Summary: Broadly neutralizing antibodies (bNAbs) against HIV can reduce viral transmission in humans, but an effective therapeutic will require unusually high breadth and potency of neutralization. We employ the OSPREY computational protein design software to engineer variants of two apex-directed bNAbs, PGT145 and PG9RSH, resulting in increases in potency of over 100-fold against some viruses. The top designed variants improve neutralization breadth from 39% to 54% at clinically relevant concentrations (IC80 < 1 μg/mL) and improve median potency (IC80) by up to 4-fold over a cross-clade panel of 208 strains. To investigate the mechanisms of improvement, we determine cryoelectron microscopy structures of each variant in complex with the HIV envelope trimer. Surprisingly, we find the largest increases in breadth to be a result of optimizing side-chain interactions with highly variable epitope residues. These results provide insight into mechanisms of neutralization breadth and inform strategies for antibody design and improvement.

Antimicrobial resistance presents a significant health care crisis. The mutation F98Y in Staphylococcus aureus dihydrofolate reductase (SaDHFR) confers resistance to the clinically important antifolate trimethoprim (TMP). Propargyl-linked antifolates (PLAs), next generation DHFR inhibitors, are much more resilient than TMP against this F98Y variant, yet this F98Y substitution still reduces efficacy of these agents. Surprisingly, differences in the enantiomeric configuration at the stereogenic center of PLAs influence the isomeric state of the NADPH cofactor. To understand the molecular basis of F98Y-mediated resistance and how PLAs’ inhibition drives NADPH isomeric states, we used protein design algorithms in the osprey protein design software suite to analyze a comprehensive suite of structural, biophysical, biochemical, and computational data. Here, we present a model showing how F98Y SaDHFR exploits a different anomeric configuration of NADPH to evade certain PLAs’ inhibition, while other PLAs remain unaffected by this resistance mechanism.

The K* algorithm provably approximates partition functions for a set of states (e.g., protein, ligand, and protein-ligand complex) to a user-specified accuracy ε. Often, reaching an ε-approximation for a particular set of partition functions takes a prohibitive amount of time and space. To alleviate some of this cost, we introduce two new algorithms into the osprey suite for protein design: fries, a Fast Removal of Inadequately Energied Sequences, and EWAK*, an Energy Window Approximation to K*. fries pre-processes the sequence space to limit a design to only the most stable, energetically favorable sequence possibilities. EWAK* then takes this pruned sequence space as input and, using a user-specified energy window, calculates K* scores using the lowest energy conformations. We expect fries/EWAK* to be most useful in cases where there are many unstable sequences in the design sequence space and when users are satisfied with enumerating the low-energy ensemble of conformations. In combination, these algorithms provably retain calculational accuracy while limiting the input sequence space and the conformations included in each partition function calculation to only the most energetically favorable, effectively reducing runtime while still enriching for desirable sequences. This combined approach led to significant speed-ups compared to the previous state-of-the-art multi-sequence algorithm, BBK*, while maintaining its efficiency and accuracy, which we show across 40 different protein systems and a total of 2,826 protein design problems. Additionally, as a proof of concept, we used these new algorithms to redesign the protein-protein interface (PPI) of the c-Raf-RBD:KRas complex. The Ras-binding domain of the protein kinase c-Raf (c-Raf-RBD) is the tightest known binder of KRas, a protein implicated in difficult-to-treat cancers. fries/EWAK* accurately retrospectively predicted the effect of 41 different sets of mutations in the PPI of the c-Raf-RBD:KRas complex. Notably, these mutations include mutations whose effect had previously been incorrectly predicted using other computational methods. Next, we used fries/EWAK* for prospective design and discovered a novel point mutation that improves binding of c-Raf-RBD to KRas in its active, GTP-bound state (KRasGTP). We combined this new mutation with two previously reported mutations (which were highly-ranked by osprey) to create a new variant of c-Raf-RBD, c-Raf-RBD(RKY). fries/EWAK* in osprey computationally predicted that this new variant binds even more tightly than the previous best-binding variant, c-Raf-RBD(RK). We measured the binding affinity of c-Raf-RBD(RKY) using a bio-layer interferometry (BLI) assay, and found that this new variant exhibits single-digit nanomolar affinity for KRasGTP, confirming the computational predictions made with fries/EWAK*. This new variant binds roughly five times more tightly than the previous best known binder and roughly 36 times more tightly than the design starting point (wild-type c-Raf-RBD). This study steps through the advancement and development of computational protein design by presenting theory, new algorithms, accurate retrospective designs, new prospective designs, and biochemical validation.

We found the reviewers’ comments very helpful, and we thank them for their suggestions which have greatly improved our manuscript. Below, we address each comment individually and summarize the resulting changes to the manuscript. While changes to the manuscript have been made throughout, the major ones are indicated in red. References (e.g., [1]) can be found at the end of this response. Added bibliographic references are not in red in the manuscript due to limitations of LaTex. In response to Reviewer 1’s questions about the significance of our designed c-Raf-RBD(RKY) variant, we attempt to answer these questions in this response to reviewers. However, we do agree to tone down our claims of significance, as she/he suggested, and have done so in the revised manuscript. We agree it is best to present our data, suggest the criteria to evaluate it, and let the reader decide. For this reason, our answers herein to Reviewer 1 are designed to explain our excitement about and interpretation of our data. But, conceding to the reviewer’s preference, the arguably over-enthusiastic language of multiple published papers in the Ras field (for example, characterizing previous 5-to 7-fold binding improvements as “superbinders” — their term — a phenomenon we discuss below on pages 10-11) is not propagated to the revised manuscript. We do feel that the intense previous search for mutations to improve Ras:Raf binding was motivated by the biomedical significance of understanding this PPI. Moreover, it is surprising to us that this search, spanning multiple papers and techniques (ranging from experimental to computational), did not discover our designed variant. This suggests that it was not an easy mutant to find, despite a great deal of previous research. Altogether, we feel this background literature strengthens the case for publication of our manuscript, since it documents that affinity

The spread of plasmid borne resistance enzymes in clinical Staphylococcus aureus isolates is rendering trimethoprim and iclaprim, both inhibitors of dihydrofolate reductase (DHFR), ineffective. Continued exploitation of these targets will require compounds that can broadly inhibit these resistance-conferring isoforms. Using a structure-based approach, we have developed a novel class of ionized nonclassical antifolates (INCAs) that capture the molecular interactions that have been exclusive to classical antifolates. These modifications allow for a greatly expanded spectrum of activity across these pathogenic DHFR isoforms, while maintaining the ability to penetrate the bacterial cell wall. Using biochemical, structural, and computational methods, we are able to optimize these inhibitors to the conserved active sites of the endogenous and trimethoprim resistant DHFR enzymes. Here, we report a series of INCA compounds that exhibit low nanomolar enzymatic activity and potent cellular activity with human selectivity against a panel of clinically relevant TMP resistant (TMPR) and methicillin resistant Staphylococcus aureus (MRSA) isolates.

KRas is a small GTPase commonly implicated in several difficult-to-treat cancers such as pancreatic ductal adenocarcinoma (PDAC). KRas normally cycles between an active, GTP-bound form and an inactive, GDP-bound form. Active KRas functions by forming protein-protein interactions (PPIs) with multiple effector proteins in order to regulate various important signal transduction pathways. However, when KRas is mutated it is constitutively active which leads to signal transduction pathway dysregulation that subsequently increases and sustains tumorigenicity and invasiveness. KRas has long been considered an "undruggable" target due to its picomolar affinity for its substrate. However, blocking the PPIs between KRas and its effectors eliminates harmful downstream effects. The tightest known binder of KRas is c-Raf, an enzyme in the ERK1/2 pathway. The Ras-binding domain (RBD) is the minimal binding domain of c-Raf that selectively binds to active, GTP-bound KRas. Previous work has measured Kd for various mutations in the KRas/c-Raf-RBD interface [1, 2]. We use OSPREY [3] (Open Source Protein REdesign for You), a state-of-the-art software package for computational structure-based protein design (CSPD), along with K∗ [4], an algorithm that estimates the binding constant for a given protein complex, to computationally predict the effect of these mutations. We compared our computational predictions to the experimental measurements and found that we can accurately predict the effect of these mutations. These results validate the accuracy of CSPD with OSPREY to target protein-protein interfaces and give us confidence that we can accurately redesign the KRas/c-Raf-RBD interface for future work towards targeting "undruggable" proteins. [1] M. Fridman, et al. Journal of Biological Chemistry, 275(39):30363-30371, 2000. [2] C. Kiel, et al. Journal of Biological Chemistry, 284(46):31893-31902, 2009. [3] P. Gainza, et al. Methods in enzymology, 523:87-107, 2013. [4] I. Georgiev, et al. Journal of computational chemistry, 29(10):1527-1542, 2008.

We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein–protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open‐source software. © 2018 Wiley Periodicals, Inc.