A Self-Consistent Approach to Rotamer and Protonation State Assignments (RAPA): Moving Beyond Single Protein Configurations.

Abstract

There are currently over 160,000 protein crystal structures obtained by X-ray diffraction with resolutions of 1.5 Å or greater in the Protein Data Bank. At these resolutions hydrogen atoms do not resolve and heavy atoms such as oxygen, carbon, and nitrogen are indistinguishable. This leads to ambiguity in the rotamer and protonation states of multiple amino acids, notably asparagine, glutamine, histidine, serine, tyrosine, and threonine. When the rotamer and protonation states of these residues change, so too does the electrochemical surface of a binding site. A variety of computational approaches have been developed to assign states for these residues by investigating all possibilities and typically deciding on a single rotamer or protonation state for each residue that is consistent with the crystal structure. Here, we posit that there are multiple rotamer and protonation states that are consistent with the resolved structure of the proteins and introduce a Rotamer and Protonation Assignment (RAPA) protocol which analyzes local hydrogen-bonding environments in the resolved structures of proteins and identifies a set of unique rotamer and protonation states that are energetically consistent with the experimentally reported crystal structure. We evaluate the RAPA-predicted configurations in molecular dynamics simulations and find that there are multiple configurations for each protein that maintain structures consistent with the X-ray results. In our initial evaluations of the RAPA protocol, we find that for most proteins (69/77) there are multiple energetically accessible rotamer and protonation state configurations however the total number is limited to 8 or fewer for most of the proteins (62 of 77). This suggests that there is no combinatorial explosion in the number of energetically accessible rotamer and protonation states for most proteins and investigating all such states is computationally feasible.