Recognizing Local and Global Structural Motifs at the Atomic Scale

Abstract

Most of the current understanding of structure-property relations at the molecular and the supramolecular scales can be formulated in terms of the stability of and the interactions between a limited number of recurring structural motifs (e.g., H-bonds, coordination polyhedra, and protein secondary structure). Here we demonstrate an algorithm to automatically recognize such patterns, based on the identification of local maxima in the probability distributions observed in atomistic computer simulations, which is robust to the dimensionality and the sparsity of the reference atomistic data. We first discuss its main features, demonstrating some on artificial data sets, and then show how it can be applied to identify coordination environments in Lennard-Jones clusters and to recognize secondary-structure patterns in the simulation of an oligopeptide. To assess the applicability of this algorithm for motifs that involve several interdependent degrees of freedom, we also employ it to identify groups of conformers of the cluster and the polypeptide, considered in their entirety. The motifs identified by analyzing atomistic simulations can be used to interpret and rationalize the stability and behavior of the system at hand, and also as a tool to accelerate sampling, in association with biased molecular dynamics schemes.

Unsupervised machine learning in atomistic simulations, between predictions and understanding

“…GMMs, and similar parametric cluster models are often criticised as the number of clusters and the initial Gaussian parameters affect the outcome of the analysis. This problem can be mitigated by using the outcome of a non-parametric clustering technique as the starting point for a GMM 39,40 .…”

Unsupervised landmark analysis for jump detection in molecular dynamics simulations

“…The descriptor changes smoothly with the Cartesian coordinates of the structure. For these reasons, SOAP descriptors have become a powerful tool to express local geometry for machinelearning applications [49] and the detection of structural motifs [35,36].…”

“…In order to overcome some of these challenges, this work introduces an algorithm for accurately and automatically analyzing molecular dynamics trajectories and detecting jumps of the mobile ion through the host lattice with minimal human supervision and no prior knowledge. This algorithm can be combined with the automatic detection of important structural motifs [35,36], leading to a versatile tool for the unsupervised analysis of trajectories and detection of diffusion events. The algorithm will be discussed in Sec.…”

Using Data-Reduction Techniques to Analyze Biomolecular Trajectories

“…Throughout this chapter we have asserted that these methods should be used to complement chemical and physical understanding and not to replace it. With this in mind an interesting recent development is the so called PAMM methodology [74,75], which uses Bayesian statistics to determine whether the arrangement of the atoms in a particular configuration resembles the canonical definition of a molecular motif such as a hydrogen bond or alpha helix.…”