Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures

t Structural genomics projects have revealed structures for a large number of proteins of unknown function. Understanding the interactions between these proteins and their ligands would provide an initial step in their functional characterization. Binding site identification methods are a fast and cost-effective way to facilitate the characterization of functionally important protein regions. In this review we describe our recently developed methods for binding site identification in the context of existing methods. The advantage of energy-based approaches is emphasized, since they provide flexibility in the identifi- cation and characterization of different types of binding site

Global analysis of SNPs, proteins and protein-protein interactions: approaches for the prioritisation of candidate disease genes.

PhDUnderstanding the etiology of complex disease remains a challenge in biology. In recent years there has been an explosion in biological data, this study investigates machine learning and network analysis methods as tools to aid candidate disease gene prioritisation, specifically relating to hypertension and cardiovascular disease. This thesis comprises four sets of analyses: Firstly, non synonymous single nucleotide polymorphisms (nsSNPs) were analysed in terms of sequence and structure based properties using a classifier to provide a model for predicting deleterious nsSNPs. The degree of sequence conservation at the nsSNP position was found to be the single best attribute but other sequence and structural attributes in combination were also useful. Predictions for nsSNPs within Ensembl have been made publicly available. Secondly, predicting protein function for proteins with an absence of experimental data or lack of clear similarity to a sequence of known function was addressed. Protein domain attributes based on physicochemical and predicted structural characteristics of the sequence were used as input to classifiers for predicting membership of large and diverse protein superfamiles from the SCOP database. An enrichment method was investigated that involved adding domains to the training dataset that are currently absent from SCOP. This analysis resulted in improved classifier accuracy, optimised classifiers achieved 66.3% for single domain proteins and 55.6% when including domains from multi domain proteins. The domains from superfamilies with low sequence similarity, share global sequence properties enabling applications to be developed which compliment profile methods for detecting distant sequence relationships. Thirdly, a topological analysis of the human protein interactome was performed. The results were combined with functional annotation and sequence based properties to build models for predicting hypertension associated proteins. The study found that predicted hypertension related proteins are not generally associated with network hubs and do not exhibit high clustering coefficients. Despite this, they tend to be closer and better connected to other hypertension proteins on the interaction network than would be expected by chance. Classifiers that combined PPI network, amino acid sequence and functional properties produced a range of precision and recall scores according to the applied 3 weights. Finally, interactome properties of proteins implicated in cardiovascular disease and cancer were studied. The analysis quantified the influential (central) nature of each protein and defined characteristics of functional modules and pathways in which the disease proteins reside. Such proteins were found to be enriched 2 fold within proteins that are influential (p<0.05) in the interactome. Additionally, they cluster in large, complex, highly connected communities, acting as interfaces between multiple processes more often than expected. An approach to prioritising disease candidates based on this analysis was proposed. Each analyses can provide some new insights into the effort to identify novel disease related proteins for cardiovascular disease

Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.

Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue-residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences

Integrated mining of feature spaces for bioinformatics domain discovery

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein\u27s surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized and cured and in which designer drugs are created. With the exponential growth of protein databases and the limitations of experimental protein structure determination, sophisticated computational methods have been developed and applied to search for, detect, and compare protein homology. Most computational tools developed for protein structure prediction are primarily based on sequence similarity searches. These approaches have improved the prediction accuracy of high sequence similarity proteins but have failed to perform well with proteins of low sequence similarity. Data mining offers unique algorithmic computational approaches that have been used widely in the development of automatic protein structure classification and prediction. In this dissertation, we present a novel approach for the integration of physico-chemical properties and effective feature extraction techniques for the classification of proteins. Our approaches overcome one of the major obstacles of data mining in protein databases, the encapsulation of different hydrophobicity residue properties into a much reduced feature space that possess high degrees of specificity and sensitivity in protein structure classification. We have developed three unique computational algorithms for coherent feature extraction on selected scale properties of the protein sequence. When plagued by the problem of the unequal cardinality of proteins, our proposed integration scheme effectively handles the varied sizes of proteins and scales well with increasing dimensionality of these sequences. We also detail a two-fold methodology for protein functional annotation. First, we exhibit our success in creating an algorithm that provides a means to integrate multiple physico-chemical properties in the form of a multi-layered abstract feature space, with each layer corresponding to a physico-chemical property. Second, we discuss a wavelet-based segmentation approach that efficiently detects regions of property conservation across all layers of the created feature space. Finally, we present a unique graph-theory based algorithmic framework for the identification of conserved hydrophobic residue interaction patterns using identified scales of hydrophobicity. We report that these discriminatory features are specific to a family of proteins, which consist of conserved hydrophobic residues that are then used for structural classification. We also present our rigorously tested validation schemes, which report significant degrees of accuracy to show that homologous proteins exhibit the conservation of physico-chemical properties along the protein backbone. We conclude our discussion by summarizing our results and contributions and by listing our goals for future research

Entropy-scaling search of massive biological data

Many datasets exhibit a well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here, we introduce a framework for similarity search based on characterizing a dataset's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the dataset is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve "compressive omics," and the general theory can be readily applied to data science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo

Visualisation and graph-theoretic analysis of a large-scale protein structural interactome

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Background Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. Results We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Conclusions Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level.Published versio

Computational Molecular Coevolution

A major goal in computational biochemistry is to obtain three-dimensional structure information from protein sequence. Coevolution represents a biological mechanism through which structural information can be obtained from a family of protein sequences. Evolutionary relationships within a family of protein sequences are revealed through sequence alignment. Statistical analyses of these sequence alignments reveals positions in the protein family that covary, and thus appear to be dependent on one another throughout the evolution of the protein family. These covarying positions are inferred to be coevolving via one of two biological mechanisms, both of which imply that coevolution is facilitated by inter-residue contact. Thus, high-quality multiple sequence alignments and robust coevolution-inferring statistics can produce structural information from sequence alone. This work characterizes the relationship between coevolution statistics and sequence alignments and highlights the implicit assumptions and caveats associated with coevolutionary inference. An investigation of sequence alignment quality and coevolutionary-inference methods revealed that such methods are very sensitive to the systematic misalignments discovered in public databases. However, repairing the misalignments in such alignments restores the predictive power of coevolution statistics. To overcome the sensitivity to misalignments, two novel coevolution-inferring statistics were developed that show increased contact prediction accuracy, especially in alignments that contain misalignments. These new statistics were developed into a suite of coevolution tools, the MIpToolset. Because systematic misalignments produce a distinctive pattern when analyzed by coevolution-inferring statistics, a new method for detecting systematic misalignments was created to exploit this phenomenon. This new method called ``local covariation\u27\u27 was used to analyze publicly-available multiple sequence alignment databases. Local covariation detected putative misalignments in a database designed to benchmark sequence alignment software accuracy. Local covariation was incorporated into a new software tool, LoCo, which displays regions of potential misalignment during alignment editing assists in their correction. This work represents advances in multiple sequence alignment creation and coevolutionary inference