Network analysis for protein dynamics

Liu, Jenny Yongjia

doi:https://doi.org/10.21985/n2-yyey-4w16

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Network analysis for protein dynamics

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Proteins and many other systems are often conceptualized as networks to access analysis methods from the field of network science. Several approaches use molecular dynamics (MD) simulations of proteins to construct networks using correlational statistics. However, in the field of network science, a well-established approach for network construction is solving the inverse problem for a network that can produce the observed correlations. We apply this inverse approach to three adhesion proteins - FimH and Siglec-8, and the SARS-CoV-2 spike protein - to identify networks that are distinct from correlation networks and instead resemble a contact map. Specifically, we use the inverse of the covariance matrix for backbone and dihedral angles. We select dihedral angles as a system of internal coordinates over external Cartesian coordinates to avoid potential distortions from structure alignment steps for proteins with hinged motion. While more computationally expensive, solving the inverse problem can remove transitive correlations to produce networks that are robust among replicate MD simulations and that have physically interpretable interactions. In the inverse covariance networks, covalent interactions are stronger than hydrogen-bonds and non-bonding interactions. This pattern is not present in correlation networks. Moreover, backbone-backbone interactions dominate the inverse covariance networks, while interactions between sidechains dominate the correlation networks. Due to the differences in the networks constructed by correlation and by solving the inverse problem, there are also differences for comparing network edge strengths, topological properties, and community structures.

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