Buigues Jorro, Pedro Juan; (2023) Developments on Enhanced Sampling and Machine Learning Analysis Techniques for Understanding Biomolecular Events. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

The research described in this work rises from the current challenges in molecular dynamics (MD) simulations. Although these simulations provide accurate and highresolution insights on the dynamics of biomolecular events, the timescales needed to observe relevant events such as ligand-unbinding, protein-protein interactions and protein folding, for instance, are not currently reachable for most scientists with classical MD methods. Additionally, MD simulations are intrinsically complex and high-dimensional, which makes it often difficult to elucidate and gain insights from. To tackle the challenges in MD, an iterative protocol for ligand unbinding followed by a machine learning (ML) analysis allowed for the investigation of the unbinding of Cyclin-Dependent Kinase 2 (CDK2) inhibitors and the long-acting muscarinic antagonists for the human Muscarinic Receptor 3 (hMR3). This approach allowed a deeper understanding of the unbinding path and the underlying protein-ligand interactions. This was achieved by obtaining an approximated transition state (TS) from the unbinding path and generating downhill simulations to train two ML models to predict the outcome. ML is a powerful tool for learning to predict from complex data. However, one of the key challenges is that many models are often considered black boxes. With explainable AI techniques it is possible to gain insights from models and understand how the relationship between input features and their predictions. In this work, we developed a protocol for assessing this in a model-agnostic way and develop a framework to test this for correlated time-series data with both 1D and 2D analytical datasets. Additionally, a problem-tailored Hamiltonian replica exchange methodology was also developed to aid in the research of systems mainly governed by electrostatic interactions. This is useful especially for phosphate-related enzymes where metal ions play a role in catalysis and active site geometries. This was tested on several systems leading to the CRISPR Cas1/Cas2 system. Results on the modelled complex hinted at a possible two-metal ion coordination in the active site due to major rearrangements and a K + ion transitioning from the bulk to form part of the coordination.

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