Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

Inactivation of cell-free HIV-1 by designing potent peptides based on mutations in the CD4 binding site

Human immunodeficiency virus type 1 (HIV-1) is a major global health problem, with over 38 million people infected worldwide. Current anti-HIV-1 drugs are limited in their ability to prevent the virus from replicating inside host cells, making them less effective as preventive measures. In contrast, viral inhibitors that inactivate the virus before it can bind to a host cell have great potential as drugs. In this study, we aimed to design mutant peptides that could block the interaction between gp120 and the CD4 receptor on host cells, thus preventing HIV-1 infection. We designed a 20-amino-acid peptide that mimicked the amino acids of the CD4 binding site and docked it to gp120. Molecular dynamics simulations were performed to calculate the energy of MMPBSA (Poisson-Boltzmann Surface Area) for each residue of the peptide, and unfavorable energy residues were identified as potential mutation points. Using MAESTRO (Multi AgEnt STability pRedictiOn), we measured ΔΔG (change in the change in Gibbs free energy) for mutations and generated a library of 240 mutated peptides using OSPREY software. The peptides were then screened for allergenicity and binding affinity. Finally, molecular dynamics simulations (via GROMACS 2020.2) and control docking (via HADDOCK 2.4) were used to evaluate the ability of four selected peptides to inhibit HIV-1 infection. Three peptides, P3 (AHRQIRQWFLTRGPNRSLWQ), P4 (VHRQIRQWFLTRGPNRSLWQ), and P9 (AHRQIRQMFLTRGPNRSLWQ), showed practical and potential as HIV inhibitors, based on their binding affinity and ability to inhibit infection. These peptides have the ability to inactivate the virus before it can bind to a host cell, thus representing a promising approach to HIV-1 prevention. Our findings suggest that mutant peptides designed to block the interaction between gp120 and the CD4 receptor have potential as HIV-1 inhibitors. These peptides could be used as preventive measures against HIV-1 transmission, and further research is needed to evaluate their safety and efficacy in clinical settings.

DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

Redesigning methionyl-tRNA synthetase for β-methionine activity with adaptive landscape flattening and experiments

Amino acids (AAs) with a noncanonical backbone would be a valuable tool for protein engineering, enabling new structural motifs and building blocks. To incorporate them into an expanded genetic code, the first, key step is to obtain an appropriate aminoacyl-tRNA synthetase. Currently, directed evolution is not available to optimize AAs with noncanonical backbones, since an appropriate selective pressure has not been discovered. Computational protein design (CPD) is an alternative. We used a new CPD method to redesign MetRS and increase its activity towards β-Met, which has an extra backbone methylene. The new method considered a few active site positions for design and used a Monte Carlo exploration of the corresponding sequence space. During the exploration, a bias energy was adaptively learned, such that the free energy landscape of the apo enzyme was flattened. Enzyme variants could then be sampled, in the presence of the ligand and the bias energy, according to their β-Met binding affinities. Eighteen predicted variants were chosen for experimental testing; 10 exhibited detectable activity for β-Met adenylation. Top predicted hits were characterized experimentally in detail. Dissociation constants, catalytic rates, and Michaelis constants for both α-Met and β-Met were measured. The best mutant retained a preference for α-Met over β-Met; however, the preference was reduced, compared to the wildtype, by a factor of 29. For this mutant, high resolution crystal structures were obtained in complex with both α-Met and β-Met, indicating that the predicted, active conformation of β-Met in the active site was retained.

Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design

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.

Protocol for predicting drug-resistant protein mutations to an ERK2 inhibitor using RESISTOR

Prospective predictions of drug-resistant protein mutants could improve the design of therapeutics less prone to resistance. Here, we describe RESISTOR, an algorithm that uses structure- and sequence-based criteria to predict resistance mutations. We demonstrate the process of using RESISTOR to predict ERK2 mutants likely to arise in melanoma ablating the efficacy of the ERK1/2 inhibitor SCH779284. RESISTOR is included in the free and open-source computational protein design software OSPREY. For complete details on the use and execution of this protocol, please refer to Guerin et al..1.

Enzyme redesign and genetic code expansion

Enzyme design is an important application of computational protein design (CPD). It can benefit enormously from the additional chemistries provided by noncanonical amino acids (ncAAs). These can be incorporated into an 'expanded' genetic code, and introduced in vivo into target proteins. The key step for genetic code expansion is to engineer an aminoacyl-transfer RNA (tRNA) synthetase (aaRS) and an associated tRNA that handles the ncAA. Experimental directed evolution has been successfully used to engineer aaRSs and incorporate over 200 ncAAs into expanded codes. But directed evolution has severe limits, and is not yet applicable to noncanonical AA backbones. CPD can help address several of its limitations, and has begun to be applied to this problem. We review efforts to redesign aaRSs, studies that designed new proteins and functionalities with the help of ncAAs, and some of the method developments that have been used, such as adaptive landscape flattening Monte Carlo, which allows an enzyme to be redesigned with substrate or transition state binding as the design target.

Advances in Rational Protein Engineering toward Functional Architectures and Their Applications in Food Science

Protein biomolecules including enzymes, cagelike proteins, and specific peptides have been continuously exploited as functional biomaterials applied in catalysis, nutrient delivery, and food preservation in food-related areas. However, natural proteins usually function well in physiological conditions, not industrial conditions, or may possess undesirable physical and chemical properties. Currently, rational protein design as a valuable technology has attracted extensive attention for the rational engineering or fabrication of ideal protein biomaterials with novel properties and functionality. This article starts with the underlying knowledge of protein folding and assembly and is followed by the introduction of the principles and strategies for rational protein design. Basic strategies for rational protein engineering involving experienced protein tailoring, computational prediction, computation redesign, and de novo protein design are summarized. Then, we focus on the recent progress of rational protein engineering or design in the application of food science, and a comprehensive summary ranging from enzyme manufacturing to cagelike protein nanocarriers engineering and antimicrobial peptides preparation is given. Overall, this review highlights the importance of rational protein engineering in food biomaterial preparation which could be beneficial for food science.

Mining the Protein Data Bank to improve prediction of changes in protein-protein binding

Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each. The method increases precision and Positive Predictive Value, and decreases Root Mean Square Error, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

Breakthroughs in computational design methods open up new frontiers for de novo protein engineering

Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.