Automated Collective Variable Discovery for MFSD2A transporter from molecular dynamics simulations

Abstract

ABSTRACTBiomolecules often exhibit complex free energy landscapes in which long-lived metastable states are separated by large energy barriers. Overcoming these barriers to robustly sample transitions between the metastable states with classical molecular dynamics (MD) simulations presents a challenge. To circumvent this issue, collective variable (CV)-based enhanced sampling MD approaches are often employed. Traditional CV selection relies on intuition and prior knowledge of the system. This approach introduces bias, which can lead to incomplete mechanistic insights. Thus, automated CV detection is desired to gain a deeper understanding of the system/process. Analysis of MD data with various machine learning algorithms, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)-based approaches have been implemented for automated CV detection. However, their performance has not been systematically evaluated on structurally and mechanistically complex biological systems. Here, we applied these methods to MD simulations of the MFSD2A (Major Facilitator Superfamily Domain 2A) lysolipid transporter in multiple functionally relevant metastable states with the goal of identifying optimal CVs that would structurally discriminate these states. Specific emphasis was on the automated detection and interpretive power of LDA-based CVs. We found that LDA methods, which included a novel gradient descent-based multiclass harmonic variant, termed GDHLDA, we developed here, outperform PCA in class separation, exhibiting remarkable consistency in extracting CVs critical for distinguishing metastable states. Furthermore, the identified CVs included features previously associated with conformational transitions in MFSD2A. Specifically, conformational shifts in transmembrane helix 7 and in residue Y294 on this helix emerged as critical features discriminating the metastable states in MFSD2A. This highlights the effectiveness of LDA-based approaches in automatically extracting from MD trajectories CVs of functional relevance that can be used to drive biased MD simulations to efficiently sample conformational transitions in the molecular system.STATEMENT OF SIGNIFICANCETo elucidate the biological mechanisms of pertinent biomolecules, it is crucial to understand their complex free energy landscapes. Such landscapes are often constructed from molecular dynamics (MD) simulations using collective variable (CV)-guided enhanced sampling methods. Identifying proper CVs for this task is critical but can be challenging with traditional intuition-based approaches. Here we propose an automated protocol for CV discovery which is based on linear discriminant analysis (LDA) for dimensionality reduction of MD data. By applying the protocol to MD simulations of the MFSD2A lysolipid transporter, a structurally and mechanistically complex biological system, we show that LDA-based methods efficiently detect system-specific CVs that accurately classify different metastable states of MFSD2A and are highly interpretable in a detailed structural context.