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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.
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Antigen Binding Reshapes Antibody Energy Landscape and Conformation Dynamics
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.
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- Award ID(s):
- 1900061
- PAR ID:
- 10343766
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Page Range / eLocation ID:
- 2519 to 2526
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BIBM52615.2021.9669830
