Fewer Dimensions, More Structures for Improved Discrete Models of Dynamics of Free versus Antigen-Bound Antibody

Abstract

Over the past decade, Markov State Models (MSM) have emerged as powerful methodologies to build discrete models of dynamics over structures obtained from Molecular Dynamics trajectories. The identification of macrostates for the MSM is a central decision that impacts the quality of the MSM but depends on both the selected representation of a structure and the clustering algorithm utilized over the featurized structures. Motivated by a large molecular system in its free and bound state, this paper investigates two directions of research, further reducing the representation dimensionality in a non-parametric, data-driven manner and including more structures in the computation. Rigorous evaluation of the quality of obtained MSMs via various statistical tests in a comparative setting firmly shows that fewer dimensions and more structures result in a better MSM. Many interesting findings emerge from the best MSM, advancing our understanding of the relationship between antibody dynamics and antibody–antigen recognition.

Evidence of variable human Fcγ receptor-Fc affinities across differentially-complexed IgG

“…The experimental techniques showing distal effects include circular dichroism, 33 nuclear magnetic resonance, 34 fluorescence correlation microscopy, 20 hydrogen-deuterium exchange mass spectrometry (HDX), 20 , 35 electron microscopy, 35 and crystallography, 36 and are further supported by extensive molecular dynamics simulations. 37 , 38 Relevant to observations in this study, crystallography studies have also demonstrated that distal loops in the CH1 domain exist in different conformations when antibodies are free versus bound to antigen, 39 and can be transmitted through linkers thought to be passive. 40 Similarly, changes in both crystal structures and the relative population of distinct conformational states of IgG related to factors such as post-translational modification have been widely reported.…”

Editorial: Special Issue “Protein Modeling and Simulation: Selected Articles from the Computational Structural Bioinformatics Workshop 2021”

“…An essential one in this direction is to move from a single-structure to a multi-structure view of protein molecules as obtaining a detailed understanding of the structure and dynamics of unbound and bound molecular systems remains a fundamental challenge. In [ 4 ], Kabir et al investigated the utility of a coarse-grained representation (based on ultrafast shape recognition) as a better state space discretization to obtain Markov State models of a higher quality with the capability to capture the multi-view structural dynamics of biological molecules and their complex, even in the absence of the associated energetics.…”