A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes

BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)

The rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources. 

In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts

Structural Dynamics and Allosteric Signaling in Ionotropic Glutamate Receptors

Ionotropic glutamate receptors (iGluRs) are ligand-gated ion channels that mediate excitatory neurotransmission events in the central nervous system. All distinct classes of iGluRs (AMPA, NMDA, Kainate) are composed of an N-terminal domain (NTD) and a ligand-binding domain (LBD) in their extracellular domain, a transmembrane domain (TMD) and an intracellular carboxy-terminal domain (CTD). Ligand binding to the LBD facilitates ion channel activation. The NTDs modulate channel gating allosterically in NMDA receptors (NMDARs). A similar function of the NTD in AMPA receptors (AMPARs) is still a matter of debate. Taking advantage of recently resolved structures of the NTD and the intact AMPAR, the main focus of this dissertation is a comprehensive examination of iGluR NTD structural dynamics, ligand binding and allosteric potential of AMPARs. We use a multiscale, multi-dimensional approach using coarse-grained network models and all-atom simulations for structural analyses and information theoretic approaches for examination of evolutionary correlations. Our major contribution has been the characterization of the global motions favored by iGluR NTD architecture. These intrinsic motions favor ligand binding in NMDAR NTDs and are also shared by other iGluR NTDs. We also identified structural determinants of flexibility in AMPARs and confirmed their role through in silico mutants. The overall similarity in collective dynamics among iGluRs hints at a putative allosteric capacity of non-NMDARs and has propelled the elucidation of interdomain and intersubunit coupling in the intact AMPAR. To this end, we identified “effector” and “sensor” regions in AMPARs using a perturbation-response technique. We identified potentially functional residues that enable information propagation between effector regions and proposed an efficient mechanism of allosteric communication based on a combination of tools including network models, graph theoretical methods and sequence analyses. Finally, we assessed the “druggability” of iGluR NTDs using molecular dynamics simulations in the presence of probe molecules containing fragments shared by drug-like molecules. Based on our study, we offer key insights into the ligand-binding landscape of iGluR NTD monomers and dimers, and we also identify a novel ligand-binding site in AMPAR dimers. These findings open an avenue of searching for molecules able to bind to iGluR NTDs and allosterically modulate receptor activity

Probabilistic Protein Design, Comparative Modeling, and the Structure of a Multidomain P53 Oligomer Bound to DNA

Proteins are the main functional components of all cellular processes, and most of them fold into unique three-dimensional shapes guided by their amino-acid sequence. Discovering the structure of a protein, or protein complexes, can provide important clues about how they perform their function. However, the chemical, physical or architectural properties of many proteins impede traditional approaches to structure determination. Two such proteins, the tumor suppressor p53 and the cholesterol processing enzyme endothelial lipase, are prime examples of problematic proteins that defy structural investigation via crystallographic methods. Therefore, new techniques must be developed to gain valuable structural insights, such as: computationally assisted protein design strategies, more efficient crystal screening, or a combination of both. We applied a statistical computationally assisted design strategy to stabilize a p53 variant consisting of two independently folding domains. The re-engineered variant retained normal DNA-binding activities, and allowed us to experimentally determine the first structure of a physiologically active multi-domain p53 tetramer bound to a full-length DNA response element. We then demonstrated how computational methodology can be used to gain functional detail of proteins in the absence of experimentally determined structures. By creating comparative models of endothelial lipase, we discovered structural features that describe function and regulation, and gained a better understanding of the mechanisms conferring substrate specificity. Additionally, traditional methods for protein structure determination, such as X-ray crystallography, require relatively large amounts of purified sample in order to screen a sufficient variety of conditions. To improve this process, we developed a novel method for protein crystal screening using a microfluidics platform. We show how it is possible to use smaller quantities of protein to screen larger varieties of conditions, in turn increasing the probability of success in obtaining crystals. Furthermore, in contrast to current crystallographic approaches, all steps from screening to crystal growth to data collection were performed within the same reaction chamber, without any manipulation of the crystal, dramatically increasing the efficiency of both time and sample required to realize the structure. Collectively, these results demonstrate how advances in computational and experimental approaches can provide structural detail for proteins in circumstances where traditional methodology fails