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UProC: tools for ultra-fast protein domain classification.

ABSTRACT: MOTIVATION: With rapidly increasing volumes of biological sequence data the functional analysis of new sequences in terms of similarities to known protein families challenges classical bioinformatics. RESULTS: The ultrafast protein classification (UProC) toolbox implements a novel algorithm ('Mosaic Matching') for large-scale sequence analysis. UProC is by three orders of magnitude faster than profile-based methods and in a metagenome simulation study achieved up to 80% higher sensitivity on unassembled 100?bp reads. AVAILABILITY AND IMPLEMENTATION: UProC is available as an open-source software at https://github.com/gobics/uproc. Precompiled databases (Pfam) are linked on the UProC homepage: http://uproc.gobics.de/. CONTACT: peter@gobics.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUBMITTER: Meinicke P

PROVIDER: S-EPMC4410661 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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UProC: tools for ultra-fast protein domain classification.

Meinicke Peter P

Bioinformatics (Oxford, England) 20141223 9

<h4>Motivation</h4>With rapidly increasing volumes of biological sequence data the functional analysis of new sequences in terms of similarities to known protein families challenges classical bioinformatics.<h4>Results</h4>The ultrafast protein classification (UProC) toolbox implements a novel algorithm ('Mosaic Matching') for large-scale sequence analysis. UProC is by three orders of magnitude faster than profile-based methods and in a metagenome simulation study achieved up to 80% higher sensi ...[more]

PMID: 25540185

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Project description:The structure, function, stability, and many other properties of a protein in a fixed environment are fully specified by its sequence, but in a manner that is difficult to discern. We present a general approach for rapidly mapping sequences directly to their energies on a pre-specified rigid backbone, an important sub-problem in computational protein design and in some methods for protein structure prediction. The cluster expansion (CE) method that we employ can, in principle, be extended to model any computable or measurable protein property directly as a function of sequence. Here we show how CE can be applied to the problem of computational protein design, and use it to derive excellent approximations of physical potentials. The approach provides several attractive advantages. First, following a one-time derivation of a CE expansion, the amount of time necessary to evaluate the energy of a sequence adopting a specified backbone conformation is reduced by a factor of 10(7) compared to standard full-atom methods for the same task. Second, the agreement between two full-atom methods that we tested and their CE sequence-based expressions is very high (root mean square deviation 1.1-4.7 kcal/mol, R2 = 0.7-1.0). Third, the functional form of the CE energy expression is such that individual terms of the expansion have clear physical interpretations. We derived expressions for the energies of three classic protein design targets-a coiled coil, a zinc finger, and a WW domain-as functions of sequence, and examined the most significant terms. Single-residue and residue-pair interactions are sufficient to accurately capture the energetics of the dimeric coiled coil, whereas higher-order contributions are important for the two more globular folds. For the task of designing novel zinc-finger sequences, a CE-derived energy function provides significantly better solutions than a standard design protocol, in comparable computation time. Given these advantages, CE is likely to find many uses in computational structural modeling.

Dataset Information

UProC: tools for ultra-fast protein domain classification.

Publications

UProC: tools for ultra-fast protein domain classification.

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