MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, C α only models, Alternative alignments, and Non-sequential alignments

Abstract

BackgroundProtein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed.ResultsWe have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, Cα only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here.ConclusionsMICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool for automatically detecting non-trivial structural relationships of proteins, such as circular permutations and segment-swapping, many of which have been identified manually by human experts so far. The source code of MICAN is freely download-able at http://www.tbp.cse.nagoya-u.ac.jp/MICAN.

High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model

“…3D protein structure alignment is a complex and time-consuming process due to complexity of 3D protein structures, huge search space, and computational complexity of algorithms used to complete the process. In the last decades, various scientists and research centers have designed and developed a variety of methods for aligning 3D protein structures, finding similarities between proteins, or classifying protein structures into groups, including VAST [9,20], DALI [11], LOCK2 [38], FATCAT [47], CE [40], FAST [49], MultiProt [39], MotifMiner [4], MICAN [26], APGM [14], DEDAL [5], CASSERT [29,33], and others. These methods rely on various representative features of 3D protein structures.…”

Landscape of protein–small ligand binding modes

“…For each pair with the same Superfamily/Family, identity of their binding sites was evaluated. The 3D structures of each pair were superimposed on the basis of their secondary structure elements, by using MICAN . When the minimum interatomic distance between the ligands is shorter than or equal to 5 Å, this pair is classified as a pair with ligands in the same binding sites.…”

Parallel Seed-Based Approach to Multiple Protein Structure Similarities Detection

“…(1) function split and solve(globalGraph) (2) INPUT: globalGraph, an alignment graph between atoms from two proteins (3) OUTPUT: globalRes, a list of the longest distinct alignments found in the graph (4) (5) ResultList globalRes = empty result list() (6) Graph[] subGraphs = split(globalGraph) (7) sort(subgraphs) (8) For each subGraph in subGraphs (9) SeedList best seeds = empty list() (10) SeedList seeds = enumerate seeds(subGraph) (11) For each seed in seeds (12) VertexSet current res = extend and filter(subGraph, seed) (13) best seeds.insert if better(seed) (14) End For (15) For each seed in best seeds (16) VertexSet current res = extend and filter(globalGraph, seed) (17) globalRes.insert if better(current res) (18) End For (19) End For…”

“…Other nonsequential structure alignment methods have been recently proposed (excellent review on this topic can be found in the very recent reference [16]). None of them is close to the approach proposed here.…”