00387nas a2200109   4500008004100000245004800041210004800089260005700137100001800194700002400212856004100236       2008                        eng d00aAssessment of protein structure predictions0 aAssessment of protein structure predictions  aNew Jersey, USAbWorld Scientific Publishing Company1 aCapriotti, E.1 aMarti-Renom, M., A.  uhttp://www.amazon.com/dp/9812778772/02500nas a2200253   4500008004100000245008800041210006900129300000700198490001400205520139900219653007701618653005701695653019901752653003901951653002701990100002402017700001402041700002402055700001802079700001502097700001502112700001302127856010602140       2007                        eng d00aThe AnnoLite and AnnoLyze programs for comparative annotation of protein structures0 aAnnoLite and AnnoLyze programs for comparative annotation of pro  aS40 v8 Suppl 43 aBACKGROUND: Advances in structural biology, including structural genomics, have resulted in a rapid increase in the number of experimentally determined protein structures. However, about half of the structures deposited by the structural genomics consortia have little or no information about their biological function. Therefore, there is a need for tools for automatically and comprehensively annotating the function of protein structures. We aim to provide such tools by applying comparative protein structure annotation that relies on detectable relationships between protein structures to transfer functional annotations. Here we introduce two programs, AnnoLite and AnnoLyze, which use the structural alignments deposited in the DBAli database. DESCRIPTION: AnnoLite predicts the SCOP, CATH, EC, InterPro, PfamA, and GO terms with an average sensitivity of  90% and average precision of  80%. AnnoLyze predicts ligand binding site and domain interaction patches with an average sensitivity of  70% and average precision of  30%, correctly localizing binding sites for small molecules in  95% of its predictions. CONCLUSION: The AnnoLite and AnnoLyze programs for comparative annotation of protein structures can reliably and automatically annotate new protein structures. The programs are fully accessible via the Internet as part of the DBAli suite of tools at http://salilab.org/DBAli/.10a*Algorithms Amino Acid Sequence Confidence Intervals Data Interpretation10aAmino Acid *Software Structure-Activity Relationship10aProtein Information Storage and Retrieval/methods Molecular Sequence Data Proteins/*chemistry/classification/*metabolism Sensitivity and Specificity Sequence Alignment/*methods Sequence Analysis10aProtein/*methods Sequence Homology10aStatistical *Databases1 aMarti-Renom, M., A.1 aRossi, A.1 aAl-Shahrour, Fatima1 aDavis, F., P.1 aPieper, U.1 aDopazo, J.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1757014702187nas 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=1650624302319nas 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=15044736