TS-AMIR: a topology string alignment method for intensive rapid protein structure comparison

Abstract

BackgroundIn structural biology, similarity analysis of protein structure is a crucial step in studying the relationship between proteins. Despite the considerable number of techniques that have been explored within the past two decades, the development of new alternative methods is still an active research area due to the need for high performance tools.ResultsIn this paper, we present TS-AMIR, a Topology String Alignment Method for Intensive Rapid comparison of protein structures. The proposed method works in two stages: In the first stage, the method generates a topology string based on the geometric details of secondary structure elements, and then, utilizes an n-gram modelling technique over entropy concept to capture similarities in these strings. This initial correspondence map between secondary structure elements is submitted to the second stage in order to obtain the alignment at the residue level. Applying the Kabsch method, a heuristic step-by-step algorithm is adopted in the second stage to align the residues, resulting in an optimal rotation matrix and minimized RMSD. The performance of the method was assessed in different information retrieval tests and the results were compared with those of CE and TM-align, representing two geometrical tools, and YAKUSA, 3D-BLAST and SARST as three representatives of linear encoding schemes. It is shown that the method obtains a high running speed similar to that of the linear encoding schemes. In addition, the method runs about 800 and 7200 times faster than TM-align and CE respectively, while maintaining a competitive accuracy with TM-align and CE.ConclusionsThe experimental results demonstrate that linear encoding techniques are capable of reaching the same high degree of accuracy as that achieved by geometrical methods, while generally running hundreds of times faster than conventional programs.

Adaptive Smith-Waterman residue match seeding for protein structural alignment

“…Constraints that confer stability or biological function are manifest at the local residue physical environment level within homologous protein families and in environment‐dependent amino acid residue substitution patterns across protein families . Information such as protein backbone conformation may be coded using structural alphabets as 1D strings that permit the rapid alignment of protein structures . Other residue physical environment descriptors, such as side‐chain hydrogen bonding and solvent accessibility, can be likewise encoded to capture evolutionary constraints in a more comprehensive manner .…”

Structural Protein Function Prediction - A Comprehensive Review

“…Therefore, globalstructure comparison-based approaches are divided into two categories based on the protein structural representation. They may be based on proteins geometric [36][37][38][39][40][41][42][43][44] or secondary structure elements (SSEs) [45][46][47][48][49][50][51].…”

“…Regarding methods based on SSEs, the much known structural alignment methods are SSAP [50] (or Sequential Structure Alignment Program) and VAST [51] (Vector Alignment Search Tool). Recently, TS-AMIR [46] or Topology String Alignment Method for Intensive Rapid comparison of protein structures which is proposed in order to reduce the complexity of the structure comparison process. They represent the protein based on the secondary structure elements (SSEs) of its backbone structure.…”

Protein Data Representation: A Survey

“…Other methods used text strings to represent the secondary structure elements of the protein [26]. These representations can be used by string matching (exact or inexact) and can be used with dynamic programming, suffix trees and hashing.…”