(342c) Comparative Role of Geometric and Chemical Parameters for the Formation of Pair-Wise Nanomolecular Complexes

The design and the prediction of the formation of pair-wise assemblies between proteins and other biomolecules in nanoscale dimensions are essential for understanding numerous biological processes. Despite the abundant protein topology data and advances in computations of self-assembled nano-complexes, the lack of generalized descriptors for the pairwise interactions of nano molecules impedes the understanding and prediction of their role in the biosystem.

Here, to understand the comprehensive parameters for the formation of nano-biomolecular complexes, the protein complex information is investigated. The protein structural data from protein databanks allow us to analyze the various descriptor sets to establish the probability for two proteins to form a complex. In addition to the chemical (CH) descriptors at the molecular level, the geometric (GE) and graph-theoretical (GT) are newly introduced to provide a universal description of the local properties of any nanostructures. GT-descriptors were calculated using graph theory and included parameters, such as Ricci curvature, multi-fractal nodal dimension, Gaussian network model were combined with GE (e.g. local chirality and pocketness), and CH (e.g. hydrophobicity and charge) features. The feature correlation dynamics validate the significant contribution of graph network features in the interaction prediction algorithms. As the proof-of-concept applications of nanomolecular complexes' interfacing sites prediction, the SARS-Cov-2 nucleocapsid protein dimer and experimentally proven protein and nanoparticle pairs are analyzed. The rapid and straightforward prediction of interface sites in pairwise molecular complexes will provide simplified design rules, new descriptors for the engineering of a wide range of pair-wise complexes of biomolecules with nanoscale dimensions.