Fold assembly of small proteins using Monte Carlo simulations driven by restraints derived from multiple sequence alignments

Abstract

The feasibility of predicting the global fold of small proteins by incorporating predicted secondary and tertiary restraints into ab initio folding simulations has been demonstrated on a test set comprised of 20 nonhomologous proteins, of which one was a blind prediction of target 42 in the recent CASP2 contest. These proteins contain from 37 to 100 residues and represent all secondary structural classes and a representative variety of global topologies. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are derived from multiple sequence alignments via a two-step process. First, seed side-chain contacts are identi®ed from correlated mutation analysis, and then a threading-based algorithm is used to expand the number of these seed contacts. A lattice-based reduced protein model and a folding algorithm designed to incorporate these predicted restraints is described. Depending upon fold complexity, it is possible to assemble native-like topologies whose coordinate root-mean-square deviation from native is between 3.0 A Ê and 6.5 A Ê . The requisite level of accuracy in side-chain contact map prediction can be roughly 25% on average, provided that about 60% of the contact predictions are correct within AE1 residue and 95% of the predictions are correct within AE4 residues. Precision in tertiary contact prediction is more critical than absolute accuracy. Furthermore, only a subset of the tertiary contacts, on the order of 25% of the total, is suf®cient for successful topology assembly. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm holds considerable promise for the prediction of the global topology of small proteins.

Toward predicting protein topology: An approach to identifying β hairpins

“…Again the results are encouraging, with several structures predicted in the low-resolution range (14,15,18,19). However, such hybrid methods are still limited to small proteins because of their large computational requirements.…”

“…Secondary structure predictions have also been incorporated into ab initio structure prediction methods (14)(15)(16)(17). Again the results are encouraging, with several structures predicted in the low-resolution range (14,15,18,19).…”

Ab-initio prediction and reliability of protein structural genomics by PROPAINOR algorithm

“…A comparison of our method with some of the recent well-received computational methods of protein structure prediction (for example, Simons et al, 1998;Ortiz et al, 1998;Huang et al, 1999;Xia et al, 2000;Osguthorpe, 1999;Simons et al, 2001) shows it to be better or comparable (Table 2). Most of these methods use scoring or fitness functions to select the optimal solutions from a large number of conformations (decoys 6 ).…”

Modelagem de proteínas por homologia

“…A principal razão é que estas leis ainda não são perfeitamente conhecidas, o que torna muito difícil obter conformações simultaneamente estáveis e funcionais a custo computacional razoável. Apesar dessas dificuldades, progressos na predição da estrutura tridimensional de peptídeos e pequenas proteínas por métodos ab initio têm sido relatados recentemente [12][13][14][15][16][17] . A ferramenta mais bem sucedida de predição de estruturas tridimensionais de proteínas é a modelagem por homologia, também conhecida como modelagem comparativa ("comparative protein modeling") [19][20][21][22][23][24][25][26] .…”

“…É possível, em princípio, predizer a estrutura tridimensional de proteínas a partir de sua estrutura primária. Este método é conhecido como modelagem de proteínas ab initio [12][13][14][15][16][17] . A determinação da estrutura secundária de proteínas por este método é um sério desafio, sendo hoje o maior problema não resolvido da biologia molecular estrutural 18 .…”