Exploring the function and evolution of proteins using domain families

Proteins are frequently composed of multiple domains which fold independently. These are often evolutionarily distinct units which can be adapted and reused in other proteins. The classification of protein domains into evolutionary families facilitates the study of their evolution and function. In this thesis such classifications are used firstly to examine methods for identifying evolutionary relationships (homology) between protein domains. Secondly a specific approach for predicting their function is developed. Lastly they are used in studying the evolution of protein complexes. Tools for identifying evolutionary relationships between proteins are central to computational biology. They aid in classifying families of proteins, giving clues about the function of proteins and the study of molecular evolution. The first chapter of this thesis concerns the effectiveness of cutting edge methods in identifying evolutionary relationships between protein domains. The identification of evolutionary relationships between proteins can give clues as to their function. The second chapter of this thesis concerns the development of a method to identify proteins involved in the same biological process. This method is based on the concept of domain fusion whereby pairs of proteins from one organism with a concerted function are sometimes found fused into single proteins in a different organism. Using protein domain classifications it is possible to identify these relationships. Most proteins do not act in isolation but carry out their function by binding to other proteins in complexes; little is understood about the evolution of such complexes. In the third chapter of this thesis the evolution of complexes is examined in two representative model organisms using protein domain families. In this work, protein domain superfamilies allow distantly related parts of complexes to be identified in order to determine how homologous units are reused

Clustering and Classification of Multi-domain Proteins

Rapid development of next-generation sequencing technology has led to an unprecedented growth in protein sequence data repositories over the last decade. Majority of these proteins lack structural and functional characterization. This necessitates design and development of fast, efficient, and sensitive computational tools and algorithms that can classify these proteins into functionally coherent groups. Domains are fundamental units of protein structure and function. Multi-domain proteins are extremely complex as opposed to proteins that have single or no domains. They exhibit network-like complex evolutionary events such as domain shuffling, domain loss, and domain gain. These events therefore, cannot be represented in the conventional protein clustering algorithms like phylogenetic reconstruction and Markov clustering. In this thesis, a multi-domain protein classification system is developed primarily based on the domain composition of protein sequences. Using the principle of co-clustering (biclustering), both proteins and domains are simultaneously clustered, where each bicluster contains a subset of proteins and domains forming a complete bipartite graph. These clusters are then converted into a network of biclusters based on the domains shared between the clusters, thereby classifying the proteins into similar protein families. We applied our biclustering network approach on a multi-domain protein family, Regulator of G-protein Signalling (RGS) proteins, where heterogeneous domain composition exists among subfamilies. Our approach showed mostly consistent clustering with the existing RGS subfamilies. The average maximum Jaccard Index scores for the clusters obtained by Markov Clustering and phylogenetic clustering methods against the biclusters were 0.64 and 0.60, respectively. Compared to other clustering methods, our approach uses auxiliary domain information of each protein, and therefore, generates more functionally coherent protein clusters and differentiates each protein subfamily from each other. Biclustered networks on complete nine proteomes showed that the number of multi-domain proteins included in connected biclusters rapidly increased with genome complexity, 48.5% in bacteria to 80% in eukaryotes. Protein clustering and classification, incorporating such wealth of additonal domain information on protein networks has wide applications and would impact functional analysis and characterization of novel proteins. Advisers: Stephen D. Scott and Etsuko N. Moriyam

Identifying metabolic enzymes with multiple types of association evidence

BACKGROUND: Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes. RESULTS: We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases. CONCLUSION: We illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities

Clustering and Classification of Multi-domain Proteins

Rapid development of next-generation sequencing technology has led to an unprecedented growth in protein sequence data repositories over the last decade. Majority of these proteins lack structural and functional characterization. This necessitates design and development of fast, efficient, and sensitive computational tools and algorithms that can classify these proteins into functionally coherent groups. Domains are fundamental units of protein structure and function. Multi-domain proteins are extremely complex as opposed to proteins that have single or no domains. They exhibit network-like complex evolutionary events such as domain shuffling, domain loss, and domain gain. These events therefore, cannot be represented in the conventional protein clustering algorithms like phylogenetic reconstruction and Markov clustering. In this thesis, a multi-domain protein classification system is developed primarily based on the domain composition of protein sequences. Using the principle of co-clustering (biclustering), both proteins and domains are simultaneously clustered, where each bicluster contains a subset of proteins and domains forming a complete bipartite graph. These clusters are then converted into a network of biclusters based on the domains shared between the clusters, thereby classifying the proteins into similar protein families. We applied our biclustering network approach on a multi-domain protein family, Regulator of G-protein Signalling (RGS) proteins, where heterogeneous domain composition exists among subfamilies. Our approach showed mostly consistent clustering with the existing RGS subfamilies. The average maximum Jaccard Index scores for the clusters obtained by Markov Clustering and phylogenetic clustering methods against the biclusters were 0.64 and 0.60, respectively. Compared to other clustering methods, our approach uses auxiliary domain information of each protein, and therefore, generates more functionally coherent protein clusters and differentiates each protein subfamily from each other. Biclustered networks on complete nine proteomes showed that the number of multi-domain proteins included in connected biclusters rapidly increased with genome complexity, 48.5% in bacteria to 80% in eukaryotes. Protein clustering and classification, incorporating such wealth of additonal domain information on protein networks has wide applications and would impact functional analysis and characterization of novel proteins. Advisers: Stephen D. Scott and Etsuko N. Moriyam

Inverse Statistical Physics of Protein Sequences: A Key Issues Review

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e.~evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.Comment: 18 pages, 7 figure

ProfPPIdb: Pairs of physical protein-protein interactions predicted for entire proteomes

Motivation Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. Results We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784