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Yokomine M, Morimoto J, Fukuda Y, Ueda T, Takeuchi K, Umezawa K, Ago H, Matsuura H, Ueno G, Senoo A, Nagatoishi S, Tsumoto K, Sando S. A high-resolution structural characterization and physicochemical study of how a peptoid binds to an oncoprotein MDM2. Chem Sci 2024; 15:7051-7060. [PMID: 38756815 PMCID: PMC11095393 DOI: 10.1039/d4sc01540a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Peptoids are a promising drug modality targeting disease-related proteins, but how a peptoid engages in protein binding is poorly understood. This is primarily due to a lack of high-resolution peptoid-protein complex structures and systematic physicochemical studies. Here, we present the first crystal structure of a peptoid bound to a protein, providing high-resolution structural information about how a peptoid binds to a protein. We previously reported a rigid peptoid, oligo(N-substituted alanine) (oligo-NSA), and developed an oligo-NSA-type peptoid that binds to MDM2. X-ray crystallographic analysis of the peptoid bound to MDM2 showed that the peptoid recognizes the MDM2 surface predominantly through the interaction of the N-substituents, while the main chain acts as a scaffold. Additionally, conformational, thermodynamic, and kinetic analysis of the peptoid and its derivatives with a less rigid main chain revealed that rigidification of the peptoid main chain contributes to improving the protein binding affinity. This improvement is thermodynamically attributed to an increased magnitude of the binding enthalpy change, and kinetically to an increased association rate and decreased dissociation rate. This study provides invaluable insights into the design of protein-targeting peptoids.
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Affiliation(s)
- Marin Yokomine
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
| | - Jumpei Morimoto
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
| | - Yasuhiro Fukuda
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
| | - Takumi Ueda
- Graduate School of Pharmaceutical Sciences, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan
| | - Koh Takeuchi
- Graduate School of Pharmaceutical Sciences, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan
| | - Koji Umezawa
- Department of Biomedical Engineering, Graduate School of Science and Technology, Shinshu University 8304 Minami-Minowa, Kami-Ina Nagano 399-4598 Japan
- Department of Biomolecular Innovation, Institute for Biomedical Sciences, Shinshu University 8304 Minami-Minowa, Kami-Ina Nagano 399-4598 Japan
| | - Hideo Ago
- RIKEN SPring-8 Center 1-1-1 Kouto Sayo Hyogo 679-5148 Japan
| | | | - Go Ueno
- RIKEN SPring-8 Center 1-1-1 Kouto Sayo Hyogo 679-5148 Japan
| | - Akinobu Senoo
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
| | - Satoru Nagatoishi
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
| | - Kouhei Tsumoto
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
- Institute of Medical Science, The University of Tokyo 4-6-1 Shirokanedai Minato-ku Tokyo 108-8639 Japan
| | - Shinsuke Sando
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan
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Raddi RM, Voelz VA. Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding. J Phys Chem B 2023; 127:10682-10690. [PMID: 38078851 DOI: 10.1021/acs.jpcb.3c04775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
In this work, we investigate the role of solvent in the binding reaction of the p53 transactivation domain (TAD) peptide to its receptor MDM2. Previously, our group generated 831 μs of explicit-solvent aggregate molecular simulation trajectory data for the MDM2-p53 peptide binding reaction using large-scale distributed computing and subsequently built a Markov State Model (MSM) of the binding reaction (Zhou et al. 2017). Here, we perform a tICA analysis and construct an MSM with similar hyperparameters while using only solvent-based structural features. We find a remarkably similar landscape but accelerated implied timescales for the slowest motions. The solvent shells contributing most to the first tICA eigenvector are those centered on Lys24 and Thr18 of the p53 TAD peptide in the range of 3-6 Å. Important solvent shells were visualized to reveal solvation and desolvation transitions along the peptide-protein binding trajectories. Our results provide a solvent-centric view of the hydrophobic effect in action for a realistic peptide-protein binding scenario.
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Affiliation(s)
- Robert M Raddi
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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Hui T, Descoteaux ML, Miao J, Lin YS. Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles. J Chem Theory Comput 2023. [PMID: 37236147 PMCID: PMC10373485 DOI: 10.1021/acs.jctc.3c00154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R2 = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R2 = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R2 = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R2 = 0.97 and R2 = 0.91 for cyclic pentapeptides and hexapeptides, respectively.
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Affiliation(s)
- Tiffani Hui
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Marc L Descoteaux
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Jiayuan Miao
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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Spiriti J, Noé F, Wong CF. Simulation of ligand dissociation kinetics from the protein kinase PYK2. J Comput Chem 2022; 43:1911-1922. [PMID: 36073605 PMCID: PMC9976590 DOI: 10.1002/jcc.26991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/11/2022] [Accepted: 08/03/2022] [Indexed: 11/09/2022]
Abstract
Early-stage drug discovery projects often focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. The kinetics of drug binding are ignored but can have significant influence on drug efficacy. Therefore, increasing attention has been paid on evaluating drug-binding kinetics early in a drug discovery process. Simulating drug-binding kinetics at the atomic level is challenging for the long time scale involved. Here, we used the transition-based reweighting analysis method (TRAM) with the Markov state model to study the dissociation of a ligand from the protein kinase PYK2. TRAM combines biased and unbiased simulations to reduce computational costs. This work used the umbrella sampling technique for the biased simulations. Although using the potential of mean force from umbrella sampling simulations with the transition-state theory over-estimated the dissociation rate by three orders of magnitude, TRAM gave a dissociation rate within an order of magnitude of the experimental value.
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Affiliation(s)
- Justin Spiriti
- Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, USA
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany,Department of Physics, Freie Universität Berlin, Berlin, Germany
| | - Chung F. Wong
- Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, USA
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Ge Y, Voelz VA. Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling. J Chem Phys 2022; 156:134115. [PMID: 35395889 PMCID: PMC8993428 DOI: 10.1063/5.0088024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ϵ parameter was tuned, and the system was placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 µs of unbiased simulation, and an 18-state Markov state model was used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional Markov state models, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal and that in most cases, only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.
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Affiliation(s)
- Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, USA
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, USA
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Voelz VA, Ge Y, Raddi RM. Reconciling Simulations and Experiments With BICePs: A Review. Front Mol Biosci 2021; 8:661520. [PMID: 34046431 PMCID: PMC8144449 DOI: 10.3389/fmolb.2021.661520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/12/2021] [Indexed: 02/04/2023] Open
Abstract
Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The Bayesian framework of BICePs enables population reweighting as a post-simulation processing step, with several advantages over existing methods, including the proper use of reference potentials, and the estimation of a Bayes factor-like quantity called the BICePs score for model selection. Here, we summarize the theory underlying this method in context with related algorithms, review the history of BICePs applications to date, and discuss current shortcomings along with future plans for improvement.
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Affiliation(s)
- Vincent A. Voelz
- Department of Chemistry, Temple University, Philadelphia, PA, United States
| | - Yunhui Ge
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, United States
| | - Robert M. Raddi
- Department of Chemistry, Temple University, Philadelphia, PA, United States
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