1
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Opuu V, Nigro G, Lazennec‐Schurdevin C, Mechulam Y, Schmitt E, Simonson T. Redesigning methionyl-tRNA synthetase for β-methionine activity with adaptive landscape flattening and experiments. Protein Sci 2023; 32:e4738. [PMID: 37518893 PMCID: PMC10451022 DOI: 10.1002/pro.4738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 08/01/2023]
Abstract
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.
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Affiliation(s)
- Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Giuliano Nigro
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Christine Lazennec‐Schurdevin
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Yves Mechulam
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Emmanuelle Schmitt
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole PolytechniqueInstitut Polytechnique de ParisPalaiseauFrance
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2
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Opuu V, Simonson T. Enzyme redesign and genetic code expansion. Protein Eng Des Sel 2023; 36:gzad017. [PMID: 37879093 DOI: 10.1093/protein/gzad017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/10/2023] [Accepted: 09/19/2023] [Indexed: 10/27/2023] Open
Abstract
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.
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Affiliation(s)
- Vaitea Opuu
- Institut Chimie Biologie Innovation (CNRS UMR8231), Ecole Supérieure de Physique et Chimie de Paris (ESPCI), 75005 Paris, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
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3
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Michael E, Saint-Jalme R, Mignon D, Simonson T. Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance. Front Mol Biosci 2023; 9:905588. [PMID: 36699702 PMCID: PMC9868620 DOI: 10.3389/fmolb.2022.905588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
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.
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4
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Chen X, Briozzo P, Machover D, Simonson T. A Computational Model for the PLP-Dependent Enzyme Methionine γ-Lyase. Front Mol Biosci 2022; 9:886358. [PMID: 35558556 PMCID: PMC9087591 DOI: 10.3389/fmolb.2022.886358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Pyridoxal-5′-phosphate (PLP) is a cofactor in the reactions of over 160 enzymes, several of which are implicated in diseases. Methionine γ-lyase (MGL) is of interest as a therapeutic protein for cancer treatment. It binds PLP covalently through a Schiff base linkage and digests methionine, whose depletion is damaging for cancer cells but not normal cells. To improve MGL activity, it is important to understand and engineer its PLP binding. We develop a simulation model for MGL, starting with force field parameters for PLP in four main states: two phosphate protonation states and two tautomeric states, keto or enol for the Schiff base moiety. We used the force field to simulate MGL complexes with each form, and showed that those with a fully-deprotonated PLP phosphate, especially keto, led to the best agreement with MGL structures in the PDB. We then confirmed this result through alchemical free energy simulations that compared the keto and enol forms, confirming a moderate keto preference, and the fully-deprotonated and singly-protonated phosphate forms. Extensive simulations were needed to adequately sample conformational space, and care was needed to extrapolate the protonation free energy to the thermodynamic limit of a macroscopic, dilute protein solution. The computed phosphate pKa was 5.7, confirming that the deprotonated, −2 form is predominant. The PLP force field and the simulation methods can be applied to all PLP enzymes and used, as here, to reveal fine details of structure and dynamics in the active site.
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Affiliation(s)
- Xingyu Chen
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Pierre Briozzo
- Institut Jean-Pierre Bourgin, INRAE-AgroParisTech, University Paris-Saclay, Paris, France
| | - David Machover
- INSERM U935-UA09, University Paris-Saclay, Hôpital Paul-Brousse, Paris, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
- *Correspondence: Thomas Simonson,
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5
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A computational protein design protocol for optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2. STAR Protoc 2022; 3:101254. [PMID: 35310078 PMCID: PMC8890969 DOI: 10.1016/j.xpro.2022.101254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
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). SARS-CoV-2 positions 455, 493, 494, and 501 at the interface with hACE2 are designed The design uses Proteus, a high-throughput computational protein design program A physics-based energy function ranks sequences and conformations An adaptive Monte Carlo protocol promotes the selection of good affinity sequences
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6
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Bouchiba Y, Ruffini M, Schiex T, Barbe S. Computational Design of Miniprotein Binders. Methods Mol Biol 2022; 2405:361-382. [PMID: 35298822 DOI: 10.1007/978-1-0716-1855-4_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.
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Affiliation(s)
- Younes Bouchiba
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France
| | - Manon Ruffini
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, Toulouse, France
| | - Thomas Schiex
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, Toulouse, France
| | - Sophie Barbe
- TBI, Université de Toulouse, CNRS, INRAE, INSA, ANITI, Toulouse, France.
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7
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Opuu V, Mignon D, Simonson T. Knowledge-Based Unfolded State Model for Protein Design. Methods Mol Biol 2022; 2405:403-424. [PMID: 35298824 DOI: 10.1007/978-1-0716-1855-4_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - David Mignon
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France.
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8
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Saikia B, Gogoi CR, Rahman A, Baruah A. Identification of an optimal foldability criterion to design misfolding resistant protein. J Chem Phys 2021; 155:144102. [PMID: 34654294 DOI: 10.1063/5.0057533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Proteins achieve their functional, active, and operative three dimensional native structures by overcoming the possibility of being trapped in non-native energy minima present in the energy landscape. The enormous and intricate interactions that play an important role in protein folding also determine the stability of the proteins. The large number of stabilizing/destabilizing interactions makes proteins to be only marginally stable as compared to the other competing structures. Therefore, there are some possibilities that they become trapped in the non-native conformations and thus get misfolded. These misfolded proteins lead to several debilitating diseases. This work performs a comparative study of some existing foldability criteria in the computational design of misfold resistant protein sequences based on self-consistent mean field theory. The foldability criteria selected for this study are Ef, Δ, and Φ that are commonly used in protein design procedures to determine the most efficient foldability criterion for the design of misfolding resistant proteins. The results suggest that the foldability criterion Δ is significantly better in designing a funnel energy landscape stabilizing the target state. The results also suggest that inclusion of negative design features is important for designing misfolding resistant proteins, but more information about the non-native conformations in terms of Φ leads to worse results compared to even simple positive design. The sequences designed using Δ show better resistance to misfolding in the Monte Carlo simulations performed in the study.
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Affiliation(s)
- Bondeepa Saikia
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
| | - Chimi Rekha Gogoi
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
| | - Aziza Rahman
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
| | - Anupaul Baruah
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
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9
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Polydorides S, Archontis G. Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2. Biophys J 2021; 120:2859-2871. [PMID: 33984310 PMCID: PMC8110322 DOI: 10.1016/j.bpj.2021.02.049] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/19/2021] [Accepted: 02/15/2021] [Indexed: 01/15/2023] Open
Abstract
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.
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10
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Corrigan RA, Qi G, Thiel AC, Lynn JR, Walker BD, Casavant TL, Lagardere L, Piquemal JP, Ponder JW, Ren P, Schnieders MJ. Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field. J Chem Theory Comput 2021; 17:2323-2341. [PMID: 33769814 DOI: 10.1021/acs.jctc.0c01286] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Computational protein design, ab initio protein/RNA folding, and protein-ligand screening can be too computationally demanding for explicit treatment of solvent. For these applications, implicit solvent offers a compelling alternative, which we describe here for the polarizable atomic multipole AMOEBA force field based on three treatments of continuum electrostatics: numerical solutions to the nonlinear and linearized versions of the Poisson-Boltzmann equation (PBE), the domain-decomposition conductor-like screening model (ddCOSMO) approximation to the PBE, and the analytic generalized Kirkwood (GK) approximation. The continuum electrostatics models are combined with a nonpolar estimator based on novel cavitation and dispersion terms. Electrostatic model parameters are numerically optimized using a least-squares style target function based on a library of 103 small-molecule solvation free energy differences. Mean signed errors for the adaptive Poisson-Boltzmann solver (APBS), ddCOSMO, and GK models are 0.05, 0.00, and 0.00 kcal/mol, respectively, while the mean unsigned errors are 0.70, 0.63, and 0.58 kcal/mol, respectively. Validation of the electrostatic response of the resulting implicit solvents, which are available in the Tinker (or Tinker-HP), OpenMM, and Force Field X software packages, is based on comparisons to explicit solvent simulations for a series of proteins and nucleic acids. Overall, the emergence of performative implicit solvent models for polarizable force fields opens the door to their use for folding and design applications.
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Affiliation(s)
- Rae A Corrigan
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa 52242, United States
| | - Guowei Qi
- Department of Biochemistry, University of Iowa, Iowa City, Iowa 52242, United States
| | - Andrew C Thiel
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa 52242, United States
| | - Jack R Lynn
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa 52242, United States
| | - Brandon D Walker
- Department of Biomedical Engineering, University of Texas in Austin, Austin, Texas 78712, United States
| | - Thomas L Casavant
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa 52242, United States
| | - Louis Lagardere
- Department of Chemistry, Sorbonne Université, F-75005 Paris, France
| | | | - Jay W Ponder
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Pengyu Ren
- Department of Biomedical Engineering, University of Texas in Austin, Austin, Texas 78712, United States
| | - Michael J Schnieders
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa 52242, United States.,Department of Biochemistry, University of Iowa, Iowa City, Iowa 52242, United States
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11
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Michael E, Polydorides S, Simonson T, Archontis G. Hybrid MC/MD for protein design. J Chem Phys 2021; 153:054113. [PMID: 32770896 DOI: 10.1063/5.0013320] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
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 pKa'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 pKa'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.
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Affiliation(s)
- Eleni Michael
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
| | - Savvas Polydorides
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Georgios Archontis
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
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12
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Abstract
This chapter describes two computational methods for PDZ-peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for conformational sampling with a Poisson-Boltzmann (PB) Linear Interaction Energy for scoring. A new CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative binding free energies. A detailed protocol is described based on the Proteus CPD software. The medium-throughput approach can be performed with standard MD and PB software, such as NAMD and Charmm. For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around 0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of parameter fitting before it can be applied, and its transferability to other protein families is still untested.
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Affiliation(s)
- Nicolas Panel
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Francesco Villa
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - David Mignon
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France.
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13
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Mignon D, Druart K, Michael E, Opuu V, Polydorides S, Villa F, Gaillard T, Panel N, Archontis G, Simonson T. Physics-Based Computational Protein Design: An Update. J Phys Chem A 2020; 124:10637-10648. [DOI: 10.1021/acs.jpca.0c07605] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- David Mignon
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Karen Druart
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Eleni Michael
- Department of Physics, University of Cyprus, PO20537, CY1678 Nicosia, Cyprus
| | - Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Savvas Polydorides
- Department of Physics, University of Cyprus, PO20537, CY1678 Nicosia, Cyprus
| | - Francesco Villa
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Thomas Gaillard
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Nicolas Panel
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
| | - Georgios Archontis
- Department of Physics, University of Cyprus, PO20537, CY1678 Nicosia, Cyprus
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128 Palaiseau, France
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14
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de Ruiter A, Oostenbrink C. Advances in the calculation of binding free energies. Curr Opin Struct Biol 2020; 61:207-212. [PMID: 32088376 DOI: 10.1016/j.sbi.2020.01.016] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 01/19/2023]
Abstract
In recent years, calculations of binding affinities from molecular simulations seem to have matured significantly. While the number of applications of such methods in drug design and biotechnology increases, the number of truly new methodological developments decreases. This review provides an overview of the current status of the field as reflected in recent publications. The focus is on the challenges that remain when using endstate, alchemical and pathway methods. For endstate methods this is the calculation of entropic contributions. For alchemical methods there are unsolved problems associated with the solvation of the active site, sampling slow degrees of freedom and when modifying the net charge. For pathway methods achieving sufficient sampling remains challenging. New trends are also highlighted, including the use of pathway methods for the quantification of protein-protein interactions.
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Affiliation(s)
- Anita de Ruiter
- Institute for Molecular Modeling and Simulation, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
| | - Chris Oostenbrink
- Institute for Molecular Modeling and Simulation, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria.
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Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power. PLoS Comput Biol 2020; 16:e1007600. [PMID: 31917825 PMCID: PMC7041857 DOI: 10.1371/journal.pcbi.1007600] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 02/25/2020] [Accepted: 12/11/2019] [Indexed: 01/30/2023] Open
Abstract
Designed enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are a complementary route. A designed enzyme should satisfy multiple criteria: stability, substrate binding, transition state binding. Such multi-objective design is computationally challenging. Two recent studies used adaptive importance sampling Monte Carlo to redesign proteins for ligand binding. By first flattening the energy landscape of the apo protein, they obtained positive design for the bound state and negative design for the unbound. We have now extended the method to design an enzyme for specific transition state binding, i.e., for its catalytic power. We considered methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. Previously, MetRS and other synthetases have been redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. Here, we have redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered by the design calculations, and 17 mutants predicted to bind MetAMP were characterized experimentally and all found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We suggest the present method should become the paradigm for computational enzyme design. Designed enzymes are of major interest. Experimental directed evolution still has significant limitations, and computational approaches are another route. Enzymes must be stable, bind substrates, and be powerful catalysts. It is challenging to design for all these properties. A method to design substrate binding was proposed recently. It used an adaptive Monte Carlo method to explore mutations of a few amino acids near the substrate. A bias energy was gradually “learned” such that, in the absence of the ligand, the simulation visited most of the possible protein mutations with comparable probabilities. Remarkably, a simulation of the protein:ligand complex, including the bias, will then preferentially sample tight-binding sequences. We generalized the method to design binding specificity. We tested it for the methionyl-tRNA synthetase enzyme, which has been engineered in order to expand the genetic code. We redesigned the enzyme to obtain variants with low activation free energies for the catalytic step. The variants proposed by the simulations were shown experimentally to be active, and the predicted activation free energies were in reasonable agreement with the experimental values. We expect the new method will become the paradigm for computational enzyme design.
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Toropova AP, Toropov AA. Application of the Monte Carlo Method for the Prediction of Behavior of Peptides. Curr Protein Pept Sci 2019; 20:1151-1157. [DOI: 10.2174/1389203720666190123163907] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 12/17/2018] [Accepted: 12/20/2018] [Indexed: 12/26/2022]
Abstract
Prediction of physicochemical and biochemical behavior of peptides is an important and attractive
task of the modern natural sciences, since these substances have a key role in life processes. The
Monte Carlo technique is a possible way to solve the above task. The Monte Carlo method is a tool with
different applications relative to the study of peptides: (i) analysis of the 3D configurations (conformers);
(ii) establishment of quantitative structure – property / activity relationships (QSPRs/QSARs); and (iii)
development of databases on the biopolymers. Current ideas related to application of the Monte Carlo
technique for studying peptides and biopolymers have been discussed in this review.
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Affiliation(s)
- Alla P. Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
| | - Andrey A. Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy
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Villa F, Simonson T. Protein pKa’s from Adaptive Landscape Flattening Instead of Constant-pH Simulations. J Chem Theory Comput 2018; 14:6714-6721. [DOI: 10.1021/acs.jctc.8b00970] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Francesco Villa
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
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Charpentier A, Mignon D, Barbe S, Cortes J, Schiex T, Simonson T, Allouche D. Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems. J Chem Inf Model 2018; 59:127-136. [DOI: 10.1021/acs.jcim.8b00510] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - David Mignon
- Laboratoire de Biochimie (CNRS UMR 7654), École Polytechnique, 91128 Palaiseau, France
| | - Sophie Barbe
- Laboratoire d’Ingénierie des Systèmes Biologiques et Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, 31077 Toulouse, France
| | - Juan Cortes
- LAAS-CNRS, Université de Toulouse, CNRS, 31400 Toulouse, France
| | - Thomas Schiex
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR 7654), École Polytechnique, 91128 Palaiseau, France
| | - David Allouche
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
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