A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N˙ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening

Expanding the Number of ‘Druggable’ Targets: Non‐Enzymes and Protein–Protein Interactions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95062/1/cbdd.12066.pd

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p

Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunone

The postgenomic era, as manifest, inter alia, by proteomics, offers unparalleled opportunities for the efficient discovery of safe, efficacious, and novel subunit vaccines targeting a tranche of modern major diseases. A negative corollary of this opportunity is the risk of becoming overwhelmed by this embarrassment of riches. Informatics techniques, working to address issues of both data management and through prediction to shortcut the experimental process, can be of enormous benefit in leveraging the proteomic revolution.In this disquisition, we evaluate proteomic approaches to the discovery of subunit vaccines, focussing on viral, bacterial, fungal, and parasite systems. We also adumbrate the impact that proteomic analysis of host-pathogen interactions can have. Finally, we review relevant methods to the prediction of immunome, with special emphasis on quantitative methods, and the subcellular localization of proteins within bacteria

Theoretical study for the inhibition ability of some bioactive imidazole derivatives against the Middle-East respiratory syndrome corona virus (MERS-Co)

The Severe Acute Respiratory Syndrome (SARS) is a serious viral life-threatening and mortal respiratory illness caused by SARS-CoV. SARS-CoV plays an essential role in the viral replication cycle. It is considered a potential target for SARS inhibitor development. A series of twenty eight bioactive imidazole compounds as possible SARS-CoV inhibitors were designed and evaluated using computational calculations. Possible binding interaction modes were proposed by molecular docking studies. Among all studied compounds, compounds 5, 15 and 22 showed most potent inhibitory activity against SARS-CoV. These results indicated that these inhibitors could be potentially developed into anti-SARS drugs

Platforms for antibiotic discovery

Abstract | The spread of resistant bacteria, leading to untreatable infections, is a major public health threat but the pace of antibiotic discovery to combat these pathogens has slowed down. Most antibiotics were originally isolated by screening soil-derived actinomycetes during the golden era of antibiotic discovery in the 1940s to 1960s. However, diminishing returns from this discovery platform led to its collapse, and efforts to create a new platform based on target-focused screening of large libraries of synthetic compounds failed, in part owing to the lack of penetration of such compounds through the bacterial envelope. This article considers strategies to re-establish viable platforms for antibiotic discovery. These include investigating untapped natural product sources such as uncultured bacteria, establishing rules of compound penetration to enable the development of synthetic antibiotics, developing species-specific antibiotics and identifying prodrugs that have the potential to eradicate dormant persisters, which are often responsible for hard-to-treat infections

Weighted Semi-Supervised Approaches for Predictive Modeling and Truth Discovery

Multi-View Learning (MVL) is a framework which combines data from heteroge- neous sources in an efficient manner in which the different views learn from each other, thereby improving the overall prediction of the task. By not combining the data from different views together, we preserve the underlying statistical property of each view thereby learning from data in their original feature space. Additionally, MVL also mitigates the problem of high dimensionality when data from multiple sources are integrated. We have exploited this property of MVL to predict chemical-target and drug-disease associations. Every chemical or drug can be represented in diverse feature spaces that could be viewed as multiple views. Similarly multi-task learning (MTL) frameworks enables the joint learning of related tasks that improves the overall performances of the tasks than learning them individually. This factor allows us to learn related targets and related diseases together. An empirical study has been carried out to study the combined effects of multi-view multi-task learning (MVMTL) to pre- dict chemical-target interactions and drug-disease associations. The first half of the thesis focuses on two methods that closely resemble MVMTL. We first explain the weighted Multi-View learning (wMVL) framework that systemat- ically learns from heterogeneous data sources by weighting the views in terms of their predictive power. We extend the work to include multi-task learning and formulate the second method called Multi-Task with weighted Multi-View Learning (MTwMVL). The performance of these two methods have been evaluated by cheminformatics data sets. iiWe change gears for the second part of this thesis towards truth discovery (TD). Truth discovery closely resembles a multi-view setting but the two strongly differ in certain aspects. While the underlying assumption in multi-view learning is that the different views have label consistency, truth finding differs in its setup where the main objective is to find the true value of an object given that different sources might conflict with each other and claim different values for that object. The sources could be considered as views and the primary strategy in truth finding is to estimate the reliability of each source and its contribution to the truth. There are many methods that address various challenges and aspects of truth discovery and we have in this thesis looked at TD in a semi-supervised setting. As the third contribution to this dissertation, we adopt a semi-supervised truth dis- covery framework in which we consider the labeled objects and unlabeled objects as two closely related tasks with one task having strong labels while the other task hav- ing weak labels. We show that a small set of ground truth helps in achieving better accuracy than the unsupervised methods

Profiling Ester Prodrug Activation: An Activity Based Protein Profiling (ABPP) Approach.

Determining the identity of prodrug activating enzyme(s) is key to understanding the mechanistic basis for enhanced cellular delivery, biodistribution, and prodrug stability. In addition, understanding species-specific prodrug sensitivities is critical for evaluating pre-clinical animal models and drug-drug interactions. Competitive Activity Based Protein Profiling (ABPP) describes an emerging chemoproteomic approach to assay active site occupancy within a mechanistically similar enzyme class within native proteomes, and has proven to be a powerful approach for activity-guided enzyme annotations. Here we describe a modified ABPP approach using direct substrate competition to determine prodrug binding enzymes of the serine hydrolase (SH) class. The ABPP approach was validated by confirming and validating that CES1 is an oseltamivir binding enzyme in intestinal cell homogenates by gel-based fluorophosphonate (FP) competition. Activation was then confirmed with recombinant hCES1. The competitive binding between oseltamivir and the FP ABPP probe was kinetically analyzed on-gel. Addition of WWL50, a mechanism-based specific carbamate inhibitor of CES1, blocked oseltamivir hydrolysis, and demonstrated exceptional selectivity across >50 active human serine hydrolases by SILAC-ABPP utilizing mass spectrometry. A second reported CES1 inhibitor, WWL79, was shown to inhibit the mouse but not human CES1. Further, complete inhibition of the hydrolysis of several additional ethyl ester prodrugs by WWL50 indicates human CES1 as their dominant activating enzyme in Caco-2 and Hep G2. Overall, we have presented a substrate-competitive activity-based protein profiling (scABPP) approach to broadly survey potential prodrug hydrolyzing enzymes, and determined a very specific hCES1 inhibitor (WWL50). The scABPP approach for surveying the SH class of hydrolase enzymes appears to be a promising methodology for new ester prodrug design and preclinical evaluation.PHDMedicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113378/1/xuha_1.pd

Potassium currents in the heart: functional roles in repolarization, arrhythmia and therapeutics

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