Abstract
BackgroundMultiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis. It has been used essentially in almost all bioinformatics tasks such as protein structure modeling, gene and protein function prediction, DNA motif recognition, and phylogenetic analysis. Therefore, improving the accuracy of multiple sequence alignment is important for advancing many bioinformatics fields.ResultsWe designed and developed a new method, MSACompro, to synergistically incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into the currently most accurate posterior probability-based MSA methods to improve the accuracy of multiple sequence alignments. The method is different from the multiple sequence alignment methods (e.g. 3D-Coffee) that use the tertiary structure information of some sequences since the structural information of our method is fully predicted from sequences. To the best of our knowledge, applying predicted relative solvent accessibility and contact map to multiple sequence alignment is novel. The rigorous benchmarking of our method to the standard benchmarks (i.e. BAliBASE, SABmark and OXBENCH) clearly demonstrated that incorporating predicted protein structural information improves the multiple sequence alignment accuracy over the leading multiple protein sequence alignment tools without using this information, such as MSAProbs, ProbCons, Probalign, T-coffee, MAFFT and MUSCLE. And the performance of the method is comparable to the state-of-the-art method PROMALS of using structural features and additional homologous sequences by slightly lower scores.ConclusionMSACompro is an efficient and reliable multiple protein sequence alignment tool that can effectively incorporate predicted protein structural information into multiple sequence alignment. The software is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.
Highlights
Multiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis
We developed MSACompro, a new multiple sequence alignment method, which effectively utilizes predicted secondary structure, relative solvent accessibility, and residueresidue contact map together with posterior alignment probabilities produced by both pair hidden Markov models and partition function as in MSAProbs [4]
Following the general scheme in MSAProbs [4], MSACompro has five main steps: (1) compute the pairwise posterior alignment probability matrices based on both pair-hidden Markov model (HMM) and partition function, considering the similarity in amino acids, secondary structure, and relative solvent accessibility; (2) generate the pairwise distance matrix from both the pairwise posterior probability matrices constructed in the first step and the pairwise contact map similarity matrices; (3) construct a guide tree based on pairwise distance matrix, and calculate sequence weights; (4) transform all the pairwise posterior matrices by a weighting scheme; (5) perform a progressive alignment by computing the profile-profile alignment from the probability matrices of all sequence pairs, and an iterative alignment to refine the results from progressive alignment
Summary
Multiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis. State of the art multiple sequence alignment methods adapt some popular techniques to improve alignment accuracy, such as iterative alignment [1], progressive alignment [2], alignment based on profile hidden Markov models [3], and posterior alignment probability transformation [4,5]. Some alignment methods, such as 3D-Coffee [6] and PROMALS3D [7], use 3D structure information to improve multiple sequence alignment, which cannot be applied to the majority of protein sequences without tertiary structures. To the best of our knowledge, applying predicted relative solvent accessibility and residue-residue contact map to multiple sequence alignment is novel
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