A novel method to compare protein structures using local descriptors

Daniluk, Paweł; Lesyng, Bogdan

doi:10.1186/1471-2105-12-344

Cited by 25 publications

(23 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of its biological or physical relevance, as well as simplicity of the evaluation, reference-dependent evaluation has been widely used [28-32]. However, some aspects of the method have been criticized; it relies heavily on the correctness of the reference alignment and it is unclear how the quality of reference alignment influence the evaluation of alignment programs [6,31,33]. …”

Section: Resultsmentioning

confidence: 99%

“…They are three sequential alignment programs: DaliLite [36], TM-align [37], and CE [38], and six non-sequential alignment programs: DEDAL [33], SNAP [22], GANGSTA+ [21], MASS [13], SCALI [16], and SAMO [18]. We selected DaliLite, TM-align and CE as representative of sequential alignment programs, because they have been known to be one of the best structure alignment programs [4,6,29].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

2013

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

2013

View full text Add to dashboard Cite

show abstract

“…Here, we compare SEQ-ALIBI, DeepAlign and Matt. Other methods which have performed well on the RIPC data set include descriptor-defined alignment, DEDAL (Daniluk and Lesyng, 2011), PROMATCH (Poleksic, 2016), protein alignment using Lagrangian alignment, PAUL (Wohlers et al, 2010), and Markovian transition of structure evolution, MATRAS (Kawabata, 2003).…”

Section: Comparison 3: Repetitions-indels-permutations-conformationalmentioning

confidence: 99%

Bayesian Protein Sequence and Structure Alignment

Fallaize

Green

Mardia

et al. 2020

Journal of the Royal Statistical Society Series C: Applied Statistics

View full text Add to dashboard Cite

Summary The structure of a protein is crucial in determining its functionality and is much more conserved than sequence during evolution. A key task in structural biology is to compare protein structures to determine evolutionary relationships, to estimate the function of newly discovered structures and to predict unknown structures. We propose a Bayesian method for protein structure alignment, with the prior on alignments based on functions which penalize ‘gaps’ in the aligned sequences. We show how a broad class of penalty functions fits into this framework, and how the resulting posterior distribution can be efficiently sampled. A commonly used gap penalty function is shown to be a special case, and we propose a new penalty function which alleviates an undesirable feature of the commonly used penalty. We illustrate our method on benchmark data sets and find that it competes well with popular tools from computational biology. Our method has the benefit of being able potentially to explore multiple competing alignments and to quantify their merits probabilistically. The framework naturally enables further information such as amino acid sequence to be included and could be adapted to other situations such as flexible proteins or domain swaps.

show abstract

“…Recently developed methods, such as Matt [7], PPM [8] and ProtDeform [9], treat proteins as flexible (rather than rigid) objects. The DEDAL algorithm [10] centers on the concept of local descriptors of protein's structure. The algorithm computes an optimal alignment by extending the similarities between the environmental descriptors in several stages.…”

Section: Introductionmentioning

confidence: 99%

Utilizing twilight zone sequence similarities to increase the accuracy of protein 3D structure comparison

Poleksić

Gray

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

View full text Add to dashboard Cite

Protein structure alignment is often used to infer a sequential alignment. The inverse problem of utilizing alignments of protein sequences to derive structural alignments has been extensively studied, but the applications are still scarce. Here we present pMatch -a novel algorithm that takes advantage of sequential profiles to compute extremely accurate protein structure alignments. Contrary to some claims that sequential information is only useful when aligning structures of proteins that share a high degree of sequence identity, we demonstrate that incorporating sequential information can increase accuracy of alignments even between proteins sharing only about 10% of identical residues. We use two widely established benchmarks to show that our method is capable of accurately aligning protein structures that exhibit substantial conformational changes, large residue insertions/deletions and circular permutations. A preliminary version of the pMatch Web server is available at

show abstract

A novel method to compare protein structures using local descriptors

Cited by 25 publications

References 58 publications

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

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

Bayesian Protein Sequence and Structure Alignment

Utilizing twilight zone sequence similarities to increase the accuracy of protein 3D structure comparison

Contact Info

Product

Resources

About