Structural similarity of loops in protein families: toward the understanding of protein evolution

BACKGROUND: Protein evolution and protein classification are usually inferred by comparing protein cores in their conserved aligned parts. Structurally aligned protein regions are separated by less conserved loop regions, where sequence and structure locally deviate from each other and do not superimpose well. RESULTS: Our results indicate that even longer protein loops can not be viewed as "random coils" and for the majority of protein families in our test set there exists a linear correlation between the measures of sequence similarity and loop structural similarity. Results suggest that distance matrices derived from the loop (dis)similarity measure may produce in some cases more reliable cluster trees compared to the distance matrices based on the conventional measures of sequence and structural (dis)similarity. CONCLUSIONS: We show that by considering "dissimilar" loop regions rather than only conserved core regions it is possible to improve our understanding of protein evolution

Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners

Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them

Long-term trends in evolution of indels in protein sequences

BACKGROUND: In this paper we describe an analysis of the size evolution of both protein domains and their indels, as inferred by changing sizes of whole domains or individual unaligned regions or "spacers". We studied relatively early evolutionary events and focused on protein domains which are conserved among various taxonomy groups. RESULTS: We found that more than one third of all domains have a statistically significant tendency to increase/decrease in size in evolution as judged from the overall domain size distribution as well as from the size distribution of individual spacers. Moreover, the fraction of domains and individual spacers increasing in size is almost twofold larger than the fraction decreasing in size. CONCLUSION: We showed that the tolerance to insertion and deletion events depends on the domain's taxonomy span. Eukaryotic domains are depleted in insertions compared to the overall test set, namely, the number of spacers increasing in size is about the same as the number of spacers decreasing in size. On the other hand, ancient domain families show some bias towards insertions or spacers which grow in size in evolution. Domains from several Gene Ontology categories also demonstrate certain tendencies for insertion or deletion events as inferred from the analysis of spacer sizes

Knowledge-based annotation of small molecule binding sites in proteins

<p>Abstract</p> <p>Background</p> <p>The study of protein-small molecule interactions is vital for understanding protein function and for practical applications in drug discovery. To benefit from the rapidly increasing structural data, it is essential to improve the tools that enable large scale binding site prediction with greater emphasis on their biological validity.</p> <p>Results</p> <p>We have developed a new method for the annotation of protein-small molecule binding sites, using inference by homology, which allows us to extend annotation onto protein sequences without experimental data available. To ensure biological relevance of binding sites, our method clusters similar binding sites found in homologous protein structures based on their sequence and structure conservation. Binding sites which appear evolutionarily conserved among non-redundant sets of homologous proteins are given higher priority. After binding sites are clustered, position specific score matrices (PSSMs) are constructed from the corresponding binding site alignments. Together with other measures, the PSSMs are subsequently used to rank binding sites to assess how well they match the query and to better gauge their biological relevance. The method also facilitates a succinct and informative representation of observed and inferred binding sites from homologs with known three-dimensional structures, thereby providing the means to analyze conservation and diversity of binding modes. Furthermore, the chemical properties of small molecules bound to the inferred binding sites can be used as a starting point in small molecule virtual screening. The method was validated by comparison to other binding site prediction methods and to a collection of manually curated binding site annotations. We show that our method achieves a sensitivity of 72% at predicting biologically relevant binding sites and can accurately discriminate those sites that bind biological small molecules from non-biological ones.</p> <p>Conclusions</p> <p>A new algorithm has been developed to predict binding sites with high accuracy in terms of their biological validity. It also provides a common platform for function prediction, knowledge-based docking and for small molecule virtual screening. The method can be applied even for a query sequence without structure. The method is available at <url>http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi</url>.</p

The relationship of protein conservation and sequence length

BACKGROUND: In general, the length of a protein sequence is determined by its function and the wide variance in the lengths of an organism's proteins reflects the diversity of specific functional roles for these proteins. However, additional evolutionary forces that affect the length of a protein may be revealed by studying the length distributions of proteins evolving under weaker functional constraints. RESULTS: We performed sequence comparisons to distinguish highly conserved and poorly conserved proteins from the bacterium Escherichia coli, the archaeon Archaeoglobus fulgidus, and the eukaryotes Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. For all organisms studied, the conserved and nonconserved proteins have strikingly different length distributions. The conserved proteins are, on average, longer than the poorly conserved ones, and the length distributions for the poorly conserved proteins have a relatively narrow peak, in contrast to the conserved proteins whose lengths spread over a wider range of values. For the two prokaryotes studied, the poorly conserved proteins approximate the minimal length distribution expected for a diverse range of structural folds. CONCLUSIONS: There is a relationship between protein conservation and sequence length. For all the organisms studied, there seems to be a significant evolutionary trend favoring shorter proteins in the absence of other, more specific functional constraints

Refining multiple sequence alignments with conserved core regions

Accurate multiple sequence alignments of proteins are very important to several areas of computational biology and provide an understanding of phylogenetic history of domain families, their identification and classification. This article presents a new algorithm, REFINER, that refines a multiple sequence alignment by iterative realignment of its individual sequences with the predetermined conserved core (block) model of a protein family. Realignment of each sequence can correct misalignments between a given sequence and the rest of the profile and at the same time preserves the family's overall block model. Large-scale benchmarking studies showed a noticeable improvement of alignment after refinement. This can be inferred from the increased alignment score and enhanced sensitivity for database searching using the sequence profiles derived from refined alignments compared with the original alignments. A standalone version of the program is available by ftp distribution () and will be incorporated into the next release of the Cn3D structure/alignment viewer

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the ‘Emperor of Maladies’. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into ‘families’ based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathway

DNA polymerase η mutational signatures are found in a variety of different types of cancer

DNA polymerase (pol) η is a specialized error-prone polymerase with at least two quite different and contrasting cellular roles: to mitigate the genetic consequences of solar UV irradiation, and promote somatic hypermutation in the variable regions of immunoglobulin genes. Misregulation and mistargeting of pol η can compromise genome integrity. We explored whether the mutational signature of pol η could be found in datasets of human somatic mutations derived from normal and cancer cells. A substantial excess of single and tandem somatic mutations within known pol η mutable motifs was noted in skin cancer as well as in many other types of human cancer, suggesting that somatic mutations in A:T bases generated by DNA polymerase η are a common feature of tumorigenesis. Another peculiarity of pol ηmutational signatures, mutations in YCG motifs, led us to speculate that error-prone DNA synthesis opposite methylated CpG dinucleotides by misregulated pol η in tumors might constitute an additional mechanism of cytosine demethylation in this hypermutable dinucleotide

ComSin: database of protein structures in bound (complex) and unbound (single) states in relation to their intrinsic disorder

Most of the proteins in a cell assemble into complexes to carry out their function. In this work, we have created a new database (named ComSin) of protein structures in bound (complex) and unbound (single) states to provide a researcher with exhaustive information on structures of the same or homologous proteins in bound and unbound states. From the complete Protein Data Bank (PDB), we selected 24 910 pairs of protein structures in bound and unbound states, and identified regions of intrinsic disorder. For 2448 pairs, the proteins in bound and unbound states are identical, while 7129 pairs have sequence identity 90% or larger. The developed server enables one to search for proteins in bound and unbound states with several options including sequence similarity between the corresponding proteins in bound and unbound states, and validation of interaction interfaces of protein complexes. Besides that, through our web server, one can obtain necessary information for studying disorder-to-order and order-to-disorder transitions upon complex formation, and analyze structural differences between proteins in bound and unbound states. The database is available at http://antares.protres.ru/comsin/