Selecting Features for Markov Modeling: A Case Study on HP35

Abstract

Markov state models represent a popular means to interpret molecular dynamics trajectories in terms of memoryless transitions between metastable conformational states. To provide a mechanistic understanding of the considered biomolecular process, these states should reflect structurally distinct conformations and ensure a timescale separation between fast intrastate and slow interstate dynamics. Adopting the folding of villin headpiece (HP35) as a well-established model problem, here we discuss the selection of suitable input coordinates or `features', such as backbone dihedral angles and interresidue distances. We show that dihedral angles account accurately for the structure of the native energy basin of HP35, while the unfolded region of the free energy landscape and the folding process are best described by tertiary contacts of the protein. To construct a contact-based model, we consider various ways to define and select contact distances, and introduce a low-pass filtering of the feature trajectory as well as a correlation-based characterization of states. Relying on input data that faithfully account for the mechanistic origin of the studied process, the states of the resulting Markov model are clearly discriminated by the features, describe consistently the hierarchical structure of the free energy landscape, and$\unicode{0x2014}$as a consequence$\unicode{0x2014}$correctly reproduce the slow timescales of the process.

References in corpus (5)

• From Louvain to Leiden: guaranteeing well-connected communities
• Identification of slow molecular order parameters for Markov model construction
• Variational Selection of Features for Molecular Kinetics
• Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories
• Nonequilibrium Modeling of the Elementary Step in PDZ3 Allosteric Communication

Cited by in corpus (5)

• Towards a Benchmark for Markov State Models: The Folding of HP35
• Learning protein-ligand unbinding pathways via single-parameter community detection
• Contact cluster modeling of allosteric communication in PDZ domains
• MDIntrinsicDimension: Dimensionality-Based Analysis of Collective Motions in Macromolecules from Molecular Dynamics Trajectories
• More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations