Biophysical Society Thematic Meeting | Hamburg 2022

Biophysics at the Dawn of Exascale Computers

Tuesday Speaker Abstracts

USING NETWORK MODELS FOR EXPLORING BIOMOLECULAR FUNCTION AT MULTIPLE SCALES FROM PROTEINS TO CHROMOSOMES Ivet Bahar ; Ivet Bahar 1 ; 1 University of Pittsburgh, Computational and Systems Biology, Pittsburgh, PA, USA Network models proved useful in the last two decades in improving our understanding of the coupled dynamics of biomolecules, from individual proteins to supramolecular. Among network models that have been developed for biological applications, elastic network models (ENMs) found wide usage in molecular biology 1 . The global motions predicted by ENMs have proven in numerous applications to provide a good description of molecular machinery and allosteric behavior, opening the way to designing allosteric modulators of protein function. Application to supramolecular structures, including cryo-EM structures, has been a major utility. A major advantage of ENMs is their simplicity and computational efficiency, which enables proteome- scale analyses, applications to large systems such as the entire chromatin, and/or combination with machine learning (ML) algorithms. Such a recent ML approach that incorporates ENM predictions in addition to sequence and structure data proved to yield an accurate assessment of the effect of mutations on function, compared to those based on sequence and structure exclusively 2,3 . Another recent adaptation to modeling human chromosomal 3D dynamics showed the close correspondence between the spatial mobilities of gene loci and the expression levels of the corresponding genes 4 . These recent developments and future directions will be discussed. References 1 1. Krieger JM, Doruker P, Scott AL, Perahia D, Bahar I. (2020) Towards Gaining Sight of Multiscale Events: Utilizing Network Models and Normal Modes in Hybrid Methods. Curr Opin Struct Biol 64:34-41. 2. Ponzoni L, Bahar I. (2018) Structural dynamics is a determinant of the functional significance of missense variants. Proc Natl Acad Sci USA 115: 4164-4163. Ponzoni L, Penaherrera DA, Oltvai ZN, and Bahar I (2020) Rhapsody: Predicting the pathogenicity of human missense variants. Bioinformatics 36:3084-3092. 4. Zhang S, Chen F, Bahar I. (2020) Differences in the Intrinsic Spatial Dynamics of the Chromatin Contribute to Cell Differentiation. Nucleic Acids Res 48, 1131-1145.

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