Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power

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.

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.

Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance

In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme "vitality", defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or "redesigned" individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other.

A computational protein design protocol for optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

The present protocol describes the computational design of the SARS-CoV-2 receptor binding motif (RBD) to identify mutations that can potentially improve binding affinity for the human ACE2 (hACE2) receptor. We focus on four positions located at the interface with the hACE2 receptor in the RBD:hACE2 complex. We conduct the design with a high-throughput computational protein design (CPD) program, Proteus, incorporating an adaptive Monte Carlo (MC) protocol that promotes the selection of sequences with good binding affinities.

For complete details on the use and execution of this protocol, please refer to Polydorides and Archontis (2021).

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SARS-CoV-2 positions 455, 493, 494, and 501 at the interface with hACE2 are designed

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The design uses Proteus, a high-throughput computational protein design program

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A physics-based energy function ranks sequences and conformations

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An adaptive Monte Carlo protocol promotes the selection of good affinity sequences

Knowledge-Based Unfolded State Model for Protein Design

The design of proteins and miniproteins is an important challenge. Designed variants should be stable, meaning the folded/unfolded free energy difference should be large enough. Thus, the unfolded state plays a central role. An extended peptide model is often used, where side chains interact with solvent and nearby backbone, but not each other. The unfolded energy is then a function of sequence composition only and can be empirically parametrized. If the space of sequences is explored with a Monte Carlo procedure, protein variants will be sampled according to a well-defined Boltzmann probability distribution. We can then choose unfolded model parameters to maximize the probability of sampling native-like sequences. This leads to a well-defined maximum likelihood framework. We present an iterative algorithm that follows the likelihood gradient. The method is presented in the context of our Proteus software, as a detailed downloadable tutorial. The unfolded model is combined with a folded model that uses molecular mechanics and a Generalized Born solvent. It was optimized for three PDZ domains and then used to redesign them. The sequences sampled are native-like and similar to a recent PDZ design study that was experimentally validated.

Peptidyl transferase center decompaction and structural constraints during early protein elongation on the ribosome

Peptide bond formation on the ribosome requires that aminoacyl-tRNAs and peptidyl-tRNAs are properly positioned on the A site and the P site of the peptidyl transferase center (PTC) so that nucleophilic attack can occur. Here we analyse some constraints associated with the induced-fit mechanism of the PTC, that promotes this positioning through a compaction around the aminoacyl ester orchestrated by U2506. The physical basis of PTC decompaction, that allows the elongated peptidyl-tRNA to free itself from that state and move to the P site of the PTC, is still unclear. From thermodynamics considerations and an analysis of published ribosome structures, the present work highlights the rational of this mechanism, in which the free-energy released by the new peptide bond is used to kick U2506 away from the reaction center. Furthermore, we show the evidence that decompaction is impaired when the nascent peptide is not yet anchored inside the exit tunnel, which may contribute to explain why the first rounds of elongation are inefficient, an issue that has attracted much interest for about two decades. Results in this field are examined in the light of the present analysis and a physico-chemical correlation in the genetic code, which suggest that elementary constraints associated with the size of the side-chain of the amino acids penalize early elongation events.

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.

Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

The coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for the coronavirus disease 2019 pandemic, and the closely related SARS-CoV coronavirus enter cells by binding at the human angiotensin converting enzyme 2 (hACE2). The stronger hACE2 affinity of SARS-CoV-2 has been connected with its higher infectivity. In this work, we study hACE2 complexes with the receptor-binding domains (RBDs) of the human SARS-CoV-2 and human SARS-CoV viruses, using all-atom molecular dynamics simulations and computational protein design with a physics-based energy function. The molecular dynamics simulations identify charge-modifying substitutions between the CoV-2 and CoV RBDs, which either increase or decrease the hACE2 affinity of the SARS-CoV-2 RBD. The combined effect of these mutations is small, and the relative affinity is mainly determined by substitutions at residues in contact with hACE2. Many of these findings are in line and interpret recent experiments. Our computational protein design calculations redesign positions 455, 493, 494, and 501 of the SARS-CoV-2 receptor binding motif, which contact hACE2 in the complex and are important for ACE2 recognition. Sampling is enhanced by an adaptive importance sampling Monte Carlo method. Sequences with increased affinity replace CoV-2 glutamine by a negative residue at position 493; serine by a nonpolar or aromatic residue or an asparagine at position 494; and asparagine by valine or threonine at position 501. Substitutions at positions 455 and 501 have a smaller effect on affinity. Substitutions suggested by our design are seen in viral sequences encountered in other species, including bat and pangolin. Our results might be used to identify potential virus strains with higher human infectivity and assist in the design of peptide-based or peptidomimetic compounds with the potential to inhibit SARS-CoV-2 binding at hACE2.

Hybrid MC/MD for protein design

Computational protein design relies on simulations of a protein structure, where selected amino acids can mutate randomly, and mutations are selected to enhance a target property, such as stability. Often, the protein backbone is held fixed and its degrees of freedom are modeled implicitly to reduce the complexity of the conformational space. We present a hybrid method where short molecular dynamics (MD) segments are used to explore conformations and alternate with Monte Carlo (MC) moves that apply mutations to side chains. The backbone is fully flexible during MD. As a test, we computed side chain acid/base constants or pK_a's in five proteins. This problem can be considered a special case of protein design, with protonation/deprotonation playing the role of mutations. The solvent was modeled as a dielectric continuum. Due to cost, in each protein we allowed just one side chain position to change its protonation state and the other position to change its type or mutate. The pK_a's were computed with a standard method that scans a range of pH values and with a new method that uses adaptive landscape flattening (ALF) to sample all protonation states in a single simulation. The hybrid method gave notably better accuracy than standard, fixed-backbone MC. ALF decreased the computational cost a factor of 13.