Search for: All records

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. 

This study elucidates the conformation dynamics of the free and antigen-bound antibody. Previous work has verified that antigen binding allosterically promotes Fc receptor recognition. Analysis of extensive molecular dynamics simulations finds that the energy landscape may play a decisive role in coordinating conformation changes but does not provide connections between the various conformational states. Here we provide such a connection. To obtain a detailed understanding of the impact of antigen binding on antibody conformation dynamics, this study utilizes Markov State Models to summarize the conformation dynamics probed in silico. We additionally equip these models with the ability to directly exploit the energy landscape view of dynamics via a computational method that detects energy basins and so allows utilizing detected basins as macrostates for the Markov State Model. Our study reveals many interesting findings and suggests that the antigen-bound form with high energy may provide many dynamic processes to further enhance co-factor binding of the antibody in the next step. 

Finding the inherent organization in the structure space of a protein molecule is central in many computational studies of proteins. Grouping or clustering tertiary structures of a protein has been leveraged to build representations of the structure-energy landscape, highlight sta- ble and semi-stable structural states, support models of structural dy- namics, and connect them to biological function. Over the years, our laboratory has introduced methods to reveal structural states and build models of state-to-state protein dynamics. These methods have also been shown competitive for an orthogonal problem known as model selection, where model refers to a computed tertiary structure. Building on this work, in this paper we present a novel, tensor factorization-based method that doubles as a non-parametric clustering method. While the method has broad applicability, here we focus and demonstrate its efficacy on the estimation of model accuracy (EMA) problem. The method outperforms state-of-the-art methods, including single-model methods that leverage deep neural networks and domain-specific insight.