1
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Lu D, Luo D, Zhang Y, Wang B. A Robust Induced Fit Docking Approach with the Combination of the Hybrid All-Atom/United-Atom/Coarse-Grained Model and Simulated Annealing. J Chem Theory Comput 2024; 20:6414-6423. [PMID: 38966989 DOI: 10.1021/acs.jctc.4c00653] [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: 07/06/2024]
Abstract
Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.
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Affiliation(s)
- Dexin Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 360015, P. R. China
| | - Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 360015, P. R. China
| | - Yuwei Zhang
- Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Centre of Biomedical Functional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 360015, P. R. China
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2
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Powers A, Yu HH, Suriana P, Koodli RV, Lu T, Paggi JM, Dror RO. Geometric Deep Learning for Structure-Based Ligand Design. ACS CENTRAL SCIENCE 2023; 9:2257-2267. [PMID: 38161364 PMCID: PMC10755842 DOI: 10.1021/acscentsci.3c00572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 01/03/2024]
Abstract
A pervasive challenge in drug design is determining how to expand a ligand-a small molecule that binds to a target biomolecule-in order to improve various properties of the ligand. Adding single chemical groups, known as fragments, is important for lead optimization tasks, and adding multiple fragments is critical for fragment-based drug design. We have developed a comprehensive framework that uses machine learning and three-dimensional protein-ligand structures to address this challenge. Our method, FRAME, iteratively determines where on a ligand to add fragments, selects fragments to add, and predicts the geometry of the added fragments. On a comprehensive benchmark, FRAME consistently improves predicted affinity and selectivity relative to the initial ligand, while generating molecules with more drug-like chemical properties than docking-based methods currently in widespread use. FRAME learns to accurately describe molecular interactions despite being given no prior information on such interactions. The resulting framework for quality molecular hypothesis generation can be easily incorporated into the workflows of medicinal chemists for diverse tasks, including lead optimization, fragment-based drug discovery, and de novo drug design.
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Affiliation(s)
- Alexander
S. Powers
- Department
of Chemistry, Stanford University, Stanford, California 94305, United States
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Helen H. Yu
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Patricia Suriana
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Rohan V. Koodli
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
- Biomedical
Informatics Program, Stanford University
School of Medicine, Stanford, California 94305, United States
| | - Tianyu Lu
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
- Department
of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Joseph M. Paggi
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Ron O. Dror
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
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3
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Elgammal WE, Elkady H, Mahdy HA, Husein DZ, Alsfouk AA, Alsfouk BA, Ibrahim IM, Elkaeed EB, Metwaly AM, Eissa IH. Rationale design and synthesis of new apoptotic thiadiazole derivatives targeting VEGFR-2: computational and in vitro studies. RSC Adv 2023; 13:35853-35876. [PMID: 38116168 PMCID: PMC10728955 DOI: 10.1039/d3ra07562a] [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: 11/05/2023] [Accepted: 11/24/2023] [Indexed: 12/21/2023] Open
Abstract
This work presents the synthesis and in vitro, and in silico analyses of new thiadiazole derivatives that are designed to mimic the pharmacophoric characteristics of vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors. A comprehensive evaluation of the inhibitory properties of the synthesized thiadiazole derivatives against the cancer cell lines MCF-7 and HepG2 identified several auspicious candidates. Among them, compound 14 showed remarkably low IC50 values of 0.04 μM and 0.18 μM against MCF-7 and HepG2, respectively. VEGFR-2 inhibitory evaluation of compound 14 revealed a promising IC50 value in the nanomolar range (103 nM). Further examination of the cell cycle revealed that compound 14 has the ability to stop the progression of the cell cycle in MCF-7 cells via G0-G1 phase arrest. Interestingly, compound 14 also demonstrated a noteworthy pro-apoptotic effect in MCF-7 cells, with notable increases in early apoptosis (16.53%) and late apoptosis (29.57%), along with a slight increase in the population of necrotic cells (5.95%). Furthermore, compound 14 showed a significant drop in MCF-7 cells' ability to migrate and heal wounds. Additionally, compound 14 promoted apoptosis by boosting BAX (6-fold) while lowering Bcl-2 (6.2-fold). The binding affinities of the synthesized candidates to their target (VEGFR-2) were confirmed by computational investigations, including molecular docking, principal component analysis of trajectories (PCAT), and molecular dynamics (MD) simulations. Additionally, compound 14's stability and reactivity were investigated using density functional theory (DFT). These thorough results highlight compound 14's potential as a lead contender for additional research in the creation of anticancer drugs that target VEGFR-2. This work establishes a foundation for promising thiadiazole derivatives for future therapeutic developments in anticancer- and angiogenesis-related scientific fields.
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Affiliation(s)
- Walid E Elgammal
- Department of Chemistry, Faculty of Science, Al-Azhar University Nasr City Cairo Egypt
| | - Hazem Elkady
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University Cairo 11884 Egypt
| | - Hazem A Mahdy
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University Cairo 11884 Egypt
| | - Dalal Z Husein
- Chemistry Department, Faculty of Science, New Valley University El-Kharja 72511 Egypt
| | - Aisha A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia
| | - Bshra A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia
| | - Ibrahim M Ibrahim
- Biophysics Department, Faculty of Science, Cairo University Giza 12613 Egypt
| | - Eslam B Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University Riyadh 13713 Saudi Arabia
| | - Ahmed M Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University Cairo 11884 Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City) Alexandria Egypt
| | - Ibrahim H Eissa
- Pharmaceutical Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University Cairo 11884 Egypt
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Simoben CV, Babiaka SB, Moumbock AFA, Namba-Nzanguim CT, Eni DB, Medina-Franco JL, Günther S, Ntie-Kang F, Sippl W. Challenges in natural product-based drug discovery assisted with in silico-based methods. RSC Adv 2023; 13:31578-31594. [PMID: 37908659 PMCID: PMC10613855 DOI: 10.1039/d3ra06831e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 10/19/2023] [Indexed: 11/02/2023] Open
Abstract
The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.
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Affiliation(s)
- Conrad V Simoben
- Center for Drug Discovery, Faculty of Science, University of Buea P.O. Box 63 Buea CM-00237 Cameroon
- Structural Genomics Consortium, University of Toronto Toronto Ontario M5G 1L7 Canada
- Department of Pharmacology & Toxicology, University of Toronto Toronto Ontario M5S 1A8 Canada
| | - Smith B Babiaka
- Center for Drug Discovery, Faculty of Science, University of Buea P.O. Box 63 Buea CM-00237 Cameroon
- Department of Chemistry, University of Buea Buea Cameroon
- Department of Microbial Bioactive Compounds, Interfaculty Institute for Microbiology and Infection Medicine, University of Tübingen 72076 Tübingen Germany
| | - Aurélien F A Moumbock
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg Freiburg Germany
| | - Cyril T Namba-Nzanguim
- Center for Drug Discovery, Faculty of Science, University of Buea P.O. Box 63 Buea CM-00237 Cameroon
- Department of Chemistry, University of Buea Buea Cameroon
| | - Donatus Bekindaka Eni
- Center for Drug Discovery, Faculty of Science, University of Buea P.O. Box 63 Buea CM-00237 Cameroon
- Department of Chemistry, University of Buea Buea Cameroon
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000 Mexico City 04510 Mexico
| | - Stefan Günther
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg Freiburg Germany
| | - Fidele Ntie-Kang
- Center for Drug Discovery, Faculty of Science, University of Buea P.O. Box 63 Buea CM-00237 Cameroon
- Department of Chemistry, University of Buea Buea Cameroon
- Institute of Pharmacy, Martin-Luther University Halle-Wittenberg Halle (Saale) Germany
| | - Wolfgang Sippl
- Institute of Pharmacy, Martin-Luther University Halle-Wittenberg Halle (Saale) Germany
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5
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Guterres H, Im W. CHARMM-GUI-Based Induced Fit Docking Workflow to Generate Reliable Protein-Ligand Binding Modes. J Chem Inf Model 2023; 63:4772-4779. [PMID: 37462607 PMCID: PMC10428204 DOI: 10.1021/acs.jcim.3c00416] [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: 03/15/2023] [Indexed: 08/15/2023]
Abstract
Molecular docking is a preferred method to predict ligand binding modes and their binding energy to target protein receptors, which is critical in early phase structure-based drug discovery. However, there is a persistent challenge in docking that can be attributed to the induced fit effect, as receptor binding sites undergo induced fit conformational changes upon ligand binding to achieve better binding modes. In this work, based on CHARMM-GUI LBS Finder& Refiner and High-Throughput Simulator, we present a straightforward CHARMM-GUI induced fit docking (CGUI-IFD) workflow to generate reliable protein-ligand binding modes. The CGUI-IFD workflow generates an ensemble of receptor binding site conformations through ligand-binding site (LBS) refinement, runs rigid receptor docking, and performs high-throughput molecular dynamics (MD) simulations of protein-ligand complex structures in explicit solvents. The results are evaluated based on the ligand root-mean-square deviation (RMSD)-based binding stability and the molecular mechanics generalized Born surface area binding energy. For a benchmark test, we used 258 cross-docking protein-ligand pairs across 41 target proteins from the Schrodinger IFD-MD data set. The application of CGUI-IFD on this data set shows 80% success rate (within 2.5 Å RMSD from the experimental structures). We expect that the CGUI-IFD workflow can be useful to generate reliable ligand binding modes for cross-docking cases.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological
Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological
Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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6
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Khan T, Raza S. Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review. Curr Top Med Chem 2023; 23:1640-1663. [PMID: 36725827 DOI: 10.2174/1568026623666230201144522] [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: 06/21/2022] [Revised: 11/14/2022] [Accepted: 12/19/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body. METHODS This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review. RESULTS Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes. CONCLUSION Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.
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Affiliation(s)
- Tahmeena Khan
- Department of Chemistry, Integral University, Lucknow, 226026, U.P., India
| | - Saman Raza
- Department of Chemistry, Isabella Thoburn College, Lucknow, 226007, U.P., India
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7
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Rooney TC, Aldred GG, Boffey HK, Willems HG, Edwards S, Chawner SJ, Scott DE, Green C, Winpenny D, Skidmore J, Clarke JH, Andrews SP. The Identification of Potent, Selective, and Brain Penetrant PI5P4Kγ Inhibitors as In Vivo-Ready Tool Molecules. J Med Chem 2022; 66:804-821. [PMID: 36516442 PMCID: PMC9841522 DOI: 10.1021/acs.jmedchem.2c01693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Owing to their central role in regulating cell signaling pathways, the phosphatidylinositol 5-phosphate 4-kinases (PI5P4Ks) are attractive therapeutic targets in diseases such as cancer, neurodegeneration, and immunological disorders. Until now, tool molecules for these kinases have been either limited in potency or isoform selectivity, which has hampered further investigation of biology and drug development. Herein we describe the virtual screening workflow which identified a series of thienylpyrimidines as PI5P4Kγ-selective inhibitors, as well as the medicinal chemistry optimization of this chemotype, to provide potent and selective tool molecules for further use. In vivo pharmacokinetics data are presented for exemplar tool molecules, along with an X-ray structure for ARUK2001607 (15) in complex with PI5P4Kγ, along with its selectivity data against >150 kinases and a Cerep safety panel.
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8
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Blay V, Radivojevic T, Allen JE, Hudson CM, Garcia Martin H. MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design. J Chem Inf Model 2022; 62:3551-3564. [PMID: 35857932 PMCID: PMC9364320 DOI: 10.1021/acs.jcim.2c00229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
![]()
The growing capabilities of synthetic biology and organic
chemistry
demand tools to guide syntheses toward useful molecules. Here, we
present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that
uses a novel approach to generate molecules predicted to meet a desired
property specification (e.g., a binding affinity of 50 nM or an octane
number of 90). MACAW describes molecules by embedding them into a
smooth multidimensional numerical space, avoiding uninformative dimensions
that previous methods often introduce. The coordinates in this embedding
provide a natural choice of features for accurately predicting molecular
properties, which we demonstrate with examples for cetane and octane
numbers, flash points, and histamine H1 receptor binding affinity.
The approach is computationally efficient and well-suited to the small-
and medium-size datasets commonly used in biosciences. We showcase
the utility of MACAW for virtual screening by identifying molecules
with high predicted binding affinity to the histamine H1 receptor
and limited affinity to the muscarinic M2 receptor, which are targets
of medicinal relevance. Combining these predictive capabilities with
a novel generative algorithm for molecules allows us to recommend
molecules with a desired property value (i.e., inverse molecular design).
We demonstrate this capability by recommending molecules with predicted
octane numbers of 40, 80, and 120, which is an important characteristic
of biofuels. Thus, MACAW augments classical retrosynthesis tools by
providing recommendations for molecules on specification.
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Affiliation(s)
- Vincent Blay
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Tijana Radivojevic
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, California 94608, United States.,DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Jonathan E Allen
- Global Security Computing Applications, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Corey M Hudson
- Sandia National Laboratories, Livermore, California 94550, United States
| | - Hector Garcia Martin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, California 94608, United States.,DOE Agile BioFoundry, Emeryville, California 94608, United States
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Bajusz D, Keserű GM. Maximizing the integration of virtual and experimental screening in hit discovery. Expert Opin Drug Discov 2022; 17:629-640. [PMID: 35671403 DOI: 10.1080/17460441.2022.2085685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches. AREAS COVERED After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies. EXPERT OPINION Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
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10
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López-López E, Fernández-de Gortari E, Medina-Franco JL. Yes SIR! On the structure-inactivity relationships in drug discovery. Drug Discov Today 2022; 27:2353-2362. [PMID: 35561964 DOI: 10.1016/j.drudis.2022.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/09/2022] [Accepted: 05/05/2022] [Indexed: 12/12/2022]
Abstract
In analogy with structure-activity relationships (SARs), which are at the core of medicinal chemistry, studying structure-inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish 'activity' and 'inactivity' with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled 'active' and 'inactive' compounds.
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Affiliation(s)
- Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico; Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07000, Mexico.
| | - Eli Fernández-de Gortari
- Department of Nanosafety, International Iberian Nanotechnology Laboratory, Braga 4715-330, Portugal
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
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11
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Warr WA, Nicklaus MC, Nicolaou CA, Rarey M. Exploration of Ultralarge Compound Collections for Drug Discovery. J Chem Inf Model 2022; 62:2021-2034. [PMID: 35421301 DOI: 10.1021/acs.jcim.2c00224] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.
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Affiliation(s)
- Wendy A Warr
- Wendy Warr & Associates, 6 Berwick Court, Holmes Chapel, Crewe, Cheshire CW4 7HZ, United Kingdom
| | - Marc C Nicklaus
- NCI, NIH, CADD Group, NCI-Frederick, Frederick, Maryland 21702, United States
| | - Christos A Nicolaou
- Discovery Chemistry, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Matthias Rarey
- Universität Hamburg, ZBH Center for Bioinformatics, 20146 Hamburg, Germany
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12
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Gu Y, Zheng S, Xu Z, Yin Q, Li L, Li J. An efficient curriculum learning-based strategy for molecular graph learning. Brief Bioinform 2022; 23:6562682. [DOI: 10.1093/bib/bbac099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/18/2022] [Accepted: 02/27/2022] [Indexed: 12/14/2022] Open
Abstract
Abstract
Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG’s encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning.
Availability: The source code is available in https://github.com/gu-yaowen/CurrMG.
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Affiliation(s)
- Yaowen Gu
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
| | - Si Zheng
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Zidu Xu
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
| | - Qijin Yin
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Liang Li
- Key Laboratory of Antibiotic Bioengineering of National Health and Family Planning Commission (NHFPC), Institute of Medicinal Biotechnology (IMB), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
| | - Jiao Li
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
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13
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Nakarin F, Boonpalit K, Kinchagawat J, Wachiraphan P, Rungrotmongkol T, Nutanong S. Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation. Molecules 2022; 27:molecules27041226. [PMID: 35209011 PMCID: PMC8878292 DOI: 10.3390/molecules27041226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 11/16/2022] Open
Abstract
A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.
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Affiliation(s)
- Fahsai Nakarin
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
- Correspondence: ; Tel.: +66-33-014-444
| | - Kajjana Boonpalit
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
| | - Jiramet Kinchagawat
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
| | - Patcharapol Wachiraphan
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
| | - Thanyada Rungrotmongkol
- Center of Excellence in Biocatalyst and Sustainable Biotechnology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand
| | - Sarana Nutanong
- School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand; (K.B.); (J.K.); (P.W.); (S.N.)
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14
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Wang HC, Zhang QX, Zhao J, Wei NN. Molecular docking and molecular dynamics simulations studies on the protective and pathogenic roles of the amyloid-β peptide between herpesvirus infection and Alzheimer's disease. J Mol Graph Model 2022; 113:108143. [DOI: 10.1016/j.jmgm.2022.108143] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 11/29/2022]
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15
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Steadman D, Atkinson BN, Zhao Y, Willis NJ, Frew S, Monaghan A, Patel C, Armstrong E, Costelloe K, Magno L, Bictash M, Jones EY, Fish PV, Svensson F. Virtual Screening Directly Identifies New Fragment-Sized Inhibitors of Carboxylesterase Notum with Nanomolar Activity. J Med Chem 2022; 65:562-578. [PMID: 34939789 DOI: 10.1021/acs.jmedchem.1c01735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Notum is a negative regulator of Wnt signaling acting through the hydrolysis of a palmitoleoylate ester, which is required for Wnt activity. Inhibitors of Notum could be of use in diseases where dysfunctional Notum activity is an underlying cause. A docking-based virtual screen (VS) of a large commercial library was used to shortlist 952 compounds for experimental validation as inhibitors of Notum. The VS was successful with 31 compounds having an IC50 < 500 nM. A critical selection process was then applied with two clusters and two singletons (1-4d) selected for hit validation. Optimization of 4d guided by structural biology identified potent inhibitors of Notum activity that restored Wnt/β-catenin signaling in cell-based models. The [1,2,4]triazolo[4,3-b]pyradizin-3(2H)-one series 4 represent a new chemical class of Notum inhibitors and the first to be discovered by a VS campaign. These results demonstrate the value of VS with well-designed docking models based on X-ray structures.
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Affiliation(s)
- David Steadman
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Benjamin N Atkinson
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Yuguang Zhao
- Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, The Henry Wellcome Building for Genomic Medicine, Roosevelt Drive, OxfordOX3 7BN, U.K
| | - Nicky J Willis
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Sarah Frew
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Amy Monaghan
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Chandni Patel
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Emma Armstrong
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Kathryn Costelloe
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Lorenza Magno
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Magda Bictash
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - E Yvonne Jones
- Division of Structural Biology, Wellcome Centre for Human Genetics, University of Oxford, The Henry Wellcome Building for Genomic Medicine, Roosevelt Drive, OxfordOX3 7BN, U.K
| | - Paul V Fish
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
| | - Fredrik Svensson
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, LondonWC1E 6BT, U.K
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Said RB, Hanachi R, Rahali S, Alkhalifah MAM, Alresheedi F, Tangour B, Hochlaf M. Evaluation of a new series of pyrazole derivatives as a potent epidermal growth factor receptor inhibitory activity: QSAR modeling using quantum-chemical descriptors. J Comput Chem 2021; 42:2306-2320. [PMID: 34609748 DOI: 10.1002/jcc.26761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 08/31/2021] [Accepted: 09/19/2021] [Indexed: 11/07/2022]
Abstract
Pyrazole derivatives correspond to a family of heterocycle molecules with important pharmacological and physiological applications. At present, we perform a density functional theory (DFT) calculations and a quantitative structure-activity relationship (QSAR) evaluation on a series of 1-(4,5-dihydro-1H-pyrazol-1-yl) ethan-1-one and 4,5-dihydro-1H-pyrazole-1-carbothioamide derivatives as an epidermal growth factor receptor (EGFR) inhibitory activity. We thus propose a virtual screening protocol based on a machine-learning study. This theoretical model relates the studied compounds' biological activity to their calculated physicochemical descriptors. Moreover, the linear regression function is used to validate the model via the evaluation of Q2 ext and Q2 cv parameters for external and internal validations, respectively. Our QSAR model shows a good correlation between observed activities IC50 and predicted ones. Our model allows us to mitigate time-consuming problems and waste chemical and biological products in the preclinical phases.
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Affiliation(s)
- Ridha Ben Said
- Laboratoire de Caractérisations, Applications et Modélisations des Matériaux, Faculté des Sciences de Tunis, Université Tunis El Manar, Tunis, Tunisie.,Department of Chemistry, College of Science and Arts, Qassim University, Ar Rass, Saudi Arabia
| | - Riadh Hanachi
- Laboratoire de Caractérisations, Applications et Modélisations des Matériaux, Faculté des Sciences de Tunis, Université Tunis El Manar, Tunis, Tunisie
| | - Seyfeddine Rahali
- Department of Chemistry, College of Science and Arts, Qassim University, Ar Rass, Saudi Arabia.,IPEIEM, Research Unit on Fundamental Sciences and Didactics, Université de Tunis El Manar, Tunis, Tunisia
| | | | - Faisal Alresheedi
- Department of Physics, College of Science, Qassim University, Buraidah, Saudi Arabia
| | - Bahoueddine Tangour
- IPEIEM, Research Unit on Fundamental Sciences and Didactics, Université de Tunis El Manar, Tunis, Tunisia
| | - Majdi Hochlaf
- Université Gustave Eiffel, COSYS/LISIS, 5 Bd Descartes, Champs sur Marne, France
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17
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Kori S, Shibahashi Y, Ekimoto T, Nishiyama A, Yoshimi S, Yamaguchi K, Nagatoishi S, Ohta M, Tsumoto K, Nakanishi M, Defossez PA, Ikeguchi M, Arita K. Structure-based screening combined with computational and biochemical analyses identified the inhibitor targeting the binding of DNA Ligase 1 to UHRF1. Bioorg Med Chem 2021; 52:116500. [PMID: 34801826 DOI: 10.1016/j.bmc.2021.116500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/02/2021] [Accepted: 11/02/2021] [Indexed: 01/04/2023]
Abstract
The accumulation of epigenetic alterations is one of the major causes of tumorigenesis. Aberrant DNA methylation patterns cause genome instability and silencing of tumor suppressor genes in various types of tumors. Therefore, drugs that target DNA methylation-regulating factors have great potential for cancer therapy. Ubiquitin-like containing PHD and RING finger domain 1 (UHRF1) is an essential factor for DNA methylation maintenance. UHRF1 is overexpressed in various cancer cells and down-regulation of UHRF1 in these cells reactivates the expression of tumor suppressor genes, thus UHRF1 is a promising target for cancer therapy. We have previously shown that interaction between the tandem Tudor domain (TTD) of UHRF1 and DNA ligase 1 (LIG1) di/trimethylated on Lys126 plays a key role in the recruitment of UHRF1 to replication sites and replication-coupled DNA methylation maintenance. An arginine binding cavity (Arg-binding cavity) of the TTD is essential for LIG1 interaction, thus the development of inhibitors that target the Arg-binding cavity could potentially repress UHRF1 function in cancer cells. To develop such an inhibitor, we performed in silico screening using not only static but also dynamic metrics based on all-atom molecular dynamics simulations, resulting in efficient identification of 5-amino-2,4-dimethylpyridine (5A-DMP) as a novel TTD-binding compound. Crystal structure of the TTD in complex with 5A-DMP revealed that the compound stably bound to the Arg-binding cavity of the TTD. Furthermore, 5A-DMP inhibits the full-length UHRF1:LIG1 interaction in Xenopus egg extracts. Our study uncovers a UHRF1 inhibitor which can be the basis of future experiments for cancer therapy.
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Affiliation(s)
- Satomi Kori
- Structural Biology Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yuki Shibahashi
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Toru Ekimoto
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Atsuya Nishiyama
- Division of Cancer Cell Biology, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Sae Yoshimi
- Structural Biology Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kosuke Yamaguchi
- Univ. Paris, Epigenetics and Cell Fate, UMR 7216 CNRS, 75013 Paris, France
| | - Satoru Nagatoishi
- Institute of Medical Sciences, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Masateru Ohta
- HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama 230-0045, Japan
| | - Kouhei Tsumoto
- Institute of Medical Sciences, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan; Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Makoto Nakanishi
- Division of Cancer Cell Biology, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | | | - Mitsunori Ikeguchi
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama 230-0045, Japan
| | - Kyohei Arita
- Structural Biology Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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18
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An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2021; 1. [PMID: 35475037 PMCID: PMC9038114 DOI: 10.1016/j.ailsci.2021.100008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.
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19
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Xu Z, Wauchope OR, Frank AT. Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking. J Chem Inf Model 2021; 61:5589-5600. [PMID: 34633194 DOI: 10.1021/acs.jcim.1c00746] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.
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Affiliation(s)
- Ziqiao Xu
- Chemistry Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Orrette R Wauchope
- Department of Natural Sciences, City University of New York, Baruch College, New York, New York 10010, United States
| | - Aaron T Frank
- Biophysics Program, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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20
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Sunoj RB. Coming of Age of Computational Chemistry from a Resilient Past to a Promising Future. Isr J Chem 2021. [DOI: 10.1002/ijch.202100106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Raghavan B. Sunoj
- Department of Chemistry Indian Institute of Technology Bombay, Powai Mumbai 400076 India
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21
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Srivastava R. Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities. ACS OMEGA 2021; 6:24891-24901. [PMID: 34604670 PMCID: PMC8482469 DOI: 10.1021/acsomega.1c03736] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Indexed: 05/10/2023]
Abstract
New chemical entities (NCEs) such as small molecules and antibody-drug conjugates have strong binding affinity for biological targets, which provide deep insights into structure-specific interactions for the design of future drugs. As structures of drugs increase in complexity, the importance of computational predictions comes into sharp focus. Knowledge of various computational tools enables us to predict the molecular properties, toxicity, and biological efficacy of the drugs and help the medicinal chemists to discover new drugs more efficiently. Newly approved drugs have higher affinities for proteins and nucleic acids and are applied for the treatment of human diseases. We have carried out the computational studies of 21 such NCEs, specifically small molecules and antibody-drug conjugates, and studied the biological efficacy of these drugs. Their bioactivity score and molecular and pharmacokinetic properties were evaluated using online computer software programs, viz., Molinspiration and Osiris Property Explorer. The SwissTargetPrediction tool was used for the efficient prediction of protein targets for the NCEs. The results indicated higher stability for the drug complexes due to a larger HOMO-LUMO gap. A high electrophilicity index reflects good electrophilic behavior and high reactivity of the drugs. Lipinski's ''rule of five'' indicated that most of the drug complexes are likely to be orally active. These drugs also showed non-mutagenic, non-tumorigenic, non-irritant, and non-effective reproductive behavior. We hope that these studies will provide an insight into molecular recognition and definitely help the medicinal chemists to design new drugs in future.
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22
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Sicho M, Liu X, Svozil D, van Westen GJP. GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics. J Cheminform 2021; 13:73. [PMID: 34563271 PMCID: PMC8465716 DOI: 10.1186/s13321-021-00550-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 09/05/2021] [Indexed: 03/05/2023] Open
Abstract
Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.
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Affiliation(s)
- M. Sicho
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
| | - X. Liu
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden, The Netherlands
| | - D. Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20 Prague 4, Czech Republic
| | - G. J. P. van Westen
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden, The Netherlands
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23
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Gaffney SG, Smaga S, Schepartz A, Townsend JP. Chemsearch: collaborative compound libraries with structure-aware browsing. BIOINFORMATICS ADVANCES 2021; 1:vbab008. [PMID: 36700113 PMCID: PMC9710581 DOI: 10.1093/bioadv/vbab008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/07/2021] [Accepted: 07/07/2021] [Indexed: 01/28/2023]
Abstract
Summary Chemsearch is a cross-platform server application for developing and managing a chemical compound library and associated data files, with an interface for browsing and search that allows for easy navigation to a compound of interest, similar compounds or compounds that have desired structural properties. With provisions for access control and centralized document and data storage, Chemsearch supports collaboration by distributed teams. Availability and implementation Chemsearch is a free and open-source Flask web application that can be linked to a Google Workspace account. Source code is available at https://github.com/gem-net/chemsearch (GPLv3 license). A Docker image allowing rapid deployment is available at https://hub.docker.com/r/cgemcci/chemsearch.
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Affiliation(s)
- Stephen G Gaffney
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06510 USA,To whom correspondence should be addressed.
| | - Sarah Smaga
- Department of Chemistry, University of California Berkeley, Berkeley, CA 94705, USA
| | - Alanna Schepartz
- Department of Chemistry, University of California Berkeley, Berkeley, CA 94705, USA,Department of Molecular & Cellular Biology, University of California Berkeley, Berkeley, CA 94705, USA
| | - Jeffrey P Townsend
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06510 USA
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24
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Medina-Franco JL, Sánchez-Cruz N, López-López E, Díaz-Eufracio BI. Progress on open chemoinformatic tools for expanding and exploring the chemical space. J Comput Aided Mol Des 2021; 36:341-354. [PMID: 34143323 PMCID: PMC8211976 DOI: 10.1007/s10822-021-00399-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 06/14/2021] [Indexed: 01/10/2023]
Abstract
The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.
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Affiliation(s)
- José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico.
| | - Norberto Sánchez-Cruz
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico
| | - Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico.,Departamento de Química y Programa de Posgrado en Farmacología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Apartado 14-740, 07000, Mexico City, Mexico
| | - Bárbara I Díaz-Eufracio
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico
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Medina-Franco JL, Martinez-Mayorga K, Fernández-de Gortari E, Kirchmair J, Bajorath J. Rationality over fashion and hype in drug design. F1000Res 2021; 10. [PMID: 34164109 PMCID: PMC8201421 DOI: 10.12688/f1000research.52676.1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/28/2021] [Indexed: 01/05/2023] Open
Abstract
The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of in silico tools comes at the risk of using them as "black boxes" without sufficient expert knowledge, leading to widespread misconceptions and problems. For example, results of computations may be taken at face value as "nothing but the truth" and data visualization may be used only to generate "pretty and colorful pictures". Computational experts might come to the rescue and help to re-direct such efforts, for example, by guiding interested novices to conduct meaningful data analysis, make scientifically sound predictions, and communicate the findings in a rigorous manner. However, this is not always ensured. This contribution aims to encourage investigators entering the CADD arena to obtain adequate computational training, communicate or collaborate with experts, and become aware of the fundamentals of computational methods and their given limitations, beyond the hype. By its very nature, this Opinion is partly subjective and we do not attempt to provide a comprehensive guide to the best practices of CADD; instead, we wish to stimulate an open discussion within the scientific community and advocate rational rather than fashion-driven use of computational methods. We take advantage of the open peer-review culture of F1000Research such that reviewers and interested readers may engage in this discussion and obtain credits for their candid personal views and comments. We hope that this open discussion forum will contribute to shaping the future practice of CADD.
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Affiliation(s)
- José L Medina-Franco
- DIFACQUIM research group, Department of Pharmacy, School of Pharmacy, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | | | - Eli Fernández-de Gortari
- Nanosafety Laboratory, International Iberian Nanotechnology Laboratory, Braga, 4715-330, Portugal
| | - Johannes Kirchmair
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Vienna, 1090, Austria
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53115, Germany
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Lee JH, Shon SY, Jeon W, Hong SJ, Ban J, Lee DS. Discovery of μ,δ-Opioid Receptor Dual-Biased Agonists That Overcome the Limitation of Prior Biased Agonists. ACS Pharmacol Transl Sci 2021; 4:1149-1160. [PMID: 34151205 DOI: 10.1021/acsptsci.1c00044] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Indexed: 11/28/2022]
Abstract
Morphine is widely used in pain management although the risk of side effects is significant. The use of biased agonists to the G protein of μ-opioid receptors has been suggested as a potential solution, although oliceridine and PZM21 have previously failed to demonstrate benefits in clinical studies. An amplification-induced confusion in the process of comparing G protein and beta-arrestin pathways may account for previously biased agonist misidentification. Here, we have devised a strategy to discover biased agonists with intrinsic efficacy. We computationally simulated 430 000 molecular dockings to the μ-opioid receptor to construct a compound library. Hits were then verified experimentally. Using the verified compounds, we performed simulations to build a second library with a common scaffold and selected compounds that showed a bias to μ- and δ-opioid receptors in a cell-based assay. Three compounds (ID110460001, ID110460002, and ID110460003) with a dual-biased agonistic effect for μ- and δ-opioid receptors were identified. These candidates are full agonists for the μ-opioid receptor and show specific binding modes. On the basis of our findings, we expect our novel compounds to act as more biased agonists compared to existing drugs, including oliceridine.
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Affiliation(s)
- Jin Hee Lee
- Research Laboratory, Ildong Pharmaceutical Co., Ltd., 20, Samsung 1-ro 1-gil, Hwaseong 18449, Korea
| | - Suh-Youn Shon
- Research Laboratory, Ildong Pharmaceutical Co., Ltd., 20, Samsung 1-ro 1-gil, Hwaseong 18449, Korea
| | - Woojin Jeon
- Research Laboratory, Ildong Pharmaceutical Co., Ltd., 20, Samsung 1-ro 1-gil, Hwaseong 18449, Korea
| | - Sung-Jun Hong
- Research Laboratory, Ildong Pharmaceutical Co., Ltd., 20, Samsung 1-ro 1-gil, Hwaseong 18449, Korea
| | - Junsu Ban
- Research Laboratory, Ildong Pharmaceutical Co., Ltd., 20, Samsung 1-ro 1-gil, Hwaseong 18449, Korea
| | - Do Sup Lee
- Research Laboratory, Ildong Pharmaceutical Co., Ltd., 20, Samsung 1-ro 1-gil, Hwaseong 18449, Korea
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Munárriz J, Gallegos M, Contreras-García J, Martín Pendás Á. Energetics of Electron Pairs in Electrophilic Aromatic Substitutions. Molecules 2021; 26:513. [PMID: 33478091 PMCID: PMC7835785 DOI: 10.3390/molecules26020513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/14/2021] [Accepted: 01/16/2021] [Indexed: 11/16/2022] Open
Abstract
The interacting quantum atoms approach (IQA) as applied to the electron-pair exhaustive partition of real space induced by the electron localization function (ELF) is used to examine candidate energetic descriptors to rationalize substituent effects in simple electrophilic aromatic substitutions. It is first shown that inductive and mesomeric effects can be recognized from the decay mode of the aromatic valence bond basin populations with the distance to the substituent, and that the fluctuation of the population of adjacent bonds holds also regioselectivity information. With this, the kinetic energy of the electrons in these aromatic basins, as well as their mutual exchange-correlation energies are proposed as suitable energetic indices containing relevant information about substituent effects. We suggest that these descriptors could be used to build future reactive force fields.
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Affiliation(s)
- Julen Munárriz
- Departamento de Química Física y Analítica, Universidad de Oviedo, 33006 Oviedo, Spain;
| | - Miguel Gallegos
- Departamento de Química Física y Analítica, Universidad de Oviedo, 33006 Oviedo, Spain;
| | | | - Ángel Martín Pendás
- Departamento de Química Física y Analítica, Universidad de Oviedo, 33006 Oviedo, Spain;
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Shen C, Weng G, Zhang X, Leung ELH, Yao X, Pang J, Chai X, Li D, Wang E, Cao D, Hou T. Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening? Brief Bioinform 2021; 22:6070382. [PMID: 33418562 DOI: 10.1093/bib/bbaa410] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 11/26/2020] [Accepted: 12/12/2020] [Indexed: 12/13/2022] Open
Abstract
Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance of target-specific MLSFs on a relatively unbiased dataset, the MLSFs trained from three representative protein-ligand interaction representations were assessed on the LIT-PCBA dataset, and the classical Glide SP SF and three types of ligand-based quantitative structure-activity relationship (QSAR) models were also utilized for comparison. Two major aspects in virtual screening campaigns, including prediction accuracy and hit novelty, were systematically explored. The calculation results illustrate that the tested target-specific MLSFs yielded generally superior performance over the classical Glide SP SF, but they could hardly outperform the 2D fingerprint-based QSAR models. Although substantial improvements could be achieved by integrating multiple types of protein-ligand interaction features, the MLSFs were still not sufficient to exceed MACCS-based QSAR models. In terms of the correlations between the hit ranks or the structures of the top-ranked hits, the MLSFs developed by different featurization strategies would have the ability to identify quite different hits. Nevertheless, it seems that target-specific MLSFs do not have the intrinsic attributes of a traditional SF and may not be a substitute for classical SFs. In contrast, MLSFs can be regarded as a new derivative of ligand-based QSAR models. It is expected that our study may provide valuable guidance for the assessment and further development of target-specific MLSFs.
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Affiliation(s)
- Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Gaoqi Weng
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Xujun Zhang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Elaine Lai-Han Leung
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, SAR, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, SAR, China
| | - Jinping Pang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Xin Chai
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dan Li
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
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Holderbach S, Adam L, Jayaram B, Wade RC, Mukherjee G. RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features. Front Mol Biosci 2020; 7:601065. [PMID: 33392260 PMCID: PMC7773945 DOI: 10.3389/fmolb.2020.601065] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 11/13/2020] [Indexed: 01/17/2023] Open
Abstract
The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback of a large number of poses that must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast pre-filtering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance that is better than that of the original RASPD method and traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.
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Affiliation(s)
- Stefan Holderbach
- Molecular and Cellular Modelling Group, Heidelberg Institute of Theoretical Studies, Heidelberg, Germany
- Institute of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University, Heidelberg, Germany
| | - Lukas Adam
- Molecular and Cellular Modelling Group, Heidelberg Institute of Theoretical Studies, Heidelberg, Germany
- Institute of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University, Heidelberg, Germany
| | - B. Jayaram
- Supercomputing Facility for Bioinformatics & Computational Biology, Department of Chemistry, Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi, India
| | - Rebecca C. Wade
- Molecular and Cellular Modelling Group, Heidelberg Institute of Theoretical Studies, Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Goutam Mukherjee
- Molecular and Cellular Modelling Group, Heidelberg Institute of Theoretical Studies, Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany
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Integrating molecular modelling methods to advance influenza A virus drug discovery. Drug Discov Today 2020; 26:503-510. [PMID: 33220433 DOI: 10.1016/j.drudis.2020.11.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/20/2020] [Accepted: 11/11/2020] [Indexed: 11/20/2022]
Abstract
Since the discovery of the anti-influenza drugs oseltamivir and zanamivir using computer-aided drug design methods, there have been significant applications of molecular modelling methodologies applied to influenza A virus drug discovery, such as molecular dynamics (MD) simulation, molecular docking, and virtual screening (VS). In this review, we provide a brief general introduction to molecular modelling in the context of drug discovery and then focus on the advances and impact of integrating these methods with specific reference to potential influenza A antiviral drug targets.
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Singh N, Decroly E, Khatib AM, Villoutreix BO. Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages. Eur J Pharm Sci 2020; 153:105495. [PMID: 32730844 PMCID: PMC7384984 DOI: 10.1016/j.ejps.2020.105495] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/16/2020] [Accepted: 07/27/2020] [Indexed: 12/28/2022]
Abstract
In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177, F-59000 Lille, France
| | | | - Abdel-Majid Khatib
- Univ. Bordeaux, Allée Geoffroy St Hilaire, 33615 Pessac, France
- INSERM, LAMC, UMR 1029, Allée Geoffroy St Hilaire, 33615 Pessac, France
- Corresponding authors.
| | - Bruno O. Villoutreix
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177, F-59000 Lille, France
- Corresponding authors.
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