Evolutionary History Of Proteins Research Articles

Automated selection of homologs to track the evolutionary history of proteins

BackgroundThe selection of distant homologs of a query protein under study is a usual and useful application of protein sequence databases. Such sets of homologs are often applied to investigate the function of a protein and the degree to which experimental results can be transferred from one organism to another. In particular, a variety of databases facilitates static browsing for orthologs. However, these resources have a limited power when identifying orthologs between taxonomically distant species. In addition, in some situations, for a given query protein, it is advantageous to compare the sets of orthologs from different specific organisms: this recursive step-wise search might give an idea of the evolutionary path of the protein as a series of consecutive steps, for example gaining or losing domains. However, a step-wise orthology search is a time-consuming task if the number of steps is high.ResultsTo illustrate a solution for this problem, we present the web tool ProteinPathTracker, which allows to track the evolutionary history of a query protein by locating homologs in selected proteomes along several evolutionary paths. Additional functionalities include locking a region of interest to follow its evolution in the discovered homologous sequences and the study of the protein function evolution by analysis of the annotations of the homologs.ConclusionsProteinPathTracker is an easy-to-use web tool that automatises the practice of looking for selected homologs in distant species in a straightforward way for non-expert users.

MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks.

Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure. The new method, MOCASSIN-prot, was applied to cluster multi-domain proteins from ten genomes. The performance of MOCASSIN-prot was compared against two protein clustering methods, Markov clustering (TRIBE-MCL) and spectral clustering (SCPS). We showed that compared to these two methods, MOCASSIN-prot, which uses both domain composition and quantitative sequence similarity information, generates fewer false positives. It achieves more functionally coherent protein clusters and better differentiates protein families. MOCASSIN-prot, implemented in Perl and Matlab, is freely available at http://bioinfolab.unl.edu/emlab/MOCASSINprot. emoriyama2@unl.edu. Supplementary data are available at Bioinformatics online.

Common Structural Core of Three-Dozen Residues Reveals Intersuperfamily Relationships.

Identification of relationships among protein families or superfamilies is a challenge. However, functionally essential protein regions typically retain structural integrity, even when the corresponding protein sequences evolve. Consequently, comparison of protein structures enables deeper phylogenetic analyses than achievable through the use of sequence information only. Here, we focus on a group of distantly related viral and cellular enzymes involved in nucleic acid or nucleotide processing and synthesis. All these enzymes share an apparently similar protein fold at their active site, which resembles the palm subdomain of the right-hand-shaped polymerases. Using a structure-based hierarchical clustering method, we identified a common structural core of 36 equivalent residues for this functionally diverse group of enzymes, representing five protein superfamilies. Based on the properties of these 36 residues, we deduced a structural distance-based tree in which the proteins were accurately clustered according to the established family classification. Within this tree, the enzymes catalyzing genomic nucleic acid replication or transcription were separated from those performing supplementary nucleic acid or nucleotide processing functions. In addition, we found that the family Y DNA polymerases are structurally more closely related to the nucleotide cyclase superfamily members than to the other members of the DNA/RNA polymerase superfamily, and these enzymes share 88 equivalent residues comprising a Β: 1- Α: 1- Α: 2- Β: 2- Β: 3- Α: 3- Β: 4- Α: 4- Β: 5 fold. The results highlight the power of structure-based hierarchical clustering in identifying remote evolutionary relationships. Furthermore, our study implies that a protein substructure of only three-dozen residues can contain a substantial amount of information on the evolutionary history of proteins.

Nhmmer: DNA homology search with profile HMMs

Summary: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome.Availability: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from http://hmmer.org. HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.Contact: wheelert@janelia.hhmi.org

SECOM: A Novel Hash Seed and Community Detection Based-Approach for Genome-Scale Protein Domain Identification

With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx.

Evolutionary Networks in the Formatted Protein Sequence Space

In our recent work, a new approach to establish sequence relatedness, by walking through the protein sequence space, was introduced. The sequence space is built from 20 amino acid long fragments of proteins from a very large collection of fully sequenced prokaryotic genomes. The fragments, points in the space, are connected, if they are closely related (high sequence identity). The connected fragments form variety of networks of sequence kinship. In this research the networks in the formatted sequence space and their topology are analyzed. For lower identity thresholds a huge network of complex structure is formed, involving up to 10% points of the space. When the threshold is increased, the major network splits into a set of smaller clusters with a wide diversity of sizes and topologies. Such "evolutionary networks" may serve as a powerful sequence annotation tool that allows one to reveal fine details in the evolutionary history of proteins.

The Stability Effects of Protein Mutations Appear to be Universally Distributed

How the thermodynamic stability effects of protein mutations (ΔΔ G) are distributed is a fundamental property related to the architecture, tolerance to mutations (mutational robustness), and evolutionary history of proteins. The stability effects of mutations also dictate the rate and dynamics of protein evolution, with deleterious mutations being the main inhibitory factor. Using the FoldX algorithm that attempts to computationally predict ΔΔ G effects of mutations, we deduced the overall distributions of stability effects for all possible mutations in 21 different globular, single domain proteins. We found that these distributions are strikingly similar despite a range of sizes and folds, and largely follow a bi-Gaussian function: The surface residues exhibit a narrow distribution with a mildly destabilizing mean ΔΔ G (∼ 0.6 kcal/mol), whereas the core residues exhibit a wider distribution with a stronger destabilizing mean (∼ 1.4 kcal/mol). Since smaller proteins have a higher fraction of surface residues, the relative weight of these single distributions correlates with size. We also found that proteins evolved in the laboratory follow an essentially identical distribution, whereas de novo designed folds show markedly less destabilizing distributions (i.e. they seem more robust to the effects of mutations). This bi-Gaussian model provides an analytical description of the predicted distributions of mutational stability effects. It comprises a novel tool for analyzing proteins and protein models, for simulating the effect of mutations under evolutionary processes, and a quantitative description of mutational robustness.

The early evolutionary history of proteins

Abstract Protein sequence comparison can be used as a guide to many past events in the history of life. However, eventually even the most slowly changing of diverging sequences reach a point beyond which useful information can be extracted. Fortunately, the comparison of three‐dimensional structure may allow us to see back to the time of the very first proteins.