Server Name (3D) Server Alias Active Since Deactived Since Weblink Server Type
Server 0 server0 2011-07-01 Devel

Development server - no detailed description available

57.5349280095 76 0 server 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

67.7131886948 1968 11 Robetta
IntFOLD2-TS server12 2012-03-02 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

63.8913880743 1625 12 IntFOLD2-TS
M4T server13 2012-03-02 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

68.3897820252 724 13 M4T
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

51.5364876698 107 17 Phyre2
Server 19 server19 2013-06-13 Devel

Development server - no detailed description available

68.4572567828 2256 19 server 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

60.8668205137 70 20 SWISS-MODEL
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

65.2037796318 723 22 RaptorX
Princeton_TEMPLATE server27 2013-09-23 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.

57.7852022506 319 27 Princeton_TEMPLATE
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.

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

60.1126354835 272 30 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.

54.4755456608 - 31 NaiveBlits
RBO Aleph server32 2014-01-07 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

59.8796185484 1339 32 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.

63.5980530922 2091 33 IntFOLD3-TS
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.

56.4085912915 193 36 NaiveBLAST
Server 45 server45 2014-07-04 Devel

Development server - no detailed description available

65.2894638655 1339 45 server 45
Server 46 server46 2014-09-24 Devel

Development server - no detailed description available

59.0589076236 256 46 server 46
Server 48 server48 2014-02-12 Devel

Development server - no detailed description available

59.9184510747 29 48 server 48
Server 49 server49 2016-11-18 Devel

Development server - no detailed description available

61.828013819 145 49 server 49
HHpredB server4 2011-07-01 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

58.1826630315 323 4 HHpredB
Server 50 server50 2015-06-12 Devel

Development server - no detailed description available

63.0320283601 3249 50 server 50
Server 54 server54 2016-02-26 Devel

Development server - no detailed description available

62.1638793817 91 54 server 54
Server 55 server55 2016-04-07 Devel

Development server - no detailed description available

65.1169351117 133 55 server 55
Server 56 server56 2016-04-16 Devel

Development server - no detailed description available

65.5009456948 1924 56 server 56
Server 57 server57 2016-04-22 Devel

Development server - no detailed description available

64.273588045 1938 57 server 57
IntFOLD4-TSb server58 2016-04-29 IntFOLD4-TSb Public - 66.0101564693 1912 58 IntFOLD4-TSb
Server 59 server59 2012-03-02 Devel

Development server - no detailed description available

61.5938234487 1332 59 server 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

61.2934883454 1788 5 IntFOLD-TS
Server 60 server60 2016-09-06 Devel

Development server - no detailed description available

45.8385577397 873 60 server 60
PRIMO server61 2016-09-26 PRIMO Public - 59.1261092539 124 61 PRIMO
PRIMO_BST_3D server62 2016-10-16 PRIMO_BST_3D Public - 58.4321771568 119 62 PRIMO_BST_3D
PRIMO_HHS_3D server63 2016-10-16 PRIMO_HHS_3D Public - 56.6854984577 128 63 PRIMO_HHS_3D
PRIMO_HHS_CL server64 2016-10-16 PRIMO_HHS_CL Public - 56.9879936191 93 64 PRIMO_HHS_CL
PRIMO_BST_CL server65 2016-10-16 PRIMO_BST_CL Public - 60.6953004947 79 65 PRIMO_BST_CL
Server 66 server66 2017-02-06 Devel

Development server - no detailed description available

22.7475000918 4600 66 server 66
Server 6 server6 2011-12-10 Devel

Development server - no detailed description available

55.8957311962 868 6 server 06
Server 7 server7 2011-12-10 Devel

Development server - no detailed description available

54.9925843309 1058 7 server 07
Server Name (LB) Server Alias Active Since Deactived Since Weblink Server Type
RaptorX-Binding server11 2013-09-12 RaptorX-Binding public - 2178 11 RaptorX-Binding
Naive Homology server1 2011-12-01 public - 218 1 Naive Homology
Naive Pocket server2 2011-12-01 public - 281 2 Naive Pocket
Naive Conservation server3 2011-12-01 public - 189 3 Naive Conservation
FunFOLD server4 2012-07-20 2014-03-25 FunFOLD public

Abstract

The accurate prediction of ligand binding residues from amino acid sequences is important for the automated functional annotation of novel proteins. In the previous two CASP experiments, the most successful methods in the function prediction category were those which used structural superpositions of 3D models and related templates with bound ligands in order to identify putative contacting residues. However, whilst most of this prediction process can be automated, visual inspection and manual adjustments of parameters, such as the distance thresholds used for each target, have often been required to prevent over prediction. Here we describe a novel method FunFOLD, which uses an automatic approach for cluster identification and residue selection. The software provided can easily be integrated into existing fold recognition servers, requiring only a 3D model and list of templates as inputs. A simple web interface is also provided allowing access to non-expert users. The method has been benchmarked against the top servers and manual prediction groups tested at both CASP8 and CASP9.

D. B., Tetchner, S. J. & McGuffin, L. J. (2011) FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins.

BMC Bioinformatics, 12, 160.

http://dx.doi.org/10.1186/1471-2105-12-160

- 4 FunFOLD
FunFOLD2-FN server5 2012-07-20 2015-06-11 FunFOLD2-FN public

Abstract

The accurate prediction of ligand binding residues from amino acid sequences is important for the automated functional annotation of novel proteins. In the previous two CASP experiments, the most successful methods in the function prediction category were those which used structural superpositions of 3D models and related templates with bound ligands in order to identify putative contacting residues. However, whilst most of this prediction process can be automated, visual inspection and manual adjustments of parameters, such as the distance thresholds used for each target, have often been required to prevent over prediction. Here we describe a novel method FunFOLD, which uses an automatic approach for cluster identification and residue selection. The software provided can easily be integrated into existing fold recognition servers, requiring only a 3D model and list of templates as inputs. A simple web interface is also provided allowing access to non-expert users. The method has been benchmarked against the top servers and manual prediction groups tested at both CASP8 and CASP9.

Roche, D. B., Buenavista, M. T., McGuffin, L. J. (2013) The FunFOLD2 server for the prediction of protein-ligand interactions.

Nucleic Acids Res., 41, W303-7.

http://dx.doi.org/10.1093%2Fnar%2Fgkt498

2111 5 FunFOLD2-FN
HHfuncs server6 2012-07-20 2014-09-04 HHfuncs public

Abstract

HHfuncs: combined homology-based and de-novo functional site prediction

HHfuncs starts by searching for homologous templates in the FireDB[1] database using HHsearch[2]. For each annotated binding site in the matched templates, HHfuncs calculates a probability that the FireDB annotation can be transferred, by multiplying three probabilities: (1) The probability that the template is homologous to the target, (2) the probability that the binding site is correctly aligned, and (3) the probability that the binding site is evolutionarily conserved between target and template. If no functional binding site can be predicted with probability >0.5, the sequence-based de novo method FRpred[3] is run to estimate the probability for each residue to form part of a functional site. If a reliable 3D model is available, the RankProp[4] algorithm is employed to identify spatial clusters of high FRpred probabilities. The top-ranked residues with probability >0.3 are predicted to form a functional site.

[1] Lopez G, Valencia A, Tress M (2007) FireDB - a database of functionally important residues from proteins of known structure. NAR 35:D219-223.

[2] Soeding J (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21:951-960.

[3] Fischer JD, Mayer CE, Söding J (2008) Prediction of protein functional residues from sequence by probability density estimation. Bioinformatics 24:613-620.

[4] Noble WS, Kuang R, Leslie C, Weston J (2005) Identifying remote protein homologs by network propagation. FEBS J. 272(20):5119-28.

- 6 HHfuncs
COACH server7 2012-11-28 COACH public

Abstract

Identification of protein-ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem. We developed two new methods, one based on binding-specific substructure comparison (TM-SITE) and another on sequence profile alignment (S-SITE), for complementary binding site predictions. The methods are tested on a set of 500 non-redundant proteins harboring 814 natural, drug-like and metal ion molecules. Starting from low-resolution protein structure predictions, the methods successfully recognize >51% of binding residues with average Matthews correlation coefficient (MCC) significantly higher (with P-value <10(-9) in student t-test) than other state-of-the-art methods, including COFACTOR, FINDSITE and ConCavity. When combining TM-SITE and S-SITE with other structure-based programs, a consensus approach (COACH) can increase MCC by 15% over the best individual predictions. COACH was examined in the recent community-wide CAMEO experiment and consistently ranked as the best method in last 22 individual datasets with the Area Under the Curve score 22.5% higher than the second best method. These data demonstrate a new robust approach to protein-ligand binding site recognition, which is ready for genome-wide structure-based function annotations.

Yang J, Roy A, Zhang Y. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment

Bioinformatics. 2013 Oct 15;29(20):2588-95.

http://dx.doi.org/10.1093/bioinformatics/btt447

887 7 COACH
FunFOLD2-LB server9 2013-07-31 2014-06-27 FunFOLD2-LB public - - 9 FunFOLD2-LB
Server Name (QE) Server Alias Active Since Deactived Since Weblink Predictor Type Server Type
Verify3d smoothed server0 2014-02-07 Standalone Public - 0 Verify3d smoothed
Server 10 server10 2014-05-16 Server Devel

Development server - no detailed description available

- 10 server 10
Server 11 server11 2014-05-23 Server Devel

Development server - no detailed description available

- 11 server 11
VoroMQA_sw5 server15 2014-12-18 VoroMQA_sw5 Server 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.

- 15 VoroMQA_sw5
EQuant 2 server16 2015-06-12 eQuant 2 Server Public - 16 eQuant 2
VoroMQA_v2 server17 2015-07-23 VoroMQA_v2 Server 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.

- 17 VoroMQA_v2
ModFOLD6 server18 2016-02-02 ModFOLD6 Server Public - 18 ModFOLD6
QMEANDisCo server19 2016-02-02 QMEANDisCo Server Public - 19 QMEANDisCo
Dfire v1.1 server1 2014-02-07 Dfire v1.1 Standalone 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

- 1 Dfire v1.1
NewQMEAN server20 2016-02-10 NewQMEAN Server Public - 20 NewQMEAN
Server 21 server21 2016-04-22 Server Devel

Development server - no detailed description available

- 21 server 21
Server 22 server22 2016-08-05 Server Devel

Development server - no detailed description available

- 22 server 22
Baseline Potential server23 2016-10-24 Standalone Public - 23 Baseline Potential
Prosa2003 server2 2014-02-07 Prosa2003 Standalone Public - 2 Prosa2003
Naive PSIBlast server3 2014-02-07 Standalone 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

- 3 Naive PSIBlast
Qmean 7.11 server4 2014-02-07 Qmean 7.11 Standalone 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

- 4 Qmean 7.11
Server 6 server6 2014-02-07 Standalone Devel

Development server - no detailed description available

- 6 server 06
ModFOLD4 server7 2014-04-11 ModFOLD4 Server 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

- 7 ModFOLD4
ProQ2 server8 2014-04-25 ProQ2 Server 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

- 8 ProQ2
Server Name (CP) Server Alias Active Since Deactived Since Weblink Server Type
BaseLine_MI server1 2016-03-14 BaseLine_MI 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 BaseLine_MI
BaseLine_MIp server2 2016-04-14 BaseLine_MIp public - - 2 BaseLine_MIp
BaseLine_MIpz server3 2016-04-14 BaseLine_MIpz public - - 3 BaseLine_MIpz
CevoMI server4 2016-04-14 CevoMI public - - 4 cevoMI