J. Comput. Biol. - Fast geometric consensus approach for protein model quality assessment.

Tópicos

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Resumo

Model quality assessment (MQA) is an integral part of protein structure prediction methods that typically generate multiple candidate models. The challenge lies in ranking and selecting the best models using a variety of physical, knowledge-based, and geometric consensus (GC)-based scoring functions. In particular, 3D-Jury and related GC methods assume that well-predicted (sub-)structures are more likely to occur frequently in a population of candidate models, compared to incorrectly folded fragments. While this approach is very successful in the context of diversified sets of models, identifying similar substructures is computationally expensive since all pairs of models need to be superimposed using MaxSub or related heuristics for structure-to-structure alignment. Here, we consider a fast alternative, in which structural similarity is assessed using 1D profiles, e.g., consisting of relative solvent accessibilities and secondary structures of equivalent amino acid residues in the respective models. We show that the new approach, dubbed 1D-Jury, allows to implicitly compare and rank N models in O(N) time, as opposed to quadratic complexity of 3D-Jury and related clustering-based methods. In addition, 1D-Jury avoids computationally expensive 3D superposition of pairs of models. At the same time, structural similarity scores based on 1D profiles are shown to correlate strongly with those obtained using MaxSub. In terms of the ability to select the best models as top candidates 1D-Jury performs on par with other GC methods. Other potential applications of the new approach, including fast clustering of large numbers of intermediate structures generated by folding simulations, are discussed as well.

Resumo Limpo

model qualiti assess mqa integr part protein structur predict method typic generat multipl candid model challeng lie rank select best model use varieti physic knowledgebas geometr consensus gcbase score function particular djuri relat gc method assum wellpredict substructur like occur frequent popul candid model compar incorrect fold fragment approach success context diversifi set model identifi similar substructur comput expens sinc pair model need superimpos use maxsub relat heurist structuretostructur align consid fast altern structur similar assess use d profil eg consist relat solvent access secondari structur equival amino acid residu respect model show new approach dub djuri allow implicit compar rank n model time oppos quadrat complex djuri relat clusteringbas method addit djuri avoid comput expens d superposit pair model time structur similar score base d profil shown correl strong obtain use maxsub term abil select best model top candid djuri perform par gc method potenti applic new approach includ fast cluster larg number intermedi structur generat fold simul discuss well

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