Post-duplication charge evolution of phosphoglucose isomerases in teleost fishes through weak selection on many amino acid sites

Background  

The partitioning of ancestral functions among duplicated genes by neutral evolution, or subfunctionalization, has been considered the primary process for the evolution of novel proteins (neofunctionalization). Nonetheless, how a subfunctionalized protein can evolve into a more adaptive protein is poorly understood, mainly due to the limitations of current analytical methods, which can detect only strong selection for amino acid substitutions involved in adaptive molecular evolution. In this study, we employed a comparative evolutionary approach to this question, focusing on differences in the structural properties of a protein, specifically the electric charge, encoded by fish-specific duplicated phosphoglucose isomerase (Pgi) genes.     

ProtRepeatsDB: a database of amino acid repeats in genomes

Background  

Genome wide and cross species comparisons of amino acid repeats is an intriguing problem in biology mainly due to the highly polymorphic nature and diverse functions of amino acid repeats. Innate protein repeats constitute vital functional and structural regions in proteins. Repeats are of great consequence in evolution of proteins, as evident from analysis of repeats in different organisms. In the post genomic era, availability of protein sequences encoded in different genomes provides a unique opportunity to perform large scale comparative studies of amino acid repeats. ProtRepeatsDB is a relational database of perfect and mismatch repeats, access to which is designed as a resource and collection of tools for detection and cross species comparisons of different types of amino acid repeats.     

Evolution of phage with chemically ambiguous proteomes

Background  

The widespread introduction of amino acid substitutions into organismal proteomes has occurred during natural evolution, but has been difficult to achieve by directed evolution. The adaptation of the translation apparatus represents one barrier, but the multiple mutations that may be required throughout a proteome in order to accommodate an alternative amino acid or analogue is an even more daunting problem. The evolution of a small bacteriophage proteome to accommodate an unnatural amino acid analogue can provide insights into the number and type of substitutions that individual proteins will require to retain functionality.     

Investigation on the role of nsSNPs in HNPCC genes – a bioinformatics approach

Background  

A central focus of cancer genetics is the study of mutations that are causally implicated in tumorigenesis. The identification of such causal mutations not only provides insight into cancer biology but also presents anticancer therapeutic targets and diagnostic markers. Missense mutations are nucleotide substitutions that change an amino acid in a protein, the deleterious effects of these mutations are commonly attributed to their impact on primary amino acid sequence and protein structure.     

The rate of the molecular clock and the cost of gratuitous protein synthesis

Background  

The nature of the protein molecular clock, the protein-specific rate of amino acid substitutions, is among the central questions of molecular evolution. Protein expression level is the dominant determinant of the clock rate in a number of organisms. It has been suggested that highly expressed proteins evolve slowly in all species mainly to maintain robustness to translation errors that generate toxic misfolded proteins. Here we investigate this hypothesis experimentally by comparing the growth rate of Escherichia coli expressing wild type and misfolding-prone variants of the LacZ protein.     

Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field

Background  

Although experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. For a considerable fraction of proteins whose structures will not be determined experimentally, computational methods can provide valuable information. The value of structural models in biological research depends critically on their quality. Development of high-accuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling.     

Use of a multi-way method to analyze the amino acid composition of a conserved group of orthologous proteins in prokaryotes

Background  

Amino acids in proteins are not used equally. Some of the differences in the amino acid composition of proteins are between species (mainly due to nucleotide composition and lifestyle) and some are between proteins from the same species (related to protein function, expression or subcellular localization, for example). As several factors contribute to the different amino acid usage in proteins, it is difficult both to analyze these differences and to separate the contributions made by each factor.     

Flexible mapping of homology onto structure with Homolmapper

Background  

Over the past decade, a number of tools have emerged for the examination of homology relationships among protein sequences in a structural context. Most recent software implementations for such analysis are tied to specific molecular viewing programs, which can be problematic for collaborations involving multiple viewing environments. Incorporation into larger packages also adds complications for users interested in adding their own scoring schemes or in analyzing proteins incorporating unusual amino acid residues such as selenocysteine.     

Combining classifiers for improved classification of proteins from sequence or structure

Background  

Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage.     

The combined effects of amino acid substitutions and indels on the evolution of structure within protein families

Background

In the process of protein evolution, sequence variations within protein families can cause changes in protein structures and functions. However, structures tend to be more conserved than sequences and functions. This leads to an intriguing question: what is the evolutionary mechanism by which sequence variations produce structural changes? To investigate this question, we focused on the most common types of sequence variations: amino acid substitutions and insertions/deletions (indels). Here their combined effects on protein structure evolution within protein families are studied.

Results

Sequence-structure correlation analysis on 75 homologous structure families (from SCOP) that contain 20 or more non-redundant structures shows that in most of these families there is, statistically, a bilinear correlation between the amount of substitutions and indels versus the degree of structure variations. Bilinear regression of percent sequence non-identity (PNI) and standardized number of gaps (SNG) versus RMSD was performed. The coefficients from the regression analysis could be used to estimate the structure changes caused by each unit of substitution (structural substitution sensitivity, SSS) and by each unit of indel (structural indel sensitivity, SIDS). An analysis on 52 families with high bilinear fitting multiple correlation coefficients and statistically significant regression coefficients showed that SSS is mainly constrained by disulfide bonds, which almost have no effects on SIDS.

Conclusions

Structural changes in homologous protein families could be rationally explained by a bilinear model combining amino acid substitutions and indels. These results may further improve our understanding of the evolutionary mechanisms of protein structures.     

Machine learning integration for predicting the effect of single amino acid substitutions on protein stability

Background  

Computational prediction of protein stability change due to single-site amino acid substitutions is of interest in protein design and analysis. We consider the following four ways to improve the performance of the currently available predictors: (1) We include additional sequence- and structure-based features, namely, the amino acid substitution likelihoods, the equilibrium fluctuations of the alpha- and beta-carbon atoms, and the packing density. (2) By implementing different machine learning integration approaches, we combine information from different features or representations. (3) We compare classification vs. regression methods to predict the sign vs. the output of stability change. (4) We allow a reject option for doubtful cases where the risk of misclassification is high.     

Structural position correlation analysis (SPCA) for protein family

Background

The proteins in a family, which perform the similar biological functions, may have very different amino acid composition, but they must share the similar 3D structures, and keep a stable central region. In the conservative structure region similar biological functions are performed by two or three catalytic residues with the collaboration of several functional residues at key positions. Communication signals are conducted in a position network, adjusting the biological functions in the protein family.

Methodology

A computational approach, namely structural position correlation analysis (SPCA), is developed to analyze the correlation relationship between structural segments (or positions). The basic hypothesis of SPCA is that in a protein family the structural conservation is more important than the sequence conservation, and the local structural changes may contain information of biology functional evolution. A standard protein P⁽⁰⁾ is defined in a protein family, which consists of the most-frequent amino acids and takes the average structure of the protein family. The foundational variables of SPCA is the structural position displacements between the standard protein P⁽⁰⁾ and individual proteins P_i of the family. The structural positions are organized as segments, which are the stable units in structural displacements of the protein family. The biological function differences of protein members are determined by the position structural displacements of individual protein P_i to the standard protein P⁽⁰⁾. Correlation analysis is used to analyze the communication network among segments.

Conclusions

The structural position correlation analysis (SPCA) is able to find the correlation relationship among the structural segments (or positions) in a protein family, which cannot be detected by the amino acid sequence and frequency-based methods. The functional communication network among the structural segments (or positions) in protein family, revealed by SPCA approach, well illustrate the distantly allosteric interactions, and contains valuable information for protein engineering study.     

Protein Evolution via Amino Acid and Codon Elimination

Background

Global residue-specific amino acid mutagenesis can provide important biological insight and generate proteins with altered properties, but at the risk of protein misfolding. Further, targeted libraries are usually restricted to a handful of amino acids because there is an exponential correlation between the number of residues randomized and the size of the resulting ensemble. Using GFP as the model protein, we present a strategy, termed protein evolution via amino acid and codon elimination, through which simplified, native-like polypeptides encoded by a reduced genetic code were obtained via screening of reduced-size ensembles.

Methodology/Principal Findings

The strategy involves combining a sequential mutagenesis scheme to reduce library size with structurally stabilizing mutations, chaperone complementation, and reduced temperature of gene expression. In six steps, we eliminated a common buried residue, Phe, from the green fluorescent protein (GFP), while retaining activity. A GFP variant containing 11 Phe residues was used as starting scaffold to generate 10 separate variants in which each Phe was replaced individually (in one construct two adjacent Phe residues were changed simultaneously), while retaining varying levels of activity. Combination of these substitutions to generate a Phe-free variant of GFP abolished fluorescence. Combinatorial re-introduction of five Phe residues, based on the activities of the respective single amino acid replacements, was sufficient to restore GFP activity. Successive rounds of mutagenesis generated active GFP variants containing, three, two, and zero Phe residues. These GFPs all displayed progenitor-like fluorescence spectra, temperature-sensitive folding, a reduced structural stability and, for the least stable variants, a reduced steady state abundance.

Conclusions/Significance

The results provide strategies for the design of novel GFP reporters. The described approach offers a means to enable engineering of active proteins that lack certain amino acids, a key step towards expanding the functional repertoire of uniquely labeled proteins in synthetic biology.     

A class frequency mixture model that adjusts for site-specific amino acid frequencies and improves inference of protein phylogeny

Background  

Widely used substitution models for proteins, such as the Jones-Taylor-Thornton (JTT) or Whelan and Goldman (WAG) models, are based on empirical amino acid interchange matrices estimated from databases of protein alignments that incorporate the average amino acid frequencies of the data set under examination (e.g JTT + F). Variation in the evolutionary process between sites is typically modelled by a rates-across-sites distribution such as the gamma (Γ) distribution. However, sites in proteins also vary in the kinds of amino acid interchanges that are favoured, a feature that is ignored by standard empirical substitution matrices. Here we examine the degree to which the pattern of evolution at sites differs from that expected based on empirical amino acid substitution models and evaluate the impact of these deviations on phylogenetic estimation.     

Exhaustive assignment of compositional bias reveals universally prevalent biased regions: analysis of functional associations in human and <Emphasis Type="Italic">Drosophila</Emphasis>

Background  

Compositionally biased (CB) regions are stretches in protein sequences made from mainly a distinct subset of amino acid residues; such regions are frequently associated with a structural role in the cell, or with protein disorder.     

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

Background  

We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of C _α traces for short proteins (up to 200 amino acids).     

Selective constraints on amino acids estimated by a mechanistic codon substitution model with multiple nucleotide changes

Background

Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices.

Results

Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins.

Conclusions/Significance

The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences.     

Accumulation of amino acid substitutions promotes irreversible structural changes in the hemagglutinin of human influenza AH3 virus during evolution

During protein evolution the amino acid substitutions accumulate with time. However, the effect of accumulation of the amino acid substitutions to structural changes has not been estimated well. We will propose that the discordance of amino acid substitution on the HA protein of influenza A virus is useful for the assessment of structural changes during evolution. Discordance value can be obtained from the experimental data of tolerance or intolerance by introducing site directed mutagenesis at the homologous positions of two HA proteins holding the same amino acid residues. The value of discordance correlated to the number of amino acid differences among proteins. In the H3HA discordance rate was calculated to be 0.45% per one amino acid change. Furthermore, discordance of amino acid substitutions suggests that tolerable amino acid substitutions in different order have a probability of promoting irreversible divergence of the HA protein to different subtypes.     

Screening of transgenic proteins expressed in transgenic food crops for the presence of short amino acid sequences identical to potential,IgE – binding linear epitopes of allergens

Background  

Transgenic proteins expressed by genetically modified food crops are evaluated for their potential allergenic properties prior to marketing, among others by identification of short identical amino acid sequences that occur both in the transgenic protein and allergenic proteins. A strategy is proposed, in which the positive outcomes of the sequence comparison with a minimal length of six amino acids are further screened for the presence of potential linear IgE-epitopes. This double track approach involves the use of literature data on IgE-epitopes and an antigenicity prediction algorithm.