Simulation intelligence for rare events in biomolecular processes

Abstract

Molecular dynamics (MD) simulations are powerful tools for investigating the dynamic behavior of complex biomolecular systems at atomistic resolution. However, the size and complexity of the configuration space pose substantial challenges to this exploration. Key cellular processes often depend on rare events, where a system must overcome energy barriers to spontaneously switch between metastable states. Extensively sampling those rare transitions, thus achieving ergodicity, is practically unfeasible for conventional, physically unbiased MD simulations. Even if we could dedicate enough computational power to this goal, we would produce massive, mostly redundant trajectory data of difficult interpretation—unless guided by prior knowledge. A more effective strategy is to carefully select the starting configurations of the simulations. In doing so, we split the problem of obtaining a long equilibrium trajectory into the more manageable task of sampling many shorter, meaningful paths. But how can we identify optimal starting configurations? Artificial intelligence (AI) offers an attractive solution. Using AI to both guide MD simulations and interpret the results is a core realization of the simulation intelligence paradigm and a foundational principle of this thesis. This work focused on both method development and applications to biologically relevant systems involving protein-protein and membrane-protein interactions. By combining rigorous path sampling with AI in an active learning framework, we demonstrated that accurate thermodynamic, kinetic, and mechanistic insights can be obtained with reasonable resource usage, even for processes occurring on time scales far beyond the reach of conventional MD simulations. In the first part of this work, we investigated the role of the amphipathic helix (AH) of the ATG3 protein in promoting LC3 lipidation on the phagophore membrane. Unsupervised machine learning (ML) revealed structural features that distinguish ATG3 AH from other membrane-sensing helices and guided the identification of an AH mutant that retains phagophore binding but compromises LC3 lipidation in vivo. Generative AI provided biologically relevant initial configurations for multiple unbiased MD simulations. Through supervised analysis and strategic reinitialization, we mapped the conformational landscape of the membrane-bound ATG3~LC3 complex and characterized the structural and dynamical differences between wild-type and mutant AHs. Based on these results, we proposed a speculative model describing the fine-tuned interplay between the AH, the lipids, and the rest of the complex. However, despite months of computation, the MD trajectories were not sufficiently long to extract comprehensive quantitative information, limiting our analysis to qualitative insights. For the remainder of this thesis, we addressed the sampling and interpretation challenges in systems shaped by rare-event transitions by adopting the AI for Molecular Mechanism Discovery (AIMMD) framework. At its core, AIMMD employs an iterative cycle where a ML model of the committor enhances path sampling simulations by orchestrating the selection of starting configurations—the shooting points (SPs)—from previously generated data. At the same time, the method uses the simulation results to train the model, further improving sampling efficiency. The committor function serves as the optimal reaction coordinate and, together with the sampled paths, can be analyzed to uncover the transition mechanism. What sets AIMMD apart from other AI-based enhanced sampling techniques is its minimal reliance on prior knowledge, its solid statistical mechanics foundations, its limited yet effective AI intervention in the simulations (still evolving from previous configurations under unbiased dynamics), and its strong emphasis on interpretability, all of which contribute to the method's robustness. In the second part of this work, we expanded the scope of AIMMD to obtain accurate free energy and transition rates estimates without the need for additional enhanced sampling simulations. To this end, we reintegrated all simulated excursions, previously considered failed attempts at generating transitions, in our sampling. We developed a data-efficient path reweighting algorithm incorporating free simulations around metastable states. Again, we relied on the committor model for optimal performance. We validated the updated method on the folding/unfolding of the mini protein chignolin, highlighting the computational advantages over standard MD simulations and the benefits of accessing the full equilibrium path ensemble. In the third part of this work, we demonstrated AIMMD's effectiveness in fully characterizing EGFR transmembrane dimerization and dissociation. In this way, we extended its applicability to a broader class of processes characterized by a single basin of attraction, where the transition is rare in only one direction. We emphasized the importance of dimer stability in triggering downstream signaling pathways and proposed AIMMD as a valuable tool for detecting stability changes under varying system conditions. Additionally, we further improved the method and its code implementation by parallelizing the computational effort, augmenting the committor training set, exploring path sampling alternatives to the original AI-guided TPS scheme, and streamlining the reweighting algorithm to improve its robustness and efficiency. In the fourth and final part of this work, we introduced an optimal rejection-free path sampling (RFPS) scheme by shifting our perspective to sampling a SP distribution. %an optimal path sampling scheme with no more rejected trials in the Markov chain. As a result, we could use all sampled paths for selecting SPs, enhancing exploration without compromising exploitation. RFPS integrates naturally into AIMMD, resulting in the updated RFPS-AIMMD algorithm, where free energy estimates are incorporated directly into the iterative cycle. RFPS-AIMMD improved committor learning through better training set coverage and demonstrated notable robustness on chignolin. This theoretical development bears broader implications for path sampling methods beyond AIMMD. Overall, the updated AIMMD framework offers a practical, interpretable, and computationally efficient alternative to long, unbiased MD simulations, with strong potential for further improvements and applications to more complex systems.