LabelHash: A Flexible and Extensible Method for Matching Structural Motifs

There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Below, we will briefly describe LabelHash, a new method for partial structure comparison. In partial structure comparison, the goal is to find the best geometric and chemical similarity between a set of 3D points called a _motif_ and a subset of a set of 3D points called the _target_. Both the motif and targets are represented as sets of labeled 3D points. A motif is ideally composed of the functionally most-relevant residues in a binding site. The labels denote the type of residue. Motif points can have multiple labels to denote that substitutions are allowed. Any subset of the target that has labels that are compatible with the motif’s labels is called a _match_. The aim is to find statistically significant matches to a structural motif. Our method preprocesses a background database of targets such as a non-redundant subset of the Protein Data Bank in such a way that we can look up in constant time partial matches to a motif. Using a variant of the previously described match augmentation algorithm (1), we obtain complete matches to our motif. The nonparametric statistical model developed by (2,3) corrects for any bias introduced by our algorithm. This bias is introduced by excluding matches that do not satisfy certain geometric constraints for efficiency reasons

Modeling regionalized volumetric differences in protein-ligand binding cavities

Identifying elements of protein structures that create differences in protein-ligand binding specificity is an essential method for explaining the molecular mechanisms underlying preferential binding. In some cases, influential mechanisms can be visually identified by experts in structural biology, but subtler mechanisms, whose significance may only be apparent from the analysis of many structures, are harder to find. To assist this process, we present a geometric algorithm and two statistical models for identifying significant structural differences in protein-ligand binding cavities. We demonstrate these methods in an analysis of sequentially nonredundant structural representatives of the canonical serine proteases and the enolase superfamily. Here, we observed that statistically significant structural variations identified experimentally established determinants of specificity. We also observed that an analysis of individual regions inside cavities can reveal areas where small differences in shape can correspond to differences in specificity

pClay: A Precise Parallel Algorithm for Comparing Molecular Surfaces

Comparing binding sites as geometric solids can reveal conserved features of protein structure that bind similar molecular fragments and varying features that select different partners. Due to the subtlety of these features, algorithmic efficiency and geometric precision are essential for comparison accuracy. For these reasons, this paper presents pClay, the first structure comparison algorithm to employ fine-grained parallelism to enhance both throughput and efficiency. We evaluated the parallel performance of pClay on both multicore workstation CPUs and a 61-core Xeon Phi, observing scaleable speedup in many thread configurations. Parallelism unlocked levels of precision that were not practical with existing methods. This precision has important applications, which we demonstrate: A statistical model of steric variations in binding cavities, trained with data at the level of precision typical of existing work, can overlook 46% of authentic steric influences on specificity (p <= .02). The same model, trained with more precise data from pClay, overlooked 0% using the same standard of statistical significance. These results demonstrate how enhanced efficiency and precision can advance the detection of binding mechanisms that influence specificity

Leveraging Structural Flexibility to Predict Protein Function

Proteins are essentially versatile and flexible molecules and understanding protein function plays a fundamental role in understanding biological systems. Protein structure comparisons are widely used for revealing protein function. However,with rigidity or partial rigidity assumption, most existing comparison methods do not consider conformational flexibility in protein structures. To address this issue, this thesis seeks to develop algorithms for flexible structure comparisons to predict one specific aspect of protein function, binding specificity. Given conformational samples as flexibility representation, we focus on two predictive problems related to specificity: aggregate prediction and individual prediction.For aggregate prediction, we have designed FAVA (Flexible Aggregate Volumetric Analysis). FAVA is the first conformationally general method to compare proteins with identical folds but different specificities. FAVA is able to correctly categorize members of protein superfamilies and to identify influential amino acids that cause different specificities. A second method PEAP (Point-based Ensemble for Aggregate Prediction) employs ensemble clustering techniques from many base clustering to predict binding specificity. This method incorporates structural motions of functional substructures and is capable of mitigating prediction errors.For individual prediction, the first method is an atomic point representation for representing flexibilities in the binding cavity. This representation is able to predict binding specificity on each protein conformation with high accuracy, and it is the first to analyze maps of binding cavity conformations that describe proteins with different specificities. Our second method introduces a volumetric lattice representation. This representation localizes solvent-accessible shape of the binding cavity by computing cavity volume in each user-defined space. It proves to be more informative than point-based representations. Last but not least, we discuss a structure-independent representation. This representation builds a lattice model on protein electrostatic isopotentials. This is the first known method to predict binding specificity explicitly from the perspective of electrostatic fields.The methods presented in this thesis incorporate the variety of protein conformations into the analysis of protein ligand binding, and provide more views on flexible structure comparisons and structure-based function annotation of molecular design

VASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand Binding Specificity

Many algorithms that compare protein structures can reveal similarities that suggest related biological functions, even at great evolutionary distances. Proteins with related function often exhibit differences in binding specificity, but few algorithms identify structural variations that effect specificity. To address this problem, we describe the Volumetric Analysis of Surface Properties (VASP), a novel volumetric analysis tool for the comparison of binding sites in aligned protein structures. VASP uses solid volumes to represent protein shape and the shape of surface cavities, clefts and tunnels that are defined with other methods. Our approach, inspired by techniques from constructive solid geometry, enables the isolation of volumetrically conserved and variable regions within three dimensionally superposed volumes. We applied VASP to compute a comparative volumetric analysis of the ligand binding sites formed by members of the steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domains and the serine proteases. Within both families, VASP isolated individual amino acids that create structural differences between ligand binding cavities that are known to influence differences in binding specificity. Also, VASP isolated cavity subregions that differ between ligand binding cavities which are essential for differences in binding specificity. As such, VASP should prove a valuable tool in the study of protein-ligand binding specificity

A Gibbs sampling strategy for mining of protein-protein interaction networks and protein structures

Complex networks are general and can be used to model phenomena that belongs to different fields of research, from biochemical applications to social networks. However, due to the intrinsic complexity of real networks, their analysis can be computationally demanding. Recently, several statistic and probabilistic analysis approaches have been designed, resulting to be much faster, flexible and effective than deterministic algorithms. Among statistical methods, Gibbs sampling is one of the simplest and most powerful algorithms for solving complex optimization problems and it has been applied in different contexts. It has shown its effectiveness in computational biology but in sequence analysis rather than in network analysis. One approach to analyze complex networks is to compare them, in order to identify similar patterns of interconnections and predict the function or the role of some unknown nodes. Thus, this motivated the main goal of the thesis: designing and implementing novel graph mining techniques based on Gibbs sampling to compare two or more complex networks. The methodology is domain-independent and can work on any complex system of interacting entities with associated attributes. However, in this thesis we focus our attention on protein analysis overcoming the strong current limitations in this area. Proteins can be analyzed from two different points of view: (i) an internal perspective, i.e. the 3D structure of the protein, (ii) an external perspective, i.e. the interactions with other macromolecules. In both cases, a comparative analysis with other proteins of the same or distinct species can reveal important clues for the function of the protein and evolutionary convergences or divergences between different organisms in the way a specific function or process is carried out. First, we present two methods based on Gibbs sampling for the comparative analysis of protein-protein interaction networks: GASOLINE and SPECTRA. GASOLINE is a stochastic and greedy algorithm to find similar groups of interacting proteins in two or more networks. It can align many networks and more quickly than the state-of-the-art methods. SPECTRA is a framework to retrieve and compare networks of proteins that interact with one another in specific healthy or tumor tissues. The aim in this case is to identify changes in protein concentration or protein "behaviour" across different tissues. SPECTRA is an adaptation of GASOLINE for weighted protein-protein interaction networks with gene expressions as node weights. It is the first algorithm proposed for multiple comparison of tissue-specific interaction networks. We also describe a Gibbs sampling based algorithm for 3D protein structure comparison, called PROPOSAL, which finds local structural similarities across two or more protein structures. Experimental results confirm our computational predictions and show that the proposed algorithms are much faster and in most cases more accurate than existing methods

Novel bioinformatics tools for epitope-based peptide vaccine design

BACKGROUND T-cells are essential in the mediation of immune responses, helping clear bacteria, viruses and cancerous cells. T-cells recognise anomalies in the cellular proteome associated with infection and neoplasms through the T-cell receptor (TCR). The most common TCRs in humans, αβ TCRs, engage processed peptide epitopes presented on the major histocompatibility complex (pMHC). TCR-pMHC interaction is critical to vaccination. In this thesis I will discuss three pieces of software and outcomes derived from them that contribute to epitope-based vaccine design. RESULTS Three pieces of software were developed to help scientists study and understand T-cell responses. The first, STACEI allows users to interrogate the TCR-pMHC crystal structures. The time consuming, error-prone analysis that previously would have to be ran manually, is replaced by a single, flexible package. The second development is the introduction of general-purpose computing on the GPU (GP-GPU) in aiding the prediction of T-cell epitopes by scanning protein datasets using data derived from combinatorial peptide libraries (CPLs). Finally, I introduce RECIPIENT, a reverse vaccinology tool (RV) that combines pangenomic and population genetics methods to predict good vaccine targets across multiple pathogen samples. CONCLUSION Across this thesis, I introduce three different methods that aid the study of T-cells that will hopefully improve future vaccine design. These methods range across data types and methodologies, with methods focusing on mechanistic understanding of the TCR-pMHC binding event; the application of GP-GPU to CPLs and using microbial genomics to aid the study and understanding of antigen-specific T-cell responses. These three methods have a significant potential for further integration, especially the structural methods

Graph-Based Approaches to Protein StructureComparison - From Local to Global Similarity

The comparative analysis of protein structure data is a central aspect of structural bioinformatics. Drawing upon structural information allows the inference of function for unknown proteins even in cases where no apparent homology can be found on the sequence level. Regarding the function of an enzyme, the overall fold topology might less important than the specific structural conformation of the catalytic site or the surface region of a protein, where the interaction with other molecules, such as binding partners, substrates and ligands occurs. Thus, a comparison of these regions is especially interesting for functional inference, since structural constraints imposed by the demands of the catalyzed biochemical function make them more likely to exhibit structural similarity. Moreover, the comparative analysis of protein binding sites is of special interest in pharmaceutical chemistry, in order to predict cross-reactivities and gain a deeper understanding of the catalysis mechanism. From an algorithmic point of view, the comparison of structured data, or, more generally, complex objects, can be attempted based on different methodological principles. Global methods aim at comparing structures as a whole, while local methods transfer the problem to multiple comparisons of local substructures. In the context of protein structure analysis, it is not a priori clear, which strategy is more suitable. In this thesis, several conceptually different algorithmic approaches have been developed, based on local, global and semi-global strategies, for the task of comparing protein structure data, more specifically protein binding pockets. The use of graphs for the modeling of protein structure data has a long standing tradition in structural bioinformatics. Recently, graphs have been used to model the geometric constraints of protein binding sites. The algorithms developed in this thesis are based on this modeling concept, hence, from a computer scientist's point of view, they can also be regarded as global, local and semi-global approaches to graph comparison. The developed algorithms were mainly designed on the premise to allow for a more approximate comparison of protein binding sites, in order to account for the molecular flexibility of the protein structures. A main motivation was to allow for the detection of more remote similarities, which are not apparent by using more rigid methods. Subsequently, the developed approaches were applied to different problems typically encountered in the field of structural bioinformatics in order to assess and compare their performance and suitability for different problems. Each of the approaches developed during this work was capable of improving upon the performance of existing methods in the field. Another major aspect in the experiments was the question, which methodological concept, local, global or a combination of both, offers the most benefits for the specific task of protein binding site comparison, a question that is addressed throughout this thesis