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
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.

Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 2004 Jul 1

32(Web Server issue):W526-31.

http://dx.doi.org/10.1093/nar/gkh468

68.18325009762447 1856.0 11_3D Robetta
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.

http://dx.doi.org/10.1093/bioinformatics/bts292

63.99523550326388 1679.0 12_3D IntFOLD2-TS
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.

http://dx.doi.org/10.1007/s10969-008-9044-9

68.53486413105777 747.0 13_3D M4T
Server 14 server14 2012-11-28 2013-02-03 Devel

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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

http://dx.doi.org/10.1038/nprot.2009.2

51.65429937488102 96.0 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
Server 19 server19 2013-06-13 Devel

Development server - no detailed description available

68.53463314300627 1837.0 19_3D ZZZserver 19
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

61.88669044538299 43.0 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.

http://dx.doi.org/10.1038/nprot.2012.085

65.90999902786047 735.0 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

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None None 29_3D ZZZserver 29
Server 2 server2 2011-07-01 2013-07-02 Devel

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None None 2_3D ZZZserver 02
SPARKS-X server30 2013-10-24 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.

http://dx.doi.org/10.1093/bioinformatics/btr350

59.5488104418253 277.0 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.

dx.doi.org/10.1093/nar/gkv357

60.15063366076969 1284.0 32_3D RBO Aleph
IntFOLD3-TS server33 2014-03-06 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.

64.23622508245957 1972.0 33_3D IntFOLD3-TS
Server 34 server34 2014-03-06 2014-03-08 Devel

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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 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.

56.45823018006951 141.0 36_3D NaiveBLAST
Server 37 server37 2014-03-05 2014-03-13 Devel

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None None 37_3D ZZZserver 37
Server 38 server38 2014-04-10 2014-04-11 Devel

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None None 38_3D ZZZserver 38
Server 39 server39 2014-04-10 2014-04-11 Devel

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Server 40 server40 2014-04-10 2014-04-11 Devel

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Server 41 server41 2014-04-10 2014-04-11 Devel

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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

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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

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62.95959043466234 59.0 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).

dx.doi.org/10.1002/prot.22499

60.12724563689078 382.0 4_3D HHpredB
Server 50 server50 2015-06-12 Devel

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64.41673158444074 2886.0 50_3D ZZZserver 50
Server 51 server51 2015-12-04 2016-07-04 Devel

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61.20074182172364 139.0 51_3D ZZZserver 51
Server 52 server52 2015-12-11 2016-03-10 Devel

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64.48519987054169 30.0 52_3D ZZZserver 52
Server 53 server53 2016-01-21 2016-04-13 Devel

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60.41403512159983 2790.0 53_3D ZZZserver 53
Server 54 server54 2016-02-26 Devel

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62.776743762003825 37.0 54_3D ZZZserver 54
Server 55 server55 2016-04-07 Devel

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65.38524156229364 50.0 55_3D ZZZserver 55
Server 56 server56 2016-04-16 2017-06-30 Devel

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65.84651650257514 1761.0 56_3D ZZZserver 56
Server 57 server57 2016-04-22 2017-06-30 Devel

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64.70009995977473 1773.0 57_3D ZZZserver 57
IntFOLD4-TS server58 2016-04-29 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.

66.26681434872228 2318.0 58_3D IntFOLD4-TS
Server 59 server59 2012-03-02 2017-08-18 Devel

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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.

http://www.ncbi.nlm.nih.gov/pubmed/21459847

61.29348834543835 1743.0 5_3D IntFOLD-TS
Server 60 server60 2016-09-06 Devel

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47.58184395331397 736.0 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.

https://doi.org/10.1371/journal.pone.0166698

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PRIMO_HHS_3D server63 2016-10-16 PRIMO_HHS_3D Public - 55.399075743631265 87.0 63_3D PRIMO_HHS_3D
PRIMO_HHS_CL server64 2016-10-16 PRIMO_HHS_CL Public - 55.38510560080127 76.0 64_3D PRIMO_HHS_CL
PRIMO_BST_CL server65 2016-10-16 PRIMO_BST_CL Public - 58.06155908839719 55.0 65_3D PRIMO_BST_CL
Server 66 server66 2017-02-06 2018-10-01 Devel

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SWISS-MODEL Beta server68 2017-05-10 2017-11-16 SWISS-MODEL Beta Public - 61.34405929009764 41.0 68_3D SWISS-MODEL Beta
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Server 6 server6 2011-12-10 Devel

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54.03789183575304 686.0 6_3D ZZZserver 06
M4T-SMOTIF-TF server70 2017-08-18 M4T-SMOTIF-TF Public - 63.36955762870258 908.0 70_3D M4T-SMOTIF-TF
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39.0346750645519 3007.0 71_3D ZZZserver 71
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IntFOLD5-TS server75 2018-03-21 IntFOLD5-TS Public - 67.30622029633123 2156.0 75_3D IntFOLD5-TS
Server 76 server76 2018-05-22 2018-07-03 Devel

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62.25234601551142 8.0 76_3D ZZZserver 76
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69.55912928112218 2306.0 87_3D ZZZserver 87
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67.75103347000804 3419.0 88_3D ZZZserver 88
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69.54990024493489 2573.0 89_3D ZZZserver 89
Server 8 server8 2011-12-10 2017-06-01 Devel

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55.736519902974614 1957.0 8_3D ZZZserver 08
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68.13131283384328 1934.0 90_3D ZZZserver 90
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65.74099230301844 756.0 91_3D ZZZserver 91
BestSingleStructuralTemplate server999 2020-02-07 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.

https://doi.org/10.1002/prot.25815

70.4075415185106 238.0 999_3D BestSingleStructuralTemplate
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.

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.

dx.doi.org/10.1002/prot.21968

0 1_QE Dfire v1.1
QMEAN 3 server20 2017-08-24 QMEAN 3 Public - 0 20_QE QMEAN 3
ModFOLD6 server21 2016-04-22 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 - 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 Devel

Development server - no detailed description available

0 35_QE ZZZserver 35
Server 36 server36 2020-04-22 Devel

Development server - no detailed description available

0 36_QE ZZZserver 36
Server 37 server37 2020-09-11 Devel

Development server - no detailed description available

0 37_QE ZZZserver 37
Server 38 server38 2020-09-11 Devel

Development server - no detailed description available

0 38_QE ZZZserver 38
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
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.

dx.doi.org/10.1002/prot.21715

0 4_QE Qmean 7.11
Server 5 server5 2014-02-07 2020-03-02 Devel

Development server - no detailed description available

0 5_QE ZZZserver 05
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 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.

10.1093/nar/gkt294

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

dx.doi.org/10.1186/1471-2105-13-224

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
Server 0 server0 2016-03-14 devel

Development server - no detailed description available

None 0_CP ZZZserver 00
Server 10 server10 2018-09-14 devel

Development server - no detailed description available

115.0 10_CP ZZZserver 10
Server 11 server11 2018-09-14 devel

Development server - no detailed description available

171.0 11_CP ZZZserver 11
Server 12 server12 2018-11-01 devel

Development server - no detailed description available

44.0 12_CP ZZZserver 12
NaiveContactMI server1 2016-03-14 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 public - 1.0 2_CP NaiveContactMIp
NaiveContactMIpz server3 2016-04-14 public - 1.0 3_CP NaiveContactMIpz
CevoMI server4 2016-04-14 public - 1.0 4_CP cevoMI
Server 5 server5 2016-04-25 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 devel

Development server - no detailed description available

1239.0 7_CP ZZZserver 07
DeepCDpred server8 2017-11-03 DeepCDpred public - None 8_CP DeepCDpred
Server 9 server9 2018-03-14 devel

Development server - no detailed description available

3113.0 9_CP ZZZserver 09