Unknown

Dataset Information

0

SAS-Pro: simultaneous residue assignment and structure superposition for protein structure alignment.


ABSTRACT: Protein structure alignment is the problem of determining an assignment between the amino-acid residues of two given proteins in a way that maximizes a measure of similarity between the two superimposed protein structures. By identifying geometric similarities, structure alignment algorithms provide critical insights into protein functional similarities. Existing structure alignment tools adopt a two-stage approach to structure alignment by decoupling and iterating between the assignment evaluation and structure superposition problems. We introduce a novel approach, SAS-Pro, which addresses the assignment evaluation and structure superposition simultaneously by formulating the alignment problem as a single bilevel optimization problem. The new formulation does not require the sequentiality constraints, thus generalizing the scope of the alignment methodology to include non-sequential protein alignments. We employ derivative-free optimization methodologies for searching for the global optimum of the highly nonlinear and non-differentiable RMSD function encountered in the proposed model. Alignments obtained with SAS-Pro have better RMSD values and larger lengths than those obtained from other alignment tools. For non-sequential alignment problems, SAS-Pro leads to alignments with high degree of similarity with known reference alignments. The source code of SAS-Pro is available for download at http://eudoxus.cheme.cmu.edu/saspro/SAS-Pro.html.

SUBMITTER: Shah SB 

PROVIDER: S-EPMC3360771 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

altmetric image

Publications

SAS-Pro: simultaneous residue assignment and structure superposition for protein structure alignment.

Shah Shweta B SB   Sahinidis Nikolaos V NV  

PloS one 20120525 5


Protein structure alignment is the problem of determining an assignment between the amino-acid residues of two given proteins in a way that maximizes a measure of similarity between the two superimposed protein structures. By identifying geometric similarities, structure alignment algorithms provide critical insights into protein functional similarities. Existing structure alignment tools adopt a two-stage approach to structure alignment by decoupling and iterating between the assignment evaluat  ...[more]

Similar Datasets

| S-EPMC4621035 | biostudies-literature
| S-EPMC3120703 | biostudies-literature
| S-EPMC2312444 | biostudies-literature
| S-EPMC8515897 | biostudies-literature
| S-EPMC4082353 | biostudies-literature
| S-EPMC7913338 | biostudies-literature
| S-EPMC8667806 | biostudies-literature
| S-EPMC2142613 | biostudies-other