We explore the ability of a simple simulated annealing procedure to assemble native-like structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear as the first two terms in a series expansion for the residue probability distributions in the protein database; the decoupling of the distance and environment dependencies of the distributions resolves the major problems with current database-derived scoring functions noted by Thomas and Dill. The simulated annealing procedure rapidly and frequently generates native-like structures for small helical proteins and better than random structures for small b sheet containing proteins. Most of the simulated structures have native-like solvent accessibility and secondary structure patterns, and thus ensembles of these structures provide a particularly challenging set of decoys for evaluating scoring functions. We investigate the effects of multiple sequence information and different types of conformational constraints on the overall performance of the method, and the ability of a variety of recently developed scoring functions to recognize the native-like conformations in the ensembles of simulated structures.

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The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures ff-helix, fi-strand and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and of the characteristic periodicity in ff-helices. Since these single-structure networks all have less than 600 adjustable weights over-fitting is avoided. To obtain a three-state prediction of ff-helix, fi-strand or coil, ensembles of single-structure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using seven-fold cross-validation on a database of 126 non-homologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthews' correlation c...

Introduction The prediction of protein three dimensional structure from sequence alone with accuracy rivalling that of experiment is an unsolved problem. However, for certain classes of small globular proteins, it is possible, in some cases, to computationally generate low resolution models of a sequence (ß 6 A C ff root mean square deviation of the coordinates (cRMSD) from the experimental structure) 1;2 . As electron microscopists have demonstrated, even low resolution models can yield valuable insights about the function of a protein. Given the large number of sequences being determined and the relatively slow progress of protein structure prediction methods, low resolution models generated by current approaches can be used to elucidate details about structure and function for proteins whose atomic structure has not been determined experimentally. a Proceedings of the Pacific Symposium on Bioc

Blind predictions of the local structure of nine CASP2 targets were made using the I-sites library of short sequence--- structure motifs, revealing strengths and weaknesses in this new knowledge-based method. Many turns between secondary structural elements were accurately predicted. Estimates of the confidence of prediction correlated well with the accuracy over the whole set. Bias toward structures used to develop the library was minimal, probably because of the extensive use of cross-validation. However, helix positions were better predicted by the PHD program. The method is likely to be sensitive to the quality of the sequence alignment. A general measure for evaluating local structure predictions is suggested. Proteins, Suppl. 1:167-- 171, 1997. r 1998 Wiley-Liss, Inc. Key words: sequence profiles building-blocks; secondary helix; strand turn knowledge-based

Multiplesequencealignmentsare aroutinetoolinproteinfoldrecognition,butmultiplestructurealignmentsarecomputationallyless cooperative.Thisworkdescribesamethodforproteinsequencethreadingandsequence -to-structure alignmentsthatusesmultiplealignedstructures, theaimbeingtoimprovemodelsfromprotein threadingcalculations.Sequencesarealignedinto afieldduetocorrespondingsitesinhomologous proteins.Onthebasisofatestsetofmorethan570 proteinpairs,theproceduredoesimprovealignmentquality, althoughnomorethanaveragingover sequences.Fortheforcefieldtested,thebenefitof structureaveragingissmallerthanthatofadding sequencesimilaritytermsoracontributionfrom secondarystructurepredictions.Althoughthereis asignificantimprovementinthequalityofsequenceto -structurealignments,thisdoesnotdirectlytranslatetoanimmediateimprovementinfoldrecogni - tioncapability.Proteins2002;47:496--505.

Previous studies of the conformations of peptides spanning the length of the a-spectrin SH3 domain suggested that SH3 domains lack independently folding substructures. Using a local structure prediction method based on the I-sites library of sequence-structure motifs, we identi®ed a seven residue peptide in the src SH3 domain predicted to adopt a nativelike structure, a type II b-turn bridging unpaired b-strands, that was not contained intact in any of the SH3 domain peptides studied earlier. NMR characterization con®rmed that the isolated peptide, FKKGERL, adopts a structure similar to that adopted in the native protein: the NOE and 3JNHa coupling constant patterns were indicative of a type II b-turn, and NOEs between the Phe and the Leu side-chains suggest that they are juxtaposed as in the prediction and the native structure. These results support the idea that high-con®dence I-sites predictions identify protein segments that are likely to form native-like structures early in folding. # 1998 Academic Press

ABSTRACT Calmodulin (CaM) signaling involves important, wide spread eukaryotic protein– protein interactions. The solved structures of CaM associated with several of its binding targets, the distinctive binding mechanism of CaM, and the significant trypsin sensitivity of the binding targets combine to indicate that the process of association likely involves coupled binding and folding for both CaM and its binding targets. Here, we use bioinformatics approaches to test the hypothesis that CaMbinding targets are intrinsically disordered. We developed a predictor of CaM-binding regions and estimated its performance. Per residue accuracy of this predictor reached 81%, which, in combination with a high recall/precision balance at the binding region level, suggests high predictability of CaMbinding partners. An analysis of putative CaMbinding proteins in yeast and human strongly indicates that their molecular functions are related to those of intrinsically disordered proteins. These findings add to the growing list of examples in which intrinsically disordered protein regions are indicated to provide the basis for cell signaling and regulation. Proteins 2006;63:398–410. © 2006 Wiley-Liss, Inc. Key words: protein–protein interactions; protein function; unfolded; unstructured

Summary: Sausage is a protein sequence threading program, but with remarkable run-time flexibility. Using different scripts, it can calculate protein sequencestructure alignments, search structure libraries, swap force fields, create models from alignments, convert file formats and analyse results. There are several different force fields which might be classed as knowledge-based, although they do not rely on Boltzmann statistics. Different force fields are used for alignment calculations and subsequent ranking of calculated models.

Introduction If you are given a protein's sequence, you might have all the information you need to predict its structure. You have the composition and (bond) topology of the system, so you only have to rearrange its atoms so they are somewhere in the major free energy basin and the problem is solved. There might be some problems with this approach. The search space grows exponentially with the number of particles. If you are able to search the conformational space for a five residue peptide this year, it might be another year or two until you can tackle six residues when your computer is several fold faster. Then, you have to have an energy or score function which really can discriminate between correct and incorrect conformations. Any score function which is fast enough to apply to more than a few hundred atoms will be full of approximations and no longer close to the best level of theory. It is also worth remembering that we are only assuming that the native protein conformat