01187nas a2200145   4500008004100000245013100041210006900172300000800241490000600249520062400255100001900879700001300898700002400911856010600935       2008                        eng d00aEvolutionary potentials: structure specific knowledge-based potentials exploiting the evolutionary record of sequence homologs0 aEvolutionary potentials structure specific knowledgebased potent  aR680 v93 aABSTRACT: We introduce a new type of knowledge-based potentials for protein structure prediction, called ’evolutionary potentials’, which are derived using a single experimental protein structure and all three-dimensional models of its homologous sequences. The new potentials have been benchmarked against other knowledge-based potentials, resulting in a significant increase in accuracy for model assessment. In contrast to standard knowledge-based potentials, we propose that evolutionary potentials capture key determinants of thermodynamic stability and specific sequence constraints required for fast folding.1 aPanjkovich, A.1 aMelo, F.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1839751702187nas a2200157   4500008004100000245009000041210006900131300001100200490000700211520142200218653023401640653001201874100001301886700002401899856010601923       2006                        eng d00aAccuracy of sequence alignment and fold assessment using reduced amino acid alphabets0 aAccuracy of sequence alignment and fold assessment using reduced  a986-950 v633 aReduced or simplified amino acid alphabets group the 20 naturally occurring amino acids into a smaller number of representative protein residues. To date, several reduced amino acid alphabets have been proposed, which have been derived and optimized by a variety of methods. The resulting reduced amino acid alphabets have been applied to pattern recognition, generation of consensus sequences from multiple alignments, protein folding, and protein structure prediction. In this work, amino acid substitution matrices and statistical potentials were derived based on several reduced amino acid alphabets and their performance assessed in a large benchmark for the tasks of sequence alignment and fold assessment of protein structure models, using as a reference frame the standard alphabet of 20 amino acids. The results showed that a large reduction in the total number of residue types does not necessarily translate into a significant loss of discriminative power for sequence alignment and fold assessment. Therefore, some definitions of a few residue types are able to encode most of the relevant sequence/structure information that is present in the 20 standard amino acids. Based on these results, we suggest that the use of reduced amino acid alphabets may allow to increasing the accuracy of current substitution matrices and statistical potentials for the prediction of protein structure of remote homologs.10aAmino Acid Sequence Amino Acids/*chemistry/classification/*metabolism Consensus Sequence Molecular Sequence Data Oxidation-Reduction *Protein Folding Proteins/*chemistry/*metabolism Sequence Alignment/*methods Structural Homology10aProtein1 aMelo, F.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1650624302131nas 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