Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity

Background: Automatic identification of structure fingerprints from a group of diverse protein structures is challenging, especially for proteins whose divergent amino acid sequences may fall into the “twilight-” or “midnight– ” zones where pair-wise sequence identities to known sequences fall below 25 % and sequence-based functional annotations often fail. Results: Here we report a novel graph database mining method and demonstrate its application to protein structure pattern identification and structure classification. The biologic motivation of our study is to recognize common structure patterns in “immunoevasins”, proteins mediating virus evasion of host immune defense. Our experimental study, using both viral and non-viral proteins, demonstrates the efficiency and efficacy of the proposed method. Conclusions: We present a theoretic framework, offer a practical software implementation for incorporating prior domain knowledge, such as substitution matrices as studied here, and devise an efficient algorithm to identify approximate matched frequent subgraphs. By doing so, we significantly expanded the analytical power of sophisticated data mining algorithms in dealing with large volume of complicated and noisy protein structure data. And without loss of generality, choice of appropriate compatibility matrices allows our method to be easily employed in domains where subgraph labels have some uncertainty

Computational Analysis of 3D Protein Structures

Ph.DDOCTOR OF PHILOSOPH

Functional classification of protein domain superfamilies for protein function annotation

Proteins are made up of domains that are generally considered to be independent evolutionary and structural units having distinct functional properties. It is now well established that analysis of domains in proteins provides an effective approach to understand protein function using a `domain grammar'. Towards this end, evolutionarily-related protein domains have been classified into homologous superfamilies in CATH and SCOP databases. An ideal functional sub-classification of the domain superfamilies into `functional families' can not only help in function annotation of uncharacterised sequences but also provide a useful framework for understanding the diversity and evolution of function at the domain level. This work describes the development of a new protocol (FunFHMMer) for identifying functional families in CATH superfamilies that makes use of sequence patterns only and hence, is unaffected by the incompleteness of function annotations, annotation biases or misannotations existing in the databases. The resulting family classification was validated using known functional information and was found to generate more functionally coherent families than other domain-based protein resources. A protein function prediction pipeline was developed exploiting the functional annotations provided by the domain families which was validated by a database rollback benchmark set of proteins and an independent assessment by CAFA 2. The functional classification was found to capture the functional diversity of superfamilies well in terms of sequence, structure and the protein-context. This aided studies on evolution of protein domain function both at the superfamily level and in specific proteins of interest. The conserved positions in the functional family alignments were found to be enriched in catalytic site residues and ligand-binding site residues which led to the development of a functional site prediction tool. Lastly, the function prediction tools were assessed for annotation of moonlighting functions of proteins and a classification of moonlighting proteins was proposed based on their structure-function relationships

Condition-specific differential subnetwork analysis for biological systems

Indiana University-Purdue University Indianapolis (IUPUI)Biological systems behave differently under different conditions. Advances in sequencing technology over the last decade have led to the generation of enormous amounts of condition-specific data. However, these measurements often fail to identify low abundance genes/proteins that can be biologically crucial. In this work, a novel text-mining system was first developed to extract condition-specific proteins from the biomedical literature. The literature-derived data was then combined with proteomics data to construct condition-specific protein interaction networks. Further, an innovative condition-specific differential analysis approach was designed to identify key differences, in the form of subnetworks, between any two given biological systems. The framework developed here was implemented to understand the differences between limb regeneration-competent Ambystoma mexicanum and –deficient Xenopus laevis. This study provides an exhaustive systems level analysis to compare regeneration competent and deficient subnetworks to show how different molecular entities inter-connect with each other and are rewired during the formation of an accumulation blastema in regenerating axolotl limbs. This study also demonstrates the importance of literature-derived knowledge, specific to limb regeneration, to augment the systems biology analysis. Our findings show that although the proteins might be common between the two given biological conditions, they can have a high dissimilarity based on their biological and topological properties in the subnetwork. The knowledge gained from the distinguishing features of limb regeneration in amphibians can be used in future to chemically induce regeneration in mammalian systems. The approach developed in this dissertation is scalable and adaptable to understand differential subnetworks between any two biological systems. This methodology will not only facilitate the understanding of biological processes and molecular functions which govern a given system but also provide novel intuitions about the pathophysiology of diseases/conditions

Graph-based methods for large-scale protein classification and orthology inference

The quest for understanding how proteins evolve and function has been a prominent and costly human endeavor. With advances in genomics and use of bioinformatics tools, the diversity of proteins in present day genomes can now be studied more efficiently than ever before. This thesis describes computational methods suitable for large-scale protein classification of many proteomes of diverse species. Specifically, we focus on methods that combine unsupervised learning (clustering) techniques with the knowledge of molecular phylogenetics, particularly that of orthology. In chapter 1 we introduce the biological context of protein structure, function and evolution, review the state-of-the-art sequence-based protein classification methods, and then describe methods used to validate the predictions. Finally, we present the outline and objectives of this thesis. Evolutionary (phylogenetic) concepts are instrumental in studying subjects as diverse as the diversity of genomes, cellular networks, protein structures and functions, and functional genome annotation. In particular, the detection of orthologous proteins (genes) across genomes provides reliable means to infer biological functions and processes from one organism to another. Chapter 2 evaluates the available computational tools, such as algorithms and databases, used to infer orthologous relationships between genes from fully sequenced genomes. We discuss the main caveats of large-scale orthology detection in general as well as the merits and pitfalls of each method in particular. We argue that establishing true orthologous relationships requires a phylogenetic approach which combines both trees and graphs (networks), reliable species phylogeny, genomic data for more than two species, and an insight into the processes of molecular evolution. Also proposed is a set of guidelines to aid researchers in selecting the correct tool. Moreover, this review motivates further research in developing reliable and scalable methods for functional and phylogenetic classification of large protein collections. Chapter 3 proposes a framework in which various protein knowledge-bases are combined into unique network of mappings (links), and hence allows comparisons to be made between expert curated and fully-automated protein classifications from a single entry point. We developed an integrated annotation resource for protein orthology, ProGMap (Protein Group Mappings, http://www.bioinformatics.nl/progmap), to help researchers and database annotators who often need to assess the coherence of proposed annotations and/or group assignments, as well as users of high throughput methodologies (e.g., microarrays or proteomics) who deal with partially annotated genomic data. ProGMap is based on a non-redundant dataset of over 6.6 million protein sequences which is mapped to 240,000 protein group descriptions collected from UniProt, RefSeq, Ensembl, COG, KOG, OrthoMCL-DB, HomoloGene, TRIBES and PIRSF using a fast and fully automated sequence-based mapping approach. The ProGMap database is equipped with a web interface that enables queries to be made using synonymous sequence identifiers, gene symbols, protein functions, and amino acid or nucleotide sequences. It incorporates also services, namely BLAST similarity search and QuickMatch identity search, for finding sequences similar (or identical) to a query sequence, and tools for presenting the results in graphic form. Graphs (networks) have gained an increasing attention in contemporary biology because they have enabled complex biological systems and processes to be modeled and better understood. For example, protein similarity networks constructed of all-versus-all sequence comparisons are frequently used to delineate similarity groups, such as protein families or orthologous groups in comparative genomics studies. Chapter 4.1 presents a benchmark study of freely available graph software used for this purpose. Specifically, the computational complexity of the programs is investigated using both simulated and biological networks. We show that most available software is not suitable for large networks, such as those encountered in large-scale proteome analyzes, because of the high demands on computational resources. To address this, we developed a fast and memory-efficient graph software, netclust (http://www.bioinformatics.nl/netclust/), which can scale to large protein networks, such as those constructed of millions of proteins and sequence similarities, on a standard computer. An extended version of this program called Multi-netclust is presented in chapter 4.2. This tool that can find connected clusters of data presented by different network data sets. It uses user-defined threshold values to combine the data sets in such a way that clusters connected in all or in either of the networks can be retrieved efficiently. Automated protein sequence clustering is an important task in genome annotation projects and phylogenomic studies. During the past years, several protein clustering programs have been developed for delineating protein families or orthologous groups from large sequence collections. However, most of these programs have not been benchmarked systematically, in particular with respect to the trade-off between computational complexity and biological soundness. In chapter 5 we evaluate three best known algorithms on different protein similarity networks and validation (or 'gold' standard) data sets to find out which one can scale to hundreds of proteomes and still delineate high quality similarity groups at the minimum computational cost. For this, a reliable partition-based approach was used to assess the biological soundness of predicted groups using known protein functions, manually curated protein/domain families and orthologous groups available in expert-curated databases. Our benchmark results support the view that a simple and computationally cheap method such as netclust can perform similar to and in cases even better than more sophisticated, yet much more costly methods. Moreover, we introduce an efficient graph-based method that can delineate protein orthologs of hundreds of proteomes into hierarchical similarity groups de novo. The validity of this method is demonstrated on data obtained from 347 prokaryotic proteomes. The resulting hierarchical protein classification is not only in agreement with manually curated classifications but also provides an enriched framework in which the functional and evolutionary relationships between proteins can be studied at various levels of specificity. Finally, in chapter 6 we summarize the main findings and discuss the merits and shortcomings of the methods developed herein. We also propose directions for future research. The ever increasing flood of new sequence data makes it clear that we need improved tools to be able to handle and extract relevant (orthological) information from these protein data. This thesis summarizes these needs and how they can be addressed by the available tools, or be improved by the new tools that were developed in the course of this research. <br/

A structural classification of protein-protein interactions for detection of convergently evolved motifs and for prediction of protein binding sites on sequence level

BACKGROUND: A long-standing challenge in the post-genomic era of Bioinformatics is the prediction of protein-protein interactions, and ultimately the prediction of protein functions. The problem is intrinsically harder, when only amino acid sequences are available, but a solution is more universally applicable. So far, the problem of uncovering protein-protein interactions has been addressed in a variety of ways, both experimentally and computationally. MOTIVATION: The central problem is: How can protein complexes with solved threedimensional structure be utilized to identify and classify protein binding sites and how can knowledge be inferred from this classification such that protein interactions can be predicted for proteins without solved structure? The underlying hypothesis is that protein binding sites are often restricted to a small number of residues, which additionally often are well-conserved in order to maintain an interaction. Therefore, the signal-to-noise ratio in binding sites is expected to be higher than in other parts of the surface. This enables binding site detection in unknown proteins, when homology based annotation transfer fails. APPROACH: The problem is addressed by first investigating how geometrical aspects of domain-domain associations can lead to a rigorous structural classification of the multitude of protein interface types. The interface types are explored with respect to two aspects: First, how do interface types with one-sided homology reveal convergently evolved motifs? Second, how can sequential descriptors for local structural features be derived from the interface type classification? Then, the use of sequential representations for binding sites in order to predict protein interactions is investigated. The underlying algorithms are based on machine learning techniques, in particular Hidden Markov Models. RESULTS: This work includes a novel approach to a comprehensive geometrical classification of domain interfaces. Alternative structural domain associations are found for 40% of all family-family interactions. Evaluation of the classification algorithm on a hand-curated set of interfaces yielded a precision of 83% and a recall of 95%. For the first time, a systematic screen of convergently evolved motifs in 102.000 protein-protein interactions with structural information is derived. With respect to this dataset, all cases related to viral mimicry of human interface bindings are identified. Finally, a library of 740 motif descriptors for binding site recognition - encoded as Hidden Markov Models - is generated and cross-validated. Tests for the significance of motifs are provided. The usefulness of descriptors for protein-ligand binding sites is demonstrated for the case of &amp;quot;ATP-binding&amp;quot;, where a precision of 89% is achieved, thus outperforming comparable motifs from PROSITE. In particular, a novel descriptor for a P-loop variant has been used to identify ATP-binding sites in 60 protein sequences that have not been annotated before by existing motif databases

Exploration des structures secondaires de l’ARN

À l’ère du numérique, valoriser les données en leur donnant un sens est un enjeu capital pour supporter la prise de décision stratégique et cela dans divers domaines, notamment dans le domaine du marketing numérique ou de la santé, ou encore, dans notre contexte, pour une meilleure compréhension de la biologie des structures des acides nucléiques. L’un des défis majeurs de la biologie structurale concerne l’étude des structures des acides ribonucléiques (ARN), les effets de ces structures et de leurs altérations sur leurs fonctions. Contribuer à cet enjeu important est l’objectif de cette thèse. Celle-ci s’inscrit principalement dans le développement de méthodes et d’outils pour l’exploration efficace des structures secondaires d’ARN. En effet, explorer les structures secondaires d’ARN contribue à lever le voile sur leur fonction et permet de mieux cerner leur implication spécifique au sein des processus cellulaires. Dans ce contexte nous avons développé le modèle des super-n-motifs qui contribue à une meilleure représentation de la complexité structurale des ARN et offre un moyen efficace d’évaluer la similarité des structures d’ARN en tenant compte de cette complexité. Le modèle des super-n-motifs facilite l’étude des ARN dont le rôle est inconnu. Il permet de poser des hypothèses sur la ou les fonctions des ARN lorsque ceux-ci partagent une similarité structurale sans équivoque. Nous avons aussi développé la plateforme structurexplor pour faciliter l’exploration des structures secondaires, c’est-à-dire de permettre, en quelques clics, de caractériser les populations de structures d’ARN en, par exemple, faisant ressortir les groupes d’ARN partageant des structures similaires. La mise en œuvre du modèle des super-n-motifs et de la plateforme structurexplor a contribué à une meilleure compréhension de la phylogénie structurale des viroïdes qui sont des agents pathogènes à ARN attaquant les plantes, phylogénie jusqu’alors basée que sur leurs séquences

Discovering discriminative and class-specific sequence and structural motifs in proteins

Finding recurring motifs is an important problem in bioinformatics. Such motifs can be used for any number of problems including sequence classi cation, label prediction, knowledge discovery and biological engineering of proteins t for a speci c purpose. Our motivation is to create a better foundation for the research and development of novel motif mining and machine learning methods that can extract class-speci c and discriminative motifs using both sequence and structural features. We propose the building blocks of a general machine learning framework to act on a biological input. This thesis present a combination of elements that are aimed to be applicable to a variety of biological problems. Ideally, the learner should only require a number of biological data instances as input that are classi- ed into a number of di erent classes as de ned by the researchers. The output should be the factors and motifs that discriminate between those classes (for reasonable, non-random class de nitions). This ideal work ow requires two main steps. First step is the representation of the biological input with features that contain the signi cant information the researcher is looking for. Due to the complexity of the macromolecules, abstract representations are required to convert the real world representation into quanti able descriptors that are suitable for motif mining and machine learning. The second step of the proposed work ow is the motif mining and knowledge discovery step. Using these informative representations, an algorithm should be able to nd discriminative, class-speci c motifs that are over-represented in one class and under-represented in the other. This thesis presents novel procedures for representation of the proteins to be used in a variety of machine learning algorithms, and two separate motif mining algorithms, one based on temporal motif mining, and the other on deep learning, that can work with the given biological data. The descriptors and the learners are applied to a wide range of computational problems encountered in life sciences

Recovery and characterization of viral diversity from aquatic short- and long-read metagenomes

Viruses are the most abundant biological entities in marine ecosystems and play an essential role in global biogeochemical cycles. They have important ecological functions as drivers of bacterial populations through lytic infections and contribute to bacterial genetic diversification. Unfortunately, their study is severely limited by the difficulty to culture and isolate them in lab conditions. Culture-independent techniques such as metagenomics can complement culture-based approaches to capture more phage diversity. However, the vast majority of viral sequences recovered through these methods are uncharacterized and therefore do not provide any information about their interactions with the bacterial community, a phenomenon that has been named “viral dark matter”. In this thesis, several bioinformatic techniques are applied to both short- and long-read metagenomic datasets to recover biological information from marine viral sequences contained therein. A pipeline for recovering viral sequences based on a reference genome was developed and applied to the study of myophages infecting the alphaproteobacterial SAR11 clade, one of the most abundant bacterioplankton groups in surface marine and freshwater ecosystems. We were able to recover 22 new genomes which include the first genomes of myophages infecting LD12, the SAR11 freshwater clade. These sequences are underrepresented in datasets derived from the viral fraction, suggesting a bias of either technical or biological nature. Surprisingly, this family of phages code for an operon which resembles the secretion system type VIII operon in Escherichia coli. The function of this phage operon is still unknown. Next, a long-read dataset from the Mediterranean Sea was explored for viral contigs to contrast phage recovery between long- and short-read datasets. The analysis revealed that while long-read assemblies resulted in viral sequences of better quality, there was a sizable amount of intra-clade viral diversity that was not included in the assemblies. This viral diversity only found in long reads is even greater than previously thought. This untapped diversity could aid biotechnological efforts as evidenced by the discovery of new endolysins. Finally, a tool (Random Forest Assignment of Hosts, or RaFAH) for assigning hosts to phage sequences obtained from metagenomic datasets was created. The tool is based on a machine learning tool trained with phage protein clusters generated de novo. Benchmarking shows that RaFAH is on par with other state-of-the-art classifiers and is able to classify phage contigs at the level of Kingdom, which makes it the first classifier to accurately detect Archaea viruses from metagenomic samples. A feature importance analysis reveals that the protein clusters with the most predictive power are those involved in host recognition.Los bacteriófagos (”fagos”) son los organismos más abundantes en los ecosistemas marinos y tienen un papel esencial en los ciclos biogeoquímicos globales. Asimismo, influencian la evolución de las poblaciones bacterianas que infectan y contribuyen a la diversificación del acervo genético bacteriano. Desgraciadamente, su estudio se ve limitado por la dificultad de cultivar y aislar estos organismos en el laboratorio. El uso de técnicas que no requieren cultivo, como la metagenómica, pueden complementar el cultivo en laboratorio para recuperar una mayor diversidad de fagos. Sin embargo, la inmensa mayoría de secuencias virales recuperadas mediante metagenómica no pueden ser caracterizadas, por lo que no proporcionan ninguna información sobre sus interacciones con la comunidad bacteriana, un fenómeno que se ha nombrado “materia oscura viral”. En esta tesis se han utilizado múltiples procesos bioinformáticos en colecciones de metagenomas de lectura corta y larga para caracterizar las secuencias virales que contienen. Se ha desarrollado un procedimiento para recuperar secuencias virales a partir de un genoma de referencia y se ha aplicado al estudio de miofagos que infectan al clado SAR11 de las Alfaproteobacteria, uno de los grupos de bacterioplankton más abundantes en agua dulce y agua salada de superficie. Se consiguió recuperar 22 nuevos genomas que incluyen el primer genoma que infecta LD12, el subclado de SAR11 de agua dulce. Estos genomas están poco representados en colecciones obtenidas de la fracción viral, lo que sugiere que las afecta un sesgo técnico o biológico. Sorprendentemente, esta familia de fagos contiene un operón similar al sistema de secreción tipo VIII de Escherichia coli. La función de este operón es aún desconocida. Asimismo, se contrastó la recuperación de secuencias víricas entre colecciones de lectura corta y larga utilizando colecciones obtenidas en el mar Mediterráneo. Los resultados muestran que aunque los ensamblajes derivados de las lecturas largas producen secuencias virales de mejor calidad, en el proceso se pierde una gran cantidad de diversidad intraclado. Esta diversidad es mucho mayor de la recuperada con lecturas cortas, y podría explotarse para aplicaciones biotecnológicas, como el descubrimiento de nuevas endolisinas. Finalmente, se desarrolló un programa (Random Forest Assignment of Hosts, o RaFAH) para asignar hospedadores a secuencias virales obtenidas de colecciones metagenómicas. El programa se basa en el uso de algoritmos de machine learning entrenados con grupos de proteínas creados de novo. RaFAH muestra un rendimiento similar a otros clasificadores de secuencias y es capaz de clasificar secuencias víricas al nivel taxonómico de Reino, siendo así el primer clasificador capaz de detectar fagos que infectan arqueas con precisión. El análisis de importancia de rasgo revela que los grupos de proteínas con mayor poder predictivo son aquellos involucrados en el reconocimiento del hospedador

Methods for the Efficient Comparison of Protein Binding Sites and for the Assessment of Protein-Ligand Complexes

In the present work, accelerated methods for the comparison of protein binding sites as well as an extended procedure for the assessment of ligand poses in protein binding sites are presented. Protein binding site comparisons are frequently used receptor-based techniques in early stages of the drug development process. Binding sites of other proteins which are similar to the binding site of the target protein can offer hints for possible side effects of a new drug prior to clinical studies. Moreover, binding site comparisons are used as an idea generator for bioisosteric replacements of individual functional groups of the newly developed drug and to unravel the function of hitherto orphan proteins. The structural comparison of binding sites is especially useful when applied on distantly related proteins as a comparison solely based on the amino acid sequence is not sufficient in such cases. Methods for the assessment of ligand poses in protein binding sites are also used in the early phase of drug development within docking programs. These programs are utilized to screen entire libraries of molecules for a possible ligand of a binding site and to furthermore estimate in which conformation the ligand will most likely bind. By employing this information, molecule libraries can be filtered for subsequent affinity assays and molecular structures can be refined with regard to affinity and selectivity