Server Name (3D) | Server Alias | Active Since | Deactived Since | Weblink | Server Type | |||||
---|---|---|---|---|---|---|---|---|---|---|
Server 0 | server0 | 2011-07-01 | 2018-11-08 | Devel | Development server - no detailed description available |
56.63929833279364 | 97.0 | 0_3D | ZZZserver 00 | |
Naive AlphaFoldDB 100 | server100 | 2021-07-23 | Naive AlphaFoldDB 100 | Public | Abstract The Naive AlphaFoldDB 100 method is a baseline that fetches models with 100% sequence identity to the target sequences from the AlphaFold Protein Structure Database at EMBL-EBI Alignents are retrieved with the FASTA REST API. Models corresponding to the top hits are cut and renumbered according to the target sequence. No remodeling is performed. Tunyasuvunakool, K., Adler, J., Wu, Z. et al. Highly accurate protein structure prediction for the human proteome. Nature (2021). DOI: 10.1038/s41586-021-03828-1 Madeira F., Pearce M., Tivey A. R. N. et al. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Research (2022) gkac240. DOI: 10.1093/nar/gkac240 |
84.145029772775 | 22.0 | 100_3D | Naive AlphaFoldDB 100 | |
PaFold | server101 | 2021-08-27 | PaFold | Public | Abstract Protein structure prediction plays an important role in drug discovery. In order to achieve robust performance, PaFold first obtains massive co-evolutional based information from raw sequence via huge protein sequence and template databases. Several layers of deep neural networks are then applied to predict the coarse-grained motifs and decoys. These, together with the previous template based information, can accurately guide the construction of 3D protein structure. The structure then undergoes several rounds of refinement based on energy minimization to achieve a more precise outcome. |
73.76410091859363 | 3612.0 | 101_3D | PaFold | |
Server 102 | server102 | 2021-10-01 | Devel | Development server - no detailed description available |
55.55268319852018 | 62.0 | 102_3D | ZZZserver 102 | ||
Server 103 | server103 | 2021-10-15 | Devel | Development server - no detailed description available |
84.02710992358425 | 4509.0 | 103_3D | ZZZserver 103 | ||
HeliXonAI | server104 | 2021-10-27 | HeliXonAI | Public | Abstract To uncover the mystery of the sequence to structure to function relationship is of great significance to the humankind for better understanding our lives. In order to achieve this dream, HeliXonAI, an integrated AI-enabled drug design platform, takes the giant leap to reach the state-of-the-art performance on protein structure prediction leveraging both informative sequence evolutionary features and modern geometric machine learning algorithms. |
85.38478597484786 | 4578.0 | 104_3D | HeliXonAI | |
PaFold_v2 | server105 | 2021-11-25 | 2022-03-31 | PaFold_v2 | Public | Abstract Following PaFold, PaFold_v2 further accelerates and improves the overall prediction performance by means of Transformer and self-attention mechanism. We also develop a novel selection algorithm based on diversity and distribution to achieve more robust intermediate data. In general, it can still give solid results even without template information and limited input data. |
68.75498682634601 | 1190.0 | 105_3D | PaFold_v2 |
RocketX | server106 | 2021-11-30 | 2022-08-15 | RocketX | Public | Abstract The successful application of deep learning has promoted a breakthrough progress in protein structure prediction. Generally, deep learning is independently used to predict the contact/distance between residues or to evaluate the accuracy of the model. RocketX integrates model evaluation into geometric constraint prediction and structural modeling to build a feedback mechanism to achieve closed-loop structural optimization. In RocketX, two different deep residual neural networks are designed to predict the inter-residue geometric constraints and evaluate the quality of the folded model, respectively. The predicted geometric constraints are used to guide the structural model folding. The model evaluation network is used to estimate the quality of the folded model, and the results are fed back to the geometric constraint prediction network to re-predict the geometric constraints and fold the new structural model. The final structural model is generated through three closed-loop iterative optimizations. |
57.580369062231384 | 1917.0 | 106_3D | RocketX |
Server 107 | server107 | 2022-01-27 | Devel | Development server - no detailed description available |
75.52637262269855 | 4832.0 | 107_3D | ZZZserver 107 | ||
Server 108 | server108 | 2022-01-27 | Devel | Development server - no detailed description available |
75.58692032471299 | 4833.0 | 108_3D | ZZZserver 108 | ||
Server 109 | server109 | 2022-01-27 | Devel | Development server - no detailed description available |
None | None | 109_3D | ZZZserver 109 | ||
Server 110 | server110 | 2022-03-10 | 2022-07-18 | Devel | Development server - no detailed description available |
86.46069367726645 | 1329.0 | 110_3D | ZZZserver 110 | |
OpenComplex | server111 | 2022-03-10 | OpenComplex | Public | Abstract OpenComplex is an open-source platform for developing protein and RNA complex models. The code is available on GitHub. |
85.25842622754696 | 960.0 | 111_3D | OpenComplex | |
SADA | server112 | 2022-03-10 | 2022-09-05 | SADA | Public | Abstract The successful application of deep learning has promoted a breakthrough progress in protein structure prediction, such as AlphaFold2. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In SADA Server, we first used protein domain boundary prediction method DomBpred [1] to predict domain boundary of input sequence. Then, AlphaFold2 is used to predict the structure of each domain sequence. Finally, a protein domain assembly method [2] is used to assemble the predicted domain model to generate the full-length model. |
83.6180413311178 | 4105.0 | 112_3D | SADA |
IntFOLD7 | server113 | 2022-03-17 | IntFOLD7 | Public | - | 81.1558888029015 | 2045.0 | 113_3D | IntFOLD7 | |
Server 114 | server114 | 2022-03-18 | 2024-04-23 | Devel | Development server - no detailed description available |
83.57152633968441 | 2724.0 | 114_3D | ZZZserver 114 | |
Server 115 | server115 | 2022-03-18 | 2024-04-23 | Devel | Development server - no detailed description available |
83.03555458229272 | 2752.0 | 115_3D | ZZZserver 115 | |
Server 116 | server116 | 2022-03-18 | 2024-04-23 | Devel | Development server - no detailed description available |
82.01481383498553 | 2668.0 | 116_3D | ZZZserver 116 | |
Server 117 | server117 | 2022-03-18 | 2024-04-23 | Devel | Development server - no detailed description available |
81.45781746346019 | 2697.0 | 117_3D | ZZZserver 117 | |
Server 118 | server118 | 2022-03-18 | 2024-04-23 | Devel | Development server - no detailed description available |
80.63600083071543 | 2671.0 | 118_3D | ZZZserver 118 | |
Server 119 | server119 | 2022-03-18 | 2024-06-17 | Devel | Development server - no detailed description available |
85.67884401378468 | 3228.0 | 119_3D | ZZZserver 119 | |
Robetta | server11 | 2012-03-16 | Robetta | Public | Abstract The Robetta server (http://robetta.bakerlab.org) provides automated tools for protein structure prediction and analysis. For structure prediction, sequences submitted to the server are parsed into putative domains and structural models are generated using either comparative modeling or de novo structure prediction methods. If a confident match to a protein of known structure is found using BLAST, PSI-BLAST, FFAS03 or 3D-Jury, it is used as a template for comparative modeling. If no match is found, structure predictions are made using the de novo Rosetta fragment insertion method. Experimental nuclear magnetic resonance (NMR) constraints data can also be submitted with a query sequence for RosettaNMR de novo structure determination. Other current capabilities include the prediction of the effects of mutations on protein–protein interactions using computational interface alanine scanning. The Rosetta protein design and protein–protein docking methodologies will soon be available through the server as well.
|
68.6499565974782 | 1717.0 | 11_3D | Robetta | |
ZJUT-DeepAssembly | server120 | 2022-03-24 | 2024-06-17 | ZJUT-DeepAssembly | Public | Abstract DeepAssembly uses a multi-domain assembly approach to predict full-length protein structures. In DeepAssembly, we first constructed a deep learning model, AffineNet, which is specially used to predict the affine transformations of inter-domain residues. Then, an energy function called Atomic Coordinate Deviation potential was designed according to the predicted affine transformations. Finally, in domain assembly module, the energy function was optimized by population-based optimization method, and the single-domain structures was assembled, so as to obtain the full-length model. |
84.50587763886759 | 4332.0 | 120_3D | ZJUT-DeepAssembly |
MultiDFold | server121 | 2022-03-25 | MultiDFold | Public | Abstract The successful application of deep learning has promoted a breakthrough progress in protein structure prediction, such as AlphaFold2, RoseTTAFold, trRosetta. The MultiDFold server is an attempt to fuse multiple information for structure prediction based on the multi-objective optimization method. |
79.638942593389 | 4422.0 | 121_3D | MultiDFold | |
MEGA-EvoGen | server122 | 2022-03-25 | 2024-06-17 | Public | - | 85.08044496664535 | 4085.0 | 122_3D | MEGA-EvoGen | |
Server 123 | server123 | 2022-03-25 | 2024-06-17 | Devel | Development server - no detailed description available |
85.3638298219105 | 4248.0 | 123_3D | ZZZserver 123 | |
Server 124 | server124 | 2022-03-25 | 2024-06-17 | Devel | Development server - no detailed description available |
85.00014608725905 | 3842.0 | 124_3D | ZZZserver 124 | |
Server 125 | server125 | 2022-03-25 | 2024-06-17 | Devel | Development server - no detailed description available |
85.85930821388516 | 3454.0 | 125_3D | ZZZserver 125 | |
Server 126 | server126 | 2022-03-25 | 2024-06-17 | Devel | Development server - no detailed description available |
85.17993898233782 | 3756.0 | 126_3D | ZZZserver 126 | |
AIRFold | server127 | 2022-03-25 | AIRFold | Public | Abstract Advanced protein structure prediction models are sensitive to the co-evolution inputs. AirFold, an automated participating server, aims to improve efficiency and performance of protein structure prediction module by exploring better representation of sequence evolutionary features. |
85.57717077122278 | 1431.0 | 127_3D | AIRFold | |
Server 128 | server128 | 2022-03-31 | 2024-07-19 | Devel | Development server - no detailed description available |
84.45670233206815 | 1724.0 | 128_3D | ZZZserver 128 | |
ManiFold | server129 | 2022-04-07 | 2024-04-23 | ManiFold | Public | - | 84.68757534472641 | 2748.0 | 129_3D | ManiFold |
IntFOLD2-TS | server12 | 2012-03-02 | 2017-12-05 | IntFOLD2-TS | Public | Abstract Modelling the 3D structures of proteins can often be enhanced if more than one fold template is used during the modelling process. However, in many cases, this may also result in poorer model quality for a given target or alignment method. There is a need for modelling protocols that can both consistently and significantly improve 3D models and provide an indication of when models might not benefit from the use of multiple target-template alignments. Here, we investigate the use of both global and local model quality prediction scores produced by ModFOLDclust2, to improve the selection of target-template alignments for the construction of multiple-template models. Additionally, we evaluate clustering the resulting population of multi- and single-template models for the improvement of our IntFOLD-TS tertiary structure prediction method. Buenavista, M. T., Roche, D. B. & McGuffin, L. J. (2012) Improvement of 3D protein models using multiple templates guided by single-template model quality assessment. Bioinformatics, 28, 1851-1857. |
63.99523550326388 | 1679.0 | 12_3D | IntFOLD2-TS |
PAthreader | server130 | 2022-04-15 | 2024-06-17 | PAthreader | Public | Abstract PAthreader is a high-precision remote template detection method by threading the PDB and AFDB libraries. AlphaFold2 is improved by PAthreader by providing better template alignment. |
84.77238764863215 | 3771.0 | 130_3D | PAthreader |
PAthreader2 | server131 | 2022-04-29 | 2024-06-17 | PAthreader2 | Public | - | 84.54825745059306 | 4013.0 | 131_3D | PAthreader2 |
Server 132 | server132 | 2022-07-02 | Devel | Development server - no detailed description available |
85.58010687168795 | 827.0 | 132_3D | ZZZserver 132 | ||
Server 133 | server133 | 2022-11-04 | 2023-06-23 | Devel | Development server - no detailed description available |
61.38684836855569 | 1209.0 | 133_3D | ZZZserver 133 | |
Server 134 | server134 | 2024-03-06 | 2024-06-17 | Devel | Development server - no detailed description available |
85.67481778169933 | 2928.0 | 134_3D | ZZZserver 134 | |
Server 135 | server135 | 2024-03-08 | 2024-06-17 | Devel | Development server - no detailed description available |
85.38197124082791 | 1972.0 | 135_3D | ZZZserver 135 | |
SAIS-Fold | server136 | 2024-03-28 | SAIS-Fold | Public | Abstract The SAIS-Fold server strives to seamlessly integrate a wide range of information sources for accurate structure prediction. It achieves this by synergistically combining an expertise-driven approach with the Protein Language Model (PLM) method. |
85.71545720939905 | 1756.0 | 136_3D | SAIS-Fold | |
ZJUT-DeepSHFold | server137 | 2024-03-29 | ZJUT-DeepSHFold | Public | Abstract DeepSHfold is a structure prediction method based on various MSA sampling. In particular, a search for structural homologous sequences based on protein language models is proposed. And for complexes, a pair-MSA strategy based on sequence structure homology scores was designed to enhance sampling. |
87.35328162323215 | 3564.0 | 137_3D | ZJUT-DeepSHFold | |
Server 138 | server138 | 2024-04-09 | Devel | Development server - no detailed description available |
84.10152579857925 | 4574.0 | 138_3D | ZZZserver 138 | ||
Server 139 | server139 | 2024-10-18 | Devel | Development server - no detailed description available |
81.36699115452559 | 204.0 | 139_3D | ZZZserver 139 | ||
M4T | server13 | 2012-03-02 | 2017-08-18 | M4T | Public | Abstract Improvements in comparative protein structure modeling for the remote target-template sequence similarity cases are possible through the optimal combination of multiple template structures and by improving the quality of target-template alignment. Recently developed MMM and M4T methods were designed to address these problems. Here we describe new developments in both the alignment generation and the template selection parts of the modeling algorithms. We set up a new scoring function in MMM to deliver more accurate target-template alignments. This was achieved by developing and incorporating into the composite scoring function a novel statistical pairwise potential that combines local and non-local terms. The non-local term of the statistical potential utilizes a shuffled reference state definition that helped to eliminate most of the false positive signal from the background distribution of pairwise contacts. The accuracy of the scoring function was further increased by using BLOSUM mutation table scores. Rykunov D, Steinberger E, Madrid-Aliste CJ, Fiser A Improved scoring function for comparative modeling using the M4T method. J Struct Funct Genomics (2009) 10(1) : 95-9. |
68.53486413105777 | 747.0 | 13_3D | M4T |
Server 140 | server140 | 2024-11-17 | Devel | Development server - no detailed description available |
None | None | 140_3D | ZZZserver 140 | ||
Server 141 | server141 | 2024-11-17 | Devel | Development server - no detailed description available |
None | None | 141_3D | ZZZserver 141 | ||
Server 14 | server14 | 2012-11-28 | 2013-02-03 | Devel | Development server - no detailed description available |
None | None | 14_3D | ZZZserver 14 | |
Server 16 | server16 | 2012-07-16 | 2013-06-12 | Devel | Development server - no detailed description available |
None | None | 16_3D | ZZZserver 16 | |
Phyre2 | server17 | 2013-03-22 | Phyre2 | Public | Abstract Determining the structure and function of a novel protein is a cornerstone of many aspects of modern biology. Over the past decades, a number of computational tools for structure prediction have been developed. It is critical that the biological community is aware of such tools and is able to interpret their results in an informed way. This protocol provides a guide to interpreting the output of structure prediction servers in general and one such tool in particular, the protein homology/analogy recognition engine (Phyre). New profile-profile matching algorithms have improved structure prediction considerably in recent years. Although the performance of Phyre is typical of many structure prediction systems using such algorithms, all these systems can reliably detect up to twice as many remote homologies as standard sequence-profile searching. Phyre is widely used by the biological community, with >150 submissions per day, and provides a simple interface to results. Phyre takes 30 min to predict the structure of a 250-residue protein. Kelley LA, Sternberg MJ. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc. 2009;4(3):363-71 |
52.7019595362079 | 91.0 | 17_3D | Phyre2 | |
Server 18 | server18 | 2013-04-12 | 2013-10-03 | Devel | Development server - no detailed description available |
None | None | 18_3D | ZZZserver 18 | |
RoseTTAFold | server19 | 2013-06-13 | RoseTTAFold | Public | Abstract DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a three-track attention-based network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces high accuracy structure predictions, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. |
70.97752482901909 | 1219.0 | 19_3D | RoseTTAFold | |
SWISS-MODEL | server20 | 2013-07-05 | SWISS-MODEL | Public | Abstract Protein structure homology modelling has become a routine technique to generate 3D models for proteins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modelling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estimation for selection of the most suitable templates and provides estimates of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org/. Biasini, M. et.al. Nucleic Acids Research; (1 July 2014) 42 (W1): W252-W258 |
64.38322274825975 | 33.0 | 20_3D | SWISS-MODEL | |
Server 21 | server21 | 2013-09-06 | 2013-09-08 | Devel | Development server - no detailed description available |
None | None | 21_3D | ZZZserver 21 | |
RaptorX | server22 | 2013-09-06 | RaptorX | Public | Abstract A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ∼35 min to finish processing a sequence of 200 amino acids. Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, Xu J. Template-based protein structure modeling using the RaptorX web server. Nature Protocols 7, 1511–1522, 2012. |
66.08485556570682 | 757.0 | 22_3D | RaptorX | |
Server 23 | server23 | 2013-09-24 | 2014-03-12 | Devel | Development server - no detailed description available |
47.03160062458616 | 589.0 | 23_3D | ZZZserver 23 | |
Server 24 | server24 | 2013-09-24 | 2014-03-12 | Devel | Development server - no detailed description available |
44.51219048646708 | 490.0 | 24_3D | ZZZserver 24 | |
Server 25 | server25 | 2013-09-24 | 2014-03-02 | Devel | Development server - no detailed description available |
56.49946289678366 | 1161.0 | 25_3D | ZZZserver 25 | |
Server 26 | server26 | 2013-09-24 | 2014-03-02 | Devel | Development server - no detailed description available |
57.77398540417929 | 1080.0 | 26_3D | ZZZserver 26 | |
Princeton_TEMPLATE | server27 | 2013-09-23 | 2019-10-18 | Princeton_TEMPLATE | Public | Abstract The Princeton_TEMPLATE (ProTEin geoMetry Prediction using simuLAtions and supporT vEctor machines) webserver is an automated template-assisted structure prediction method that produces 5 model predictions for targets with sufficient homology to a template. First, the method does single or multiple domain alignments to template structures contained in our newly constructed template library. Next, sequence alignments are generated using the SPARKS-X Fold Recognition Software developed by the Yaoqi Zhou Lab. Next, the method generates many local minima based on the constraints generated using torsion-angle dynamics in Cyana. Each of these local minima are next relaxed using Rosetta Fast Relax with constraints on the coordinates to remain near the start in order to repack the side-chains and to make movements that will enhance the number of hyrogen bonds. This ensemble of structures is refined using components of the Princeton_TIGRESS refinement protocol. Namely, the structures are passed through a support vector machines based function evaluation that is capable of identifying models that are improved relative to the naive lowest-energy model. The model selected by the SVM is then refined via all atom molecular dynamics simulatons in CHARMM using the FACTS implicit solvent model. The final prediction and refined structure is sent to the user. The methodology has been benchmarked on all CASP10 targets. Khoury, G. A. et.al. Proteins: Structure, Function, and Bioinformatics 2014, 82 (5), 794-814. |
57.37828218265193 | 290.0 | 27_3D | Princeton_TEMPLATE |
Server 28 | server28 | 2013-10-02 | 2013-10-11 | Devel | Development server - no detailed description available |
None | None | 28_3D | ZZZserver 28 | |
Server 29 | server29 | 2013-10-02 | 2013-10-11 | Devel | Development server - no detailed description available |
None | None | 29_3D | ZZZserver 29 | |
Server 2 | server2 | 2011-07-01 | 2013-07-02 | Devel | Development server - no detailed description available |
None | None | 2_3D | ZZZserver 02 | |
SPARKS-X | server30 | 2013-10-24 | SPARKS-X | Public | Abstract In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area. Yang Y, Faraggi E, Zhao H, Zhou Y. Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates.Bioinformatics. 2011 Aug 1;27(15):2076-82. |
59.28810607378706 | 319.0 | 30_3D | SPARKS-X | |
NaiveBlits | server31 | 2013-12-20 | 2013-12-20 | Public | Abstract The NaiveBlits method servers as baseline by relying on the first hit found by HHblits as template and predicting the protein structure by calling MODELLER. |
54.47554566076892 | 140.0 | 31_3D | NaiveBlits | |
RBO Aleph | server32 | 2014-01-07 | 2017-08-14 | RBO Aleph | Public | Abstract RBO Aleph is a novel protein structure prediction web server for template-based modeling, protein contact prediction and ab initio structure prediction. The server has a strong emphasis on modeling difficult protein targets for which templates cannot be detected. RBO Aleph's unique features are (i) the use of combined evolutionary and physicochemical information to perform residue–residue contact prediction and (ii) leveraging this contact information effectively in conformational space search. RBO Aleph emerged as one of the leading approaches to ab initio protein structure prediction and contact prediction during the most recent Critical Assessment of Protein Structure Prediction experiment (CASP11, 2014). In addition to RBO Aleph's main focus on ab initio modeling, the server also provides state-of-the-art template-based modeling services. Based on template availability, RBO Aleph switches automatically between template-based modeling and ab initio prediction based on the target protein sequence, facilitating use especially for non-expert users. The RBO Aleph web server offers a range of tools for visualization and data analysis, such as the visualization of predicted models, predicted contacts and the estimated prediction error along the model's backbone. The server is accessible at http://compbio.robotics.tu-berlin.de/rbo_aleph/. Mabrouk, M., Putz, I., Werner, T., Schneider, M., Neeb, M., Bartels, P. and Brock, O., 2015. RBO Aleph: leveraging novel information sources for protein structure prediction. Nucleic acids research, 43(W1), pp.W343-W348. |
60.15063366076969 | 1284.0 | 32_3D | RBO Aleph |
IntFOLD3-TS | server33 | 2014-03-06 | 2023-12-14 | IntFOLD3-TS | Public | Abstract Modelling the 3D structures of proteins can often be enhanced if more than one fold template is used during the modelling process. However, in many cases, this may also result in poorer model quality for a given target or alignment method. There is a need for modelling protocols that can both consistently and significantly improve 3D models and provide an indication of when models might not benefit from the use of multiple target-template alignments. Here, we investigate the use of both global and local model quality prediction scores produced by ModFOLDclust3, to improve the selection of target-template alignments for the construction of multiple-template models. Additionally, we evaluate clustering the resulting population of multi- and single-template models for the improvement of our IntFOLD-TS tertiary structure prediction method. McGuffin, L.J., Atkins, J., Salehe, B.R., Shuid, A.N. & Roche, D.B. (2015) IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences. Nucleic Acids Research, 43, W169-73. |
64.28501280116731 | 2115.0 | 33_3D | IntFOLD3-TS |
Server 34 | server34 | 2014-03-06 | 2014-03-08 | Devel | Development server - no detailed description available |
None | None | 34_3D | ZZZserver 34 | |
Server 35 | server35 | 2014-03-06 | 2014-03-08 | Devel | Development server - no detailed description available |
None | None | 35_3D | ZZZserver 35 | |
NaiveBLAST | server36 | 2012-01-06 | NaiveBLAST | Public | Abstract The NaiveBlast method servers as baseline by relying on the first hit found by BLAST as template and predicting the protein structure by calling MODELLER. |
56.56122375337957 | 102.0 | 36_3D | NaiveBLAST | |
Server 37 | server37 | 2014-03-05 | 2014-03-13 | Devel | Development server - no detailed description available |
None | None | 37_3D | ZZZserver 37 | |
Server 38 | server38 | 2014-04-10 | 2014-04-11 | Devel | Development server - no detailed description available |
None | None | 38_3D | ZZZserver 38 | |
Server 39 | server39 | 2014-04-10 | 2014-04-11 | Devel | Development server - no detailed description available |
None | None | 39_3D | ZZZserver 39 | |
Server 40 | server40 | 2014-04-10 | 2014-04-11 | Devel | Development server - no detailed description available |
None | None | 40_3D | ZZZserver 40 | |
Server 41 | server41 | 2014-04-10 | 2014-04-11 | Devel | Development server - no detailed description available |
None | None | 41_3D | ZZZserver 41 | |
Server 42 | server42 | 2014-04-16 | 2016-03-24 | Devel | Development server - no detailed description available |
62.844128930438906 | 2254.0 | 42_3D | ZZZserver 42 | |
Server 43 | server43 | 2014-07-01 | 2015-01-27 | Devel | Development server - no detailed description available |
59.73826558073739 | 415.0 | 43_3D | ZZZserver 43 | |
Server 44 | server44 | 2014-06-19 | 2015-05-14 | Devel | Development server - no detailed description available |
60.69658702070063 | 1346.0 | 44_3D | ZZZserver 44 | |
Server 45 | server45 | 2014-07-04 | 2020-08-24 | Devel | Development server - no detailed description available |
65.94995814607229 | 1185.0 | 45_3D | ZZZserver 45 | |
Server 46 | server46 | 2014-09-24 | 2017-03-14 | Devel | Development server - no detailed description available |
58.972407330473 | 274.0 | 46_3D | ZZZserver 46 | |
Server 47 | server47 | 2014-10-24 | 2015-12-18 | Devel | Development server - no detailed description available |
57.454842620529234 | 51.0 | 47_3D | ZZZserver 47 | |
Server 48 | server48 | 2014-02-12 | 2017-11-30 | Devel | Development server - no detailed description available |
59.838523960616016 | 32.0 | 48_3D | ZZZserver 48 | |
Server 49 | server49 | 2016-11-18 | Devel | Development server - no detailed description available |
62.96905602189761 | 58.0 | 49_3D | ZZZserver 49 | ||
HHpredB | server4 | 2011-07-01 | 2019-08-02 | HHpredB | Public | Abstract Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8. Hildebrand A., Remmert M., Biegert A., and Söding J. Fast and accurate automatic structure prediction with HHpred. Proteins. 77 Suppl 9:128-132 (2009). |
60.12724563689078 | 382.0 | 4_3D | HHpredB |
Server 50 | server50 | 2015-06-12 | Devel | Development server - no detailed description available |
64.41673158444074 | 2886.0 | 50_3D | ZZZserver 50 | ||
Server 51 | server51 | 2015-12-04 | 2016-07-04 | Devel | Development server - no detailed description available |
61.20074182172364 | 139.0 | 51_3D | ZZZserver 51 | |
Server 52 | server52 | 2015-12-11 | 2016-03-10 | Devel | Development server - no detailed description available |
64.48519987054169 | 30.0 | 52_3D | ZZZserver 52 | |
Server 53 | server53 | 2016-01-21 | 2016-04-13 | Devel | Development server - no detailed description available |
60.41403512159983 | 2790.0 | 53_3D | ZZZserver 53 | |
Server 54 | server54 | 2016-02-26 | 2023-05-23 | Devel | Development server - no detailed description available |
63.08782721888612 | 30.0 | 54_3D | ZZZserver 54 | |
Server 55 | server55 | 2016-04-07 | 2023-05-23 | Devel | Development server - no detailed description available |
65.72995505301147 | 42.0 | 55_3D | ZZZserver 55 | |
Server 56 | server56 | 2016-04-16 | 2017-06-30 | Devel | Development server - no detailed description available |
65.84651650257514 | 1761.0 | 56_3D | ZZZserver 56 | |
Server 57 | server57 | 2016-04-22 | 2017-06-30 | Devel | Development server - no detailed description available |
64.70009995977473 | 1773.0 | 57_3D | ZZZserver 57 | |
IntFOLD4-TS | server58 | 2016-04-29 | 2023-12-14 | IntFOLD4-TS | Public | Abstract For our automated predictions we developed the IntFOLD4-TS protocol, which integrates the ModFOLD6_rank method for scoring the multiple-template models that were generated using a number of alternative sequence-structure alignments. Overall, our selection of top models and Accuracy Self Estimate (ASE) scores using ModFOLD6_rank was an improvement on our previous approaches. Our IntFOLD4-TS method was developed with the aim of identifying, and then attempting to fix, the local errors in an initial pool of single template models via iterative multi-template modeling. The method attempts to exploit our previous CASP successes in accurately predicting local errors in our models by taking the global and local per-residue errors into consideration during the multiple template selection stage.The pipeline can be broken down into two major stages: (1) single template modeling with ASE scoring and (2) QA guided multiple template modeling with ASE scoring. McGuffin, L.J., Shuid, A.M., Kempster, R., Maghrabi, A.H.A., Nealon J.O., Salehe, B.R., Atkins, J.D. & Roche, D.B. (2017) Accurate Template Based Modelling in CASP12 using the IntFOLD4-TS, ModFOLD6 and ReFOLD methods. Proteins: Structure, Function, and Bioinformatics, 86 Suppl 1, 335-344. McGuffin, L.J., Atkins, J., Salehe, B.R., Shuid, A.N. & Roche, D.B. (2015) IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences. Nucleic Acids Research, 43, W169-73. |
66.04786115340963 | 2740.0 | 58_3D | IntFOLD4-TS |
Server 59 | server59 | 2012-03-02 | 2017-08-18 | Devel | Development server - no detailed description available |
63.522968082055456 | 1203.0 | 59_3D | ZZZserver 59 | |
IntFOLD-TS | server5 | 2011-12-01 | 2015-07-16 | IntFOLD-TS | Public | Abstract The IntFOLD-TS method was developed according to the guiding principle that the model quality assessment (QA) would be the most critical stage for our template-based modeling pipeline. Thus, the IntFOLD-TS method firstly generates numerous alternate models, using in-house versions of several different sequence-structure alignment methods, which are then ranked in terms of global quality using our top performing QA method-ModFOLDclust2. In addition to the predicted global quality scores, the predictions of local errors are also provided in the resulting coordinate files, using scores that represent the predicted deviation of each residue in the model from the equivalent residue in the native structure. The IntFOLD-TS method was found to generate high quality 3D models for many of the CASP9 targets, whilst also providing highly accurate predictions of their per-residue errors. This important information may help to make the 3D models that are produced by the IntFOLD-TS method more useful for guiding future experimental work Roche, D. B., Buenavista, M. T., Tetchner, S. J. & McGuffin, L. J. (2011) The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic Acids Res., 39, W171-6. |
61.29348834543835 | 1743.0 | 5_3D | IntFOLD-TS |
Server 60 | server60 | 2016-09-06 | Devel | Development server - no detailed description available |
50.57450257275137 | 789.0 | 60_3D | ZZZserver 60 | ||
PRIMO | server61 | 2016-09-26 | PRIMO | Public | Abstract The development of automated servers to predict the three-dimensional structure of proteins has seen much progress over the years. These servers make calculations simpler, but largely exclude users from the process. In this study, we present the PRotein Interactive MOdeling (PRIMO) pipeline for homology modeling of protein monomers. The pipeline eases the multi-step modeling process, and reduces the workload required by the user, while still allowing engagement from the user during every step. Default parameters are given for each step, which can either be modified or supplemented with additional external input. PRIMO has been designed for users of varying levels of experience with homology modeling. The pipeline incorporates a user-friendly interface that makes it easy to alter parameters used during modeling. During each stage of the modeling process, the site provides suggestions for novice users to improve the quality of their models. PRIMO provides functionality that allows users to also model ligands and ions in complex with their protein targets. Herein, we assess the accuracy of the fully automated capabilities of the server, including a comparative analysis of the available alignment programs, as well as of the refinement levels used during modeling. The tests presented here demonstrate the reliability of the PRIMO server when producing a large number of protein models. While PRIMO does focus on user involvement in the homology modeling process, the results indicate that in the presence of suitable templates, good quality models can be produced even without user intervention. This gives an idea of the base level accuracy of PRIMO, which users can improve upon by adjusting parameters in their modeling runs. The accuracy of PRIMO’s automated scripts is being continuously evaluated by the CAMEO (Continuous Automated Model EvaluatiOn) project. Hatherley R, Brown DK, Glenister M, Tastan Bishop Ö (2016) PRIMO: An Interactive Homology Modeling Pipeline. PLOS ONE 11(11): e0166698. |
57.13137374680096 | 89.0 | 61_3D | PRIMO | |
PRIMO_BST_3D | server62 | 2016-10-16 | PRIMO_BST_3D | Public | - | 55.26334400167857 | 90.0 | 62_3D | PRIMO_BST_3D | |
PRIMO_HHS_3D | server63 | 2016-10-16 | PRIMO_HHS_3D | Public | - | 54.81293976048101 | 97.0 | 63_3D | PRIMO_HHS_3D | |
PRIMO_HHS_CL | server64 | 2016-10-16 | PRIMO_HHS_CL | Public | - | 54.912185952475156 | 90.0 | 64_3D | PRIMO_HHS_CL | |
PRIMO_BST_CL | server65 | 2016-10-16 | PRIMO_BST_CL | Public | - | 57.120324842193796 | 86.0 | 65_3D | PRIMO_BST_CL | |
Server 66 | server66 | 2017-02-06 | 2018-10-01 | Devel | Development server - no detailed description available |
22.747500091791153 | 4601.0 | 66_3D | ZZZserver 66 | |
Server 67 | server67 | 2017-05-02 | 2022-09-06 | Devel | Development server - no detailed description available |
61.40288552445061 | 1061.0 | 67_3D | ZZZserver 67 | |
SWISS-MODEL Beta | server68 | 2017-05-10 | 2017-11-16 | SWISS-MODEL Beta | Public | - | 61.34405929009764 | 41.0 | 68_3D | SWISS-MODEL Beta |
Server 69 | server69 | 2017-08-14 | 2022-09-12 | Devel | Development server - no detailed description available |
58.206684603714024 | 694.0 | 69_3D | ZZZserver 69 | |
Server 6 | server6 | 2011-12-10 | Devel | Development server - no detailed description available |
53.45226352662217 | 591.0 | 6_3D | ZZZserver 06 | ||
M4T-SMOTIF-TF | server70 | 2017-08-18 | 2021-08-05 | M4T-SMOTIF-TF | Public | - | 63.96192896507752 | 873.0 | 70_3D | M4T-SMOTIF-TF |
Server 71 | server71 | 2017-10-06 | 2020-10-26 | Devel | Development server - no detailed description available |
39.0346750645519 | 3007.0 | 71_3D | ZZZserver 71 | |
Server 72 | server72 | 2017-10-26 | 2020-10-26 | Devel | Development server - no detailed description available |
51.54575922299479 | 1946.0 | 72_3D | ZZZserver 72 | |
Server 73 | server73 | 2017-12-09 | 2020-03-23 | Devel | Development server - no detailed description available |
48.52390794575899 | 2374.0 | 73_3D | ZZZserver 73 | |
Server 74 | server74 | 2018-03-14 | 2020-03-23 | Devel | Development server - no detailed description available |
None | None | 74_3D | ZZZserver 74 | |
IntFOLD5-TS | server75 | 2018-03-21 | IntFOLD5-TS | Public | - | 66.83114747104267 | 2198.0 | 75_3D | IntFOLD5-TS | |
Server 76 | server76 | 2018-05-22 | 2018-07-03 | Devel | Development server - no detailed description available |
62.25234601551142 | 8.0 | 76_3D | ZZZserver 76 | |
Server 77 | server77 | 2018-10-12 | 2024-08-12 | Devel | Development server - no detailed description available |
54.44697697731016 | 567.0 | 77_3D | ZZZserver 77 | |
Server 78 | server78 | 2018-11-08 | 2024-08-12 | Devel | Development server - no detailed description available |
56.85487904385298 | 197.0 | 78_3D | ZZZserver 78 | |
Server 79 | server79 | 2018-12-08 | 2022-02-21 | Devel | Development server - no detailed description available |
45.72434781230882 | 1044.0 | 79_3D | ZZZserver 79 | |
Server 7 | server7 | 2011-12-10 | Devel | Development server - no detailed description available |
52.316138479510485 | 754.0 | 7_3D | ZZZserver 07 | ||
Server 80 | server80 | 2019-05-23 | 2022-02-17 | Devel | Development server - no detailed description available |
63.515566557885684 | 410.0 | 80_3D | ZZZserver 80 | |
Server 81 | server81 | 2019-05-23 | 2022-02-17 | Devel | Development server - no detailed description available |
59.309304480190896 | 106.0 | 81_3D | ZZZserver 81 | |
Server 82 | server82 | 2019-06-01 | Devel | Development server - no detailed description available |
69.00686647225204 | 19.0 | 82_3D | ZZZserver 82 | ||
Server 83 | server83 | 2019-08-23 | 2020-12-18 | Devel | Development server - no detailed description available |
61.52068755390915 | 2047.0 | 83_3D | ZZZserver 83 | |
Server 84 | server84 | 2019-12-06 | 2020-12-18 | Devel | Development server - no detailed description available |
69.82649179411105 | 1201.0 | 84_3D | ZZZserver 84 | |
Server 85 | server85 | 2019-12-06 | 2020-12-18 | Devel | Development server - no detailed description available |
69.25880212096725 | 2591.0 | 85_3D | ZZZserver 85 | |
Server 86 | server86 | 2019-12-06 | 2020-12-18 | Devel | Development server - no detailed description available |
68.27859888984779 | 3306.0 | 86_3D | ZZZserver 86 | |
Server 87 | server87 | 2019-12-16 | 2020-12-18 | Devel | Development server - no detailed description available |
70.15829256709 | 2653.0 | 87_3D | ZZZserver 87 | |
Server 88 | server88 | 2019-12-16 | 2020-12-18 | Devel | Development server - no detailed description available |
68.70760284463302 | 3507.0 | 88_3D | ZZZserver 88 | |
TFold | server89 | 2020-01-10 | 2020-12-18 | tFold | Public | Abstract The tFold server integrates three innovative techniques for high-accuracy protein structure prediction. We adopt the “multi-source fusion” strategy to fully exploit co-evolution patterns embedded in multiple groups of MSA data. An ultra-deep criss-cross attention residual network is developed to accurately predict both inter-residue distance and orientation relationships. We further develop the “template-based free modeling - TBFM” framework to combine both TBM-based tertiary structure information and FM-based distance and orientation predictions to generate high-quality structure predictions. Han, Y., Zhuang, Q., Sun, B. et al. Crystal structure of steroid reductase SRD5A reveals conserved steroid reduction mechanism. Nat Commun 12, 449 (2021). DOI: 10.1038/s41467-020-20675-2. |
70.08415085757339 | 2880.0 | 89_3D | tFold |
Server 8 | server8 | 2011-12-10 | 2017-06-01 | Devel | Development server - no detailed description available |
55.736519902974614 | 1957.0 | 8_3D | ZZZserver 08 | |
IntFOLD6-TS | server90 | 2020-02-20 | IntFOLD6-TS | Public | - | 67.21130421537735 | 1831.0 | 90_3D | IntFOLD6-TS | |
Server 91 | server91 | 2020-08-18 | 2022-07-15 | Devel | Development server - no detailed description available |
66.08631086267164 | 666.0 | 91_3D | ZZZserver 91 | |
Server 92 | server92 | 2020-10-23 | 2020-12-31 | Devel | Development server - no detailed description available |
None | None | 92_3D | ZZZserver 92 | |
Server 93 | server93 | 2020-10-23 | 2022-08-08 | Devel | Development server - no detailed description available |
None | None | 93_3D | ZZZserver 93 | |
Server 94 | server94 | 2020-11-27 | 2022-09-05 | Devel | Development server - no detailed description available |
63.807651800641295 | 2155.0 | 94_3D | ZZZserver 94 | |
Server 95 | server95 | 2020-12-02 | 2024-04-23 | Devel | Development server - no detailed description available |
57.821119267766065 | 3489.0 | 95_3D | ZZZserver 95 | |
PureAF2_orig | server96 | 2021-07-23 | 2022-12-20 | Public | - | 85.9411205518447 | 2096.0 | 96_3D | pureAF2_orig | |
PureAF2_notemp | server97 | 2021-07-23 | 2022-12-20 | Public | - | 85.11075270283331 | 2427.0 | 97_3D | pureAF2_notemp | |
ZlxFold | server98 | 2021-07-23 | 2022-12-20 | Public | - | 83.64024620237608 | 3471.0 | 98_3D | ZlxFold | |
BestSingleStructuralTemplate | server999 | 2020-02-07 | BestSingleStructuralTemplate | Public | Abstract The BestSingleStructuralTemplate method serves as "post-diction" baseline, representing an upper limit for single template models. The best templates are discovered by structural superposition of the target reference structures with all PDB structures using TM‐align. The top 20 of the obtained structural alignments serve as input for the subsequent template‐based modeling. Modeling is performed with SWISS‐MODEL's modeling engine ProMod3. Termini beyond the region covered by the template structure are modeled by a low‐complexity Monte Carlo sampling approach. The final models are ranked by lDDT, and the top scoring model is selected for that particular target. Haas, J, Gumienny, R, Barbato, A, et al. Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO). Proteins. 2019; 87: 1378–1387. |
71.16715276488833 | 282.0 | 999_3D | BestSingleStructuralTemplate | |
Naive AlphaFoldDB 90 | server99 | 2021-07-23 | Naive AlphaFoldDB 90 | Public | Abstract The Naive AlphaFoldDB 90 method is a baseline that fetches models with at least 90% sequence identity to the target sequences from the AlphaFold Protein Structure Database at EMBL-EBI. Alignents are retrieved with the FASTA REST API. Models corresponding to the top hits are downloaded and minimal modelling required to map the AlphaFold model to the target sequence is performed with ProMod3 using its default modelling pipeline. Tunyasuvunakool, K., Adler, J., Wu, Z. et al. Highly accurate protein structure prediction for the human proteome. Nature (2021). DOI: 10.1038/s41586-021-03828-1 Studer, G., Tauriello, G., Bienert S. et al. ProMod3—A versatile homology modelling toolbox. PLOS Computational Biology (2021) 17(1): e1008667. DOI: 10.1371/journal.pcbi.1008667 Madeira F., Pearce M., Tivey A. R. N. et al. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Research (2022) gkac240. DOI: 10.1093/nar/gkac240 |
83.17602421084696 | 20.0 | 99_3D | Naive AlphaFoldDB 90 | |
Server 9 | server9 | 2012-01-13 | 2015-01-12 | Devel | Development server - no detailed description available |
59.6175534696201 | 151.0 | 9_3D | ZZZserver 09 |
Server Name (QE) | Server Alias | Active Since | Deactived Since | Weblink | Predictor Type | Server Type | ||||
---|---|---|---|---|---|---|---|---|---|---|
Verify3d smoothed | server0 | 2014-02-07 | 2019-06-22 | Public | - | 0 | 0_QE | Verify3d smoothed | ||
Server 10 | server10 | 2014-05-16 | 2018-04-24 | Devel | Development server - no detailed description available |
0 | 10_QE | ZZZserver 10 | ||
Server 11 | server11 | 2014-05-23 | 2018-04-24 | Devel | Development server - no detailed description available |
0 | 11_QE | ZZZserver 11 | ||
Server 12 | server12 | 2014-10-16 | 2016-08-31 | Devel | Development server - no detailed description available |
0 | 12_QE | ZZZserver 12 | ||
Server 13 | server13 | 2014-12-04 | 2015-06-11 | Devel | Development server - no detailed description available |
0 | 13_QE | ZZZserver 13 | ||
Server 14 | server14 | 2014-12-18 | 2015-06-11 | Devel | Development server - no detailed description available |
0 | 14_QE | ZZZserver 14 | ||
VoroMQA_sw5 | server15 | 2014-12-18 | VoroMQA_sw5 | Public | Abstract VoroMQA_sw5 server uses the first version of VoroMQA ("Voronoi diagram-based Model Quality Assessment"), a new method for the estimation of protein structure quality. It combines the idea of statistical potentials with the advanced use of the Voronoi tessellation of atomic balls. The new method uses contact areas instead of distances for describing and seamlessly integrating both explicit interactions between protein atoms and implicit interactions of protein atoms with solvent. The method produces scores at atomic, residue and global levels. The VoroMQA version used by the VoroMQA_sw5 server is generally identical to the version that participated in CASP11 with one important modification: local residue scores are smoothed along the sequence using a triangular window of 5 residues on each side of a central residue position. Kliment Olechnovič and Česlovas Venclovas. VoroMQA: Assessment of protein structure quality using interatomic contact areas. Proteins 2017; 85:1131–1145. DOI: 10.1002/prot.25278. |
0 | 15_QE | VoroMQA_sw5 | ||
EQuant 2 | server16 | 2015-06-12 | 2020-05-19 | eQuant 2 | Public | - | 0 | 16_QE | eQuant 2 | |
VoroMQA_v2 | server17 | 2015-07-23 | VoroMQA_v2 | Public | Abstract VoroMQA_v2 server uses the second version of VoroMQA ("Voronoi diagram-based Model Quality Assessment"), a new method for the estimation of protein structure quality. It combines the idea of statistical potentials with the advanced use of the Voronoi tessellation of atomic balls. The new method uses contact areas instead of distances for describing and seamlessly integrating both explicit interactions between protein atoms and implicit interactions of protein atoms with solvent. In addition, the second version of VoroMQA utilizes the Voronoi tessellation of balls to describe the orientation of contacts. The method produces scores at atomic, residue and global levels. The web server is available at bioinformatics.ibt.lt/wtsam/voromqa. The VoroMQA version used by the VoroMQA_v2 server is generally identical to the version that participated in CASP12. |
0 | 17_QE | VoroMQA_v2 | ||
ModFOLD6 | server18 | 2016-02-02 | 2018-02-23 | ModFOLD6 | Public | - | 0 | 18_QE | ModFOLD6 | |
QMEANDisCo 2 | server19 | 2016-02-02 | 2019-05-24 | QMEANDisCo 2 | Public | - | 0 | 19_QE | QMEANDisCo 2 | |
Dfire v1.1 | server1 | 2014-02-07 | 2019-06-22 | Dfire v1.1 | Public | Abstract Proteins fold into unique three-dimensional structures by specific, orientation-dependent interactions between amino acid residues. Here, we extract orientation-dependent interactions from protein structures by treating each polar atom as a dipole with a direction. The resulting statistical energy function successfully refolds 13 out of 16 fully unfolded secondary-structure terminal regions of 10–23 amino acid residues in 15 small proteins. Dissecting the orientation-dependent energy function reveals that the orientation preference between hydrogen-bonded atoms is not enough to account for the structural specificity of proteins. The result has significant implications on the theoretical and experimental searches for specific interactions involved in protein folding and molecular recognition between proteins and other biologically active molecules. Yang Y., Zhou Y.. Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins. 2008 Aug;72(2):793-803. |
0 | 1_QE | Dfire v1.1 | |
QMEAN 3 | server20 | 2017-08-24 | QMEAN 3 | Public | Abstract Motivation: Quality assessment of protein structures is an important part of experimental structure validation and plays a crucial role in protein structure prediction, where the predicted models may contain substantial errors. Most current scoring functions are primarily designed to rank alternative models of the same sequence supporting model selection, whereas the prediction of the absolute quality of an individual protein model has received little attention in the field. However, reliable absolute quality estimates are crucial to assess the suitability of a model for specific biomedical applications. Results: In this work, we present a new absolute measure for the quality of protein models, which provides an estimate of the ‘degree of nativeness’ of the structural features observed in a model and describes the likelihood that a given model is of comparable quality to experimental structures. Model quality estimates based on the QMEAN scoring function were normalized with respect to the number of interactions. The resulting scoring function is independent of the size of the protein and may therefore be used to assess both monomers and entire oligomeric assemblies. Model quality scores for individual models are then expressed as ‘Z-scores’ in comparison to scores obtained for high-resolution crystal structures. We demonstrate the ability of the newly introduced QMEAN Z-score to detect experimentally solved protein structures containing significant errors, as well as to evaluate theoretical protein models. In a comprehensive QMEAN Z-score analysis of all experimental structures in the PDB, membrane proteins accumulate on one side of the score spectrum and thermostable proteins on the other. Proteins from the thermophilic organism Thermatoga maritima received significantly higher QMEAN Z-scores in a pairwise comparison with their homologous mesophilic counterparts, underlining the significance of the QMEAN Z-score as an estimate of protein stability. Benkert, P., Biasini, M., Schwede, T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27, 343-350 (2011). |
0 | 20_QE | QMEAN 3 | ||
ModFOLD6 | server21 | 2016-04-22 | 2023-12-14 | ModFOLD6 | Public | - | 0 | 21_QE | ModFOLD6 | |
Server 22 | server22 | 2016-08-05 | Devel | Development server - no detailed description available |
0 | 22_QE | ZZZserver 22 | |||
Baseline Potential | server23 | 2016-10-24 | Public | Abstract The “BaselinePotential” server implements a classical distance based statistical potential as described by Sippl and coworkers. Statistics have been extracted for pairwise distances between all chemically distinguishable heavy atoms in the 20 naturally occurring amino acids. Histograms have been built with a bin size of 0.5 Å and maximal distance of 10 Å, neglecting all interactions from residues being closer than 4 in sequence. The underlying data is composed of a non-redundant set of experimentally determined protein structures (2995 culled chains from the PISCES webserver with max pairwise sequence identity of 20% and X-Ray resolution better than 1.6 Å). The resulting potential functions are applied on all pairwise interactions and per residue scores are estimated by averaging all outcomes of interactions a residue is involved in. A subsequent sequential smoothing applies a Gaussian filter with a standard deviation of 4 residues to reduce noise. To avoid amino acid specific biases, a linear model is trained for all 20 naturally occurring amino acids to predict per-residue lDDT scores. |
0 | 23_QE | Baseline Potential | |||
QMEANDISCO beta | server24 | 2017-06-18 | 2017-08-24 | QMEANDISCO beta | Public | Abstract Estimating the quality of a protein model is crucial to determine its utility and potential applications. Global quality estimates can already give a general impression of a model’s applicability or allow selecting a model in a set of alternatives. Extending this concept to a local per residue scale gives much more detailed insights to a protein model and opens a full range of possible applications. We have therefore extend the local quality estimation capabilities of QMEAN by harnessing distance constraints from the rapidly increasing amount of experimentally determined structural information. We improved the established quality estimation tool QMEAN and enhanced its local quality estimation capabilities with a new term based on distance constraints - DisCo. QMEAN and QME-ANDisCo have been successfully tested and compared to other state of the art local quality estimation tools on a wide variety of test sets. The careful data analysis revealed that both methods particularly stand out in distinguishing wrongly from correctly modelled residues in models of reasonable overall fold. |
0 | 24_QE | QMEANDISCO beta | |
Server 25 | server25 | 2017-08-24 | 2017-11-02 | Devel | Development server - no detailed description available |
0 | 25_QE | ZZZserver 25 | ||
Server 26 | server26 | 2017-08-24 | 2019-07-01 | Devel | Development server - no detailed description available |
0 | 26_QE | ZZZserver 26 | ||
Server 27 | server27 | 2018-02-09 | 2019-05-16 | Devel | Development server - no detailed description available |
0 | 27_QE | ZZZserver 27 | ||
ModFOLD7_lDDT | server28 | 2018-02-22 | ModFOLD7_lDDT | Public | - | 0 | 28_QE | ModFOLD7_lDDT | ||
QMEANDisCo 3 | server29 | 2018-04-20 | QMEANDisCo 3 | Public | Abstract Motivation Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score. Results DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times. Studer, G., Rempfer, C., Waterhouse, A.M., Gumienny, G., Haas, J., Schwede, T. QMEANDisCo - distance constraints applied on model quality estimation. Bioinformatics 36, 1765-1771 (2020). |
0 | 29_QE | QMEANDisCo 3 | ||
Prosa2003 | server2 | 2014-02-07 | 2019-06-22 | Prosa2003 | Public | - | 0 | 2_QE | Prosa2003 | |
Server 30 | server30 | 2018-05-04 | 2018-11-17 | Devel | Development server - no detailed description available |
0 | 30_QE | ZZZserver 30 | ||
ProQ3D_LDDT | server31 | 2018-11-18 | ProQ3D_LDDT | Public | - | 0 | 31_QE | ProQ3D_LDDT | ||
ProQ3 | server32 | 2018-11-18 | ProQ3 | Public | - | 0 | 32_QE | ProQ3 | ||
ProQ3D | server33 | 2018-11-18 | ProQ3D | Public | - | 0 | 33_QE | ProQ3D | ||
Server 34 | server34 | 2020-01-15 | Devel | Development server - no detailed description available |
0 | 34_QE | ZZZserver 34 | |||
Server 35 | server35 | 2020-02-20 | 2021-01-08 | Devel | Development server - no detailed description available |
0 | 35_QE | ZZZserver 35 | ||
Server 36 | server36 | 2020-04-22 | 2022-07-19 | Devel | Development server - no detailed description available |
0 | 36_QE | ZZZserver 36 | ||
Server 37 | server37 | 2020-09-11 | 2022-07-19 | Devel | Development server - no detailed description available |
0 | 37_QE | ZZZserver 37 | ||
Server 38 | server38 | 2020-09-11 | 2022-07-19 | Devel | Development server - no detailed description available |
0 | 38_QE | ZZZserver 38 | ||
ModFOLD8 | server39 | 2021-01-08 | ModFOLD8 | Public | - | 0 | 39_QE | ModFOLD8 | ||
Naive PSIBlast | server3 | 2014-02-07 | 2019-06-22 | Public | Abstract The Baseline predictor runs PSI-BLAST with the most recent version of the NCBI "NR" database. Then, from the generated PSI-BLAST profiles, we consider the PSSM score of the target sequence residues as naive indicator of local quality. Specifically, PSI-BLAST is launched as follows: blastpgp -d [nr database] -i [target FASTA sequence] -e 1e-10 -J t -u 1 -j 3 |
0 | 3_QE | Naive PSIBlast | ||
DeepUMQA | server40 | 2021-11-26 | 2024-06-17 | DeepUMQA | Public | Abstract Protein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for describing residue-level topological information. Design features that can further reflect residue-level topology when combined with deep learning methods are therefore crucial to improve the performance of model quality assessment. We developed an Ultrafast Shape Recognition (USR)-based residue-level single model quality assessment method, termed DeepUMQA. The residue-level USR feature is used to map the topological information of the model to each residue, thereby characterizing the relationship between the residue and the topological structure (i.e. the topological information of the residue), which is combined with the residue voxelization feature (i.e. the local structure information of the residue), secondary structure, distances and Rosetta energy terms, and use three-dimensional convo- lution, two-dimensional convolution and residual networks to predict the quality of the protein model. Experimental results show that the USR feature is complementary to the voxelization feature in describing residues from local and topological aspects, and it can thus significantly improve the performance of model quality assessment. Sai-Sai Guo#, Jun Liu#, Xiao-Gen Zhou, Gui-Jun Zhang*, DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning, Bioinformatics, Volume 38, Issue 7, 1 April 2022, Pages 1895–1903. |
0 | 40_QE | DeepUMQA | |
Atom_ProteinQA | server41 | 2021-12-17 | 2022-12-20 | Atom_ProteinQA | Public | Abstract Protein model quality assessment (ProteinQA) is a fundamental task, which is essential for biologically relevant applications, i.e., protein structure refinement, protein design, etc. Previous works aim to conduct the ProteinQA only in the global whole structure or per-residue levels, ignoring potentially usable and precise cues from a fine-grained per-atom perspective. In this study, we propose an atom-level ProteinQA model, named Atom-ProteinQA, in which two innovative modules are designed to extract geometric and topological atom-level relationships respectively. Extensive experiments show that our proposed Atom-ProteinQA outperforms previous methods by a large margin, regardless of residue-level or atom-level assessment. |
0 | 41_QE | Atom_ProteinQA | |
Server 42 | server42 | 2022-02-25 | 2022-12-20 | Devel | Development server - no detailed description available |
0 | 42_QE | ZZZserver 42 | ||
DeepUMQA2 | server43 | 2022-03-04 | 2024-06-17 | DeepUMQA2 | Public | - | 0 | 43_QE | DeepUMQA2 | |
ModFOLD9 | server44 | 2022-03-17 | ModFOLD9 | Public | - | 0 | 44_QE | ModFOLD9 | ||
ModFOLD9_pure | server45 | 2022-03-17 | ModFOLD9_pure | Public | - | 0 | 45_QE | ModFOLD9_pure | ||
ZJUT-GraphCPLMQA | server46 | 2022-04-08 | ZJUT-GraphCPLMQA | Public |
GraphCPLMQA: Assessing protein model quality based on deep graph coupling networks using protein language model. |
0 | 46_QE | ZJUT-GraphCPLMQA | ||
MEGA-Assessment | server47 | 2022-05-27 | 2024-06-17 | Public | - | 0 | 47_QE | MEGA-Assessment | ||
Server 48 | server48 | 2022-06-10 | 2024-04-22 | Devel | Development server - no detailed description available |
0 | 48_QE | ZZZserver 48 | ||
Server 49 | server49 | 2022-07-01 | 2024-06-17 | Devel | Development server - no detailed description available |
0 | 49_QE | ZZZserver 49 | ||
Qmean 7.11 | server4 | 2014-02-07 | 2017-08-31 | Qmean 7.11 | Public | Abstract QMEAN, which stands for Qualitative Model Energy ANalysis, is a composite scoring function describing the major geometrical aspects of protein structures. Five different structural descriptors are used. The local geometry is analyzed by a new kind of torsion angle potential over three consecutive amino acids. A secondary structure-specific distance-dependent pairwise residue-level potential is used to assess long-range interactions. A solvation potential describes the burial status of the residues. Two simple terms describing the agreement of predicted and calculated secondary structure and solvent accessibility, respectively, are also included. Benkert P, Tosatto SC, Schomburg D. QMEAN: A comprehensive scoring function for model quality assessment. Proteins. 2008 Apr;71(1):261-77. |
0 | 4_QE | Qmean 7.11 | |
Server 50 | server50 | 2022-10-28 | 2023-01-09 | Devel | Development server - no detailed description available |
0 | 50_QE | ZZZserver 50 | ||
Server 51 | server51 | 2023-11-10 | Devel | Development server - no detailed description available |
0 | 51_QE | ZZZserver 51 | |||
Server 52 | server52 | 2023-11-17 | Devel | Development server - no detailed description available |
0 | 52_QE | ZZZserver 52 | |||
Server 53 | server53 | 2023-12-01 | Devel | Development server - no detailed description available |
0 | 53_QE | ZZZserver 53 | |||
Server 54 | server54 | 2023-12-01 | Devel | Development server - no detailed description available |
0 | 54_QE | ZZZserver 54 | |||
Server 55 | server55 | 2023-12-01 | Devel | Development server - no detailed description available |
0 | 55_QE | ZZZserver 55 | |||
Server 56 | server56 | 2023-12-01 | Devel | Development server - no detailed description available |
0 | 56_QE | ZZZserver 56 | |||
Server 57 | server57 | 2023-12-01 | Devel | Development server - no detailed description available |
0 | 57_QE | ZZZserver 57 | |||
Server 58 | server58 | 2023-12-01 | Devel | Development server - no detailed description available |
0 | 58_QE | ZZZserver 58 | |||
Server 59 | server59 | 2023-12-01 | Devel | Development server - no detailed description available |
0 | 59_QE | ZZZserver 59 | |||
Server 5 | server5 | 2014-02-07 | 2020-03-02 | Devel | Development server - no detailed description available |
0 | 5_QE | ZZZserver 05 | ||
ZJUT-MultiViewMQA | server60 | 2024-04-12 | ZJUT-MultiViewMQA | Public |
MultiViewMQA is a protein model quality evaluation method based on a multi-view network. It employs graph attention networks, Transformer, and 3D CNNs to process features of different dimensions of proteins individually, thus capturing better protein feature representations and evaluating protein model quality. |
0 | 60_QE | ZJUT-MultiViewMQA | ||
Server 6 | server6 | 2014-02-07 | 2019-06-22 | Devel | Development server - no detailed description available |
0 | 6_QE | ZZZserver 06 | ||
ModFOLD4 | server7 | 2014-04-11 | 2021-10-22 | ModFOLD4 | Public | Abstract The ModFOLD4 server deploys a quasi-single-model QA algorithm. This means that the method preserves the predictive power of pure clustering-based methods while also being capable of making predictions for a single model at a time. If the server receives multiple models then it will make use of a full clustering approach; however, if only a single model is submitted, then it will operate in quasi-single-model mode with comparable accuracy. McGuffin L.J., Buenavista M.T. and Roche D.B. The ModFOLD4 server for the quality assessment of 3D protein models. Nucleic Acids Res. 2013 Jul;41(Web Server issue):W368-72. |
0 | 7_QE | ModFOLD4 | |
ProQ2 | server8 | 2014-04-25 | ProQ2 | Public | Abstract ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local. Ray A., Lindahl E. and Wallner B. Improved model quality assessment using ProQ2. BMC Bioinformatics 2012, 13:224 |
0 | 8_QE | ProQ2 | ||
Server 9 | server9 | 2014-05-09 | 2016-03-24 | Devel | Development server - no detailed description available |
0 | 9_QE | ZZZserver 09 |
Server Name (CP) | Server Alias | Active Since | Deactived Since | Weblink | Server Type | ||||
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Server 0 | server0 | 2016-03-14 | 2020-02-14 | devel |
Development server - no detailed description available |
None | 0_CP | ZZZserver 00 | |
Server 10 | server10 | 2018-09-14 | 2020-02-14 | devel |
Development server - no detailed description available |
115.0 | 10_CP | ZZZserver 10 | |
Server 11 | server11 | 2018-09-14 | 2020-02-14 | devel |
Development server - no detailed description available |
171.0 | 11_CP | ZZZserver 11 | |
Server 12 | server12 | 2018-11-01 | 2020-02-14 | devel |
Development server - no detailed description available |
44.0 | 12_CP | ZZZserver 12 | |
NaiveContactMI | server1 | 2016-03-14 | 2020-02-14 | public |
As a baseline for the contact prediction, we use mutual information (MI) to predict the most likely contacts from a multiple sequence alignment (MSA) obtained with HHblits. Specifically the MSA for the target protein is obtained from a search against the UniProt nr20 database. For our calculation of MI, we use the small number correction introduced by Buslje et al. [Bulsje et al, Bioinformatics 25:1125-1131, 2009], which simply consists in adding a small number (here 0.05) to the number of observations of any residue pair when calculating the probabilities. Gaps are not counted as an amino acid type and are therefore not included in the calculation of MI, whereas we always use the total number of sequences in the alignment as normalisation factor in the calculation of the probabilities. This effectively penalises columns with gaps in the calculation of MI. |
1.0 | 1_CP | NaiveContactMI | |
NaiveContactMIp | server2 | 2016-04-14 | 2020-02-14 | public | - | 1.0 | 2_CP | NaiveContactMIp | |
NaiveContactMIpz | server3 | 2016-04-14 | 2020-02-14 | public | - | 1.0 | 3_CP | NaiveContactMIpz | |
CevoMI | server4 | 2016-04-14 | 2020-02-14 | public | - | 1.0 | 4_CP | cevoMI | |
Server 5 | server5 | 2016-04-25 | 2020-02-14 | devel |
Development server - no detailed description available |
None | 5_CP | ZZZserver 05 | |
DeepCDpred-deactivated | server6 | 2017-09-25 | 2017-11-03 | DeepCDpred-deactivated | public | - | None | 6_CP | DeepCDpred-deactivated |
Server 7 | server7 | 2017-09-25 | 2020-02-14 | devel |
Development server - no detailed description available |
1239.0 | 7_CP | ZZZserver 07 | |
DeepCDpred | server8 | 2017-11-03 | 2020-02-14 | DeepCDpred | public | - | None | 8_CP | DeepCDpred |
Server 9 | server9 | 2018-03-14 | 2020-02-14 | devel |
Development server - no detailed description available |
3113.0 | 9_CP | ZZZserver 09 |