02608nas a2200217   4500008004100000245009500041210006900136300001200205490000600217520172500223653008401948653002102032653012902053653002102182100001602203700001302219700001402232700002402246700001402270856010602284       2007                        eng d00aCharacterization of protein hubs by inferring interacting motifs from protein interactions0 aCharacterization of protein hubs by inferring interacting motifs  a1761-710 v33 aThe characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.10aAmino Acid Motifs Amino Acid Sequence Binding Sites Computer Simulation *Models10aChemical *Models10aMolecular Molecular Sequence Data Protein Binding Protein Interaction Mapping/*methods Proteins/*chemistry Sequence Analysis10aProtein/*methods1 aAragues, R.1 aSali, A.1 aBonet, J.1 aMarti-Renom, M., A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1794170502005nas 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=1675160602167nas a2200301   4500008004100000245008600041210006900127300001100196490000700207520100200214653012701216653002601343653013701369653003901506653001801545100002001563700001901583700001901602700001901621700001401640700002401654700001701678700001801695700001301713700001901726700001401745856010601759       2005                        eng d00aThe C-type lectin fold as an evolutionary solution for massive sequence variation0 aCtype lectin fold as an evolutionary solution for massive sequen  a886-920 v123 aOnly few instances are known of protein folds that tolerate massive sequence variation for the sake of binding diversity. The most extensively characterized is the immunoglobulin fold. We now add to this the C-type lectin (CLec) fold, as found in the major tropism determinant (Mtd), a retroelement-encoded receptor-binding protein of Bordetella bacteriophage. Variation in Mtd, with its approximately 10(13) possible sequences, enables phage adaptation to Bordetella spp. Mtd is an intertwined, pyramid-shaped trimer, with variable residues organized by its CLec fold into discrete receptor-binding sites. The CLec fold provides a highly static scaffold for combinatorial display of variable residues, probably reflecting a different evolutionary solution for balancing diversity against stability from that in the immunoglobulin fold. Mtd variants are biased toward the receptor pertactin, and there is evidence that the CLec fold is used broadly for sequence variation by related retroelements.10aAmino Acid Sequence Bacterial Outer Membrane Proteins/*chemistry Bacteriophages/*metabolism Bordetella/*virology Evolution10aBordetella/*chemistry10aC-Type/*chemistry Molecular Sequence Data Protein Conformation Protein Folding Viral Proteins/*chemistry/*genetics Virulence Factors10aMolecular Genetic Variation Genome10aViral Lectins1 aMcMahon, S., A.1 aMiller, J., L.1 aLawton, J., A.1 aKerkow, D., E.1 aHodes, A.1 aMarti-Renom, M., A.1 aDoulatov, S.1 aNarayanan, E.1 aSali, A.1 aMiller, J., F.1 aGhosh, P.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16170324