Protein-protein interactions are among today’s most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more “traditional” drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of “decoys.” Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new “Talaris” update and the “pwSHO” solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta’s ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta’s ability to identify small-molecule inhibitors of protein-protein interactions.

Protein design aims at designing new protein molecules of desired structure and functionality. One of the major obstacles to large-scale protein design is the extensive time and manpower requirements for experimental validation of the designed sequences. Recent advances in protein structure prediction have provided potentials for automated assessment of the designed sequences via folding simulations. We present a new protocol for protein design-and-validation. The sequence space is initially searched by Monte Carlo sampling guided by a public atomic potential, with candidate sequences selected by the clustering of sequence decoys. The designed sequences are then assessed by I-TASSER folding simulations, which generate full-length atomic structural models by the iterative assembly of threading fragments. The protocol is tested on 52 non-homologous single-domain proteins, with average sequence identity of 24% between the designed and native sequences. Despite this low sequence identity, 3D models predicted for the first designed sequence have an RMSD <2 Å to the target structure in 62% of cases. This percentage increases to 77% if we consider the 3D models from the top 10 designed sequences. Such a striking consistency between the target structure and the structural prediction from nonhomologous sequences, despite the fact that the design and folding algorithms adopt completely different force fields, indicates that the design algorithm captures the features essential to the global fold of the target. On average, the designed sequences have a free energy that is 0.39 kcal/(mol·residue) lower than in the native, potentially affording a greater stability to synthesized target folds.

In this report we investigated, within a group of closely related single domain camelid antibodies (VHHs), the relationship between binding affinity and neutralizing activity as it pertains to ricin, a fast-acting toxin and biothreat agent. The V1C7-like VHHs (V1C7, V2B9, V2E8, and V5C1) are similar in amino acid sequence, but differ in their binding affinities and toxin-neutralizing activities. Using the X-ray crystal structure of V1C7 in complex with ricin’s enzymatic subunit (RTA) as a template, Rosetta-based homology modeling coupled with energetic decomposition led us to predict that a single pairwise interaction between Arg29 on V5C1 and Glu67 on RTA was responsible for the difference in ricin toxin binding affinity between V1C7, a weak neutralizer, and V5C1, a moderate neutralizer. This prediction was borne out experimentally: substitution of Arg for Gly at position 29 enhanced V1C7’s binding affinity for ricin, whereas the reverse (i.e., Gly for Arg at position 29) diminished V5C1’s binding affinity by >10 fold. As expected, the V5C1R29G mutant was largely devoid of toxin-neutralizing activity. However, the toxin-neutralizing activity of the V1C7G29R mutant was not correspondingly improved, indicating that in the V1C7 family binding affinity alone does not account for differences in antibody function. V1C7 and V5C1, as well as their respective point mutants, recognized indistinguishable epitopes on RTA, at least at the level of sensitivity afforded by hydrogen-deuterium mass spectrometry. The results of this study have implications for engineering therapeutic antibodies because they demonstrate that even subtle differences in epitope specificity can account for important differences in antibody function.

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