Proteins comparison through probabilistic optimal structure local alignment.

Giovanni Micale,Alfredo Ferro,Alfredo Pulvirenti,Rosalba Giugno

doi:10.3389/fgene.2014.00302

Giovanni Micale, Alfredo Ferro + Show 2 more

Open Access

https://doi.org/10.3389/fgene.2014.00302

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Abstract

Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.

Highlights

Protein function is commonly deduced by sequence analysis
In the first one we analyzed the performance of our method and the effects of input parameters, using the 33 structures of Skolnick’s dataset benchmark (Lancia et al, 2001), a set of large protein domains which has been used in several recent studies related to structural comparison of proteins (Pulim et al, 2008; Di Lena et al, 2010)
In the second case study, we compared PROPOSAL to SMAP (Xie and Bourne, 2008; Xie et al, 2009) and ProBis (Konc and Janezic, 2010, 2012), two algorithms for local pairwise structural alignment, on a dataset of known motifs derived from the literature and taken from the Catalytic Site Atlas (CSA) (Furnham et al, 2013)

Summary

Introduction

Protein function is commonly deduced by sequence analysis. On the other hand, most protein interactions, such as catalytic activity or gene regulation (transcription, maturation, etc.), depend on sub-regions of their 3D structures, called structural or binding motifs. Havranek and Baker (2009) show that the identification of protein-DNA interactions can help discover placements for the protein backbone. Havranek and Baker (2009) show that the identification of protein-DNA interactions can help discover placements for the protein backbone. Most protein interactions, such as catalytic activity or gene regulation (transcription, maturation, etc.), depend on sub-regions of their 3D structures, called structural or binding motifs. This contributes to identify the desired position and interaction of the side-chain atoms, which are responsible for protein function. Structural comparison is usually performed by local alignments since these are more sensitive than the global ones. Some of them start from a specified motif (called template) in a query protein structure and search for similarities in a reference set of 3D structures

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Conclusion