ProBias: a web-server for the identification of user-specified types of compositionally biased segments in protein sequences

Most proteins contain compositionally biased segments (CBS) in which one or more amino acid types are significantly overrepresented. CBS that contain amino acids with similar chemical properties can have functional and structural importance. This article describes ProBias, a web-server that searches a protein sequence for CBS composed of user-specified amino acid types. ProBias utilizes the discrete scan statistics to estimate statistical significance of CBS and is able to detect even subtle local deviations from the random independence model. The web-server also analyzes the global compositional bias of the input sequence. In the case of novel proteins that lack functional annotation, statistically significant CBS reported by ProBias can be used to guide the search for potential functionally important sites or domains. AVAILABILITY: Freely available at http://lcg.rit.albany.edu/ProBias. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Prebiotically plausible mechanisms increase compositional diversity of nucleic acid sequences

During the origin of life, the biological information of nucleic acid polymers must have increased to encode functional molecules (the RNA world). Ribozymes tend to be compositionally unbiased, as is the vast majority of possible sequence space. However, ribonucleotides vary greatly in synthetic yield, reactivity and degradation rate, and their non-enzymatic polymerization results in compositionally biased sequences. While natural selection could lead to complex sequences, molecules with some activity are required to begin this process. Was the emergence of compositionally diverse sequences a matter of chance, or could prebiotically plausible reactions counter chemical biases to increase the probability of finding a ribozyme? Our in silico simulations using a two-letter alphabet show that template-directed ligation and high concatenation rates counter compositional bias and shift the pool toward longer sequences, permitting greater exploration of sequence space and stable folding. We verified experimentally that unbiased DNA sequences are more efficient templates for ligation, thus increasing the compositional diversity of the pool. Our work suggests that prebiotically plausible chemical mechanisms of nucleic acid polymerization and ligation could predispose toward a diverse pool of longer, potentially structured molecules. Such mechanisms could have set the stage for the appearance of functional activity very early in the emergence of life.     

Compositional pressure and translational selection determine codon usage in the extremely GC-poor unicellular eukaryote Entamoeba histolytica

It is widely accepted that the compositional pressure is the only factor shaping codon usage in unicellular species displaying extremely biased genomic compositions. This seems to be the case in the prokaryotes Mycoplasma capricolum, Rickettsia prowasekii and Borrelia burgdorferi (GC-poor), and in Micrococcus luteus (GC-rich). However, in the GC-poor unicellular eukaryotes Dictyostelium discoideum and Plasmodium falciparum, there is evidence that selection, acting at the level of translation, influences codon choices. This is a twofold intriguing finding, since (1) the genomic GC levels of the above mentioned eukaryotes are lower than the GC% of any studied bacteria, and (2) bacteria usually have larger effective population sizes than eukaryotes, and hence natural selection is expected to overcome more efficiently the randomizing effects of genetic drift among prokaryotes than among eukaryotes. In order to gain a new insight about this problem, we analysed the patterns of codon preferences of the nuclear genes of Entamoeba histolytica, a unicellular eukaryote characterised by an extremely AT-rich genome (GC = 25%). The overall codon usage is strongly biased towards A and T in the third codon positions, and among the presumed highly expressed sequences, there is an increased relative usage of a subset of codons, many of which are C-ending. Since an increase in C in third codon positions is 'against' the compositional bias, we conclude that codon usage in E. histolytica, as happens in D. discoideum and P. falciparum, is the result of an equilibrium between compositional pressure and selection. These findings raise the question of why strongly compositionally biased eukaryotic cells may be more sensitive to the (presumed) slight differences among synonymous codons than compositionally biased bacteria.     

Compositional Bias May Affect Both DNA-Based and Protein-Based Phylogenetic Reconstructions

It is now well-established that compositional bias in DNA sequences can adversely affect phylogenetic analysis based on those sequences. Phylogenetic analyses based on protein sequences are generally considered to be more reliable than those derived from the corresponding DNA sequences because it is believed that the use of encoded protein sequences circumvents the problems caused by nucleotide compositional biases in the DNA sequences. There exists, however, a correlation between AT/GC bias at the nucleotide level and content of AT- and GC-rich codons and their corresponding amino acids. Consequently, protein sequences can also be affected secondarily by nucleotide compositional bias. Here, we report that DNA bias not only may affect phylogenetic analysis based on DNA sequences, but also drives a protein bias which may affect analyses based on protein sequences. We present a striking example where common phylogenetic tools fail to recover the correct tree from complete animal mitochondrial protein-coding sequences. The data set is very extensive, containing several thousand sites per sequence, and the incorrect phylogenetic trees are statistically very well supported. Additionally, neither the use of the LogDet/paralinear transform nor removal of positions in the protein alignment with AT- or GC-rich codons allowed recovery of the correct tree. Two taxa with a large compositional bias continually group together in these analyses, despite a lack of close biological relatedness. We conclude that even protein-based phylogenetic trees may be misleading, and we advise caution in phylogenetic reconstruction using protein sequences, especially those that are compositionally biased. Received: 19 February 1998 / Accepted: 28 August 1998     

Comparison of sequence masking algorithms and the detection of biased protein sequence regions

MOTIVATION: Separation of protein sequence regions according to their local information complexity and subsequent masking of low complexity regions has greatly enhanced the reliability of function prediction by sequence similarity. Comparisons with alternative methods that focus on compositional sequence bias rather than information complexity measures have shown that removal of compositional bias yields at least as sensitive and much more specific results. Besides the application of sequence masking algorithms to sequence similarity searches, the study of the masked regions themselves is of great interest. Traditionally, however, these have been neglected despite evidence of their functional relevance. RESULTS: Here we demonstrate that compositional bias seems to be a more effective measure for the detection of biologically meaningful signals. Typical results on proteins are compared to results for sequences that have been randomized in various ways, conserving composition and local correlations for individual proteins or the entire set. It is remarkable that low-complexity regions have the same form of distribution in proteins as in randomized sequences, and that the signal from randomized sequences with conserved local correlations and amino acid composition almost matches the signal from proteins. This is not the case for sequence bias, which hence seems to be a genuinely biological phenomenon in contrast to patches of low complexity.     

Issues in predicting protein function from sequence

Identifying homologues, defined as genes that arose from a common evolutionary ancestor, is often a relatively straightforward task, thanks to recent advances made in estimating the statistical significance of sequence similarities found from database searches. The extent by which homologues possess similarities in function, however, is less amenable to statistical analysis. Consequently, predicting function by homology is a qualitative, rather than quantitative, process and requires particular care to be taken. This review focuses on the various approaches that have been developed to predict function from the scale of the atom to that of the organism. Similarities in homologues' functions differ considerably at each of these different scales and also vary for different domain families. It is argued that due attention should be paid to all available clues to function, including orthologue identification, conservation of particular residue types, and the co-occurrence of domains in proteins. Pitfalls in database searching methods arising from amino acid compositional bias and database size effects are also discussed.     

Identifying compositionally homogeneous and nonhomogeneous domains within the human genome using a novel segmentation algorithm

It has been suggested that the mammalian genome is composed mainly of long compositionally homogeneous domains. Such domains are frequently identified using recursive segmentation algorithms based on the Jensen–Shannon divergence. However, a common difficulty with such methods is deciding when to halt the recursive partitioning and what criteria to use in deciding whether a detected boundary between two segments is real or not. We demonstrate that commonly used halting criteria are intrinsically biased, and propose IsoPlotter, a parameter-free segmentation algorithm that overcomes such biases by using a simple dynamic halting criterion and tests the homogeneity of the inferred domains. IsoPlotter was compared with an alternative segmentation algorithm, D_JS, using two sets of simulated genomic sequences. Our results show that IsoPlotter was able to infer both long and short compositionally homogeneous domains with low GC content dispersion, whereas D_JS failed to identify short compositionally homogeneous domains and sequences with low compositional dispersion. By segmenting the human genome with IsoPlotter, we found that one-third of the genome is composed of compositionally nonhomogeneous domains and the remaining is a mixture of many short compositionally homogeneous domains and relatively few long ones.     

Conformational preferences of non-polar amino acid residues: an additional factor in amyloid formation

Amyloid consists of β-sheet polymers and is associated with disease and with functional assemblies. Amyloid-forming proteins differ widely in native structures and sequences. We describe here how conformational preferences of non-polar amino acid residues can affect amyloid formation. The most non-polar residues promote either β-strands (Val, Ile, Phe, and Cys, VIFC) or α-helices (Leu, Ala, and Met, LAM), while the most polar residues promote only α-helices. For 12 proteins associated with disease, the localizations of the amyloid core regions are known. Eleven of these contain segments that are biased for VIFC, but essentially lack segments that are biased for LAM. For the amyloid β-peptide associated with Alzheimer’s disease and an amyloidogenic fragment of the prion protein, observed effects of mutations support that VIFC bias favors formation of β-sheet aggregates and amyloid, while LAM bias prevents it. VIFC and LAM profiles combine information on secondary structure propensities and polarity, and add a simple criterion to the prediction of amyloidogenic regions.     

LEON: multiple aLignment Evaluation Of Neighbours

Sequence alignments are fundamental to a wide range of applications, including database searching, functional residue identification and structure prediction techniques. These applications predict or propagate structural/functional/evolutionary information based on a presumed homology between the aligned sequences. If the initial hypothesis of homology is wrong, no subsequent application, however sophisticated, can be expected to yield accurate results. Here we present a novel method, LEON, to predict homology between proteins based on a multiple alignment of complete sequences (MACS). In MACS, weak signals from distantly related proteins can be considered in the overall context of the family. Intermediate sequences and the combination of individual weak matches are used to increase the significance of low-scoring regions. Residue composition is also taken into account by incorporation of several existing methods for the detection of compositionally biased sequence segments. The accuracy and reliability of the predictions is demonstrated in large-scale comparisons with structural and sequence family databases, where the specificity was shown to be >99% and the sensitivity was estimated to be ~76%. LEON can thus be used to reliably identify the complex relationships between large multidomain proteins and should be useful for automatic high-throughput genome annotations, 2D/3D structure predictions, protein–protein interaction predictions etc.     

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.     

Interactome-wide prediction of short, disordered protein interaction motifs in humans

Many of the specific functions of intrinsically disordered protein segments are mediated by Short Linear Motifs (SLiMs) interacting with other proteins. Well known examples include SLiMs that interact with 14-3-3, PDZ, SH2, SH3, and WW domains but the true extent and diversity of SLiM-mediated interactions is largely unknown. Here, we attempt to expand our knowledge of human SLiMs by applying in silico SLiM prediction to the human interactome. Combining data from seven different interaction databases, we analysed approximately 6000 protein-centred and 1600 domain-centred human interaction datasets of 3+ unrelated proteins that interact with a common partner. Results were placed in context through comparison to randomised datasets of similar size and composition. The search returned thousands of evolutionarily conserved, intrinsically disordered occurrences of hundreds of significantly enriched recurring motifs, including many that have never been previously identified (). In addition to True Positive results for at least 25 different known SLiMs, a striking number of "off-target" proteins/domains also returned significantly enriched known motifs. Often, this was due to the non-independence of the datasets, with many proteins sharing interaction partners or contributing interactions to multiple domain datasets. The majority of these motif classes, however, were also found to be significantly enriched in one or more randomised datasets. This highlights the need for care when interpreting motif predictions of this nature but also raises the possibility that SLiM occurrences may be successfully identified independently of interaction data. Although not as compositionally biased as previous studies, patterns matching known SLiMs tended to cluster into a few large groups of similar sequence, while novel predictions tended to be more distinctive and less abundant. Whether this is due to ascertainment bias or a true functional composition bias of SLiMs is not clear and warrants further investigation.     

CAST: an iterative algorithm for the complexity analysis of sequence tracts. Complexity analysis of sequence tracts

MOTIVATION: Sensitive detection and masking of low-complexity regions in protein sequences. Filtered sequences can be used in sequence comparison without the risk of matching compositionally biased regions. The main advantage of the method over similar approaches is the selective masking of single residue types without affecting other, possibly important, regions. RESULTS: A novel algorithm for low-complexity region detection and selective masking. The algorithm is based on multiple-pass Smith-Waterman comparison of the query sequence against twenty homopolymers with infinite gap penalties. The output of the algorithm is both the masked query sequence for further analysis, e.g. database searches, as well as the regions of low complexity. The detection of low-complexity regions is highly specific for single residue types. It is shown that this approach is sufficient for masking database query sequences without generating false positives. The algorithm is benchmarked against widely available algorithms using the 210 genes of Plasmodium falciparum chromosome 2, a dataset known to contain a large number of low-complexity regions. AVAILABILITY: CAST (version 1.0) executable binaries are available to academic users free of charge under license. Web site entry point, server and additional material: http://www.ebi.ac.uk/research/cgg/services/cast/     

Measurement errors in line transect surveys where detectability varies with distance and size

When using bivariate line transect methods to estimate the biomass density of a tightly clustered biological population, it is generally assumed that both the perpendicular distance from the trackline to the cluster and the cluster size, or biomass, are measured without error. This is unlikely to be the case in practice. In this article, assuming additive mean zero errors in distance and multiplicative errors in size, we develop an estimator of density that corrects for these errors. We use the method of moments for the case of gamma cluster size, randomly placed transect lines, and the generalized exponential detection function. We derive results that show that it may not be necessary to correct for errors in distance or size when the distance and size estimates are not biased. When the size estimates are biased, the biomass density estimate has approximately the same bias as the size estimates. The work is illustrated in the context of annual aerial surveys for juvenile southern bluefin tuna in the Great Australian Bight.     

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.     

Composition strand asymmetries in prokaryotic genomes: mutational bias and biased gene orientation

Most prokaryotic genomes display strand compositional asymmetries, but the reasons for these biases remain unclear. When the distribution of gene orientation is biased, as it often is, this may induce a bias in composition, as codon frequencies are not identical. We show here that this effect can be estimated and removed, and that the residual base skews are the highest at third base codon positions and lower at first and second positions. This strongly suggests that compositional asymmetries result from 1) a replication-related mutational bias that is filtered through selective pressure and/or from 2) an uneven distribution of gene orientation. In most cases, the mutational bias alters the codon usage and amino acid frequencies of the leading and the lagging strand. However, these features are not ubiquitous amongst prokaryotes, and the biological reasons for them remain to be found.     

Antigen-antibody interface properties: Composition, residue interactions, and features of 53 non-redundant structures

The structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how Ab recognize Ag as well as how Ag are folded to present surfaces for Ag recognition. As such, the Ab surface holds information about Ag folding that resides with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, a data set comprised of 53 non-redundant 3D structures of Ag-Ab complexes was analyzed. We assessed the physical and biochemical features of the Ag-Ab interfaces and the degree to which favored interactions exist between amino acid residues on the corresponding interface surfaces. Amino acid compositional analysis of the interfaces confirmed the dominance of TYR in the Ab paratope-containing surface (PCS), with almost two fold greater abundance than any other residue. Additionally TYR had a much higher than expected presence in the PCS compared to the surface of the whole antibody (defined as the occurrence propensity), along with aromatics PHE, TRP, and to a lesser degree HIS and ILE. In the Ag epitope-containing surface (ECS), there were slightly increased occurrence propensities of TRP and TYR relative to the whole Ag surface, implying an increased significance over the compositionally most abundant LYS>ASN>GLU>ASP>ARG. This examination encompasses a large, diverse set of unique Ag-Ab crystal structures that help explain the biological range and specificity of Ag-Ab interactions. This analysis may also provide a measure of the significance of individual amino acid residues in phage display analysis of Ag binding.     

SEGMENT: identifying compositional domains in DNA sequences

MOTIVATION: DNA sequences are formed by patches or domains of different nucleotide composition. In a few simple sequences, domains can simply be identified by eye; however, most DNA sequences show a complex compositional heterogeneity (fractal structure), which cannot be properly detected by current methods. Recently, a computationally efficient segmentation method to analyse such nonstationary sequence structures, based on the Jensen-Shannon entropic divergence, has been described. Specific algorithms implementing this method are now needed. RESULTS: Here we describe a heuristic segmentation algorithm for DNA sequences, which was implemented on a Windows program (SEGMENT). The program divides a DNA sequence into compositionally homogeneous domains by iterating a local optimization procedure at a given statistical significance. Once a sequence is partitioned into domains, a global measure of sequence compositional complexity (SCC), accounting for both the sizes and compositional biases of all the domains in the sequence, is derived. SEGMENT computes SCC as a function of the significance level, which provides a multiscale view of sequence complexity.     

Recovering motifs from biased genomes: application of signal correction

A significant problem in biological motif analysis arises when the background symbol distribution is biased (e.g. high/low GC content in the case of DNA sequences). This can lead to overestimation of the amount of information encoded in a motif. A motif can be depicted as a signal using information theory (IT). We apply two concepts from IT, distortion and patterned interference (a type of noise), to model genomic and codon bias respectively. This modeling approach allows us to correct a raw signal to recover signals that are weakened by compositional bias. The corrected signal is more likely to be discriminated from a biased background by a macromolecule. We apply this correction technique to recover ribosome-binding site (RBS) signals from available sequenced and annotated prokaryotic genomes having diverse compositional biases. We observed that linear correction was sufficient for recovering signals even at the extremes of these biases. Further comparative genomics studies were made possible upon correction of these signals. We find that the average Euclidian distance between RBS signal frequency matrices of different genomes can be significantly reduced by using the correction technique. Within this reduced average distance, we can find examples of class-specific RBS signals. Our results have implications for motif-based prediction, particularly with regards to the estimation of reliable inter-genomic model parameters.     

大气向下长波辐射参数化模型在长白山地区的适用

利用中国科学院长白山森林生态系统定位站的近地面气象观测数据,分析评价了目前被广泛使用的8个晴天与8个云天大气长波辐射参数化模型的模拟性能.结果表明： 晴天时Satterlund模型最适用,其偏差(BIAS)与均方根误差(RMSE)分别是-23.34和28.55 W·m^-2;系数校正后,虽然其参数值变化不大,但其模拟效果有很大提高,BIAS与RMSE分别降低为-6.33和18.08 W·m^-2;云天时Jacobs模型最准确,BIAS和RMSE只有0.38和29.29 W·m^-2.对模型中大气发射率的敏感性分析表明,大气发射率对水汽压的变化最敏感,对温度的变化不敏感.应用优选模型(晴天和云天)得到的模拟值与观测值的日变化趋势基本一致,但在云量发生突变的节点上模拟效果不太理想.
     

On the correlation between composition and site-specific evolutionary rate: implications for phylogenetic inference

Model-based phylogenetic reconstruction methods traditionally assume homogeneity of nucleotide frequencies among sequence sites and lineages. Yet, heterogeneity in base composition is a characteristic shared by most biological sequences. Compositional variation in time, reflected in the compositional biases among contemporary sequences, has already been extensively studied, and its detrimental effects on phylogenetic estimates are known. However, fewer studies have focused on the effects of spatial compositional heterogeneity within genes. We show here that different sites in an alignment do not always share a unique compositional pattern, and we provide examples where nucleotide frequency trends are correlated with the site-specific rate of evolution in RNA genes. Spatial compositional heterogeneity is shown to affect the estimation of evolutionary parameters. With standard phylogenetic methods, estimates of equilibrium frequencies are found to be biased towards the composition observed at fast-evolving sites. Conversely, the ancestral composition estimates of some time-heterogeneous but spatially homogeneous methods are found to be biased towards frequencies observed at invariant and slow-evolving sites. The latter finding challenges the result of a previous study arguing against a hyperthermophilic last universal ancestor from the low apparent G + C content of its rRNA sequences. We propose a new model to account for compositional variation across sites. A Gaussian process prior is used to allow for a smooth change in composition with evolutionary rate. The model has been implemented in the phylogenetic inference software PHASE, and Bayesian methods can be used to obtain the model parameters. The results suggest that this model can accurately capture the observed trends in present-day RNA sequences.