Assignment of Protonation States in Proteins and Ligands: Combining pKa Prediction with Hydrogen Bonding Network Optimization

Abstract

Among the many applications of molecular modeling, drug design is probably the one with the highest demands on the accuracy of the underlying structures. During lead optimization, the position of every atom in the binding site should ideally be known with high precision to identify those chemical modifications that are most likely to increase drug affinity. Unfortunately, X-ray crystallography at common resolution yields an electron density map that is too coarse, since the chemical elements and their protonation states cannot be fully resolved.This chapter describes the steps required to fill in the missing knowledge, by devising an algorithm that can detect and resolve the ambiguities. First, the pK (a) values of acidic and basic groups are predicted. Second, their potential protonation states are determined, including all permutations (considering for example protons that can jump between the oxygens of a phosphate group). Third, those groups of atoms are identified that can adopt alternative but indistinguishable conformations with essentially the same electron density. Fourth, potential hydrogen bond donors and acceptors are located. Finally, all these data are combined in a single "configuration energy function," whose global minimum is found with the SCWRL algorithm, which employs dead-end elimination and graph theory. As a result, one obtains a complete model of the protein and its bound ligand, with ambiguous groups rotated to the best orientation and with protonation states assigned considering the current pH and the H-bonding network. An implementation of the algorithm has been available since 2008 as part of the YASARA modeling & simulation program.

The Drug Diazaborine Blocks Ribosome Biogenesis by Inhibiting the AAA-ATPase Drg1

“…Initially, 10 models were built using the two closest templates in the Protein Data Bank (1R7R and 3CF3, which are both crystal structures of murine p97 protein) and five slightly different alignments per template (21). The modeling procedure involved the SCWRL algorithm (22) for side chain rotamer prediction and hydrogen bonding network optimization (23), as well as an energy minimization with explicit solvent shell (19) to generate the final models. Surprisingly, the models based on template 3CF3 (24) scored consistently better, even though 1R7R was solved at higher resolution (3.6 versus 4.25 Å).…”

Phosphorylation of Leukotriene C4 Synthase at Serine 36 Impairs Catalytic Activity

“…The p70S6k is a serine/threonine-specific kinase localized both in the cytosol and nucleus (20). In this study, we show that the predominant p70S6k phosphorylation site on LTC4S is Ser 36 . Prediction of Candidate Phosphorylation Site(s)-Initially, we used an online phosphorylation prediction tool, NetPhos 2.0 Server, to identify the potential phosphorylation site(s) based on sequence information using an artificial neural network method (21).…”

“…In addition, the positional shift of the loop and its interaction with the neighboring subunit affect active site access. Thus, our mutational and kinetic data, together with molecular simulations, suggest that phosphorylation of Ser 36 inhibits the catalytic function of LTC4S by interference with the catalytic machinery.…”

“…The enzyme activity of S36E increased linearly with increasing LTA 4 concentrations during the steady-state kinetics analysis, indicating poor lipid substrate binding. The Ser 36 is located in a loop region close to the entrance of the proposed substrate binding pocket. Comparative molecular dynamics indicated that Ser 36 upon phosphorylation will pull the first luminal loop of LTC4S toward the neighboring subunit of the functional homotrimer, thereby forming hydrogen bonds with Arg 104 in the adjacent subunit.…”

Structural studies of UBXN2A and mortalin interaction and the putative role of silenced UBXN2A in preventing response to chemotherapy

“…Models were examined for accuracy by comparison with the 2.8-Å crystal structure of the nucleotide-binding domain of mortalin (PDB entry 4KBO). Hydrogens were added and side chains were optimized using a rotamer library (SCWRL), steepest descent, and semi-empirical quantum mechanics (MOPAC) in YASARA Structure (Krieger et al 2012;Krieger and Vriend 2015). The homology model was inspected and validated using the protein structure validation suite (Bhattacharya et al 2007).…”