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Zhang Z, Shen WX, Liu Q, Zitnik M. Efficient Generation of Protein Pockets with PocketGen. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581968. [PMID: 38464121 PMCID: PMC10925136 DOI: 10.1101/2024.02.25.581968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Designing protein-binding proteins is critical for drug discovery. However, the AI-based design of such proteins is challenging due to the complexity of ligand-protein interactions, the flexibility of ligand molecules and amino acid side chains, and sequence-structure dependencies. We introduce PocketGen, a deep generative model that simultaneously produces both the residue sequence and atomic structure of the protein regions where ligand interactions occur. PocketGen ensures consistency between sequence and structure by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The bilevel graph transformer captures interactions at multiple scales, including atom, residue, and ligand levels. To enhance sequence refinement, PocketGen integrates a structural adapter into the protein language model, ensuring that structure-based predictions align with sequence-based predictions. PocketGen can generate high-fidelity protein pockets with superior binding affinity and structural validity. It operates ten times faster than physics-based methods and achieves a 95% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets. Additionally, it attains an amino acid recovery rate exceeding 64%.
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Affiliation(s)
- Zaixi Zhang
- State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Wan Xiang Shen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Qi Liu
- State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
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Zhang H, Liu Y, Liu X, Wang C, Guo M. Equivariant score-based generative diffusion framework for 3D molecules. BMC Bioinformatics 2024; 25:203. [PMID: 38816718 DOI: 10.1186/s12859-024-05810-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Molecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing generative methods with deep learning technology offers an effective approach to streamline the search space for molecular design and save costs. This paper introduces a novel E(3)-equivariant score-based diffusion framework for 3D molecular generation via SDEs, aiming to address the constraints of unified Gaussian diffusion methods. Within the proposed framework EMDS, the complete diffusion is decomposed into separate diffusion processes for distinct components of the molecular feature space, while the modeling processes also capture the complex dependency among these components. Moreover, angle and torsion angle information is integrated into the networks to enhance the modeling of atom coordinates and utilize spatial information more effectively. RESULTS Experiments on the widely utilized QM9 dataset demonstrate that our proposed framework significantly outperforms the state-of-the-art methods in all evaluation metrics for 3D molecular generation. Additionally, ablation experiments are conducted to highlight the contribution of key components in our framework, demonstrating the effectiveness of the proposed framework and the performance improvements of incorporating angle and torsion angle information for molecular generation. Finally, the comparative results of distribution show that our method is highly effective in generating molecules that closely resemble the actual scenario. CONCLUSION Through the experiments and comparative results, our framework clearly outperforms previous 3D molecular generation methods, exhibiting significantly better capacity for modeling chemically realistic molecules. The excellent performance of EMDS in 3D molecular generation brings novel and encouraging opportunities for tackling challenging biomedical molecule and protein scenarios.
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Affiliation(s)
- Hao Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yang Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Cheng Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China
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Zhang R, Yuan R, Tian B. PointGAT: A Quantum Chemical Property Prediction Model Integrating Graph Attention and 3D Geometry. J Chem Theory Comput 2024; 20:4115-4128. [PMID: 38727259 DOI: 10.1021/acs.jctc.3c01420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Predicting quantum chemical properties is a fundamental challenge for computational chemistry. While the development of graph neural networks has advanced molecular representation learning and property prediction, their performance could be further enhanced by incorporating three-dimensional (3D) structural geometry into two-dimensional (2D) molecular graph representation. In this study, we introduce the PointGAT model for quantum molecular property prediction, which integrates 3D molecular coordinates with graph-attention modeling. Comparison with other current models in molecular prediction tasks showed that PointGAT could provide higher predictive accuracy in various benchmark data sets from MoleculeNet, including ESOL, FreeSolv, Lipop, HIV, and 6 out of 12 tasks of the QM9 data set. To further examine PointGAT prediction of quantum mechanical (QM) energies, we constructed a C10 data set comprising 11,841 charged and chiral carbocation intermediates with QM energies calculated at the DM21/6-31G*//B3LYP/6-31G* levels. Notably, PointGAT achieved an R2 value of 0.950 and an MAE of 1.616 kcal/mol, outperforming even the best-performing graph neural network model with a reduction of 0.216 kcal/mol in MAE and an improvement of 0.050 in R2. Additional ablation studies indicated that incorporating molecular geometry into the model resulted in markedly higher predictive accuracy, reducing the MAE value from 1.802 to 1.616 kcal/mol. Moreover, visualization of PointGAT atomic attention weights suggested its predictions were interpretable. Findings in this study support the application of PointGAT as a powerful and versatile tool for quantum chemical property prediction that can facilitate high-accuracy modeling for fundamental exploration of chemical space as well as drug design and molecular engineering.
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Affiliation(s)
- Rong Zhang
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Rongqing Yuan
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Boxue Tian
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
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Ju W, Fang Z, Gu Y, Liu Z, Long Q, Qiao Z, Qin Y, Shen J, Sun F, Xiao Z, Yang J, Yuan J, Zhao Y, Wang Y, Luo X, Zhang M. A Comprehensive Survey on Deep Graph Representation Learning. Neural Netw 2024; 173:106207. [PMID: 38442651 DOI: 10.1016/j.neunet.2024.106207] [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: 08/28/2023] [Revised: 01/23/2024] [Accepted: 02/21/2024] [Indexed: 03/07/2024]
Abstract
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future.
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Affiliation(s)
- Wei Ju
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Zheng Fang
- School of Intelligence Science and Technology, Peking University, Beijing, 100871, China
| | - Yiyang Gu
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Zequn Liu
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Qingqing Long
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100086, China
| | - Ziyue Qiao
- Artificial Intelligence Thrust, The Hong Kong University of Science and Technology, Guangzhou, 511453, China
| | - Yifang Qin
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Jianhao Shen
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Fang Sun
- Department of Computer Science, University of California, Los Angeles, 90095, USA
| | - Zhiping Xiao
- Department of Computer Science, University of California, Los Angeles, 90095, USA
| | - Junwei Yang
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Jingyang Yuan
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Yusheng Zhao
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China
| | - Yifan Wang
- School of Information Technology & Management, University of International Business and Economics, Beijing, 100029, China
| | - Xiao Luo
- Department of Computer Science, University of California, Los Angeles, 90095, USA.
| | - Ming Zhang
- School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China.
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Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
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Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Du H, Jiang D, Zhang O, Wu Z, Gao J, Zhang X, Wang X, Deng Y, Kang Y, Li D, Pan P, Hsieh CY, Hou T. A flexible data-free framework for structure-based de novo drug design with reinforcement learning. Chem Sci 2023; 14:12166-12181. [PMID: 37969589 PMCID: PMC10631243 DOI: 10.1039/d3sc04091g] [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: 08/06/2023] [Accepted: 10/11/2023] [Indexed: 11/17/2023] Open
Abstract
Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.
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Affiliation(s)
- Hongyan Du
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dejun Jiang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Odin Zhang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Zhenxing Wu
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Junbo Gao
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xujun Zhang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Xiaorui Wang
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology Macao 999078 China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
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