Harnessing Computational and Structural Biology Platforms for Drug Discovery

点击翻译按钮获取中文摘要

Drug discovery is expensive and laborious, and although computational tools are cheaper relative to their experimental counterparts, they represent a significant time cost in what is still an iterative and subjective process. Computational aspects of drug discovery are particularly relevant during the COVID-19 pandemic, exemplified by the recent Moonshot effort. This began as a particularly large fragment screening against SARS-CoV-2's main protease and now involves crowdsourcing a set of easily synthesised and diverse fragments in order to create an anti-viral drug compound that will be vital for long term management of the pandemic. The Moonshot effort is composed of scientists from all over the world who have submitted a combined total of over 15,000 fragments. Pooled computer resources are then used to generate lead compounds, which can then be synthesised and tested for efficacy. This process of fragment-based drug discovery (FBDD), in general, involves using small molecule hits from a screening process (e.g. X-ray crystolographic screening) and optimising them to produce leads. These optimised fragments can show an increased affinity for their targets by multiple orders of magnitude. FBDD allows rational and target-driven lead generation, and the field is still growing with dozens of drugs currently in clinical trials. However as of yet only 4 drugs that FBDD has contributed to, have been brought to market. The question this project sets out to answer is:Can this sort of workflow be automated by using individual structural biology/drug discovery platforms in an integrated way for particular bacterial targets? Can the automated pipeline be used by a non-specialist? Individual tools will be assessed for reliability, accuracy and extensibility and a method of integrating them will be developed, using python. An example of a promising (Python-based) tool that can be incorporated into an automated workflow in the Computer-Aided Drug Design (CADD) pipeline is DeLinker. DeLinker is a machine learning approach for fragment linking and scaffold hopping, which addresses the lack of 3D generative fragment-linking software. It can produce linkers using spatial information of two initial fragments, utilising the distance between them and their relative orientations to produce novel linkers not in the initial training database.The automated Python workflow will be validated by optimising leads for two bacterial targets, the transcriptional regulator PrfA in Listeria monocytogenes and the toxin-antitoxin system of Mycobacterium tuberculosis. L. monocytogenes is a food-borne pathogen that causes listerosis, with major outbreaks occuring on an annual basis. A potential target is the virulence machinery in L. monocytogenes and is non-bactericidal, so has less chance of resistance relative to traditional antibacterial approaches. There are known inhibitors for an intraprotein 'tunnel' previously identified in L. monocytogenes using ring-fused 2-pyridone hetero-cycles that reduced virulence by binding and attenuating PrfA, so this particular target is ripe for a fragment-linking approach to develop inhibitors with antivirulence properties, containing that moiety. M. tuberculosis (TB) is the number one cause of death from infectious disease. Toxin-antitoxin systems regulate cellular processes and are therapeutic targets, and the toxin in TB, MbcT, is bactericidal unless neutralized by its antitoxin MbcA, and causes rapid cell death. The search for a small molecule inhibitor for the MbcTA (toxin-antitoxin) complex or the inactivate MbcA antitoxin could be an avenue to combat TB.