Harnessing Computational and Structural Biology Platforms for Drug Discovery

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.

The CDT has five primary beneficiaries:
The CDT cohort
Our students will receive an innovative training experience making them highly employable and equipping them with the necessary knowledge and skillset in science and enterprise to become future innovators and leaders. The potential for careers in the field is substantial and students graduating from the CDT will be sought after by employers. The Life Sciences Industrial strategy states that nearly half of businesses cite a shortage of graduates as an issue in their ability to recruit talent. Collectively, the industrial partners directly involved in the co-creation of the proposal have identified recruitment needs over the next decade that already significantly exceed the output of the CDT cohort.
Life science industries
The cohort will make a vital contribution to the UK life sciences industry, filling the skills gap in this vital part of the economy and providing a talented workforce, able to instantly focus on industry relevant challenges. Through co-creation, industrial partners have shaped the training of future employees. Additional experience in management and entrepreneurship, as well as peer-to-peer activities and the beginning of a professional network provided by the cohort programme will enable graduates to become future leaders. Through direct involvement in the CDT and an ongoing programme of dissemination, stakeholders will benefit from the research and continue to contribute to its evolution. Instrument manufacturers will gain new applications for their technologies, pharmaceutical and biotech companies will gain new opportunities for drug discovery projects through new insight into disease and new methods and techniques.
Health and Society
Research outputs will ultimately benefit healthcare providers and patients in relevant areas, such as cancer, ageing and infection. Pathways to such impact are provided by involvement of industrial partners specialising in translational research and enabling networks such as the Northern Health Science Alliance, the First for Pharma group and the NHS, who will all be partners. Moreover, graduates of the CDT will provide future healthcare solutions throughout their careers in pharmaceuticals, biotechnology, contract research industries and academia.
UK economy
The cohort will contribute to growth in the life sciences industry, providing innovations that will be the vehicle for economic growth. Nationally, the Life Sciences Industrial Strategy Health Advanced Research Programme seeks to create two entirely new industries in the field over the next ten years. Regionally, medicines research is a central tenet of the Northern Powerhouse Strategy. The CDT will create new opportunities for the local life sciences sector, Inspiration for these new industries will come from researchers with an insight into both molecular and life sciences as evidenced by notable successes in the recent past. For example, the advent of Antibody Drug Conjugates and Proteolysis Targeting Chimeras arose from interdisciplinary research in this area, predominantly in the USA and have led to significant wealth and job creation. Providing a cohort of insightful, innovative and entrepreneurial scientists will help to ensure the UK remains at the forefront of future developments, in line with the aim of the Industrial Strategy of building a country confident, outward looking and fit for the future.
Institutions
Both host institutions will benefit hugely from hosting the CDT. The enhancement to the research culture provided by the presence of a diverse and international cohort of talented students will be beneficial to all researchers allied to the theme areas of the programme, who will also benefit from attending many of the scientific and networking events. The programme will further strengthen the existing scientific and cultural links between Newcastle and Durham and will provide a vehicle for new collaborative research.