Determination of Nucleotide-Nucleotide and Nucleotide-Amino Acid Binding Interactions from All-Atom...

Biomolecular condensates emerge from multivalent interactions between proteins and nucleic acids and are frequently modeled by using coarse-grained molecular dynamics simulations. The parametrization of these models critically depends on atomistic data describing the underlying molecular interactions. In this work, we employ all-atom molecular dynamics simulations and potential-of-mean-force (PMF) calculations to investigate the landscape of interactions between RNA nucleotides and protein amino acids. We begin by characterizing nucleotide-nucleotide binding modes through canonical base-pairing analysis, observing notable agreement in the predictions from both the AMBER03ws and CHARMM36 force fields. Further rationalization of different nucleotide-nucleotide interaction modes involves the calculation of PMFs for ribose-ribose, phosphate-phosphate, and RNA tertiary interactions such as G-quadruplex formation. We also examine the effect of salt concentration on these interactions, finding a reduction in electrostatic self-repulsion for phosphate-phosphate binding upon increasing the ionic strength. Expanding our analysis to amino acids, we first benchmark the performance of both AMBER03ws and a99SB-disp force fields for describing pairwise amino acid interactions, and then, we evaluate different nucleotide-amino acid binding profiles. Our findings reveal a subset of amino acidsLys and Arg (positively charged), Asp and Glu (negatively charged), and Gln, Ser, and Asn (polar residues)that consistently engage with the nitrogenous bases of different nucleotides. Such binding is primarily mediated by hydrogen bonding and, in some cases, cation-π interactions. Furthermore, we identify strong π-π stacking interactions with aromatic residues and phosphate-Arg contacts as key contributors to condensate cohesion in RNA-protein condensates. Our comprehensive analysis provides a detailed library of nucleotide-amino acid interactions, offering quantitative insights to inform coarse-grained model parametrization and deepening our understanding of condensate self-assembly, nucleic acid recognition, and phase-separation regulation at the submolecular scale.