Dataset Information

AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain-domain interaction prediction.

ABSTRACT: Most proteins consist of multiple domains, independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form new protein architectures. Template-based modeling methods can often detect homologous templates for individual domains, but templates that could be used to model the entire query protein are often not available.We have developed a fast docking algorithm ab initio domain assembly (AIDA) for assembling multi-domain protein structures, guided by the ab initio folding potential. This approach can be extended to discontinuous domains (i.e. domains with 'inserted' domains). When tested on experimentally solved structures of multi-domain proteins, the relative domain positions were accurately found among top 5000 models in 86% of cases. AIDA server can use domain assignments provided by the user or predict them from the provided sequence. The latter approach is particularly useful for automated protein structure prediction servers. The blind test consisting of 95 CASP10 targets shows that domain boundaries could be successfully determined for 97% of targets.The AIDA package as well as the benchmark sets used here are available for download at http://ffas.burnham.org/AIDA/.adam@sanfordburnham.orgSupplementary data are available at Bioinformatics online.

SUBMITTER: Xu D

PROVIDER: S-EPMC4481839 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain-domain interaction prediction.

Xu Dong D Jaroszewski Lukasz L Li Zhanwen Z Godzik Adam A

Bioinformatics (Oxford, England) 20150219 13

<h4>Motivation</h4>Most proteins consist of multiple domains, independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form new protein architectures. Template-based modeling methods can often detect homologous templates for individual domains, but templates that could be used to model the entire query protein are often not available.<h4>Results</h4>We have developed a fast docking algorithm ab initio domain assembly (AIDA) for assembling multi-domain ...[more]

PMID: 25701568

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Project description:We have developed a new combined approach for ab initio protein structure prediction. The protein conformation is described as a lattice chain connecting C(alpha) atoms, with attached C(beta) atoms and side-chain centers of mass. The model force field includes various short-range and long-range knowledge-based potentials derived from a statistical analysis of the regularities of protein structures. The combination of these energy terms is optimized through the maximization of correlation for 30 x 60,000 decoys between the root mean square deviation (RMSD) to native and energies, as well as the energy gap between native and the decoy ensemble. To accelerate the conformational search, a newly developed parallel hyperbolic sampling algorithm with a composite movement set is used in the Monte Carlo simulation processes. We exploit this strategy to successfully fold 41/100 small proteins (36 approximately 120 residues) with predicted structures having a RMSD from native below 6.5 A in the top five cluster centroids. To fold larger-size proteins as well as to improve the folding yield of small proteins, we incorporate into the basic force field side-chain contact predictions from our threading program PROSPECTOR where homologous proteins were excluded from the data base. With these threading-based restraints, the program can fold 83/125 test proteins (36 approximately 174 residues) with structures having a RMSD to native below 6.5 A in the top five cluster centroids. This shows the significant improvement of folding by using predicted tertiary restraints, especially when the accuracy of side-chain contact prediction is >20%. For native fold selection, we introduce quantities dependent on the cluster density and the combination of energy and free energy, which show a higher discriminative power to select the native structure than the previously used cluster energy or cluster size, and which can be used in native structure identification in blind simulations. These procedures are readily automated and are being implemented on a genomic scale.

Dataset Information

AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain-domain interaction prediction.

Publications

AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain-domain interaction prediction.

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