Development of computational approaches for structural classification, analysis and prediction of molecular recognition regions in proteins

The vast and growing volume of 3D protein structural data stored in the PDB contains abundant information about macromolecular complexes, and hence, data about protein interfaces. Non-covalent contacts between amino acids are the basis of protein interactions, and they are responsible for binding afinity and specificity in biological processes. In addition, water networks in protein interfaces can also complement direct interactions contributing significantly to molecular recognition, although their exact role is still not well understood. It is estimated that protein complexes in the PDB are substantially underrepresented due to their crystallization dificulties. Methods for automatic classifification and description of the protein complexes are essential to study protein interfaces, and to propose putative binding regions. Due to this strong need, several protein-protein interaction databases have been developed. However, most of them do not take into account either protein-peptide complexes, solvent information or a proper classification of the binding regions, which are fundamental components to provide an accurate description of protein interfaces. In the firest stage of my thesis, I developed the SCOWLP platform, a database and web application that structurally classifies protein binding regions at family level and defines accurately protein interfaces at atomic detail. The analysis of the results showed that protein-peptide complexes are substantially represented in the PDB, and are the only source of interacting information for several families. By clustering the family binding regions, I could identify 9,334 binding regions and 79,803 protein interfaces in the PDB. Interestingly, I observed that 65% of protein families interact to other molecules through more than one region and in 22% of the cases the same region recognizes different protein families. The database and web application are open to the research community (www.scowlp.org) and can tremendously facilitate high-throughput comparative analysis of protein binding regions, as well as, individual analysis of protein interfaces. SCOWLP and the other databases collect and classify the protein binding regions at family level, where sequence and structure homology exist. Interestingly, it has been observed that many protein families also present structural resemblances within each other, mostly across folds. Likewise, structurally similar interacting motifs (binding regions) have been identified among proteins with different folds and functions. For these reasons, I decided to explore the possibility to infer protein binding regions independently of their fold classification. Thus, I performed the firest systematic analysis of binding region conservation within all protein families that are structurally similar, calculated using non-sequential structural alignment methods. My results indicate there is a substantial molecular recognition information that could be potentially inferred among proteins beyond family level. I obtained a 6 to 8 fold enrichment of binding regions, and identified putative binding regions for 728 protein families that lack binding information. Within the results, I found out protein complexes from different folds that present similar interfaces, confirming the predictive usage of the methodology. The data obtained with my approach may complement the SCOWLP family binding regions suggesting alternative binding regions, and can be used to assist protein-protein docking experiments and facilitate rational ligand design. In the last part of my thesis, I used the interacting information contained in the SCOWLP database to help understand the role that water plays in protein interactions in terms of affinity and specificity. I carried out one of the firest high-throughput analysis of solvent in protein interfaces for a curated dataset of transient and obligate protein complexes. Surprisingly, the results highlight the abundance of water-bridged residues in protein interfaces (40.1% of the interfacial residues) that reinforces the importance of including solvent in protein interaction studies (14.5% extra residues interacting only water- mediated). Interestingly, I also observed that obligate and transient interfaces present a comparable amount of solvent, which contrasts the old thoughts saying that obligate protein complexes are expected to exhibit similarities to protein cores having a dry and hydrophobic interfaces. I characterized novel features of water-bridged residues in terms of secondary structure, temperature factors, residue composition, and pairing preferences that differed from direct residue-residue interactions. The results also showed relevant aspects in the mobility and energetics of water-bridged interfacial residues. Collectively, my doctoral thesis work can be summarized in the following points: 1. I developed SCOWLP, an improved framework that identiffies protein interfaces and classifies protein binding regions at family level. 2. I developed a novel methodology to predict alternative binding regions among structurally similar protein families independently of the fold they belong to. 3. I performed a high-throughput analysis of water-bridged interactions contained in SCOWLP to study the role of solvent in protein interfaces. These three components of my thesis represent novel methods for exploiting existing structural information to gain insights into protein- protein interactions, key mechanisms to understand biological processes

Hub Promiscuity in Protein-Protein Interaction Networks

Hubs are proteins with a large number of interactions in a protein-protein interaction network. They are the principal agents in the interaction network and affect its function and stability. Their specific recognition of many different protein partners is of great interest from the structural viewpoint. Over the last few years, the structural properties of hubs have been extensively studied. We review the currently known features that are particular to hubs, possibly affecting their binding ability. Specifically, we look at the levels of intrinsic disorder, surface charge and domain distribution in hubs, as compared to non-hubs, along with differences in their functional domains

Molecular characterization and evolutionary plasticity of protein-protein interfaces

Abstract The sequencing of the human genome provides the parts list for understanding cellular processes. However, as 70% of eukaryotic genes work through multi-protein systems, it is only through detailed study of the interactions of these components, that a more complete, systems-level understanding can be gained. This thesis is centred on the establishment of PICCOLO - a comprehensive database of structurally characterized protein interactions. In generating the resource, issues of interface definition, quaternary structure, data redundancy, structural environment and interaction type are addressed. The resource enables a variety of analyses to be performed concerning interface properties including residue propensity, hydropathy, polarity, interface size, sequence entropy and residue contact preference. PICCOLO has been applied to probing the patterns of substitutions that are accepted in protein interfaces across evolution, and whether these patterns are distinguishable from those seen in other structural environments. The derivation of a high-quality set of multiple structural alignments in the form of the database TOCCATA, a prerequisite for such analysis, is described, as well as procedures to derive environment-specific substitution tables. The Blundell group has contributed a series of methods to predict the likely effect of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on protein stability, function and interactions in order to triage the large volumes of data created from high-throughput genetic screening studies, enabling prioritization of those nsSNPs most likely to be phenotypically detrimental. PICCOLO's contribution to these predictions is described. Historically there has been little focus on protein-protein interactions as drug targets for small-molecule therapeutics. However, alanine-scanning mutagenesis studies have revealed that only a subset of residues contribute the greater part of free energy to binding - so-called "hot-spots". Molecular characterization of hot-spots performed using PICCOLO, probes the molecular basis underlying this important phenomenon leading to the possibility of predictive methods to identify hot-spots 'in silico'

Characterization, classification and alignment of protein-protein interfaces

Protein structural models provide essential information for the research on protein-protein interactions. In this dissertation, we describe two projects on the analysis of protein interactions using structural information. The focus of the first is to characterize and classify different types of interactions. We discriminate between biological obligate and biological non-obligate interactions, and crystal packing contacts. To this end, we defined six interface properties and used them to compare the three types of interactions in a hand-curated dataset. Based on the analysis, a classifier, named NOXclass, was constructed using a support vector machine algorithm in order to generate predictions of interaction types. NOXclass was tested on a non-redundant dataset of 243 protein-protein interactions and reaches an accuracy of 91.8%. The program is benecial for structural biologists for the interpretation of protein quaternary structures and to form hypotheses about the nature of proteinprotein interactions when experimental data are yet unavailable. In the second part of the dissertation, we present Galinter, a novel program for the geometrical comparison of protein-protein interfaces. The Galinter program aims at identifying similar patterns of different non-covalent interactions at interfaces. It is a graph-based approach optimized for aligning non-covalent interactions. A scoring scheme was developed for estimating the statistical signicance of the alignments. We tested the Galinter method on a published dataset of interfaces. Galinter alignments agree with those delivered by methods based on interface residue comparison and backbone structure comparison. In addition, we applied Galinter on four medically relevant examples of protein mimicry. Our results are consistent with previous human-curated analysis. The Galinter program provides an intuitive method of comparative analysis and visualization of binding modes and may assist in the prediction of interaction partners, and the design and engineering of protein interactions and interaction inhibitors

Prediction of protein-protein interaction types using machine learning approaches

Prediction and analysis of protein-protein interactions (PPIs) is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. One of the important problems surrounding PPIs is the identification and prediction of different types of complexes, which are characterized by properties such as type and numbers of proteins that interact, stability of the proteins, and also duration of the interactions. This thesis focuses on studying the temporal and stability aspects of the PPIs mostly using structural data. We have addressed the problem of predicting obligate and non-obligate protein complexes, as well as those aspects related to transient versus permanent because of the importance of non-obligate and transient complexes as therapeutic targets for drug discovery and development. We have presented a computational model to predict-protein interaction types using our proposed physicochemical features of desolvation and electrostatic energies and also structural and sequence domain-based features. To achieve a comprehensive comparison and demonstrate the strength of our proposed features to predict PPI types, we have also computed a wide range of previously used properties for prediction including physical features of interface area, chemical features of hydrophobicity and amino acid composition, physicochemical features of solvent-accessible surface area (SASA) and atomic contact vectors (ACV). After extracting the main features of the complexes, a variety of machine learning approaches have been used to predict PPI types. The prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative and relevant properties to distinguish between these two types of complexes Our computational results on different datasets confirm that using our proposed physicochemical features of desolvation and electrostatic energies lead to significant improvements on prediction performance. Moreover, using structural and sequence domains of CATH and Pfam and doing biological analysis help us to achieve a better insight on obligate and non-obligate complexes and their interactions

Efficient comprehensive scoring of docked proteincomplexes - a machine learning approach

Biological systems and processes rely on a complex network of molecular interactions. The association of biological macromolecules is a fundamental biochemical phenomenon and an unsolved theoretical problem crucial for the understanding of complex living systems. The term protein-protein docking describes the computational prediction of the assembly of protein complexes from the individual subunits. Docking algorithms generally produce a large number of putative protein complexes. In most cases, some of these conformations resemble the native complex structure within an acceptable degree of structural similarity. A major challenge in the field of docking is to extract the near-native structure(s) out of this considerably large pool of solutions, the so called scoring or ranking problem. It has been the aim of this work to develop methods for the efficient and accurate detection of near-native conformations in the scoring or ranking process of docked protein-protein complexes. A series of structural, chemical, biological and physical properties are used in this work to score docked protein-protein complexes. These properties include specialised energy functions, evolutionary relationship, class specific residue interface propensities, gap volume, buried surface area, empiric pair potentials on residue and atom level as well as measures for the tightness of fit. Efficient comprehensive scoring functions have been developed using probabilistic Support Vector Machines in combination with this array of properties on the largest currently available protein-protein docking benchmark. The established scoring functions are shown to be specific for certain types of protein-protein complexes and are able to detect near-native complex conformations from large sets of decoys with high sensitivity. The specific complex classes are Enzyme-Inhibitor/Substrate complexes, Antibody-Antigen complexes and a third class denoted as "Other" complexes which holds all test cases not belonging to either of the two previous classes. The three complex class specific scoring functions were tested on the docking results of 99 complexes in their unbound form for the above mentioned categories. Defining success as scoring a 'true' result with a p-value of better than 0.1, the scoring schemes were found to be successful in 93%, 78% and 63% of the examined cases, respectively. The ranking of near-native structures can be drastically improved, leading to a significant enrichment of near-native complex conformations in the top ranks. It could be shown that the developed scoring schemes outperform five other previously published scoring functions

Machine learning for the prediction of protein-protein interactions

The prediction of protein-protein interactions (PPI) has recently emerged as an important problem in the fields of bioinformatics and systems biology, due to the fact that most essential cellular processes are mediated by these kinds of interactions. In this thesis we focussed in the prediction of co-complex interactions, where the objective is to identify and characterize protein pairs which are members of the same protein complex. Although high-throughput methods for the direct identification of PPI have been developed in the last years. It has been demonstrated that the data obtained by these methods is often incomplete and suffers from high false-positive and false-negative rates. In order to deal with this technology-driven problem, several machine learning techniques have been employed in the past to improve the accuracy and trustability of predicted protein interacting pairs, demonstrating that the combined use of direct and indirect biological insights can improve the quality of predictive PPI models. This task has been commonly viewed as a binary classification problem. However, the nature of the data creates two major problems. Firstly, the imbalanced class problem due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly, the selection of negative examples is based on some unreliable assumptions which could introduce some bias in the classification results. The first part of this dissertation addresses these drawbacks by exploring the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilize examples of just one class to generate a predictive model which is consequently independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We designed and carried out a performance evaluation study of several OCC methods for this task. We also undertook a comparative performance evaluation with several conventional learning techniques. Furthermore, we pay attention to a new potential drawback which appears to affect the performance of PPI prediction. This is associated with the composition of the positive gold standard set, which contain a high proportion of examples associated with interactions of ribosomal proteins. We demonstrate that this situation indeed biases the classification task, resulting in an over-optimistic performance result. The prediction of non-ribosomal PPI is a much more difficult task. We investigate some strategies in order to improve the performance of this subtask, integrating new kinds of data as well as combining diverse classification models generated from different sets of data. In this thesis, we undertook a preliminary validation study of the new PPI predicted by using OCC methods. To achieve this, we focus in three main aspects: look for biological evidence in the literature that support the new predictions; the analysis of predicted PPI networks properties; and the identification of highly interconnected groups of proteins which can be associated with new protein complexes. Finally, this thesis explores a slightly different area, related to the prediction of PPI types. This is associated with the classification of PPI structures (complexes) contained in the Protein Data Bank (PDB) data base according to its function and binding affinity. Considering the relatively reduced number of crystalized protein complexes available, it is not possible at the moment to link these results with the ones obtained previously for the prediction of PPI complexes. However, this could be possible in the near future when more PPI structures will be available

At the nexus of three kingdoms: the genome of the mycorrhizal fungus Gigaspora margarita provides insights into plant, endobacterial and fungal interactions.

As members of the plant microbiota, arbuscular mycorrhizal fungi (AMF, Glomeromycotina) symbiotically colonize plant roots. AMF also possess their own microbiota, hosting some uncultivable endobacteria. Ongoing research has revealed the genetics underlying plant responses to colonization by AMF, but the fungal side of the relationship remains in the dark. Here, we sequenced the genome of Gigaspora margarita, a member of the Gigasporaceae in an early diverging group of the Glomeromycotina. In contrast to other AMF, G. margarita may host distinct endobacterial populations and possesses the largest fungal genome so far annotated (773.104 Mbp), with more than 64% transposable elements. Other unique traits of the G. margarita genome include the expansion of genes for inorganic phosphate metabolism, the presence of genes for production of secondary metabolites and a considerable number of potential horizontal gene transfer events. The sequencing of G. margarita genome reveals the importance of its immune system, shedding light on the evolutionary pathways that allowed early diverging fungi to interact with both plants and bacteria