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Sun Q, Wang H, Xie J, Wang L, Mu J, Li J, Ren Y, Lai L. Computer-Aided Drug Discovery for Undruggable Targets. Chem Rev 2025. [PMID: 40423592 DOI: 10.1021/acs.chemrev.4c00969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2025]
Abstract
Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.
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Affiliation(s)
- Qi Sun
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, Sichuan 610213, China
| | - Hanping Wang
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Juan Xie
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Liying Wang
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Junxi Mu
- Peking-Tsinghua Center for Life Science, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Junren Li
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yuhao Ren
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Luhua Lai
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Science, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, Sichuan 610213, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences, Peking University, Beijing 100871, China
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Ikizawa S, Hori T, Wijaya TN, Kono H, Bai Z, Kimizono T, Lu W, Tran DP, Kitao A. PaCS-Toolkit: Optimized Software Utilities for Parallel Cascade Selection Molecular Dynamics (PaCS-MD) Simulations and Subsequent Analyses. J Phys Chem B 2024; 128:3631-3642. [PMID: 38578072 PMCID: PMC11033871 DOI: 10.1021/acs.jpcb.4c01271] [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] [Received: 02/27/2024] [Revised: 03/26/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024]
Abstract
Parallel cascade selection molecular dynamics (PaCS-MD) is an enhanced conformational sampling method conducted as a "repetition of time leaps in parallel worlds", comprising cycles of multiple molecular dynamics (MD) simulations performed in parallel and selection of the initial structures of MDs for the next cycle. We developed PaCS-Toolkit, an optimized software utility enabling the use of different MD software and trajectory analysis tools to facilitate the execution of the PaCS-MD simulation and analyze the obtained trajectories, including the preparation for the subsequent construction of the Markov state model. PaCS-Toolkit is coded with Python, is compatible with various computing environments, and allows for easy customization by editing the configuration file and specifying the MD software and analysis tools to be used. We present the software design of PaCS-Toolkit and demonstrate applications of PaCS-MD variations: original targeted PaCS-MD to peptide folding; rmsdPaCS-MD to protein domain motion; and dissociation PaCS-MD to ligand dissociation from adenosine A2A receptor.
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Affiliation(s)
- Shinji Ikizawa
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Tatsuki Hori
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Tegar Nurwahyu Wijaya
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
- Department
of Chemistry, Universitas Pertamina, Jl. Teuku Nyak Arief, Simprug, Jakarta 12220, Indonesia
| | - Hiroshi Kono
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Zhen Bai
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Tatsuhiro Kimizono
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Wenbo Lu
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Duy Phuoc Tran
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
| | - Akio Kitao
- School
of Life Science and Technology, Tokyo Institute
of Technology, 2-12-2 Ookayama, Meguro, Tokyo 152-8550, Japan
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Maruyama Y, Igarashi R, Ushiku Y, Mitsutake A. Analysis of Protein Folding Simulation with Moving Root Mean Square Deviation. J Chem Inf Model 2023; 63:1529-1541. [PMID: 36821519 PMCID: PMC10015464 DOI: 10.1021/acs.jcim.2c01444] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
We apply moving root-mean-square deviation (mRMSD), which does not require a reference structure, as a method for analyzing protein dynamics. This method can be used to calculate the root-mean-square deviation (RMSD) of structure between two specified time points and to analyze protein dynamics behavior through time series analysis. We applied this method to the Trp-cage trajectory calculated by the Anton supercomputer and found that it shows regions of stable states as well as the conventional RMSD. In addition, we extracted a characteristic structure in which the side chains of Asp1 and Arg16 form hydrogen bonds near the most stable structure of the Trp-cage. We also determined that ≥20 ns is an appropriate time interval to investigate protein dynamics using mRMSD. Applying this method to NuG2 protein, we found that mRMSD can be used to detect regions of metastable states in addition to the stable state. This method can be applied to molecular dynamics simulations of proteins whose stable structures are unknown.
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Affiliation(s)
- Yutaka Maruyama
- OMRON SINIC X Corporation, Tokyo 113-0033, Japan.,Department of Physics, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan
| | - Ryo Igarashi
- OMRON SINIC X Corporation, Tokyo 113-0033, Japan
| | | | - Ayori Mitsutake
- Department of Physics, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan
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Yasuda T, Morita R, Shigeta Y, Harada R. Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins. J Chem Inf Model 2022; 62:3442-3452. [PMID: 35786886 DOI: 10.1021/acs.jcim.2c00573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.
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Affiliation(s)
- Takunori Yasuda
- College of Biological Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-0821, Japan
| | - Rikuri Morita
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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