02308nas a2200325   4500008004100000245009900041210006900140300001200209490000700221520117300228653001501401653003601416653004401452653003601496653009601532653005201628100001501680700001401695700001701709700001601726700001401742700001901756700001501775700001501790700001601805700002401821700001801845700001301863856010601876       2009                        eng d00aMODBASE, a database of annotated comparative protein structure models and associated resources0 aMODBASE a database of annotated comparative protein structure mo  aD347-540 v373 aMODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).10a*Databases10aMolecular Mutation Polymorphism10aProtein Genomics Humans Ligands *Models10aProtein User-Computer Interface10aSingle Nucleotide Protein Folding Protein Interaction Domains and Motifs *Protein Structure10aTertiary Proteins/genetics *Structural Homology1 aPieper, U.1 aEswar, N.1 aWebb, B., M.1 aEramian, D.1 aKelly, L.1 aBarkan, D., T.1 aCarter, H.1 aMankoo, P.1 aKarchin, R.1 aMarti-Renom, M., A.1 aDavis, F., P.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1894828202005nas 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=1842876702131nas a2200217   4500008004100000245007200041210006900113300001200182490000700194520144000201653001201641653002101653653003601674100001601710700001701726700001401743700001301757700001301770700002401783856010601807       2006                        eng d00aA composite score for predicting errors in protein structure models0 acomposite score for predicting errors in protein structure model  a1653-660 v153 aReliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.10a*Models10aMolecular Models10aTheoretical Proteins/*chemistry1 aEramian, D.1 aShen, M., Y.1 aDevos, D.1 aMelo, F.1 aSali, A.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1675160602671nas 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=16381869