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Clyde A. Ultrahigh Throughput Protein-Ligand Docking with Deep Learning. Methods Mol Biol 2022; 2390:301-319. [PMID: 34731475 DOI: 10.1007/978-1-0716-1787-8_13] [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: 06/13/2023]
Abstract
Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.
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Affiliation(s)
- Austin Clyde
- Department of Computer Science, University of Chicago, Chicago, IL, USA.
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
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2
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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3
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Matthew AN, Leidner F, Lockbaum GJ, Henes M, Zephyr J, Hou S, Desaboini NR, Timm J, Rusere LN, Ragland DA, Paulsen JL, Prachanronarong K, Soumana DI, Nalivaika EA, Yilmaz NK, Ali A, Schiffer CA. Drug Design Strategies to Avoid Resistance in Direct-Acting Antivirals and Beyond. Chem Rev 2021; 121:3238-3270. [PMID: 33410674 PMCID: PMC8126998 DOI: 10.1021/acs.chemrev.0c00648] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Drug resistance is prevalent across many diseases, rendering therapies ineffective with severe financial and health consequences. Rather than accepting resistance after the fact, proactive strategies need to be incorporated into the drug design and development process to minimize the impact of drug resistance. These strategies can be derived from our experience with viral disease targets where multiple generations of drugs had to be developed to combat resistance and avoid antiviral failure. Significant efforts including experimental and computational structural biology, medicinal chemistry, and machine learning have focused on understanding the mechanisms and structural basis of resistance against direct-acting antiviral (DAA) drugs. Integrated methods show promise for being predictive of resistance and potency. In this review, we give an overview of this research for human immunodeficiency virus type 1, hepatitis C virus, and influenza virus and the lessons learned from resistance mechanisms of DAAs. These lessons translate into rational strategies to avoid resistance in drug design, which can be generalized and applied beyond viral targets. While resistance may not be completely avoidable, rational drug design can and should incorporate strategies at the outset of drug development to decrease the prevalence of drug resistance.
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Affiliation(s)
- Ashley N. Matthew
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
- Virginia Commonwealth University
| | - Florian Leidner
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Gordon J. Lockbaum
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Mina Henes
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Jacqueto Zephyr
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Shurong Hou
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Nages Rao Desaboini
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Jennifer Timm
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
- Rutgers University
| | - Linah N. Rusere
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
- Raybow Pharmaceutical
| | - Debra A. Ragland
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
- University of North Carolina, Chapel Hill
| | - Janet L. Paulsen
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
- Schrodinger, Inc
| | - Kristina Prachanronarong
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
- Icahn School of Medicine at Mount Sinai
| | - Djade I. Soumana
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
- Cytiva
| | - Ellen A. Nalivaika
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Nese Kurt Yilmaz
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Akbar Ali
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
| | - Celia A Schiffer
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States
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Xia J, Wang Z, Huan Y, Xue W, Wang X, Wang Y, Liu Z, Hsieh JH, Zhang L, Wu S, Shen Z, Zhang H, Wang XS. Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists. J Chem Inf Model 2020; 60:1202-1214. [PMID: 32050066 DOI: 10.1021/acs.jcim.9b01030] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Farnesoid X receptor (FXR) agonists can reverse dysregulated bile acid metabolism, and thus, they are potential therapeutics to prevent and treat nonalcoholic fatty liver disease. The low success rate of FXR agonists' R&D and the side effects of clinical candidates such as obeticholic acid make it urgent to discover new chemotypes. Unfortunately, structure-based virtual screening (SBVS) that can speed up drug discovery has rarely been reported with success for FXR, which was likely hindered by the failure in addressing protein flexibility. To address this issue, we devised human FXR (hFXR)-specific ensemble learning models based on pose filters from 24 agonist-bound hFXR crystal structures and coupled them to traditional SBVS approaches of the FRED docking plus Chemgauss4 scoring function. It turned out that the hFXR-specific pose filter ensemble (PFE) was able to improve ligand enrichment significantly, which rendered 3RUT-based SBVS with its PFE the ideal approach for FXR agonist discovery. By screening of the Specs chemical library and in vitro FXR transactivation bioassay, we identified a new class of FXR agonists with compound XJ034 as the representative, which would have been missed if the PFE was not coupled. Following that, we performed in-depth biological studies which demonstrated that XJ034 resulted in a downtrend of intracellular triglyceride in vitro, significantly decreased the serum/liver TG in high fat diet-induced C57BL/6J obese mice, and more importantly, showed metabolic stabilities in both plasma and liver microsomes. To provide insight into further structure-based lead optimization, we solved the crystal structure of hFXR complexed with compound XJ034, uncovering a unique hydrogen bond between compound XJ034 and residue Y375. The current work highlights the power of our pose filter-based ensemble learning approach in terms of scaffold hopping and provides a promising lead compound for further development.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zhenyi Wang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.,Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment and SUSTech-HKU Joint Laboratories for Matrix Biology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yi Huan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Wenjie Xue
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Xing Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Yuxi Wang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Jui-Hua Hsieh
- Kelly Government Solutions, Research Triangle Park, North Carolina 27709, United States
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zhufang Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Hongmin Zhang
- Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment and SUSTech-HKU Joint Laboratories for Matrix Biology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR); Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, D.C. 20059, United States
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5
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Abstract
Virtual screening (VS) is an efficient hit-finding tool. Its distinctive strength is that it allows one to screen compound libraries that are not available in the lab. Moreover, structure-based (SB) VS also enables an understanding of how the hit compounds bind the protein target, thus laying ground work for the rational hit-to-lead progression. SBVS requires a very limited experimental effort and is particularly well suited for academic labs and small biotech companies that, unlike pharmaceutical companies, do not have physical access to quality small-molecule libraries. Here, we describe SBVS of commercial compound libraries for Mer kinase inhibitors. The screening protocol relies on the docking algorithm Glide complemented by a post-docking filter based on structural protein-ligand interaction fingerprints (SPLIF).
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Lenselink EB, Jespers W, van Vlijmen HWT, IJzerman AP, van Westen GJP. Interacting with GPCRs: Using Interaction Fingerprints for Virtual Screening. J Chem Inf Model 2016; 56:2053-2060. [PMID: 27626908 DOI: 10.1021/acs.jcim.6b00314] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The expanding number of crystal structures of G protein-coupled receptors (GPCRs) has increased the knowledge on receptor function and their ability to recognize ligands. Although structure-based virtual screening has been quite successful on GPCRs, scores obtained by docking are typically not indicative for ligand affinity. Methods capturing interactions between protein and ligand in a more explicit manner, such as interaction fingerprints (IFPs), have been applied as an addition or alternative to docking. Originally IFPs captured the interactions of amino acid residues with ligands with specific definitions for the various interaction types. More complex IFPs now capture atom-atom interactions, such as in SYBYL, or fragment-fragment co-occurrences such as in SPLIF. Overall, most of the IFPs have been studied in comparison with docking in retrospective studies. For GPCRs it remains unclear which IFP should be used, if at all, and in what manner. Thus, the performance between five different IFPs was compared on five different representative GPCRs, including several extensions of the original implementations,. Results show that the more detailed IFPs, SYBYL and SPLIF, perform better than the other IFPs (Deng, Credo, and Elements). SPLIF was further tuned based on the number of poses, fingerprint similarity coefficient, and using an ensemble of structures. Enrichments were obtained that were significantly higher than initial enrichments and those obtained by 2D-similarity. With the increase in available crystal structures for GPCRs, and given that IFPs such as SPLIF enhance enrichment in virtual screens, it is anticipated that IFPs will be used in conjunction with docking, especially for GPCRs with a large binding pocket.
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Affiliation(s)
- Eelke B Lenselink
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , 2333 CC Leiden, The Netherlands
| | - Willem Jespers
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , 2333 CC Leiden, The Netherlands
| | - Herman W T van Vlijmen
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , 2333 CC Leiden, The Netherlands
| | - Adriaan P IJzerman
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , 2333 CC Leiden, The Netherlands
| | - Gerard J P van Westen
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , 2333 CC Leiden, The Netherlands
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Anighoro A, Bajorath J. Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor. J Comput Aided Mol Des 2016; 30:447-56. [DOI: 10.1007/s10822-016-9918-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Accepted: 06/18/2016] [Indexed: 12/31/2022]
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Baladi T, Abet V, Piguel S. State-of-the-art of small molecule inhibitors of the TAM family: the point of view of the chemist. Eur J Med Chem 2015; 105:220-37. [PMID: 26498569 DOI: 10.1016/j.ejmech.2015.10.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Revised: 10/01/2015] [Accepted: 10/03/2015] [Indexed: 01/04/2023]
Abstract
The TAM family of tyrosine kinases receptors (Tyro3, Axl and Mer) is implicated in cancer development, autoimmune reactions and viral infection and is therefore emerging as an effective and attractive therapeutic target. To date, only a few small molecules have been intentionally designed to block the TAM kinases, while most of the inhibitors were developed for blocking different protein kinases and then identified through selectivity profile studies. This minireview will examine in terms of chemical structure the different compounds able to act on either one, two or three TAM kinases with details about structure-activity relationships, drug-metabolism and pharmacokinetics properties where they exist.
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Affiliation(s)
- Tom Baladi
- Institut Curie/UMR9187-U1196, 91405 Orsay cedex, France; Univ Paris Sud, 91405 Orsay cedex, France
| | | | - Sandrine Piguel
- Institut Curie/UMR9187-U1196, 91405 Orsay cedex, France; Univ Paris Sud, 91405 Orsay cedex, France.
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