Improving phylogenetic inference with a semiempirical amino acid substitution model.

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi‐nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life.

BackgroundSequence matching is extremely important for applications throughout biology, particularly for discovering information such as functional and evolutionary relationships, and also for discriminating between unimportant and disease mutants. At present the functions of a large fraction of genes are unknown; improvements in sequence matching will improve gene annotations. Universal amino acid substitution matrices such as Blosum62 are used to measure sequence similarities and to identify distant homologues, regardless of the structure class. However, such single matrices do not take into account important structural information evident within the different topologies of proteins and treats substitutions within all protein folds identically. Others have suggested that the use of structural information can lead to significant improvements in sequence matching but this has not yet been very effective. Here we develop novel substitution matrices that include not only general sequence information but also have a topology specific component that is unique for each CATH topology. This novel feature of using a combination of sequence and structure information for each protein topology significantly improves the sequence matching scores for the sequence pairs tested. We have used a novel multi-structure alignment method for each homology level of CATH in order to extract topological information.ResultsWe obtain statistically significant improved sequence matching scores for 73 % of the alpha helical test cases. On average, 61 % of the test cases showed improvements in homology detection when structure information was incorporated into the substitution matrices. On average z-scores for homology detection are improved by more than 54 % for all cases, and some individual cases have z-scores more than twice those obtained using generic matrices. Our topology specific similarity matrices also outperform other traditional similarity matrices and single matrix based structure methods. When default amino acid substitution matrix in the Psi-blast algorithm is replaced by our structure-based matrices, the structure matching is significantly improved over conventional Psi-blast. It also outperforms results obtained for the corresponding HMM profiles generated for each topology.ConclusionsWe show that by incorporating topology-specific structure information in addition to sequence information into specific amino acid substitution matrices, the sequence matching scores and homology detection are significantly improved. Our topology specific similarity matrices outperform other traditional similarity matrices, single matrix based structure methods, also show improvement over conventional Psi-blast and HMM profile based methods in sequence matching. The results support the discriminatory ability of the new amino acid similarity matrices to distinguish between distant homologs and structurally dissimilar pairs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1198-z) contains supplementary material, which is available to authorized users.

Motivation: Although many amino acid substitution matrices have been developed, it has not been well understood which is the best for similarity searches, especially for remote homology detection. Therefore, we collected information related to existing matrices, condensed it and derived a novel matrix that can detect more remote homology than ever.Results: Using principal component analysis with existing matrices and benchmarks, we developed a novel matrix, which we designate as MIQS. The detection performance of MIQS is validated and compared with that of existing general purpose matrices using SSEARCH with optimized gap penalties for each matrix. Results show that MIQS is able to detect more remote homology than the existing matrices on an independent dataset. In addition, the performance of our developed matrix was superior to that of CS-BLAST, which was a novel similarity search method with no amino acid matrix. We also evaluated the alignment quality of matrices and methods, which revealed that MIQS shows higher alignment sensitivity than that with the existing matrix series and CS-BLAST. Fundamentally, these results are expected to constitute good proof of the availability and/or importance of amino acid matrices in sequence analysis. Moreover, with our developed matrix, sophisticated similarity search methods such as sequence–profile and profile–profile comparison methods can be improved further.Availability and implementation: Newly developed matrices and datasets used for this study are available at http://csas.cbrc.jp/Ssearch/.Contact:k-tomii@aist.go.jpSupplementary information:Supplementary data are available at Bioinformatics online

The single-matrix amino acid (AA) substitution models are widely used in phylogenetic analyses; however, they are unable to properly model the heterogeneity of AA substitution rates among sites. The multi-matrix mixture models can handle the site rate heterogeneity and outperform the single-matrix models. Estimating multi-matrix mixture models is a complex process and no computer program is available for this task. In this study, we implemented a computer program of the so-called QMix based on the algorithm of LG4X and LG4M with several enhancements to automatically estimate multi-matrix mixture models from large datasets. QMix employs QMaker algorithm instead of XRATE algorithm to accurately and rapidly estimate the parameters of models. It is able to estimate mixture models with different number of matrices and supports multi-threading computing to efficiently estimate models from thousands of genes. We re-estimate mixture models LG4X and LG4M from 1471 HSSP alignments. The re-estimated models (HP4X and HP4M) are slightly better than LG4X and LG4M in building maximum likelihood trees from HSSP and TreeBASE datasets. QMix program required about 10 hours on a computer with 18 cores to estimate a mixture model with four matrices from 200 HSSP alignments. It is easy to use and freely available for researchers.

Construction of non-symmetric substitution matrices derived from proteomes with biased amino acid distributions

Aligned amino acid sequences of three functionally independent samples of transmembrane (TM) transport proteins have been analyzed. The concept of TM-kernel is proposed as the most probable transmembrane region of a sequence. The average amino acid composition of TM-kernels differs from the published amino acid composition of transmembrane segments. TM-kernels contain more alanines, glycines, and less polar, charged, and aromatic residues in contrast to non-TM-proteins. There are also differences between TM-kernels of bacterial and eukaryotic proteins. We have constructed amino acid substitution matrices for bacterial TM-kernels, named the BATMAS (BActerial Transmembrane MAtrix of Substitutions) series. In TM-kernels, polar and charged residues, as well as proline and tyrosine, are highly conserved, whereas there are more substitutions within the group of hydrophobic residues, in contrast to non-TM-proteins that have fewer, relatively more conserved, hydrophobic residues. These results demonstrate that alignment of transmembrane proteins should be based on at least two amino acid substitution matrices, one for loops (e.g., the BLOSUM series) and one for TM-segments (the BATMAS series), and the choice of the TM-matrix should be different for eukaryotic and bacterial proteins.

Information about protein sequences of ancestral organisms is important for identifying critical amino acid substitutions that have caused the functional change of proteins in evolution. Using computer simulation, we studied the accuracy of ancestral amino acids inferred by two currently available methods (maximum-parsimony [MP] and maximum-likelihood [ML] methods) in addition to a distance method, which was newly developed in this paper. All three methods give reliable inference when the divergence of amino acid sequences is low. When the extent of sequence divergence is high, however, the ML and distance methods give more accurate results than the MP method, particularly when the phylogenetic tree includes long branches. The accuracy of inferred ancestral amino acids does not change very much when a few present-day sequences are added or eliminated. When an incorrect model of amino acid substitution is used for the ML and distance methods, the accuracy decreases, but it is still higher than that for the MP method. When the tree topology used is partially incorrect, the accuracy in the correct part of the tree is virtually unaffected. The posterior probability of inferred ancestral amino acids computed by the ML and distance methods is an unbiased estimate of the true probability when a correct substitution model is used but may become an overestimate when a simpler model is used.

Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.

We report a study of networks constructed from mutation patterns observed in biology. These networks form evolutionary trajectories, which allow for both frequent substitution of closely related structures, and a small evolutionary distance between any two structures. These two properties define the small-world phenomenon. The mutation behavior between tokens in an evolvable artificial chemistry determines its ability to explore evolutionary space. This concept is underrepresented in previous work on string-based chemistries. We argue that small-world mutation networks will confer better exploration of the evolutionary space than either random or fully regular mutation strategies. We calculate network statistics from two data sets: amino acid substitution matrices, and codon-level single point mutations. The first class are observed data from protein alignments; while the second class is defined by the standard genetic code that is used to translate RNA into amino acids. We report a methodology for creating small-world mutation networks for artificial chemistries with arbitrary node count and connectivity. We argue that ALife systems would benefit from this approach, as it delivers a more viable exploration of evolutionary space.

A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally for Protein. MSA has wide range of applications and to cite few of them such as phylogenetic analysis, protein pattern identification, protein domain identification, prediction of protein structure, structural similarity of amino acids and to get evolutionary similarity. ClustalW2 is a general purpose global multiple sequence alignment program for proteins. It produces biologically meaningful multiple sequence alignments of divergent sequences. The output from ClustalW2 shows the best match for the selected sequences and lines up them in such a way that the identities, similarities and differences can be easily understood. Evolutionary relationships can be seen by creating Cladograms or Phylograms. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. In this paper, protein sequences of few legume species from UNIPROT database were taken and focused on MSA for protein sequences for these tree species of family Leguminosae, where ClustalW2 tool have used to generate biological data. The results are discussed with the help of Cladograms and Phylograms for selected tree species.

One of the most important and difficult challenges in the research of molecular evolution is modeling the process of amino acid substitutions. Although single-matrix models, such as the LG model, are popular, their capability to properly capture the heterogeneity of the substitution process across sites is still questioned. Several mixture models with multiple matrices have been introduced and shown to offer advantages over single-matrix models. Current general mixture models assume the reversibility of the evolutionary process, implying that substitution rates between any two amino acids are equal in both forward and backward directions. This assumption is not based on biological properties but rather on computational simplicity. The well-known hypothesis is that more realistic models can yield more accurate evolutionary inferences; therefore, our aim is to estimate more biologically realistic models. To this end, we relax the assumption of reversibility and introduce two new general non-reversible 4-matrix mixture models, called nT4M and nT4X. Using alignments from HSSP and TreeBASE databases as data, our newly estimated models outperformed all single-matrix models and almost all reversible mixture models. Moreover, the new non-reversible mixture models enable us to infer rooted trees.

Amino acid substitutions at position 69 of the reverse transcriptase of human immunodeficiency virus type 1 are frequent in zalcitabine-naive antiretroviral-drug-experienced patients.

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are proteins or protein-domains that do not have a single native structure, rather, they are a class of flexible peptides that can rapidly adopt multiple conformations. IDPs are quite abundant, and their dynamic characteristics provide unique advantages for various biological processes. The field of "unstructured biology" has emerged, in part, because of numerous computational studies that had identified the unique characteristics of IDPs and IDRs. The package 'idpr', short for Intrinsically Disordered Proteins in R, implements several R functions that match the established characteristics of IDPs to protein sequences of interest. This includes calculations of residue composition, charge-hydropathy relationships, and predictions of intrinsic disorder. Additionally, idpr integrates several amino acid substitution matrices and calculators to supplement IDP-based workflows. Overall, idpr aims to integrate tools for the computational analysis of IDPs within R, facilitating the analysis of these important, yet under-characterized, proteins. The idpr package can be downloaded from Bioconductor (https://bioconductor.org/packages/idpr/).

The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

Waardenburg syndrome (WS) type 1 is an autosomal dominant disorder characterized by sensorineural hearing loss, pigmentary abnormalities of the eye, hair, and skin, and dystopia canthorum. The phenotype is variable and affected individuals may exhibit only one or a combination of several of the associated features. To assess the relationship between phenotype and gene defect, clinical and genotype data on 48 families (271 WS individuals) collected by members of the Waardenburg Consortium were pooled. Forty-two unique mutations in the PAX3 gene, previously identified in these families, were grouped in five mutation categories: amino acid (AA) substitution in the paired domain, AA substitution in the homeodomain, deletion of the Ser-Thr-Pro-rich region, deletion of the homeodomain and the Ser-Thr-Pro-rich region, and deletion of the entire gene. These mutation classes are based on the structure of the PAX3 gene and were chosen to group mutations predicted to have similar defects in the gene product. Association between mutation class and the presence of hearing loss, eye pigment abnormality, skin hypopigmentation, or white forelock was evaluated using generalized estimating equations, which allowed for incorporation of a correlation structure that accounts for potential similarity among members of the same family. Odds for the presence of eye pigment abnormality, white forelock, and skin hypopigmentation were 2, 8, and 5 times greater, respectively, for individuals with deletions of the homeodomain and the Pro-Ser-Thr-rich region compared to individuals with an AA substitution in the homeodomain. Odds ratios that differ significantly from 1.0 for these traits may indicate that the gene products resulting from different classes of mutations act differently in the expression of WS. Although a suggestive association was detected for hearing loss with an odds ratio of 2.6 for AA substitution in the paired domain compared with AA substitution in the homeodomain, this odds ratio did not differ significantly from 1.0.