Genome bias influences amino acid choices: analysis of amino acid substitution and re-compilation of substitution matrices exclusive to an AT-biased genome

The genomic era has seen a remarkable increase in the number of genomes being sequenced and annotated. Nonetheless, annotation remains a serious challenge for compositionally biased genomes. For the preliminary annotation, popular nucleotide and protein comparison methods such as BLAST are widely employed. These methods make use of matrices to score alignments such as the amino acid substitution matrices. Since a nucleotide bias leads to an overall bias in the amino acid composition of proteins, it is possible that a genome with nucleotide bias may have introduced atypical amino acid substitutions in its proteome. Consequently, standard matrices fail to perform well in sequence analysis of these genomes. To address this issue, we examined the amino acid substitution in the AT-rich genome of Plasmodium falciparum, chosen as a reference and reconstituted a substitution matrix in the genome's context. The matrix was used to generate protein sequence alignments for the parasite proteins that improved across the functional regions. We attribute this to the consistency that may have been achieved amid the target and background frequencies calculated exclusively in our study. This study has important implications on annotation of proteins that are of experimental interest but give poor sequence alignments with standard conventional matrices.     

Analysis of differences in amino acid substitution patterns, using multilevel G-tests

In this paper, a new algorithm is presented, which makes possible multilevel comparison of BLOSUM protein substitution matrices based on data from different groups of organisms. As an example, a comparison between substitution matrices based on data from two groups of bacterial genomes with different GC content is presented. Our approach includes evaluating the number of amino acid pairs in BLOCKS databases created separately for the two groups of bacteria using protein sequences deposited in the COG database. Differences of distributions of amino acid pair counts are tested using the chi-squared based G-test. Different analysis levels make it possible to distinguish different patterns of amino acid substitution. Application of the algorithm reveals statistically significant differences in amino acid substitution patterns between AT-rich and GC-rich groups of bacterial organisms. The differences are particularly visible in the overall substitution pattern, amino acid conservation pattern and in comparison of substitution patterns for single amino acids. The algorithm presented in this paper can be considered a novel method for multi-level comparison of amino acid substitution patterns. The presented approach is not limited to bacterial organisms and BLOSUM substitution matrices. Statistically significant differences between substitution patterns in the two groups of bacterial organisms with respect to amino acid conservation pattern can be the evidence of different rate of evolutionary change between AT-rich and GC-rich bacterial organisms.     

Protein database searches using compositionally adjusted substitution matrices

Almost all protein database search methods use amino acid substitution matrices for scoring, optimizing, and assessing the statistical significance of sequence alignments. Much care and effort has therefore gone into constructing substitution matrices, and the quality of search results can depend strongly upon the choice of the proper matrix. A long-standing problem has been the comparison of sequences with biased amino acid compositions, for which standard substitution matrices are not optimal. To address this problem, we have recently developed a general procedure for transforming a standard matrix into one appropriate for the comparison of two sequences with arbitrary, and possibly differing compositions. Such adjusted matrices yield, on average, improved alignments and alignment scores when applied to the comparison of proteins with markedly biased compositions. Here we review the application of compositionally adjusted matrices and consider whether they may also be applied fruitfully to general purpose protein sequence database searches, in which related sequence pairs do not necessarily have strong compositional biases. Although it is not advisable to apply compositional adjustment indiscriminately, we describe several simple criteria under which invoking such adjustment is on average beneficial. In a typical database search, at least one of these criteria is satisfied by over half the related sequence pairs. Compositional substitution matrix adjustment is now available in NCBI's protein-protein version of blast.     

The construction of amino acid substitution matrices for the comparison of proteins with non-standard compositions

MOTIVATION: Amino acid substitution matrices play a central role in protein alignment methods. Standard log-odds matrices, such as those of the PAM and BLOSUM series, are constructed from large sets of protein alignments having implicit background amino acid frequencies. However, these matrices frequently are used to compare proteins with markedly different amino acid compositions, such as transmembrane proteins or proteins from organisms with strongly biased nucleotide compositions. It has been argued elsewhere that standard matrices are not ideal for such comparisons and, furthermore, a rationale has been presented for transforming a standard matrix for use in a non-standard compositional context. RESULTS: This paper presents the mathematical details underlying the compositional adjustment of amino acid or DNA substitution matrices.     

Plastid Genome Phylogeny and a Model of Amino Acid Substitution for Proteins Encoded by Chloroplast DNA

Maximum likelihood (ML) phylogenies based on 9,957 amino acid (AA) sites of 45 proteins encoded in the plastid genomes of Cyanophora, a diatom, a rhodophyte (red algae), a euglenophyte, and five land plants are compared with respect to several properties of the data, including between-site rate variation and aberrant amino acid composition in individual species. Neighbor-joining trees from AA LogDet distances and ML analyses are seen to be congruent when site rate variability was taken into account. Four feasible trees are identified in these analyses, one of which is preferred, and one of which is almost excluded by statistical criteria. A transition probability matrix for the general reversible Markov model of amino acid substitutions is estimated from the data, assuming each of these four trees. In all cases, the tree with diatom and rhodophyte as sister taxa was clearly favored. The new transition matrix based on the best tree, called cpREV, takes into account distinct substitution patterns in plastid-encoded proteins and should be useful in future ML inferences using such data. A second rate matrix, called cpREV*, based on a weighted sum of rate matrices from different trees, is also considered. Received: 3 June 1999 / Accepted: 26 November 1999     

Nucleotide bias causes a genomewide bias in the amino acid composition of proteins

We analyzed the nucleotide contents of several completely sequenced genomes, and we show that nucleotide bias can have a dramatic effect on the amino acid composition of the encoded proteins. By surveying the genes in 21 completely sequenced eubacterial and archaeal genomes, along with the entire Saccharomyces cerevisiae genome and two Plasmodium falciparum chromosomes, we show that biased DNA encodes biased proteins on a genomewide scale. The predicted bias affects virtually all genes within the genome, and it could be clearly seen even when we limited the analysis to sets of homologous gene sequences. Parallel patterns of compositional bias were found within the archaea and the eubacteria. We also found a positive correlation between the degree of amino acid bias and the magnitude of protein sequence divergence. We conclude that mutational bias can have a major effect on the molecular evolution of proteins. These results could have important implications for the interpretation of protein-based molecular phylogenies and for the inference of functional protein adaptation from comparative sequence data.     

基于进化信息改进蛋白质定点突变稳定性预测准确率

定点突变后蛋白质稳定性的增加还是降低,是分子生物学和蛋白质工程的核心问题之一,也是目前生物信息学研究的重要领域。基于蛋白质序列信息对蛋白质定点突变后的稳定性进行预测的方法,因其简易、适用面广而得到广泛的研究应用。通过对编码策略（coding schemes）的探索,发现不同编码策略对预测准确率有较大影响,并发现基于进化信息的BLOSUM打分矩阵可以用于蛋白质定点突变稳定性预测,具有较高的预测准确率。应用基于BLOSUM62打分矩阵的神经网络（ANN）和支持向量机（SVM）算法,可以改进蛋白质定点突变后稳定性的预测,而且ANN+ BLOSUM62在1623条序列的数据集上的实测结果优于目前国际通用的几款预测 软件。     

Insights from the analysis of conserved motifs and permitted amino acid exchanges in the human, the fly and the worm GPCR clusters

G-protein coupled receptors (GPCRs) belong to biologically important and functionally diverse and largest super family of membrane proteins. GPCRs retain a characteristic membrane topology of seven alpha helices with three intracellular, three extracellular loops and flanking N' and C' terminal residues. Subtle differences do exist in the helix boundaries (TM-domain), loop lengths, sequence features such as conserved motifs, and substituting amino acid patterns and their physiochemical properties amongst these sequences (clusters) at intra-genomic and inter-genomic level (please re-phrase into 2 statements for clarity). In the current study, we employ prediction of helix boundaries and scores derived from amino acid substitution exchange matrices to identify the conserved amino acid residues (motifs) as consensus in aligned set of homologous GPCR sequences. Co-clustered GPCRs from human and other genomes, organized as 32 clusters, were employed to study the amino acid conservation patterns and species-specific or cluster-specific motifs. Critical analysis on sequence composition and properties provide clues to connect functional relevance within and across genome for vast practical applications such as design of mutations and understanding of disease-causing genetic abnormalities.     

节肢动物线粒体基因组碱基组成特征分析

利用93个节肢动物线粒体基因组数据,分析了线粒体基因组的碱基组成,及对氨基酸组成的影响。研究表明:(1)节肢动物线粒体基因组GC含量较低,分布范围较窄(13.28%～39.64%)。基因组GC含量与密码子第三位置的GC含量间的相关性(r=0.9432,p<0.01)比密码子第一、二位置上的相关性强。(2)在密码子的三个不同位置上均可以观察到C<->T和A<->G相互取代的现象。(3)从NC.004529和NC.003979两个序列的对比研究中可以发现碱基组成变化会引起氨基酸组成的变化,这种变化不仅体现在不同的物种之间,而且也体现在同一基因组内部的不同基因之间,这些影响可能是相互的。表明节肢动物线粒体基因组中的碱基变化是受多种因素共同作用的结果。     

Context-specific amino acid substitution matrices and their use in the detection of protein homologs

The sequence homology detection relies on score matrices, which reflect the frequency of amino acid substitutions observed in a dataset of homologous sequences. The substitution matrices in popular use today are usually constructed without consideration of the structural context in which the substitution takes place. Here, we present amino acid substitution matrices specific for particular polar-nonpolar environment of the amino acid. As expected, these matrices [context-specific substitution matrices (CSSMs)] show striking differences from the popular BLOSUM62 matrix, which does not include structural information. When incorporated into BLAST and PSI-BLAST, CSSM outperformed BLOSUM matrices as assessed by ROC curve analyses of the number of true and false hits and by the accuracy of the sequence alignments to the hit sequences. These findings are also of relevance to profile-profile-based methods of homology detection, since CSSMs may help build a better profile. Profiles generated for protein sequences in PDB using CSSM-PSI-BLAST will be made available for searching via RPSBLAST through our web site http://lmbbi.nci.nih.gov/.     

Amino acid substitution matrices from an information theoretic perspective

Protein sequence alignments have become an important tool for molecular biologists. Local alignments are frequently constructed with the aid of a "substitution score matrix" that specifies a score for aligning each pair of amino acid residues. Over the years, many different substitution matrices have been proposed, based on a wide variety of rationales. Statistical results, however, demonstrate that any such matrix is implicitly a "log-odds" matrix, with a specific target distribution for aligned pairs of amino acid residues. In the light of information theory, it is possible to express the scores of a substitution matrix in bits and to see that different matrices are better adapted to different purposes. The most widely used matrix for protein sequence comparison has been the PAM-250 matrix. It is argued that for database searches the PAM-120 matrix generally is more appropriate, while for comparing two specific proteins with suspected homology the PAM-200 matrix is indicated. Examples discussed include the lipocalins, human alpha 1 B-glycoprotein, the cystic fibrosis transmembrane conductance regulator and the globins.     

Evolutionary pressures on apicoplast transit peptides

Malaria parasites (species of the genus Plasmodium) harbor a relict chloroplast (the apicoplast) that is the target of novel antimalarials. Numerous nuclear-encoded proteins are translocated into the apicoplast courtesy of a bipartite N-terminal extension. The first component of the bipartite leader resembles a standard signal peptide present at the N-terminus of secreted proteins that enter the endomembrane system. Analysis of the second portion of the bipartite leaders of P. falciparum, the so-called transit peptide, indicates similarities to plant transit peptides, although the amino acid composition of P. falciparum transit peptides shows a strong bias, which we rationalize by the extraordinarily high AT content of P. falciparum DNA. 786 plastid transit peptides were also examined from several other apicomplexan parasites, as well as from angiosperm plants. In each case, amino acid biases were correlated with nucleotide AT content. A comparison of a spectrum of organisms containing primary and secondary plastids also revealed features unique to secondary plastid transit peptides. These unusual features are explained in the context of secondary plastid trafficking via the endomembrane system.     

Models of amino acid substitution and applications to mitochondrial protein evolution

Models of amino acid substitution were developed and compared using maximum likelihood. Two kinds of models are considered. "Empirical" models do not explicitly consider factors that shape protein evolution, but attempt to summarize the substitution pattern from large quantities of real data. "Mechanistic" models are formulated at the codon level and separate mutational biases at the nucleotide level from selective constraints at the amino acid level. They account for features of sequence evolution, such as transition-transversion bias and base or codon frequency biases, and make use of physicochemical distances between amino acids to specify nonsynonymous substitution rates. A general approach is presented that transforms a Markov model of codon substitution into a model of amino acid replacement. Protein sequences from the entire mitochondrial genomes of 20 mammalian species were analyzed using different models. The mechanistic models were found to fit the data better than empirical models derived from large databases. Both the mutational distance between amino acids (determined by the genetic code and mutational biases such as the transition-transversion bias) and the physicochemical distance are found to have strong effects on amino acid substitution rates. A significant proportion of amino acid substitutions appeared to have involved more than one codon position, indicating that nucleotide substitutions at neighboring sites may be correlated. Rates of amino acid substitution were found to be highly variable among sites.      

Amino Acid Usage Is Asymmetrically Biased in AT- and GC-Rich Microbial Genomes

Introduction

Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. Since only a small fraction of prokaryotic genomes is not protein coding even a minor change in genomic base composition will induce profound protein changes. We examined how amino acid and codon frequencies were distributed in over 2000 microbial genomes and how these distributions were affected by base compositional changes. In addition, we wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias. To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates.

Results

We found that genomic amino acid frequencies carried a stronger phylogenetic signal than codon frequencies, but that this signal was weak compared to that of genomic %AT. Further, in contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes. AAUB was also associated with relative entropy; genomes with low AAUB contained more random mutations as a consequence of relaxed purifying selection than genomes with higher AAUB.

Conclusion

Genomic base composition has a substantial effect on both amino acid- and codon frequencies in bacterial genomes. While phylogeny influenced amino acid usage more in GC-rich genomes, AT-content was driving amino acid usage in AT-rich genomes. We found the GAMM model to be an excellent tool to analyze the genomic data used in this study.     

Mutational bias affects protein evolution in flowering plants

Amino acid sequences from several thousand homologous gene pairs were compared for two plant genomes, Oryza sativa and Arabidopsis thaliana. The Arabidopsis genes all have similar G+C (guanine plus cytosine) contents, whereas their homologs in rice span a wide range of G+C levels. The results show that those rice genes that display increased divergence in their nucleotide composition (specifically, increased G+C content) showed a corresponding, predictable change in the amino acid compositions of the encoded proteins relative to their Arabidopsis homologs. This trend was not seen in a "control" set of rice genes that had nucleotide contents closer to their Arabidopsis homologs. In addition to showing an overall difference in the amino acid composition of the homologous proteins, we were also able to investigate the biased patterns of amino acid substitution since the divergence of these two species. We found that the amino acid exchange matrix was highly asymmetric when comparing the High G+C rice genes with their Arabidopsis homologs. Finally, we investigated the possible causes of this biased pattern of sequence evolution. Our results indicate that the biased pattern of protein evolution is the consequence, rather than the cause, of the corresponding changes in nucleotide content. In fact, there is an even more marked asymmetry in the patterns of substitution at synonymous nucleotide sites. Surprisingly, there is a very strong negative correlation between the level of nucleotide bias and the length of the coding sequences within the rice genome. This difference in gene length may provide important clues about the underlying mechanisms.     

Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets

Reduced or simplified amino acid alphabets group the 20 naturally occurring amino acids into a smaller number of representative protein residues. To date, several reduced amino acid alphabets have been proposed, which have been derived and optimized by a variety of methods. The resulting reduced amino acid alphabets have been applied to pattern recognition, generation of consensus sequences from multiple alignments, protein folding, and protein structure prediction. In this work, amino acid substitution matrices and statistical potentials were derived based on several reduced amino acid alphabets and their performance assessed in a large benchmark for the tasks of sequence alignment and fold assessment of protein structure models, using as a reference frame the standard alphabet of 20 amino acids. The results showed that a large reduction in the total number of residue types does not necessarily translate into a significant loss of discriminative power for sequence alignment and fold assessment. Therefore, some definitions of a few residue types are able to encode most of the relevant sequence/structure information that is present in the 20 standard amino acids. Based on these results, we suggest that the use of reduced amino acid alphabets may allow to increasing the accuracy of current substitution matrices and statistical potentials for the prediction of protein structure of remote homologs.     

Information theoretic perspective on genome clustering

Shannon’s information theoretic perspective of communication helps one to understand the storage and processing of information in one-dimensional sequences. An information theoretic analysis of 937 available completely sequenced prokaryotic genomes and 238 eukaryotic chromosomes is presented. Information content (Id) values were used to cluster these chromosomes. Chargaff’s second parity rule i.e compositional self-complementarity, an empirical fact is observed in all the genomes, except for the proteobacteria Candidatus Hodgkinia cicadicola. High information content, arising out of biased base composition in all the 14 chromosomes of Plasmodium falciparum is found among two other genomes of prokaryotes viz. Buchnera aphidicola str. Cc (Cinara cedri) and Candidatus Carsonella ruddii PV. Despite size and compositional variations, both prokaryotic and eukaryotic genomes do not deviate significantly from an equiprobable and random situation. Eukaryotic chromosomes of an organism tend to have similar informational restraints as seen when a simple distance based method is used to cluster them. In eukaryotes, in certain cases, Id values are also similar for the two arms (p and q arm) of the chromosomes. The results of this current study confirm that the information content can provide insights into the clustering of genomes and the evolution of messaging strategies of the genomes. An efficient and robust Perl CGI standalone tool is created based on this information theory algorithm for the analysis of the whole genomes and is made available at https://github.com/AlagurajVeluchamy/InformationTheory.     

Causal analysis of CpG suppression in the Mycoplasma genome

Some bacterial genomes are known to have low CpG dinucleotide frequencies. While their causes are not clearly understood, the frequency of CpG is suppressed significantly in the genome of Mycoplasma genitalium, but not in that of Mycoplasma pneumoniae. We compared orthologous gene pairs of the two closely related species to analyze CpG substitution patterns between these two genomes. We also divided genome sequences into three regions: protein-coding, noncoding, and RNA-coding, and obtained the CpG frequencies for each region for each organism. It was found that the observed/expected ratio of CpG dinucleotides is low in both the protein-coding and noncoding regions; while that ratio is in the normal range in the RNA-coding region. Our results indicate that CpG suppression of the Mycoplasma genome is not caused by (1) biased usage amino acid; (2) biased usage of synonymous codon; or (3) methylation effects by the CpG methyltransferase in the genomes of their hosts. Instead, we consider it likely that a certain global pressure, such as genome-wide pressure for the advantages of DNA stability or replication, has the effect of decreasing CpG over the entire genome, which, in turn, resulted in the biased codon usage.     

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes.