Integrating HDX-MS and Molecular Dynamics to investigate the mechanism of activation of the RORy by multiple small molecule modulators

Detailed information on the mechanism of activation is enigmatic for many proteins. Protein mechanism and function is a product of both the structure and the dynamics of the system. Dynamical behaviour is invisible to standard structural studies such as X-ray crystallography and CryoEM. To improve our understanding of how protein drug targets respond to ligands requirements the development of methods that can probe dynamics. Hydrogen-Deuterium Exchange Mass-Spectrometry (HDX-MS) is a widely used experimental technique that provides quantitative information on the protein's amide hydrogen bonding network, thereby facilitating the exploration of protein conformational dynamics.

HDX-MS stands out to other complementary biophysical techniques as it has advantages over many other methods in that there is no limit to the size of protein it can study and it can probe proteins in conditions similar to the native environment. As such HDX-MS is extensively used in small molecule, antibody, and vaccine applications.

In small molecule research, HDX-MS can report on both direct binding events and allosteric modulation. In studies of conformational mechanism, the full deconvolution of HDX data into structural representation can be bypassed by restricting the conformational state of proteins. These sorts of experimental studies are often complemented by Molecular Dynamics (MD) simulations that can be used to monitor the conformational dynamics of proteins and their complexes.

The frequent use of HDX-MS in antibody therapeutics research underscores its position as a routinely utilized branch of Analytical Science. The experimental method combines Biological Informatics and Computational and Theoretical Chemistry. HDX-MS involves breaking down a protein into peptides, the peptide fragment/protein sequences must then be matched which can be challenging due to the number of different peptide/mass possibilities. MD simulations are therefore used to help resolve gaps in the resulting data and to help relate the spatially distributed exchange rates to structural functions. As proteins are inherently Non-Linear Systems, the MD simulations require careful implementation to reweight the dynamical observations to simulated structures, to link dynamics to protein function.

Presently, two main regimes exist for performing HDX-MS studies: experiment rich and simulation rich. A primary research objective is to compare the two approaches and apply the latest techniques from both HDX-MS as well as MD simulation. This goal will be achieved by the combination interdisciplinary experience of the team: experimental HDX-MS (Srinath Krishnamurthy, OMass), computational chemistry (Maria Musgaard, OMass), theoretical methods in HDX-MS (Oliver Crook, Oxford) and structural biology and statistics (Charlotte Deane, Oxford).

In this project, we will develop an integrative approach between HDX-MS and MD to investigate the mechanism of activation of receptors with multiple small molecule modulators. Beyond simply contrasting best practices, one intriguing area we plan to explore is the Graphics and Visualisation of these data, the inherent high dimensionality can make interpretation challenging.

A wealth of HDX-MS data from published studies are available and provide a testbed to compare different approaches. In addition, OMass has several current studies for exploration as well as the possibility to supplement the data with further experiments. This will enable us to investigate the latest advancements in HDX-MS experiments.

Initially we will leverage the wealth of data generated in previous work on a library of structural dynamics from RORy modulators by HDX-MS (38 compounds studied via HDX-MS and around 60 pdb structures) to determine whether the correct protein-ligand complex conformations can be predicted or selected from MD trajectories attempting to reveal the conformational dynamics of activation.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.