02044nas a2200181   4500008004100000245008700041210006900128260001300197300001100210490000700221520141100228100002401639700002201663700002501685700002101710700001701731856011401748       2009                        eng d00aAlignment of multiple protein structures based on sequence and structure features.0 aAlignment of multiple protein structures based on sequence and s  c2009 Sep  a569-740 v223 a<p>Comparing the structures of proteins is crucial to gaining insight into protein evolution and function. Here, we align the sequences of multiple protein structures by a dynamic programming optimization of a scoring function that is a sum of an affine gap penalty and terms dependent on various sequence and structure features (SALIGN). The features include amino acid residue type, residue position, residue accessible surface area, residue secondary structure state and the conformation of a short segment centered on the residue. The multiple alignment is built by following the ’guide’ tree constructed from the matrix of all pairwise protein alignment scores. Importantly, the method does not depend on the exact values of various parameters, such as feature weights and gap penalties, because the optimal alignment across a range of parameter values is found. Using multiple structure alignments in the HOMSTRAD database, SALIGN was benchmarked against MUSTANG for multiple alignments as well as against TM-align and CE for pairwise alignments. On the average, SALIGN produces a 15% improvement in structural overlap over HOMSTRAD and 14% over MUSTANG, and yields more equivalent structural positions than TM-align and CE in 90% and 95% of cases, respectively. The utility of accurate multiple structure alignment is illustrated by its application to comparative protein structure modeling.</p>1 aMadhusudhan, M., S.1 aWebb, Benjamin, M1 aMarti-Renom, Marc, A1 aEswar, Narayanan1 aSali, Andrej  uhttp://clinbioinfosspa.es/content/alignment-multiple-protein-structures-based-sequence-and-structure-features02151nas a2200277   4500008004100000245005200041210005100093300001100144490000700155520104000162653008701202653005701289653019401346653003901540653002701579100002401606700001501630700002401645700001401669700001401683700001801697700002401715700001501739700001301754856010601767       2007                        eng d00aDBAli tools: mining the protein structure space0 aDBAli tools mining the protein structure space  aW393-70 v353 aThe DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.10a*Algorithms Amino Acid Sequence Computational Biology/*methods Data Interpretation10aAmino Acid *Software Structure-Activity Relationship10aProtein Internet Molecular Sequence Data Protein Conformation Proteins/*chemistry/classification/*metabolism Pseudomonas aeruginosa/*metabolism Sequence Alignment/*methods Sequence Analysis10aProtein/*methods Sequence Homology10aStatistical *Databases1 aMarti-Renom, M., A.1 aPieper, U.1 aMadhusudhan, M., S.1 aRossi, A.1 aEswar, N.1 aDavis, F., P.1 aAl-Shahrour, Fatima1 aDopazo, J.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1747851302005nas a2200253   4500008004100000245005800041210005800099300001300157490001400170520105600184653008801240653002101328653012901349653003101478100001401509700001301523700002401536700002401560700001601584700001701600700001501617700001301632856010601645       2006                        eng d00aComparative protein structure modeling using Modeller0 aComparative protein structure modeling using Modeller  aUnit 5 60 vChapter 53 aFunctional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.10aAlgorithms Amino Acid Sequence Computer Simulation Crystallography/*methods *Models10aChemical *Models10aMolecular Molecular Sequence Data Protein Conformation Protein Folding Proteins/*chemistry/*ultrastructure Sequence Analysis10aProtein/*methods *Software1 aEswar, N.1 aWebb, B.1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aEramian, D.1 aShen, M., Y.1 aPieper, U.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1842876702671nas a2200349   4500008004100000245009900041210006900140300001100209490000700220520147800227653002901705653002701734653004401761653005601805653004001861653008301901100001501984700001401999700001802013700001602031700002402047700001402071700002402085700001602109700001702125700001602142700001702158700001402175700001302189700001302202856010602215       2006                        eng d00aMODBASE: a database of annotated comparative protein structure models and associated resources0 aMODBASE a database of annotated comparative protein structure mo  aD291-50 v343 aMODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).10aBinding Sites *Databases10aMolecular Polymorphism10aProtein Humans Internet Ligands *Models10aProtein Systems Integration User-Computer Interface10aSingle Nucleotide Protein Structure10aTertiary Proteins/*chemistry/genetics/metabolism Software *Structural Homology1 aPieper, U.1 aEswar, N.1 aDavis, F., P.1 aBraberg, H.1 aMadhusudhan, M., S.1 aRossi, A.1 aMarti-Renom, M., A.1 aKarchin, R.1 aWebb, B., M.1 aEramian, D.1 aShen, M., Y.1 aKelly, L.1 aMelo, F.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1638186902070nas a2200205   4500008004100000245006600041210006500107300001100172490000700183520128000190653004201470653002501512653010401537653004001641100002401681700002401705700001601729700001301745856010601758       2006                        eng d00aVariable gap penalty for protein sequence-structure alignment0 aVariable gap penalty for protein sequencestructure alignment  a129-330 v193 aThe penalty for inserting gaps into an alignment between two protein sequences is a major determinant of the alignment accuracy. Here, we present an algorithm for finding a globally optimal alignment by dynamic programming that can use a variable gap penalty (VGP) function of any form. We also describe a specific function that depends on the structural context of an insertion or deletion. It penalizes gaps that are introduced within regions of regular secondary structure, buried regions, straight segments and also between two spatially distant residues. The parameters of the penalty function were optimized on a set of 240 sequence pairs of known structure, spanning the sequence identity range of 20-40%. We then tested the algorithm on another set of 238 sequence pairs of known structures. The use of the VGP function increases the number of correctly aligned residues from 81.0 to 84.5% in comparison with the optimized affine gap penalty function; this difference is statistically significant according to Student’s t-test. We estimate that the new algorithm allows us to produce comparative models with an additional approximately 7 million accurately modeled residues in the approximately 1.1 million proteins that are detectably related to a known structure.10aAlgorithms Amino Acid Sequence Models10aAmino Acid *Software10aMolecular Molecular Sequence Data Proteins/*chemistry Sequence Alignment/*methods Sequence Analysis10aProtein/*methods *Sequence Homology1 aMadhusudhan, M., S.1 aMarti-Renom, M., A.1 aSanchez, R.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1642384602319nas a2200181   4500008004100000245005300041210005300094300001200147490000700159520157000166653006801736653008501804653008101889100002401970700002401994700001302018856010602031       2004                        eng d00aAlignment of protein sequences by their profiles0 aAlignment of protein sequences by their profiles  a1071-870 v133 aThe accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.10a*Algorithms Amino Acid Sequence Computational Biology Databases10aProtein Markov Chains Molecular Sequence Data *Protein Folding Protein Structure10aTertiary Proteins/*chemistry *Sequence Alignment Sequence Homology *Software1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1504473603269nas a2200337   4500008004100000245010000041210006900141300001200210490000700222520201600229653008002245653005102325653005202376653014002428100001502568700001402583700001602597700002402613700001802637700001902655700001702674700001402691700002402705700001402729700001302743700001902756700001802775700001902793700001302812856010602825       2004                        eng d00aMODBASE, a database of annotated comparative protein structure models, and associated resources0 aMODBASE a database of annotated comparative protein structure mo  aD217-220 v323 aMODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1,26,629 models for domains in 659,495 out of 1,182,126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24,113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb).10aAmino Acid Sequence Animals Binding Sites *Computational Biology *Databases10aMolecular Molecular Sequence Data Polymorphism10aProtein Genomics Humans Internet Ligands Models10aSingle Nucleotide Protein Binding Protein Conformation Proteins/*chemistry/genetics Sequence Alignment Software User-Computer Interface1 aPieper, U.1 aEswar, N.1 aBraberg, H.1 aMadhusudhan, M., S.1 aDavis, F., P.1 aStuart, A., C.1 aMirkovic, N.1 aRossi, A.1 aMarti-Renom, M., A.1 aFiser, A.1 aWebb, B.1 aGreenblatt, D.1 aHuang, C., C.1 aFerrin, T., E.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1468139802057nas a2200301   4500008004100000245006000041210005900101300001100160490000700171520105900178653002501237653001201262653007701274653003201351653007901383100001601462700001901478700002401497700001901521700002401540700001401564700001401578700001401592700001701606700001301623700001301636856010601649       2003                        eng d00aEVA: Evaluation of protein structure prediction servers0 aEVA Evaluation of protein structure prediction servers  a3311-50 v313 aEVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.10aAutomation Databases10aProtein10aProtein Internet *Protein Conformation Protein Folding Protein Structure10aProtein Structural Homology10aSecondary Proteins/chemistry Reproducibility of Results *Sequence Analysis1 aKoh, I., Y.1 aEyrich, V., A.1 aMarti-Renom, M., A.1 aPrzybylski, D.1 aMadhusudhan, M., S.1 aEswar, N.1 aGrana, O.1 aPazos, F.1 aValencia, A.1 aSali, A.1 aRost, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1282431501823nas a2200289   4500008004100000245006600041210006600107300001200173490000700185520083500192653004601027653002001073653011301093653003201206100001401238700001301252700001701265700001401282700001801296700001501314700001901329700002401348700002401372700001801396700001301414856010601427       2003                        eng d00aTools for comparative protein structure modeling and analysis0 aTools for comparative protein structure modeling and analysis  a3375-800 v313 aThe following resources for comparative protein structure modeling and analysis are described (http://salilab.org): MODELLER, a program for comparative modeling by satisfaction of spatial restraints; MODWEB, a web server for automated comparative modeling that relies on PSI-BLAST, IMPALA and MODELLER; MODLOOP, a web server for automated loop modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparative models based on distant known structures; MODBASE, a comprehensive database of annotated comparative models for all sequences detectably related to a known structure; MODVIEW, a Netscape plugin for Linux that integrates viewing of multiple sequences and structures; and SNPWEB, a web server for structure-based prediction of the functional impact of a single amino acid substitution.10aAmino Acid *Software *Structural Homology10aInternet Models10aMolecular Protein Folding Proteins/chemistry Reproducibility of Results Sequence Alignment Sequence Homology10aProtein Systems Integration1 aEswar, N.1 aJohn, B.1 aMirkovic, N.1 aFiser, A.1 aIlyin, V., A.1 aPieper, U.1 aStuart, A., C.1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aYerkovich, B.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1282433101632nas a2200193   4500008004100000245007000041210006900111300001100180490000700191520092300198653004001121653008301161100002401244700002401268700001401292700001301306700001301319856010601332       2002                        eng d00aReliability of assessment of protein structure prediction methods0 aReliability of assessment of protein structure prediction method  a435-400 v103 a<p>The reliability of ranking of protein structure modeling methods is assessed. The assessment is based on the parametric Student’s t test and the nonparametric Wilcox signed rank test of statistical significance of the difference between paired samples. The approach is applied to the ranking of the comparative modeling methods tested at the fourth meeting on Critical Assessment of Techniques for Protein Structure Prediction (CASP). It is shown that the 14 CASP4 test sequences may not be sufficient to reliably distinguish between the top eight methods, given the model quality differences and their standard deviations. We suggest that CASP needs to be supplemented by an assessment of protein structure prediction methods that is automated, continuous in time, based on several criteria applied to a large number of models, and with quantitative statistical reliability assigned to each characterization.</p>10a*Computer Simulation Humans *Models10aMolecular *Protein Conformation Proteins/*chemistry Reproducibility of Results1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aFiser, A.1 aRost, B.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1200544101566nas a2200229   4500008004100000245008100041210006900122300001100191490000700202520078900209653007500998100001901073700002401092700001901116700002401135700001401159700001401173700001701187700001301204700001301217856010601230       2001                        eng d00aEVA: continuous automatic evaluation of protein structure prediction servers0 aEVA continuous automatic evaluation of protein structure predict  a1242-30 v173 aEvaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. AVAILABILITY: http://cubic.bioc.columbia.edu/eva. CONTACT: eva@cubic.bioc.columbia.edu10aAutomation Internet *Protein Conformation Proteins/*analysis *Software1 aEyrich, V., A.1 aMarti-Renom, M., A.1 aPrzybylski, D.1 aMadhusudhan, M., S.1 aFiser, A.1 aPazos, F.1 aValencia, A.1 aSali, A.1 aRost, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11751240