1
|
Burger PB, Hu X, Balabin I, Muller M, Stanley M, Joubert F, Kaiser TM. FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology. J Chem Inf Model 2024; 64:3812-3825. [PMID: 38651738 PMCID: PMC11094716 DOI: 10.1021/acs.jcim.4c00071] [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: 01/12/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.
Collapse
Affiliation(s)
- Pieter B. Burger
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Xiaohu Hu
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ilya Balabin
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Morné Muller
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Megan Stanley
- Microsoft
Research AI4Science, 21 Station Road, Cambridge CB1 2FB, U.K.
| | - Fourie Joubert
- Centre
for Bioinformatics and Computational Biology, Department of Biochemistry,
Genetics and Microbiology, University of
Pretoria, Pretoria 0001, South Africa
| | - Thomas M. Kaiser
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| |
Collapse
|
2
|
Summa CM, Langford DP, Dinshaw SH, Webb J, Rick SW. Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding. J Chem Theory Comput 2024; 20:2812-2819. [PMID: 38538531 DOI: 10.1021/acs.jctc.4c00057] [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: 04/10/2024]
Abstract
Computer simulation methods can aid in the rational design of drugs aimed at a specific target, typically a protein. The affinity of a drug for its target is given by the free energy of binding. Binding can be further characterized by the enthalpy and entropy changes in the process. Methods exist to determine exact free energies, enthalpies, and entropies that are dependent only on the quality of the potential model and adequate sampling of conformational degrees of freedom. Entropy and enthalpy are roughly an order of magnitude more difficult to calculate than the free energy. This project combines a replica exchange method for enhanced sampling, designed to be efficient for protein-sized systems, with free energy calculations. This approach, replica exchange with dynamical scaling (REDS), uses two conventional simulations at different temperatures so that the entropy can be found from the temperature dependence of the free energy. A third replica is placed between them, with a modified Hamiltonian that allows it to span the temperature range of the conventional replicas. REDS provides temperature-dependent data and aids in sampling. It is applied to the bromodomain-containing protein 4 (BRD4) system. We find that for the force fields used, the free energies are accurate but the entropies and enthalpies are not, with the entropic contribution being too positive. Reproducing the entropy and enthalpy of binding appears to be a more stringent test of the force fields than reproducing the free energy.
Collapse
Affiliation(s)
- Christopher M Summa
- Department of Computer Science, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Dillon P Langford
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Sam H Dinshaw
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Jennifer Webb
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Steven W Rick
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| |
Collapse
|
3
|
Ries B, Alibay I, Swenson DWH, Baumann HM, Henry MM, Eastwood JRB, Gowers RJ. Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations. J Chem Theory Comput 2024; 20:1862-1877. [PMID: 38330251 PMCID: PMC10941767 DOI: 10.1021/acs.jctc.3c01206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.
Collapse
Affiliation(s)
- Benjamin Ries
- Medicinal
Chemistry, Boehringer Ingelheim Pharma GmbH
& Co KG, Birkendorfer Str 65, 88397 Biberach an der Riss, Germany
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Irfan Alibay
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - David W. H. Swenson
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Hannah M. Baumann
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Michael M. Henry
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
- Computational
and Systems Biology Program, Sloan Kettering
Institute, Memorial Sloan Kettering Cancer Center, New York, 1275 New York, United States
| | - James R. B. Eastwood
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Richard J. Gowers
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| |
Collapse
|
4
|
Wang S, Liu F, Li P, Wang JN, Mo Y, Lin B, Mei Y. Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations. Phys Chem Chem Phys 2024; 26:5377-5386. [PMID: 38269624 DOI: 10.1039/d3cp05582e] [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: 01/26/2024]
Abstract
Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.
Collapse
Affiliation(s)
- Shipeng Wang
- School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Fengjiao Liu
- School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Pengfei Li
- Single Particle, LLC, 10531 4S Commons Dr 166-629, San Diego, CA 92127, USA
| | - Jia-Ning Wang
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
| | - Yan Mo
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Bin Lin
- Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang 110016, China.
| | - Ye Mei
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| |
Collapse
|
5
|
Bashir Y, Noor F, Ahmad S, Tariq MH, Qasim M, Tahir Ul Qamar M, Almatroudi A, Allemailem KS, Alrumaihi F, Alshehri FF. Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer. J Biomol Struct Dyn 2024; 42:1099-1109. [PMID: 37021492 DOI: 10.1080/07391102.2023.2198017] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/28/2023] [Indexed: 04/07/2023]
Abstract
Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Yasir Bashir
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - Fatima Noor
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - Sajjad Ahmad
- Department of Health and Biological Sciences, Abasyn University, Peshawar, Pakistan
| | | | - Muhammad Qasim
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - Muhammad Tahir Ul Qamar
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - Ahmad Almatroudi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Khaled S Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Faris Alrumaihi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Faez Falah Alshehri
- College of Applied Medical Sciences, Shaqra University, Aldawadmi, Saudi Arabia
| |
Collapse
|
6
|
Kralj S, Jukič M, Bahun M, Kranjc L, Kolarič A, Hodošček M, Ulrih NP, Bren U. Identification of Triazolopyrimidinyl Scaffold SARS-CoV-2 Papain-Like Protease (PL pro) Inhibitor. Pharmaceutics 2024; 16:169. [PMID: 38399230 PMCID: PMC10893172 DOI: 10.3390/pharmaceutics16020169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/25/2024] Open
Abstract
The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its companion disease, COVID-19, has reminded us of the importance of basic coronaviral research. In this study, a comprehensive approach using molecular docking, in vitro assays, and molecular dynamics simulations was applied to identify potential inhibitors for SARS-CoV-2 papain-like protease (PLpro), a key and underexplored viral enzyme target. A focused protease inhibitor library was initially created and molecular docking was performed using CmDock software (v0.2.0), resulting in the selection of hit compounds for in vitro testing on the isolated enzyme. Among them, compound 372 exhibited promising inhibitory properties against PLpro, with an IC50 value of 82 ± 34 μM. The compound also displayed a new triazolopyrimidinyl scaffold not yet represented within protease inhibitors. Molecular dynamics simulations demonstrated the favorable binding properties of compound 372. Structural analysis highlighted its key interactions with PLpro, and we stress its potential for further optimization. Moreover, besides compound 372 as a candidate for PLpro inhibitor development, this study elaborates on the PLpro binding site dynamics and provides a valuable contribution for further efforts in pan-coronaviral PLpro inhibitor development.
Collapse
Affiliation(s)
- Sebastjan Kralj
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova Ulica 17, SI-2000 Maribor, Slovenia
| | - Marko Jukič
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova Ulica 17, SI-2000 Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška Ulica 8, SI-6000 Koper, Slovenia
- Institute of Enviormental Protection and Sensors, Beloruska Ulica 7, SI-2000 Maribor, Slovenia
| | - Miha Bahun
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana, Slovenia
| | - Luka Kranjc
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana, Slovenia
- National Institute of Biology, Večna Pot 111, SI-1000 Ljubljana, Slovenia
| | - Anja Kolarič
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova Ulica 17, SI-2000 Maribor, Slovenia
| | - Milan Hodošček
- National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Nataša Poklar Ulrih
- Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana, Slovenia
| | - Urban Bren
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova Ulica 17, SI-2000 Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška Ulica 8, SI-6000 Koper, Slovenia
- Institute of Enviormental Protection and Sensors, Beloruska Ulica 7, SI-2000 Maribor, Slovenia
| |
Collapse
|
7
|
Janela T, Bajorath J. Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs. J Chem Inf Model 2023; 63:7032-7044. [PMID: 37943257 DOI: 10.1021/acs.jcim.3c01530] [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: 11/10/2023]
Abstract
Potency predictions are popular in compound design and optimization but are complicated by intrinsic limitations. Moreover, even for nonlinear methods, activity cliffs (ACs, formed by structural analogues with large potency differences) represent challenging test cases for compound potency predictions. We have devised a new test system for potency predictions, including AC compounds, that is based on partitioned matched molecular pairs (MMP) and makes it possible to monitor prediction accuracy at the level of analogue pairs with increasing potency differences. The results of systematic predictions using different machine learning and control methods on MMP-based data sets revealed increasing prediction errors when potency differences between corresponding training and test compounds increased, including large prediction errors for AC compounds. At the global level, these prediction errors were not apparent due to the statistical dominance of analogue pairs with small potency differences. Test compounds from such pairs were accurately predicted and determined the observed global prediction accuracy. Shapley value analysis, an explainable artificial intelligence approach, was applied to identify structural features determining potency predictions using different methods. The analysis revealed that numerical predictions of different regression models were determined by features that were shared by MMP partner compounds or absent in these compounds, with opposing effects. These findings provided another rationale for accurate predictions of similar potency values for structural analogues and failures in predicting the potency of AC compounds.
Collapse
Affiliation(s)
- Tiago Janela
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
- Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
| |
Collapse
|
8
|
Bobrovs R, Drunka L, Kanepe I, Jirgensons A, Caflisch A, Salvalaglio M, Jaudzems K. Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors. J Chem Inf Model 2023; 63:6890-6899. [PMID: 37801405 DOI: 10.1021/acs.jcim.3c00826] [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: 10/08/2023]
Abstract
Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.
Collapse
Affiliation(s)
- Raitis Bobrovs
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Laura Drunka
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Iveta Kanepe
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Aigars Jirgensons
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Matteo Salvalaglio
- Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom
| | - Kristaps Jaudzems
- Latvian Institute of Organic Synthesis, Aizkraukles 21, Riga LV1006, Latvia
| |
Collapse
|
9
|
Janela T, Bajorath J. Rationalizing general limitations in assessing and comparing methods for compound potency prediction. Sci Rep 2023; 13:17816. [PMID: 37857835 PMCID: PMC10587074 DOI: 10.1038/s41598-023-45086-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023] Open
Abstract
Compound potency predictions play a major role in computational drug discovery. Predictive methods are typically evaluated and compared in benchmark calculations that are widely applied. Previous studies have revealed intrinsic limitations of potency prediction benchmarks including very similar performance of increasingly complex machine learning methods and simple controls and narrow error margins separating machine learning from randomized predictions. However, origins of these limitations are currently unknown. We have carried out an in-depth analysis of potential reasons leading to artificial outcomes of potency predictions using different methods. Potency predictions on activity classes typically used in benchmark settings were found to be determined by compounds with intermediate potency close to median values of the compound data sets. The potency of these compounds was consistently predicted with high accuracy, without the need for learning, which dominated the results of benchmark calculations, regardless of the activity classes used. Taken together, our findings provide a clear rationale for general limitations of compound potency benchmark predictions and a basis for the design of alternative test systems for methodological comparisons.
Collapse
Affiliation(s)
- Tiago Janela
- B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Department of Life Science Informatics and Data Science, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany
| | - Jürgen Bajorath
- B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Department of Life Science Informatics and Data Science, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany.
| |
Collapse
|
10
|
Yu J, Li Z, Chen G, Kong X, Hu J, Wang D, Cao D, Li Y, Huo R, Wang G, Liu X, Jiang H, Li X, Luo X, Zheng M. Computing the relative binding affinity of ligands based on a pairwise binding comparison network. NATURE COMPUTATIONAL SCIENCE 2023; 3:860-872. [PMID: 38177766 PMCID: PMC10766524 DOI: 10.1038/s43588-023-00529-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/05/2023] [Indexed: 01/06/2024]
Abstract
Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrödinger's FEP+, which is much more computationally intensive and requires substantial expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 473%. Finally, for the convenience of users, a web service for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface.
Collapse
Affiliation(s)
- Jie Yu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Information Science and Technology, Shanghai Tech University, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Zhaojun Li
- College of Computer and Information Engineering, Dezhou University, Dezhou City, China
- Development Department, Suzhou Alphama Biotechnology Co., Ltd, Suzhou City, China
| | - Geng Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Xiangtai Kong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jie Hu
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Duanhua Cao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yanbei Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Ruifeng Huo
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Gang Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaohong Liu
- Development Department, Suzhou Alphama Biotechnology Co., Ltd, Suzhou City, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
- State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, China.
| |
Collapse
|
11
|
Conflitti P, Raniolo S, Limongelli V. Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness. J Chem Theory Comput 2023; 19:6047-6061. [PMID: 37656199 PMCID: PMC10536999 DOI: 10.1021/acs.jctc.3c00641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 09/02/2023]
Abstract
Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.
Collapse
Affiliation(s)
- Paolo Conflitti
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Stefano Raniolo
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
| | - Vittorio Limongelli
- Faculty
of Biomedical Sciences, Euler Institute, Universitá della Svizzera italiana (USI), 6900 Lugano, Switzerland
- Department
of Pharmacy, University of Naples “Federico
II”, 80131 Naples, Italy
| |
Collapse
|
12
|
Chen H, Bajorath J. Meta-learning for transformer-based prediction of potent compounds. Sci Rep 2023; 13:16145. [PMID: 37752164 PMCID: PMC10522638 DOI: 10.1038/s41598-023-43046-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design.
Collapse
Affiliation(s)
- Hengwei Chen
- Department of Life Science Informatics and Data Science, B-IT, Lamarr Institute for Machine Learning and Artificial Intelligence, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, Lamarr Institute for Machine Learning and Artificial Intelligence, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany.
| |
Collapse
|
13
|
Karwounopoulos J, Kaupang Å, Wieder M, Boresch S. Calculations of Absolute Solvation Free Energies with Transformato─Application to the FreeSolv Database Using the CGenFF Force Field. J Chem Theory Comput 2023; 19:5988-5998. [PMID: 37616333 PMCID: PMC10500982 DOI: 10.1021/acs.jctc.3c00691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Indexed: 08/26/2023]
Abstract
We recently introduced transformato, an open-source Python package for the automated setup of large-scale calculations of relative solvation and binding free energy differences. Here, we extend the capabilities of transformato to the calculation of absolute solvation free energy differences. After careful validation against the literature results and reference calculations with the PERT module of CHARMM, we used transformato to compute absolute solvation free energies for most molecules in the FreeSolv database (621 out of 642). The force field parameters were obtained with the program cgenff (v2.5.1), which derives missing parameters from the CHARMM general force field (CGenFF v4.6). A long-range correction for the Lennard-Jones interactions was added to all computed solvation free energies. The mean absolute error compared to the experimental data is 1.12 kcal/mol. Our results allow a detailed comparison between the AMBER and CHARMM general force fields and provide a more in-depth understanding of the capabilities and limitations of the CGenFF small molecule parameters.
Collapse
Affiliation(s)
- Johannes Karwounopoulos
- Faculty
of Chemistry, Institute of Computational Biological Chemistry, University of Vienna, Währingerstr. 17, 1090 Vienna, Austria
- Vienna
Doctoral School of Chemistry (DoSChem), University of Vienna, Währingerstr. 42, 1090 Vienna, Austria
| | - Åsmund Kaupang
- Department
of Pharmacy, Section for Pharmaceutical Chemistry, University of Oslo, 0316 Oslo, Norway
| | - Marcus Wieder
- Department
of Pharmaceutical Sciences, Pharmaceutical Chemistry Division, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria
| | - Stefan Boresch
- Faculty
of Chemistry, Institute of Computational Biological Chemistry, University of Vienna, Währingerstr. 17, 1090 Vienna, Austria
| |
Collapse
|
14
|
Arias HR, Pierce SR, Germann AL, Xu SQ, Ortells MO, Sakamoto S, Manetti D, Romanelli MN, Hamachi I, Akk G. Chemical, Pharmacological, and Structural Characterization of Novel Acrylamide-Derived Modulators of the GABA A Receptor. Mol Pharmacol 2023; 104:115-131. [PMID: 37316350 PMCID: PMC10441626 DOI: 10.1124/molpharm.123.000692] [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: 03/01/2023] [Revised: 05/22/2023] [Accepted: 06/01/2023] [Indexed: 06/16/2023] Open
Abstract
Acrylamide-derived compounds have been previously shown to act as modulators of members of the Cys-loop transmitter-gated ion channel family, including the mammalian GABAA receptor. Here we have synthesized and functionally characterized the GABAergic effects of a series of novel compounds (termed "DM compounds") derived from the previously characterized GABAA and the nicotinic α7 receptor modulator (E)-3-furan-2-yl-N-p-tolyl-acrylamide (PAM-2). Fluorescence imaging studies indicated that the DM compounds increase apparent affinity to the transmitter by up to 80-fold in the ternary αβγ GABAA receptor. Using electrophysiology, we show that the DM compounds, and the structurally related (E)-3-furan-2-yl-N-phenylacrylamide (PAM-4), have concurrent potentiating and inhibitory effects that can be isolated and observed under appropriate recording conditions. The potentiating efficacies of the DM compounds are similar to those of neurosteroids and benzodiazepines (ΔG ∼ -1.5 kcal/mol). Molecular docking, functionally confirmed by site-directed mutagenesis experiments, indicate that receptor potentiation is mediated by interactions with the classic anesthetic binding sites located in the transmembrane domain of the intersubunit interfaces. Inhibition by the DM compounds and PAM-4 was abolished in the receptor containing the α1(V256S) mutation, suggestive of similarities in the mechanism of action with that of inhibitory neurosteroids. Functional competition and mutagenesis experiments, however, indicate that the sites mediating inhibition by the DM compounds and PAM-4 differ from those mediating the action of the inhibitory steroid pregnenolone sulfate. SIGNIFICANCE STATEMENT: We have synthesized and characterized the actions of novel acrylamide-derived compounds on the mammalian GABAA receptor. We show that the compounds have concurrent potentiating effects mediated by the classic anesthetic binding sites, and inhibitory actions that bear mechanistic resemblance to but do not share binding sites with, the inhibitory steroid pregnenolone sulfate.
Collapse
Affiliation(s)
- Hugo R Arias
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Spencer R Pierce
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Allison L Germann
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Sophia Q Xu
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Marcelo O Ortells
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Seiji Sakamoto
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Dina Manetti
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Maria Novella Romanelli
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Itaru Hamachi
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| | - Gustav Akk
- Department of Pharmacology and Physiology, Oklahoma State University College of Osteopathic Medicine, Tahlequah, Oklahoma (H.R.A.); Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (S.R.P., A.L.G., S.Q.X., G.A.); Facultad de Medicina, Universidad de Morón, Morón, and CONICET, Argentina (M.O.O.); Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, Japan (S.S., I.H.); Department of Neurosciences, Psychology, Drug Research and Child Health Section of Pharmaceutical and Nutraceutical Sciences, University of Florence, Florence, Italy (D.M., M.N.R.); The Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, Missouri (G.A.)
| |
Collapse
|
15
|
Tang R, Wang Z, Xiang S, Wang L, Yu Y, Wang Q, Deng Q, Hou T, Sun H. Uncovering the Kinetic Characteristics and Degradation Preference of PROTAC Systems with Advanced Theoretical Analyses. JACS AU 2023; 3:1775-1789. [PMID: 37388700 PMCID: PMC10301679 DOI: 10.1021/jacsau.3c00195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 07/01/2023]
Abstract
Proteolysis-targeting chimeras (PROTACs), which can selectively induce the degradation of target proteins, represent an attractive technology in drug discovery. A large number of PROTACs have been reported, but due to the complicated structural and kinetic characteristics of the target-PROTAC-E3 ligase ternary interaction process, the rational design of PROTACs is still quite challenging. Here, we characterized and analyzed the kinetic mechanism of MZ1, a PROTAC that targets the bromodomain (BD) of the bromodomain and extra terminal (BET) protein (Brd2, Brd3, or Brd4) and von Hippel-Lindau E3 ligase (VHL), from the kinetic and thermodynamic perspectives of view by using enhanced sampling simulations and free energy calculations. The simulations yielded satisfactory predictions on the relative residence time and standard binding free energy (rp > 0.9) for MZ1 in different BrdBD-MZ1-VHL ternary complexes. Interestingly, the simulation of the PROTAC ternary complex disintegration illustrates that MZ1 tends to remain on the surface of VHL with the BD proteins dissociating alone without a specific dissociation direction, indicating that the PROTAC prefers more to bind with E3 ligase at the first step in the formation of the target-PROTAC-E3 ligase ternary complex. Further exploration of the degradation difference of MZ1 in different Brd systems shows that the PROTAC with higher degradation efficiency tends to leave more lysine exposed on the target protein, which is guaranteed by the stability (binding affinity) and durability (residence time) of the target-PROTAC-E3 ligase ternary complex. It is quite possible that the underlying binding characteristics of the BrdBD-MZ1-VHL systems revealed by this study may be shared by different PROTAC systems as a general rule, which may accelerate rational PROTAC design with higher degradation efficiency.
Collapse
Affiliation(s)
- Rongfan Tang
- Department
of Medicinal Chemistry, China Pharmaceutical
University, Nanjing 210009, Jiangsu, P. R. China
| | - Zhe Wang
- Innovation
Institute for Artificial Intelligence in Medicine of Zhejiang University,
College of Pharmaceutical Sciences, Zhejiang
University, Hangzhou 310058, Zhejiang, P. R. China
| | - Sutong Xiang
- Department
of Medicinal Chemistry, China Pharmaceutical
University, Nanjing 210009, Jiangsu, P. R. China
| | - Lingling Wang
- Department
of Medicinal Chemistry, China Pharmaceutical
University, Nanjing 210009, Jiangsu, P. R. China
| | - Yang Yu
- Department
of Medicinal Chemistry, China Pharmaceutical
University, Nanjing 210009, Jiangsu, P. R. China
| | - Qinghua Wang
- Department
of Medicinal Chemistry, China Pharmaceutical
University, Nanjing 210009, Jiangsu, P. R. China
| | - Qirui Deng
- Department
of Medicinal Chemistry, China Pharmaceutical
University, Nanjing 210009, Jiangsu, P. R. China
| | - Tingjun Hou
- Innovation
Institute for Artificial Intelligence in Medicine of Zhejiang University,
College of Pharmaceutical Sciences, Zhejiang
University, Hangzhou 310058, Zhejiang, P. R. China
| | - Huiyong Sun
- Department
of Medicinal Chemistry, China Pharmaceutical
University, Nanjing 210009, Jiangsu, P. R. China
| |
Collapse
|
16
|
Alamri MA. Bioinformatics and network pharmacology-based study to elucidate the multi-target pharmacological mechanism of the indigenous plants of Medina valley in treating HCV-related hepatocellular carcinoma. Saudi Pharm J 2023; 31:1125-1138. [PMID: 37293382 PMCID: PMC10244409 DOI: 10.1016/j.jsps.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 04/03/2023] [Indexed: 06/10/2023] Open
Abstract
The incidence of Hepatocellular Carcinoma (HCC) in Saudi Arabia is not surprising given the relatively high prevalence of hepatitis C virus (HCV) infection. Hepatitis C is also common in Saudi Arabia with a prevalence rate of 1% to 3% of the population, which further increases the risk of HCC. The incidence of HCC has been increasing in recent years, with HCV-related HCC accounting for a significant proportion of cases. Traditional medicine has long been a part of Saudi Arabian culture, and many medicinal plants have been used for centuries to treat various ailments, including cancer. Following that, this study combines network pharmacology with bioinformatics approaches to potentially revolutionize HCV-related HCC treatment by identifying effective phytochemicals of indigenous plants of Medina valley. Eight indigenous plants including Rumex vesicarius, Withania somnifera, Rhazya stricta, Heliotropium arbainense, Asphodelus fistulosus, Pulicaria incise, Commicarpus grandiflorus, and Senna alexandrina, were selected for the initial screening of potential drug-like compounds. At first, the information related to active compounds of eight indigenous plants was retrieved from public databases and through literature review which was later combined with differentially expressed genes (DEGs) obtained through microarray datasets. Later, a compound-target genes-disease network was constructed which uncovered that kaempferol, rhazimol, beta-sitosterol, 12-Hydroxy-3-keto-bisnor-4-cholenic acid, 5-O-caffeoylquinic acid, 24-Methyldesmosterol, stigmasterone, fucosterol, and withanolide_J decisively contributed to the cell growth and proliferation by affecting ALB and PTGS2 proteins. Moreover, the molecular docking and Molecular Dynamic (MD) simulation of 20 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. But the findings were not validated in actual patients, so further investigation is needed to confirm the potential use of selected medicinal plants towards HCV-related HC.
Collapse
Affiliation(s)
- Mubarak A. Alamri
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
| |
Collapse
|
17
|
Chung MKJ, Miller RJ, Novak B, Wang Z, Ponder JW. Accurate Host-Guest Binding Free Energies Using the AMOEBA Polarizable Force Field. J Chem Inf Model 2023; 63:2769-2782. [PMID: 37075788 PMCID: PMC10878370 DOI: 10.1021/acs.jcim.3c00155] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
A grand challenge of computational biophysics is accurate prediction of interactions between molecules. Molecular dynamics (MD) simulations have recently gained much interest as a tool to directly compute rigorous intermolecular binding affinities. The choice of a fixed point-charge or polarizable multipole force field used in MD is a topic of ongoing discussion. To compare alternative methods, we participated in the SAMPL7 and SAMPL8 Gibb octaacid host-guest challenges to assess the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) polarizable multipole force field. Advantages of AMOEBA over fixed charge models include improved representation of molecular electrostatic potentials and better description of water occupying the unligated host cavity. Prospective predictions for 26 host-guest systems exhibit a mean unsigned error vs experiment of 0.848 kcal/mol across all absolute binding free energies, demonstrating excellent agreement between computational and experimental results. In addition, we explore two topics related to the inclusion of ions in MD simulations: use of a neutral co-alchemical protocol and the effect of salt concentration on binding affinity. Use of the co-alchemical method minimally affects computed energies, but salt concentration significantly perturbs our binding results. Higher salt concentration strengthens binding through classical charge screening. In particular, added Na+ ions screen negatively charged carboxylate groups near the binding cavity, thereby diminishing repulsive coulomb interactions with negatively charged guests. Overall, the AMOEBA results demonstrate the accuracy available through a force field providing a detailed energetic description of the four octaacid hosts and 13 charged organic guests. Use of the AMOEBA polarizable atomic multipole force field in conjunction with an alchemical free energy protocol can achieve chemical accuracy in application to realistic molecular systems.
Collapse
Affiliation(s)
- Moses K. J. Chung
- Medical Scientist Training Program, Washington University School of Medicine, Saint Louis, MO 63110, USA
- Department of Physics, Washington University in St. Louis, Saint Louis, MO 63130, USA
| | - Ryan J. Miller
- Department of Chemistry, Washington University in St. Louis, Saint Louis, MO 63130, USA
| | - Borna Novak
- Medical Scientist Training Program, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Zhi Wang
- Department of Chemistry, Washington University in St. Louis, Saint Louis, MO 63130, USA
| | - Jay W. Ponder
- Department of Chemistry, Washington University in St. Louis, Saint Louis, MO 63130, USA
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, Saint Louis, MO 63110, USA
| |
Collapse
|
18
|
Chen H, Bajorath J. Designing highly potent compounds using a chemical language model. Sci Rep 2023; 13:7412. [PMID: 37150793 PMCID: PMC10164739 DOI: 10.1038/s41598-023-34683-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/05/2023] [Indexed: 05/09/2023] Open
Abstract
Compound potency prediction is a major task in medicinal chemistry and drug design. Inspired by the concept of activity cliffs (which encode large differences in potency between similar active compounds), we have devised a new methodology for predicting potent compounds from weakly potent input molecules. Therefore, a chemical language model was implemented consisting of a conditional transformer architecture for compound design guided by observed potency differences. The model was evaluated using a newly generated compound test system enabling a rigorous assessment of its performance. It was shown to predict known potent compounds from different activity classes not encountered during training. Moreover, the model was capable of creating highly potent compounds that were structurally distinct from input molecules. It also produced many novel candidate compounds not included in test sets. Taken together, the findings confirmed the ability of the new methodology to generate structurally diverse highly potent compounds.
Collapse
Affiliation(s)
- Hengwei Chen
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany.
| |
Collapse
|
19
|
Losev TV, Gerasimov IS, Panova MV, Lisov AA, Abdyusheva YR, Rusina PV, Zaletskaya E, Stroganov OV, Medvedev MG, Novikov FN. Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design. J Chem Inf Model 2023; 63:1239-1248. [PMID: 36763797 DOI: 10.1021/acs.jcim.2c01212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Bioisosteres are molecules that differ in substituents but still have very similar shapes. Bioisosteric replacements are ubiquitous in modern drug design, where they are used to alter metabolism, change bioavailability, or modify activity of the lead compound. Prediction of relative affinities of bioisosteres with computational methods is a long-standing task; however, the very shape closeness makes bioisosteric substitutions almost intractable for computational methods, which use standard force fields. Here, we design a quantum mechanical (QM)-cluster approach based on the GFN2-xTB semi-empirical quantum-chemical method and apply it to a set of H → F bioisosteric replacements. The proposed methodology enables advanced prediction of biological activity change upon bioisosteric substitution of -H with -F, with the standard deviation of 0.60 kcal/mol, surpassing the ChemPLP scoring function (0.83 kcal/mol), and making QM-based ΔΔG estimation comparable to ∼0.42 kcal/mol standard deviation of in vitro experiment. The speed of the method and lack of tunable parameters makes it affordable in current drug research.
Collapse
Affiliation(s)
- Timofey V Losev
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation.,Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russian Federation.,A.N. Nesmeyanov Institute of Organoelement Compounds of Russian Academy of Sciences, Vavilov Str. 28, 119991 Moscow, Russian Federation
| | - Igor S Gerasimov
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation.,Department of Chemistry, Kyungpook National University, Daegu 41566, South Korea
| | - Maria V Panova
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation
| | - Alexey A Lisov
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation
| | - Yana R Abdyusheva
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation.,National Research University Higher School of Economics, Myasnitskaya Street 20, 101000 Moscow, Russian Federation
| | - Polina V Rusina
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation
| | - Eugenia Zaletskaya
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation.,National Research University Higher School of Economics, Myasnitskaya Street 20, 101000 Moscow, Russian Federation
| | - Oleg V Stroganov
- BioMolTech Corp., 226 York Mills Rd, Toronto, Ontario M2L 1L1, Canada
| | - Michael G Medvedev
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation
| | - Fedor N Novikov
- N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky prospect 47, 119991 Moscow, Russian Federation.,National Research University Higher School of Economics, Myasnitskaya Street 20, 101000 Moscow, Russian Federation
| |
Collapse
|
20
|
Crawford B, Timalsina U, Quach CD, Craven NC, Gilmer JB, McCabe C, Cummings PT, Potoff JJ. MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC. J Chem Inf Model 2023; 63:1218-1228. [PMID: 36791286 DOI: 10.1021/acs.jcim.2c01498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script. All relevant simulation parameters are encoded within the workflow, ensuring reproducible simulations. MoSDeF-GOMC's capabilities are illustrated through a number of examples, including prediction of the adsorption isotherm for CO2 in IRMOF-1, free energies of hydration for neon and radon over a broad temperature range, and the vapor-liquid coexistence curve of a four-component surrogate for the jet fuel S-8. The MoSDeF-GOMC software is available on GitHub at https://github.com/GOMC-WSU/MoSDeF-GOMC.
Collapse
Affiliation(s)
- Brad Crawford
- Department of Chemical Engineering, Wayne State University, Detroit, Michigan 48202-4050, United States
| | - Umesh Timalsina
- Institute for Software Integrated Systems (ISIS), Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Co D Quach
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1604, United States.,Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Nicholas C Craven
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States.,Interdisciplinary Material Science Program, Vanderbilt University, Nashville, Tennessee 37235-0106, United States
| | - Justin B Gilmer
- Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States.,Interdisciplinary Material Science Program, Vanderbilt University, Nashville, Tennessee 37235-0106, United States
| | - Clare McCabe
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1604, United States.,Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Peter T Cummings
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1604, United States.,Multiscale Modeling and Simulation (MuMS) Center, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Jeffrey J Potoff
- Department of Chemical Engineering, Wayne State University, Detroit, Michigan 48202-4050, United States
| |
Collapse
|
21
|
Freire TS, Caracelli I, Zukerman-Schpector J, Friedman R. Resistance to a tyrosine kinase inhibitor mediated by changes to the conformation space of the kinase. Phys Chem Chem Phys 2023; 25:6175-6183. [PMID: 36752538 DOI: 10.1039/d2cp05549j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Gilteritinib is a highly selective and effective inhibitor of the FLT3/ITD mutated protein, and is used successfully in treating acute myeloid leukaemia (AML). Unfortunately, tumour cells gradually develop resistance to gilteritinib due to mutations in the molecular drug target. The atomistic details behind this observed resistance are not clear, since the protein structure of the complex is only available in the inactive state, while the drug binds better to the active state. To overcome this limitation, we used a computer-aided approach where we docked gilteritinib to the active site of FLT3/ITD and calculated the Gibbs free energy difference between the binding energies of the parental and mutant enzymes. These calculations agreed with experimental estimations for one mutation (F691L) but not the other (D698N). To further understand how these mutations operate, we used metadynamics simulations to study the conformational landscape of the activation process. Both mutants show a lower activation energy barrier which suggests that they are more likely to adopt an active state until inhibited, making the mutant enzymes more active. This suggests that a higher efficiency of tyrosine kinases contributes to resistance not only against type 2 but also against type 1 kinase inhibitors.
Collapse
Affiliation(s)
- Thales Souza Freire
- Department of Physics, Federal University of São Carlos, São Carlos-SP, Brazil
| | - Ignez Caracelli
- Department of Physics, Federal University of São Carlos, São Carlos-SP, Brazil
| | | | - Ran Friedman
- Department of Chemistry and Biomedical Sciences, Linnæus University, 391 82 Kalmar, Sweden.
| |
Collapse
|
22
|
Li Y, Liu R, Liu J, Luo H, Wu C, Li Z. An Open Source Graph-Based Weighted Cycle Closure Method for Relative Binding Free Energy Calculations. J Chem Inf Model 2023; 63:561-570. [PMID: 36583975 DOI: 10.1021/acs.jcim.2c01076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle closure (cc) has proven to be an effective method in improving the calculation accuracy by correcting the hysteresis (summation of errors) of each closed cycle to the theoretical value 0. However, a primary hypothesis was made in the current cycle closure method that the hysteresis is evenly distributed to all paths, which is unlikely to be true in practice and may limit the further improvement of the calculation accuracy when better error estimation methods are available. Moreover, being a closed source software makes the current cycle closure method unachievable in many studies. In this paper, a newly implemented open source graph-based weighted cycle closure (wcc) algorithm was developed and introduced, not only including functions from the original cc method but also containing a new wcc method which can consider different error contributions from different paths and further improve the calculation accuracy. The wcc program also provides a new path-independent molecular error calculation method, which can be quite useful in many studies (like structure-activity relationship (SAR)) compared with the path-dependent method of the original cc program.
Collapse
Affiliation(s)
- Yishui Li
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha410073, Hunan, P.R. China.,Laboratory of Software Engineering for Complex System, National University of Defense Technology, Changsha410073, Hunan, P.R. China
| | - Runduo Liu
- School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou510275, Guangdong, P.R. China
| | - Jie Liu
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha410073, Hunan, P.R. China.,Laboratory of Software Engineering for Complex System, National University of Defense Technology, Changsha410073, Hunan, P.R. China
| | - Haibin Luo
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Pharmaceutical Sciences, Hainan University, Haikou570228, Hainan, P.R. China
| | - Chengkun Wu
- State Key Laboratory of High-Performance Computing, National University of Defense Technology, Changsha410073, Hunan, P.R. China
| | - Zhe Li
- School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou510275, Guangdong, P.R. China
| |
Collapse
|
23
|
Gorin G, Vastola JJ, Fang M, Pachter L. Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments. Nat Commun 2022; 13:7620. [PMID: 36494337 PMCID: PMC9734650 DOI: 10.1038/s41467-022-34857-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 11/09/2022] [Indexed: 12/13/2022] Open
Abstract
The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.
Collapse
Affiliation(s)
- Gennady Gorin
- grid.20861.3d0000000107068890Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125 USA
| | - John J. Vastola
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
| | - Meichen Fang
- grid.20861.3d0000000107068890Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125 USA
| | - Lior Pachter
- grid.20861.3d0000000107068890Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125 USA ,grid.20861.3d0000000107068890Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 USA
| |
Collapse
|
24
|
Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
Collapse
Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| |
Collapse
|
25
|
Adhesion of Bis-Salphen-Based Coordination Polymers to Graphene: Insights from Free Energy Perturbation Study. Polymers (Basel) 2022; 14:polym14214525. [DOI: 10.3390/polym14214525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022] Open
Abstract
Manipulation of nanoscale objects using molecular self-assembly is a potent tool to achieve large scale nanopatterning with small effort. Coordination polymers of bis-salphen compounds based on zinc have demonstrated their ability to align carbon nanotubes into micro-scale networks with an unusual “rings-and-rods” pattern. This paper investigates how the compounds interact with pristine and functionalized graphene using density functional theory calculations and molecular dynamic simulations. Using the free energy perturbation method we will show how the addition of phenyl side groups to the core compound and functionalization of graphene affect the stability, mobility and conformation adopted by a dimer of bis-(Zn)salphen compound adsorbed on graphene surface and what it can reveal about the arrangement of chains of bis-(Zn)salphen polymer around carbon nanotubes during the self-assembly of microscale networks.
Collapse
|
26
|
Gohda K. Conformational Analysis of the Loop-to-Helix Transition of the α-Helix3 Plastic Region in the N-Terminal Domain of Human Hsp90α by a Computational Biochemistry Approach. J Chem Inf Model 2022; 62:5699-5714. [PMID: 36278922 DOI: 10.1021/acs.jcim.2c00984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Hsp90 is a chaperone protein aiding in correct protein folding and attractive for drug discovery. The structure of human Hsp90α N-terminal domain (NTD) is intriguing since the α-helix3 region of the ATP-binding site in the NTD plastically changes its conformation, i.e., loop-out, loop-in, and helical conformations, according to the bound inhibitor type. The plastic region structure is known to influence the mode of inhibition-inhibitors bound to a helix have a longer residence time in the complex, which is a factor of in vivo-active drugs, compared with loop binders. In this study, we analyzed the loop-to-helix transition of the plastic region through binding of a helix binder by a computational biochemistry approach. To generate the helical transition from the loop, the resorcinol inhibitor C1 complexed with a loop-in structure was alchemically transformed to the C10 inhibitor, which is known as a helix binder. The loop in the C1 complex possesses Leu107 tightly binding to the hydrophobic subpocket, considered as a key residue for the plasticity. From 10 × 1 μs simulations after the alchemical transformation, the helical transition was observed with a 29% success rate. Conformational analysis of the simulations identified residues possibly associated with the helical transition. The implementation of additional simulations (dihedral-constrained and in silico mutant simulations) led to a statistically significant increase in the transition success rate to 78%, as observed in Asn105 psi-constrained simulation. Therefore, we concluded that the Asn105 psi dihedral angle is most likely involved in the helical transition by a change of the dihedral angle to gauche-negative.
Collapse
Affiliation(s)
- Keigo Gohda
- Computer-aided Molecular Modeling Research Center, Kansai (CAMM-Kansai), 3-32-302, Tsuto-Otsuka, Nishinomiya 663-8241, Japan
| |
Collapse
|
27
|
Bassman Oftelie L, Klymko K, Liu D, Tubman NM, de Jong WA. Computing Free Energies with Fluctuation Relations on Quantum Computers. PHYSICAL REVIEW LETTERS 2022; 129:130603. [PMID: 36206437 DOI: 10.1103/physrevlett.129.130603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/07/2022] [Accepted: 08/05/2022] [Indexed: 06/16/2023]
Abstract
As a central thermodynamic property, free energy enables the calculation of virtually any equilibrium property of a physical system, allowing for the construction of phase diagrams and predictions about transport, chemical reactions, and biological processes. Thus, methods for efficiently computing free energies, which in general is a difficult problem, are of great interest to broad areas of physics and the natural sciences. The majority of techniques for computing free energies target classical systems, leaving the computation of free energies in quantum systems less explored. Recently developed fluctuation relations enable the computation of free energy differences in quantum systems from an ensemble of dynamic simulations. While performing such simulations is exponentially hard on classical computers, quantum computers can efficiently simulate the dynamics of quantum systems. Here, we present an algorithm utilizing a fluctuation relation known as the Jarzynski equality to approximate free energy differences of quantum systems on a quantum computer. We discuss under which conditions our approximation becomes exact, and under which conditions it serves as a strict upper bound. Furthermore, we successfully demonstrate a proof of concept of our algorithm using the transverse field Ising model on a real quantum processor. As quantum hardware continues to improve, we anticipate that our algorithm will enable computation of free energy differences for a wide range of quantum systems useful across the natural sciences.
Collapse
Affiliation(s)
| | | | - Diyi Liu
- School of Mathematics, University of Minnesota, Minnesota 55455, USA
| | - Norm M Tubman
- NASA Ames Research Center, Mountain View, California 94035, USA
| | - Wibe A de Jong
- Lawrence Berkeley National Lab, Berkeley, California 94720
| |
Collapse
|
28
|
Rieder SR, Ries BJ, Kubincová A, Champion C, Barros EP, Hünenberger PH, Riniker S. Leveraging the Sampling Efficiency of RE-EDS in OpenMM Using a Shifted Reaction-Field With an Atom-Based Cutoff. J Chem Phys 2022; 157:104117. [DOI: 10.1063/5.0107935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Replica-exchange enveloping distribution sampling (RE-EDS) is a pathway-independent multistate free-energy method, currently implemented in the GROMOS software package for molecular dynamics (MD) simulations. It has a high intrinsic sampling efficiency as the interactions between the unperturbed particles have to be calculated only once for multiple end-states. As a result, RE-EDS is an attractive method for the calculation of relative solvation and binding free energies. An essential requirement for reaching this high efficiency is the separability of the nonbonded interactions into solute-solute, solute-environment, and environment-environment contributions. Such a partitioning is trivial when using a Coulomb term with a reaction-field (RF) correction to model the electrostatic interactions, but not when using lattice- sum schemes. To avoid cutoff artifacts, the RF correction is typically used in combination with a charge-group based cutoff, which is not supported by most small-molecule force fields and other MD engines. To address this issue, we investigate the combination of RE-EDS simulations with a recently introduced RF scheme including a shifting function that enables the rigorous calculation of RF electrostatics with atom-based cutoffs. The resulting approach is validated by calculating solvation free energies with the generalized AMBER force field (GAFF) in water and chloroform using both the GROMOS software package and a proof-of-concept implementation in OpenMM.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zurich D-CHAB, Switzerland
| |
Collapse
|
29
|
Patel LA, Chau P, Debesai S, Darwin L, Neale C. Drug Discovery by Automated Adaptation of Chemical Structure and Identity. J Chem Theory Comput 2022; 18:5006-5024. [PMID: 35834740 DOI: 10.1021/acs.jctc.1c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.
Collapse
|
30
|
Rieder SR, Ries B, Schaller K, Champion C, Barros EP, Hünenberger PH, Riniker S. Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules. J Chem Inf Model 2022; 62:3043-3056. [PMID: 35675713 PMCID: PMC9241072 DOI: 10.1021/acs.jcim.2c00383] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
Free-energy differences
between pairs of end-states can be estimated
based on molecular dynamics (MD) simulations using standard pathway-dependent
methods such as thermodynamic integration (TI), free-energy perturbation,
or Bennett’s acceptance ratio. Replica-exchange enveloping
distribution sampling (RE-EDS), on the other hand, allows for the
sampling of multiple end-states in a single simulation without the
specification of any pathways. In this work, we use the RE-EDS method
as implemented in GROMOS together with generalized AMBER force-field
(GAFF) topologies, converted to a GROMOS-compatible format with a
newly developed GROMOS++ program amber2gromos, to
compute relative hydration free energies for a series of benzene derivatives.
The results obtained with RE-EDS are compared to the experimental
data as well as calculated values from the literature. In addition,
the estimated free-energy differences in water and in vacuum are compared
to values from TI calculations carried out with GROMACS. The hydration
free energies obtained using RE-EDS for multiple molecules are found
to be in good agreement with both the experimental data and the results
calculated using other free-energy methods. While all considered free-energy
methods delivered accurate results, the RE-EDS calculations required
the least amount of total simulation time. This work serves as a validation
for the use of GAFF topologies with the GROMOS simulation package
and the RE-EDS approach. Furthermore, the performance of RE-EDS for
a large set of 28 end-states is assessed with promising results.
Collapse
Affiliation(s)
- Salomé R Rieder
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Benjamin Ries
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Kay Schaller
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Candide Champion
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Emilia P Barros
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Philippe H Hünenberger
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| |
Collapse
|
31
|
Maier S, Thapa B, Erickson J, Raghavachari K. Comparative assessment of QM-based and MM-based models for prediction of protein-ligand binding affinity trends. Phys Chem Chem Phys 2022; 24:14525-14537. [PMID: 35661842 DOI: 10.1039/d2cp00464j] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Methods which accurately predict protein-ligand binding strengths are critical for drug discovery. In the last two decades, advances in chemical modelling have enabled steadily accelerating progress in the discovery and optimization of structure-based drug design. Most computational methods currently used in this context are based on molecular mechanics force fields that often have deficiencies in describing the quantum mechanical (QM) aspects of molecular binding. In this study, we show the competitiveness of our QM-based Molecules-in-Molecules (MIM) fragmentation method for characterizing binding energy trends for seven different datasets of protein-ligand complexes. By using molecular fragmentation, the MIM method allows for accelerated QM calculations. We demonstrate that for classes of structurally similar ligands bound to a common receptor, MIM provides excellent correlation to experiment, surpassing the more popular Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) methods. The MIM method offers a relatively simple, well-defined protocol by which binding trends can be ascertained at the QM level and is suggested as a promising option for lead optimization in structure-based drug design.
Collapse
Affiliation(s)
- Sarah Maier
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA.
| | - Bishnu Thapa
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA. .,Lilly Research Laboratories, Eli Lilly & Co., Indianapolis, Indiana 47285, USA
| | - Jon Erickson
- Lilly Research Laboratories, Eli Lilly & Co., Indianapolis, Indiana 47285, USA
| | | |
Collapse
|
32
|
Patel D, Cox BD, Kasthuri M, Mengshetti S, Bassit L, Verma K, Ollinger-Russell O, Amblard F, Schinazi RF. In silico design of a novel nucleotide antiviral agent by free energy perturbation. Chem Biol Drug Des 2022; 99:801-815. [PMID: 35313085 PMCID: PMC9175506 DOI: 10.1111/cbdd.14042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/03/2022] [Accepted: 03/05/2022] [Indexed: 11/30/2022]
Abstract
Nucleoside analogs are the backbone of antiviral therapies. Drugs from this class undergo processing by host or viral kinases to form the active nucleoside triphosphate species that selectively inhibits the viral polymerase. It is the central hypothesis that the nucleoside triphosphate analog must be a favorable substrate for the viral polymerase and the nucleoside precursor must be a satisfactory substrate for the host kinases to inhibit viral replication. Herein, free energy perturbation (FEP) was used to predict substrate affinity for both host and viral enzymes. Several uridine 5'-monophosphate prodrug analogs known to inhibit hepatitis C virus (HCV) were utilized in this study to validate the use of FEP. Binding free energies to the host monophosphate kinase and viral RNA-dependent RNA polymerase (RdRp) were calculated for methyl-substituted uridine analogs. The 2'-C-methyl-uridine and 4'-C-methyl-uridine scaffolds delivered favorable substrate binding to the host kinase and HCV RdRp that were consistent with results from cellular antiviral activity in support of our new approach. In a prospective evaluation, FEP results suggest that 2'-C-dimethyl-uridine scaffold delivered favorable monophosphate and triphosphate substrates for both host kinase and HCV RdRp, respectively. Novel 2'-C-dimethyl-uridine monophosphate prodrug was synthesized and exhibited sub-micromolar inhibition of HCV replication. Using this novel approach, we demonstrated for the first time that nucleoside analogs can be rationally designed that meet the multi-target requirements for antiviral activity.
Collapse
Affiliation(s)
- Dharmeshkumar Patel
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Bryan D. Cox
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Mahesh Kasthuri
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Seema Mengshetti
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Leda Bassit
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Kiran Verma
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Olivia Ollinger-Russell
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Franck Amblard
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| | - Raymond F. Schinazi
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, 1760 Haygood Dr., Atlanta, GA, 30322, USA
| |
Collapse
|
33
|
Piskorz TK, Martí-Centelles V, Young TA, Lusby PJ, Duarte F. Computational Modeling of Supramolecular Metallo-organic Cages-Challenges and Opportunities. ACS Catal 2022; 12:5806-5826. [PMID: 35633896 PMCID: PMC9127791 DOI: 10.1021/acscatal.2c00837] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/09/2022] [Indexed: 01/18/2023]
Abstract
![]()
Self-assembled
metallo-organic
cages have emerged as promising
biomimetic platforms that can encapsulate whole substrates akin to
an enzyme active site. Extensive experimental work has enabled access
to a variety of structures, with a few notable examples showing catalytic
behavior. However, computational investigations of metallo-organic
cages are scarce, not least due to the challenges associated with
their modeling and the lack of accurate and efficient protocols to
evaluate these systems. In this review, we discuss key molecular principles
governing the design of functional metallo-organic cages, from the
assembly of building blocks through binding and catalysis. For each
of these processes, computational protocols will be reviewed, considering
their inherent strengths and weaknesses. We will demonstrate that
while each approach may have its own specific pitfalls, they can be
a powerful tool for rationalizing experimental observables and to
guide synthetic efforts. To illustrate this point, we present several
examples where modeling has helped to elucidate fundamental principles
behind molecular recognition and reactivity. We highlight the importance
of combining computational and experimental efforts to speed up supramolecular
catalyst design while reducing time and resources.
Collapse
Affiliation(s)
- Tomasz K. Piskorz
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Vicente Martí-Centelles
- Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, Valencia 46022, Spain
| | - Tom A. Young
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Paul J. Lusby
- EaStCHEM School of Chemistry, University of Edinburgh, Joseph Black Building, David Brewster Road, Edinburgh, Scotland EH9 3FJ, United Kingdom
| | - Fernanda Duarte
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, United Kingdom
| |
Collapse
|
34
|
Parameterization and Application of the General Amber Force Field to Model Fluoro Substituted Furanose Moieties and Nucleosides. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092616. [PMID: 35565967 PMCID: PMC9101125 DOI: 10.3390/molecules27092616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/07/2022] [Accepted: 04/14/2022] [Indexed: 11/16/2022]
Abstract
Molecular mechanics force field calculations have historically shown significant limitations in modeling the energetic and conformational interconversions of highly substituted furanose rings. This is primarily due to the gauche effect that is not easily captured using pairwise energy potentials. In this study, we present a refinement to the set of torsional parameters in the General Amber Force Field (gaff) used to calculate the potential energy of mono, di-, and gem-fluorinated nucleosides. The parameters were optimized to reproduce the pseudorotation phase angle and relative energies of a diverse set of mono- and difluoro substituted furanose ring systems using quantum mechanics umbrella sampling techniques available in the IpolQ engine in the Amber suite of programs. The parameters were developed to be internally consistent with the gaff force field and the TIP3P water model. The new set of angle and dihedral parameters and partial charges were validated by comparing the calculated phase angle probability to those obtained from experimental nuclear magnetic resonance experiments.
Collapse
|
35
|
Tang R, Chen P, Wang Z, Wang L, Hao H, Hou T, Sun H. Characterizing the stabilization effects of stabilizers in protein-protein systems with end-point binding free energy calculations. Brief Bioinform 2022; 23:6565618. [PMID: 35395683 DOI: 10.1093/bib/bbac127] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/10/2022] [Accepted: 03/15/2022] [Indexed: 02/06/2023] Open
Abstract
Drug design targeting protein-protein interactions (PPIs) associated with the development of diseases has been one of the most important therapeutic strategies. Besides interrupting the PPIs with PPI inhibitors/blockers, increasing evidence shows that stabilizing the interaction between two interacting proteins may also benefit the therapy, such as the development of various types of molecular glues/stabilizers that mostly work by stabilizing the two interacting proteins to regulate the downstream biological effects. However, characterizing the stabilization effect of a stabilizer is usually hard or too complicated for traditional experiments since it involves ternary interactions [protein-protein-stabilizer (PPS) interaction]. Thus, developing reliable computational strategies will facilitate the discovery/design of molecular glues or PPI stabilizers. Here, by fully analyzing the energetic features of the binary interactions in the PPS ternary complex, we systematically investigated the performance of molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) methods on characterizing the stabilization effects of stabilizers in 14-3-3 systems. The results show that both MM/PBSA and MM/GBSA are powerful tools in distinguishing the stabilizers from the decoys (with area under the curves of 0.90-0.93 for all tested cases) and are reasonable for ranking protein-peptide interactions in the presence or absence of stabilizers as well (with the average Pearson correlation coefficient of ~0.6 at a relatively high dielectric constant for both methods). Moreover, to give a detailed picture of the stabilization effects, the stabilization mechanism is also analyzed from the structural and energetic points of view for individual systems containing strong or weak stabilizers. This study demonstrates a potential strategy to accelerate the discovery of PPI stabilizers.
Collapse
Affiliation(s)
- Rongfan Tang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| | - Pengcheng Chen
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, Zhejiang, P. R. China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Lingling Wang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| | - Haiping Hao
- State Key Laboratory of Natural Medicines, Key Lab of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, 210009 Nanjing, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| |
Collapse
|
36
|
McNutt AT, Koes DR. Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network. J Chem Inf Model 2022; 62:1819-1829. [PMID: 35380443 PMCID: PMC9038699 DOI: 10.1021/acs.jcim.1c01497] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Relative binding free energy (RBFE, also referred to as ΔΔG) methods allow the estimation of binding free energy changes after small changes to a ligand scaffold. Here, we propose and evaluate a Siamese convolutional neural network (CNN) for the prediction of RBFE between two bound ligands. We show that our multitask loss is able to improve on a previous state-of-the-art Siamese network for RBFE prediction via increased regularization of the latent space. The Siamese network architecture is well suited to the prediction of RBFE in comparison to a standard CNN trained on the same data (Pearson's R of 0.553 and 0.5, respectively). When evaluated on a left-out protein family, our Siamese CNN shows variability in its RBFE predictive performance depending on the protein family being evaluated (Pearson's R ranging from -0.44 to 0.97). RBFE prediction performance can be improved during generalization by injecting only a few examples (few-shot learning) from the evaluation data set during model training.
Collapse
Affiliation(s)
- Andrew T McNutt
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - David Ryan Koes
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| |
Collapse
|
37
|
Goel H, Hazel A, Yu W, Jo S, MacKerell AD. Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design. NEW J CHEM 2022; 46:919-932. [PMID: 35210743 PMCID: PMC8863107 DOI: 10.1039/d1nj04028f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.
Collapse
Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Sunhwan Jo
- SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States., SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States.,, Tel: 410-706-7442, Fax: 410-706-5017
| |
Collapse
|
38
|
Wang J, Zhang Y, Nie W, Luo Y, Deng L. Computational anti-COVID-19 drug design: progress and challenges. Brief Bioinform 2022; 23:bbab484. [PMID: 34850817 PMCID: PMC8690229 DOI: 10.1093/bib/bbab484] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/14/2022] Open
Abstract
Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.
Collapse
Affiliation(s)
- Jinxian Wang
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, 150001, Harbin, China
| | - Wenjuan Nie
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
| | - Yi Luo
- School of Science, The University of Auckland,Auckland 1010, Auckland, New Zealand
| | - Lei Deng
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
| |
Collapse
|
39
|
Ioppolo A, Eccles M, Groth D, Verdile G, Agostino M. Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators. Molecules 2021; 27:176. [PMID: 35011410 PMCID: PMC8746326 DOI: 10.3390/molecules27010176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 11/17/2022] Open
Abstract
γ-Secretase is an intramembrane aspartyl protease that is important in regulating normal cell physiology via cleavage of over 100 transmembrane proteins, including Amyloid Precursor Protein (APP) and Notch family receptors. However, aberrant proteolysis of substrates has implications in the progression of disease pathologies, including Alzheimer's disease (AD), cancers, and skin disorders. While several γ-secretase inhibitors have been identified, there has been toxicity observed in clinical trials associated with non-selective enzyme inhibition. To address this, γ-secretase modulators have been identified and pursued as more selective agents. Recent structural evidence has provided an insight into how γ-secretase inhibitors and modulators are recognized by γ-secretase, providing a platform for rational drug design targeting this protease. In this study, docking- and pharmacophore-based screening approaches were evaluated for their ability to identify, from libraries of known inhibitors and modulators with decoys with similar physicochemical properties, γ-secretase inhibitors and modulators. Using these libraries, we defined strategies for identifying both γ-secretase inhibitors and modulators incorporating an initial pharmacophore-based screen followed by a docking-based screen, with each strategy employing distinct γ-secretase structures. Furthermore, known γ-secretase inhibitors and modulators were able to be identified from an external set of bioactive molecules following application of the derived screening strategies. The approaches described herein will inform the discovery of novel small molecules targeting γ-secretase.
Collapse
Affiliation(s)
- Alicia Ioppolo
- Curtin Health and Innovation Research Institute, Curtin Medical School, Curtin University, Bentley, WA 6102, Australia; (A.I.); (M.E.); (D.G.); (G.V.)
| | - Melissa Eccles
- Curtin Health and Innovation Research Institute, Curtin Medical School, Curtin University, Bentley, WA 6102, Australia; (A.I.); (M.E.); (D.G.); (G.V.)
| | - David Groth
- Curtin Health and Innovation Research Institute, Curtin Medical School, Curtin University, Bentley, WA 6102, Australia; (A.I.); (M.E.); (D.G.); (G.V.)
| | - Giuseppe Verdile
- Curtin Health and Innovation Research Institute, Curtin Medical School, Curtin University, Bentley, WA 6102, Australia; (A.I.); (M.E.); (D.G.); (G.V.)
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia
| | - Mark Agostino
- Curtin Health and Innovation Research Institute, Curtin Medical School, Curtin University, Bentley, WA 6102, Australia; (A.I.); (M.E.); (D.G.); (G.V.)
- Curtin Institute for Computation, Curtin University, Bentley, WA 6102, Australia
| |
Collapse
|
40
|
Zara L, Efrém NL, van Muijlwijk-Koezen JE, de Esch IJP, Zarzycka B. Progress in Free Energy Perturbation: Options for Evolving Fragments. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 40:36-42. [PMID: 34916020 DOI: 10.1016/j.ddtec.2021.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 01/18/2023]
Abstract
One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.
Collapse
Affiliation(s)
- Lorena Zara
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Nina-Louisa Efrém
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Jacqueline E van Muijlwijk-Koezen
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Barbara Zarzycka
- Division of Medicinal Chemistry, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands..
| |
Collapse
|
41
|
Golyshev VM, Pyshnyi DV, Lomzov AA. Calculation of Energy for RNA/RNA and DNA/RNA Duplex Formation by Molecular Dynamics Simulation. Mol Biol 2021. [DOI: 10.1134/s002689332105006x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract
The development of approaches for predictive calculation of hybridization properties of various nucleic acid (NA) derivatives is the basis for the rational design of the NA-based constructs. Modern advances in computer modeling methods provide the feasibility of these calculations. We have analyzed the possibility of calculating the energy of DNA/RNA and RNA/RNA duplex formation using representative sets of complexes (65 and 75 complexes, respectively). We used the classical molecular dynamics (MD) method, the MMPBSA or MMGBSA approaches to calculate the enthalpy (ΔH°) component, and the quasi-harmonic approximation (Q-Harm) or the normal mode analysis (NMA) methods to calculate the entropy (ΔS°) contribution to the Gibbs energy ($$\Delta G_{{37}}^{^\circ }$$ ) of the NA complex formation. We have found that the MMGBSA method in the analysis of the MD trajectory of only the NA duplex and the empirical linear approximation allow calculation of the enthalpy of formation of the DNA, RNA, and hybrid duplexes of various lengths and GC content with an accuracy of 8.6%. Within each type of complex, the combination of rather efficient MMGBSA and Q-Harm approaches being applied to the trajectory of only the bimolecular complex makes it possible to calculate the $$\Delta G_{{37}}^{^\circ }$$ of the duplex formation with an error value of 10%. The high accuracy of predictive calculation for different types of natural complexes (DNA/RNA, DNA/RNA, and RNA/RNA) indicates the possibility of extending the considered approach to analogs and derivatives of nucleic acids, which gives a fundamental opportunity in the future to perform rational design of new types of NA-targeted sequence-specific compounds.
Collapse
|
42
|
S El Salamouni N, Buckley BJ, Jiang L, Huang M, Ranson M, Kelso MJ, Yu H. Disruption of Water Networks is the Cause of Human/Mouse Species Selectivity in Urokinase Plasminogen Activator (uPA) Inhibitors Derived from Hexamethylene Amiloride (HMA). J Med Chem 2021; 65:1933-1945. [PMID: 34898192 DOI: 10.1021/acs.jmedchem.1c01423] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The urokinase plasminogen activator (uPA) plays a critical role in tumor cell invasion and migration and is a promising antimetastasis target. 6-Substituted analogues of 5-N,N-(hexamethylene)amiloride (HMA) are potent and selective uPA inhibitors that lack the diuretic and antikaliuretic properties of the parent drug amiloride. However, the compounds display pronounced selectivity for human over mouse uPA, thus confounding interpretation of data from human xenograft mouse models of cancer. Here, computational and experimental findings reveal that residue 99 is a key contributor to the observed species selectivity, whereby enthalpically unfavorable expulsion of a water molecule by the 5-N,N-hexamethylene ring occurs when residue 99 is Tyr (as in mouse uPA). Analogue 7 lacking the 5-N,N-hexamethylene ring maintained similar water networks when bound to human and mouse uPA and displayed reduced selectivity, thus supporting this conclusion. The study will guide further optimization of dual-potent human/mouse uPA inhibitors from the amiloride class as antimetastasis drugs.
Collapse
Affiliation(s)
- Nehad S El Salamouni
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, NSW 2522, Australia.,Molecular Horizons, University of Wollongong, Wollongong, NSW 2522, Australia.,Illawarra Health and Medical Research Institute, Wollongong, NSW 2522, Australia
| | - Benjamin J Buckley
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, NSW 2522, Australia.,Molecular Horizons, University of Wollongong, Wollongong, NSW 2522, Australia.,Illawarra Health and Medical Research Institute, Wollongong, NSW 2522, Australia.,CONCERT-Translational Cancer Research Centre, Sydney, NSW 2750, Australia
| | - Longguang Jiang
- National Joint Biomedical Engineering Research Centre on Photodynamic Technologies, Fuzhou University, Fujian 350116, China
| | - Mingdong Huang
- National Joint Biomedical Engineering Research Centre on Photodynamic Technologies, Fuzhou University, Fujian 350116, China
| | - Marie Ranson
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, NSW 2522, Australia.,Molecular Horizons, University of Wollongong, Wollongong, NSW 2522, Australia.,Illawarra Health and Medical Research Institute, Wollongong, NSW 2522, Australia.,CONCERT-Translational Cancer Research Centre, Sydney, NSW 2750, Australia
| | - Michael J Kelso
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, NSW 2522, Australia.,Molecular Horizons, University of Wollongong, Wollongong, NSW 2522, Australia.,Illawarra Health and Medical Research Institute, Wollongong, NSW 2522, Australia
| | - Haibo Yu
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, NSW 2522, Australia.,Molecular Horizons, University of Wollongong, Wollongong, NSW 2522, Australia.,Illawarra Health and Medical Research Institute, Wollongong, NSW 2522, Australia
| |
Collapse
|
43
|
Giustiniano M, Gruber CW, Kent CN, Trippier PC. Back to the Medicinal Chemistry Future. J Med Chem 2021; 64:15515-15518. [PMID: 34719927 DOI: 10.1021/acs.jmedchem.1c01788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mariateresa Giustiniano
- Department of Pharmacy, University of Naples Federico II, via D. Montesano 49, 80131 Napoli, Italy
| | - Christian W Gruber
- Medical University of Vienna, Center for Physiology and Pharmacology, Schwsrzspanierstr. 17, 1090 Vienna, Austria
| | - Caitlin N Kent
- Integrated Drug Discovery, Sanofi R&D, Waltham, Massachusetts 02451, United States
| | - Paul C Trippier
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States
| |
Collapse
|
44
|
Goel H, Hazel A, Ustach VD, Jo S, Yu W, MacKerell AD. Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation. Chem Sci 2021; 12:8844-8858. [PMID: 34257885 PMCID: PMC8246086 DOI: 10.1039/d1sc01781k] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/24/2021] [Indexed: 01/18/2023] Open
Abstract
Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.
Collapse
Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Vincent D Ustach
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Sunhwan Jo
- SilcsBio LLC 8 Market Place, Suite 300 Baltimore Maryland 21201 USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
- SilcsBio LLC 8 Market Place, Suite 300 Baltimore Maryland 21201 USA
| |
Collapse
|
45
|
Zhang L, Domeniconi G, Yang CC, Kang SG, Zhou R, Cong G. CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method. BMC Bioinformatics 2021; 22:338. [PMID: 34157976 PMCID: PMC8218488 DOI: 10.1186/s12859-021-04214-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/18/2021] [Indexed: 01/18/2023] Open
Abstract
Background Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04214-4.
Collapse
Affiliation(s)
- Leili Zhang
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA.
| | - Giacomo Domeniconi
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA.
| | - Chih-Chieh Yang
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA
| | - Seung-Gu Kang
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA
| | - Ruhong Zhou
- ZheJiang University, 688 Yuhangtang Road, Hangzhou, 310027, China
| | - Guojing Cong
- Oak Ridge national laboratory, 1 Bethel Valley Rd, 37830, Oak Ridge, TN, USA
| |
Collapse
|
46
|
Sainas S, Giorgis M, Circosta P, Gaidano V, Bonanni D, Pippione AC, Bagnati R, Passoni A, Qiu Y, Cojocaru CF, Canepa B, Bona A, Rolando B, Mishina M, Ramondetti C, Buccinnà B, Piccinini M, Houshmand M, Cignetti A, Giraudo E, Al-Karadaghi S, Boschi D, Saglio G, Lolli ML. Targeting Acute Myelogenous Leukemia Using Potent Human Dihydroorotate Dehydrogenase Inhibitors Based on the 2-Hydroxypyrazolo[1,5- a]pyridine Scaffold: SAR of the Biphenyl Moiety. J Med Chem 2021; 64:5404-5428. [PMID: 33844533 PMCID: PMC8279415 DOI: 10.1021/acs.jmedchem.0c01549] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Indexed: 02/08/2023]
Abstract
The connection with acute myelogenous leukemia (AML) of dihydroorotate dehydrogenase (hDHODH), a key enzyme in pyrimidine biosynthesis, has attracted significant interest from pharma as a possible AML therapeutic target. We recently discovered compound 1, a potent hDHODH inhibitor (IC50 = 1.2 nM), able to induce myeloid differentiation in AML cell lines (THP1) in the low nM range (EC50 = 32.8 nM) superior to brequinar's phase I/II clinical trial (EC50 = 265 nM). Herein, we investigate the 1 drug-like properties observing good metabolic stability and no toxic profile when administered at doses of 10 and 25 mg/kg every 3 days for 5 weeks (Balb/c mice). Moreover, in order to identify a backup compound, we investigate the SAR of this class of compounds. Inside the series, 17 is characterized by higher potency in inducing myeloid differentiation (EC50 = 17.3 nM), strong proapoptotic properties (EC50 = 20.2 nM), and low cytotoxicity toward non-AML cells (EC30(Jurkat) > 100 μM).
Collapse
Affiliation(s)
- Stefano Sainas
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| | - Marta Giorgis
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| | - Paola Circosta
- Department
of Clinical and Biological Sciences, University
of Turin, Regione Gonzole 10, Orbassano, Turin 10043, Italy
- Molecular
Biotechnology Center, University of Turin, Via Nizza 52, Turin 10126, Italy
| | - Valentina Gaidano
- Department
of Clinical and Biological Sciences, University
of Turin, Regione Gonzole 10, Orbassano, Turin 10043, Italy
- Division
of Hematology, AO SS Antonio e Biagio e
Cesare Arrigo, Via Venezia
16, Alessandria 15121, Italy
| | - Davide Bonanni
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| | - Agnese C. Pippione
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| | - Renzo Bagnati
- Department
of Environmental Health Sciences, Istituto
di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano 20156, Italy
| | - Alice Passoni
- Department
of Environmental Health Sciences, Istituto
di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano 20156, Italy
| | - Yaqi Qiu
- Laboratory
of Tumor Microenvironment, Candiolo Cancer
Institute, FPO, IRCCS, Candiolo, Strada Provinciale, 142-KM 3.95, Candiolo, Turin 10060, Italy
- Higher
Education Mega Center, Institutes for Life Sciences, South China University of Technology, Guangzhou 510641, China
| | - Carina Florina Cojocaru
- Laboratory
of Tumor Microenvironment, Candiolo Cancer
Institute, FPO, IRCCS, Candiolo, Strada Provinciale, 142-KM 3.95, Candiolo, Turin 10060, Italy
| | - Barbara Canepa
- Gem
Forlab srl, Via Ribes,
5, Colleretto Giacosa, Turin 10010, Italy
| | - Alessandro Bona
- Gem
Chimica srl, Via Maestri
del Lavoro, 25, Busca, Cuneo 12022, Italy
| | - Barbara Rolando
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| | - Mariia Mishina
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| | - Cristina Ramondetti
- Department
of Oncology, University of Turin, Via Michelangelo 27/B, Turin 10125, Italy
| | - Barbara Buccinnà
- Department
of Oncology, University of Turin, Via Michelangelo 27/B, Turin 10125, Italy
| | - Marco Piccinini
- Department
of Oncology, University of Turin, Via Michelangelo 27/B, Turin 10125, Italy
| | - Mohammad Houshmand
- Department
of Clinical and Biological Sciences, University
of Turin, Regione Gonzole 10, Orbassano, Turin 10043, Italy
- Molecular
Biotechnology Center, University of Turin, Via Nizza 52, Turin 10126, Italy
| | - Alessandro Cignetti
- Division
of Hematology and Cell Therapy, AO Ordine
Mauriziano, Largo Filippo Turati, 62, Turin 10128, Italy
| | - Enrico Giraudo
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
- Laboratory
of Tumor Microenvironment, Candiolo Cancer
Institute, FPO, IRCCS, Candiolo, Strada Provinciale, 142-KM 3.95, Candiolo, Turin 10060, Italy
| | - Salam Al-Karadaghi
- Department
of Biochemistry and Structural Biology, Lund University, Naturvetarvägen 14, Box 124, Lund 221 00, Sweden
| | - Donatella Boschi
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| | - Giuseppe Saglio
- Department
of Clinical and Biological Sciences, University
of Turin, Regione Gonzole 10, Orbassano, Turin 10043, Italy
- Division
of Hematology and Cell Therapy, AO Ordine
Mauriziano, Largo Filippo Turati, 62, Turin 10128, Italy
| | - Marco L. Lolli
- Department
of Drug Science and Technology, University
of Turin, Via P. Giuria 9, Turin 10125, Italy
| |
Collapse
|
47
|
Baumann HM, Gapsys V, de Groot BL, Mobley DL. Challenges Encountered Applying Equilibrium and Nonequilibrium Binding Free Energy Calculations. J Phys Chem B 2021; 125:4241-4261. [PMID: 33905257 PMCID: PMC8240641 DOI: 10.1021/acs.jpcb.0c10263] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Binding free energy calculations have become increasingly valuable to drive decision making in drug discovery projects. However, among other issues, inadequate sampling can reduce accuracy, limiting the value of the technique. In this paper, we apply absolute binding free energy calculations to ligands binding to T4 lysozyme L99A and HSP90 using equilibrium and nonequilibrium approaches. We highlight sampling problems encountered in these systems, such as slow side chain rearrangements and slow changes of water placement upon ligand binding. These same types of challenges are also likely to show up in other protein-ligand systems, and we propose some strategies to diagnose and test for such problems in alchemical free energy calculations. We also explore similarities and differences in how the equilibrium and the nonequilibrium approaches handle these problems. Our results show the large amount of work still to be done to make free energy calculations robust and reliable and provide insight for future research in this area.
Collapse
Affiliation(s)
- Hannah M Baumann
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, D-37077 Göttingen, Germany
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, D-37077 Göttingen, Germany
| | - David L Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
- Department of Chemistry, University of California, Irvine, California 92617, United States
| |
Collapse
|
48
|
Mishra CB, Pandey P, Sharma RD, Malik MZ, Mongre RK, Lynn AM, Prasad R, Jeon R, Prakash A. Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach. Brief Bioinform 2021; 22:1346-1360. [PMID: 33386025 PMCID: PMC7799228 DOI: 10.1093/bib/bbaa378] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/03/2020] [Accepted: 11/26/2020] [Indexed: 01/18/2023] Open
Abstract
The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.
Collapse
Affiliation(s)
| | - Preeti Pandey
- Department of Chemistry & Biochemistry, University of Oklahoma, OK, USA
| | | | - Md Zubbair Malik
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Raj Kumar Mongre
- College of Pharmacy, Sookmyung Women’s University, Seoul, South Korea
| | - Andrew M Lynn
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Rajendra Prasad
- Amity Institute of Biotechnology and is the dean of Faculty of Science Engineering and Technology, Amity University Haryana, Haryana 122413, India
| | - Raok Jeon
- College of Pharmacy, Sookmyung Women’s University, Seoul, South Korea
| | - Amresh Prakash
- Amity Institute of Integrative Sciences and Health, Amity Institute of Integrative Sciences and Health, Amity University, Haryana
| |
Collapse
|
49
|
Williams-Noonan BJ, Todorova N, Kulkarni K, Aguilar MI, Yarovsky I. An Active Site Inhibitor Induces Conformational Penalties for ACE2 Recognition by the Spike Protein of SARS-CoV-2. J Phys Chem B 2021; 125:2533-2550. [PMID: 33657325 PMCID: PMC7945587 DOI: 10.1021/acs.jpcb.0c11321] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/28/2021] [Indexed: 12/12/2022]
Abstract
The novel RNA virus, severe acute respiratory syndrome coronavirus II (SARS-CoV-2), is currently the leading cause of mortality in 2020, having led to over 1.6 million deaths and infecting over 75 million people worldwide by December 2020. While vaccination has started and several clinical trials for a number of vaccines are currently underway, there is a pressing need for a cure for those already infected with the virus. Of particular interest in the design of anti-SARS-CoV-2 therapeutics is the human protein angiotensin converting enzyme II (ACE2) to which this virus adheres before entry into the host cell. The SARS-CoV-2 virion binds to cell-surface bound ACE2 via interactions of the spike protein (s-protein) on the viral surface with ACE2. In this paper, we use all-atom molecular dynamics simulations and binding enthalpy calculations to determine the effect that a bound ACE2 active site inhibitor (MLN-4760) would have on the binding affinity of SARS-CoV-2 s-protein with ACE2. Our analysis indicates that the binding enthalpy could be reduced for s-protein adherence to the active site inhibitor-bound ACE2 protein by as much as 1.48-fold as an upper limit. This weakening of binding strength was observed to be due to the destabilization of the interactions between ACE2 residues Glu-35, Glu-37, Tyr-83, Lys-353, and Arg-393 and the SARS-CoV-2 s-protein receptor binding domain (RBD). The conformational changes were shown to lead to weakening of ACE2 interactions with SARS-CoV-2 s-protein, therefore reducing s-protein binding strength. Further, we observed increased conformational lability of the N-terminal helix and a conformational shift of a significant portion of the ACE2 motifs involved in s-protein binding, which may affect the kinetics of the s-protein binding when the small molecule inhibitor is bound to the ACE2 active site. These observations suggest potential new ways for interfering with the SARS-CoV-2 adhesion by modulating ACE2 conformation through distal active site inhibitor binding.
Collapse
Affiliation(s)
| | - Nevena Todorova
- School of Engineering, RMIT
University, Melbourne, Victoria 3001, Australia
| | - Ketav Kulkarni
- Department of Biochemistry and Molecular Biology,
Monash University, Clayton, Victoria 3800,
Australia
| | - Marie-Isabel Aguilar
- Department of Biochemistry and Molecular Biology,
Monash University, Clayton, Victoria 3800,
Australia
| | - Irene Yarovsky
- School of Engineering, RMIT
University, Melbourne, Victoria 3001, Australia
| |
Collapse
|
50
|
Blake S, Hemming I, Heng JIT, Agostino M. Structure-Based Approaches to Classify the Functional Impact of ZBTB18 Missense Variants in Health and Disease. ACS Chem Neurosci 2021; 12:979-989. [PMID: 33621064 DOI: 10.1021/acschemneuro.0c00758] [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: 01/18/2023] Open
Abstract
The Cys2His2 type zinc finger is a motif found in many eukaryotic transcription factor proteins that facilitates binding to genomic DNA so as to influence cellular gene expression. One such transcription factor is ZBTB18, characterized as a repressor that orchestrates the development of mammalian tissues including skeletal muscle and brain during embryogenesis. In humans, it has been recognized that disease-associated ZBTB18 missense variants mapping to the coding sequence of the zinc finger domain influence sequence-specific DNA binding, disrupt transcriptional regulation, and impair neural circuit formation in the brain. Furthermore, general population ZBTB18 missense variants that influence DNA binding and transcriptional regulation have also been documented within this domain; however, the molecular traits that explain why some variants cause disease while others do not are poorly understood. Here, we have applied five structure-based approaches to evaluate their ability to discriminate between disease-associated and general population ZBTB18 missense variants. We found that thermodynamic integration and Residue Scanning in the Schrodinger Biologics Suite were the best approaches for distinguishing disease-associated variants from general population variants. Our results demonstrate the effectiveness of structure-based approaches for the functional characterization of missense alleles to DNA binding, zinc finger transcription factor protein-coding genes that underlie human health and disease.
Collapse
Affiliation(s)
- Steven Blake
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
| | - Isabel Hemming
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- The Faculty of Health and Medical Sciences, Medical School, The University of Western Australia, Crawley, Western Australia 6009, Australia
| | - Julian Ik-Tsen Heng
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
| | - Mark Agostino
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
- Curtin Institute for Computation, Curtin University, Bentley, Western Australia, Australia
| |
Collapse
|