1
|
Ahamad S, Junaid IT, Gupta D. Computational Design of Novel Tau-Tubulin Kinase 1 Inhibitors for Neurodegenerative Diseases. Pharmaceuticals (Basel) 2024; 17:952. [PMID: 39065802 PMCID: PMC11280166 DOI: 10.3390/ph17070952] [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: 05/20/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
The tau-tubulin kinase 1 (TTBK1) protein is a casein kinase 1 superfamily member located at chromosome 6p21.1. It is expressed explicitly in the brain, particularly in the cytoplasm of cortical and hippocampal neurons. TTBK1 has been implicated in the phosphorylation and aggregation of tau in Alzheimer's disease (AD). Considering its significance in AD, TTBK1 has emerged as a promising target for AD treatment. In the present study, we identified novel TTBK1 inhibitors using various computational techniques. We performed a virtual screening-based docking study followed by E-pharmacophore modeling, cavity-based pharmacophore, and ligand design techniques and found ZINC000095101333, LD7, LD55, and LD75 to be potential novel TTBK1 lead inhibitors. The docking results were complemented by Molecular Mechanics/Generalized Born Surface Area (MMGBSA) calculations. The molecular dynamics (MD) simulation studies at a 500 ns scale were carried out to monitor the behavior of the protein toward the identified ligands. Pharmacological and ADME/T studies were carried out to check the drug-likeness of the compounds. In summary, we identified a new series of compounds that could effectively bind the TTBK1 receptor. The newly designed compounds are promising candidates for developing therapeutics targeting TTBK1 for AD.
Collapse
Affiliation(s)
- Shahzaib Ahamad
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Iqbal Taliy Junaid
- Malaria Biology, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India;
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| |
Collapse
|
2
|
Lima Neto JX, Bezerra KS, Barbosa ED, Araujo RL, Galvão DS, Lyra ML, Oliveira JIN, Akash S, Jardan YAB, Nafidi HA, Bourhia M, Fulco UL. Investigation of protein-protein interactions and hotspot region on the NSP7-NSP8 binding site in NSP12 of SARS-CoV-2. Front Mol Biosci 2024; 10:1325588. [PMID: 38304231 PMCID: PMC10830813 DOI: 10.3389/fmolb.2023.1325588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/22/2023] [Indexed: 02/03/2024] Open
Abstract
Background: The RNA-dependent RNA polymerase (RdRp) complex, essential in viral transcription and replication, is a key target for antiviral therapeutics. The core unit of RdRp comprises the nonstructural protein NSP12, with NSP7 and two copies of NSP8 (NSP81 and NSP82) binding to NSP12 to enhance its affinity for viral RNA and polymerase activity. Notably, the interfaces between these subunits are highly conserved, simplifying the design of molecules that can disrupt their interaction. Methods: We conducted a detailed quantum biochemical analysis to characterize the interactions within the NSP12-NSP7, NSP12-NSP81, and NSP12-NSP82 dimers. Our objective was to ascertain the contribution of individual amino acids to these protein-protein interactions, pinpointing hotspot regions crucial for complex stability. Results: The analysis revealed that the NSP12-NSP81 complex possessed the highest total interaction energy (TIE), with 14 pairs of residues demonstrating significant energetic contributions. In contrast, the NSP12-NSP7 complex exhibited substantial interactions in 8 residue pairs, while the NSP12-NSP82 complex had only one pair showing notable interaction. The study highlighted the importance of hydrogen bonds and π-alkyl interactions in maintaining these complexes. Intriguingly, introducing the RNA sequence with Remdesivir into the complex resulted in negligible alterations in both interaction energy and geometric configuration. Conclusion: Our comprehensive analysis of the RdRp complex at the protein-protein interface provides invaluable insights into interaction dynamics and energetics. These findings can guide the design of small molecules or peptide/peptidomimetic ligands to disrupt these critical interactions, offering a strategic pathway for developing effective antiviral drugs.
Collapse
Affiliation(s)
- José Xavier Lima Neto
- Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Katyanna Sales Bezerra
- Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Emmanuel Duarte Barbosa
- Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Roniel Lima Araujo
- Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | | | | | - Jonas Ivan Nobre Oliveira
- Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Shopnil Akash
- Department of Pharmacy, Daffodil International University, Dhaka, Bangladesh
| | - Yousef A. Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Hiba-Allah Nafidi
- Department of Food Science, Faculty of Agricultural and Food Sciences, Laval University, Quebec City, QC, Canada
| | - Mohammed Bourhia
- Department of Chemistry and Biochemistry, Faculty of Medicine and Pharmacy, Ibn Zohr University, Laayoune, Morocco
| | - Umberto Laino Fulco
- Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| |
Collapse
|
3
|
Mohebbinia Z, Firouzi R, Karimi-Jafari MH. Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set. J Biomol Struct Dyn 2024:1-11. [PMID: 38165642 DOI: 10.1080/07391102.2023.2299742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 12/20/2023] [Indexed: 01/04/2024]
Abstract
Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Zainab Mohebbinia
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
| | - Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
| | | |
Collapse
|
4
|
Li L, Liu S, Wang B, Liu F, Xu S, Li P, Chen Y. An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches. Int J Mol Sci 2023; 24:13953. [PMID: 37762253 PMCID: PMC10530957 DOI: 10.3390/ijms241813953] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.
Collapse
Affiliation(s)
- Linwei Li
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Songtao Liu
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
- Key Laboratory of Pesticide, College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China
| | - Bi Wang
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Fei Liu
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Shu Xu
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Pirui Li
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| | - Yu Chen
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China; (L.L.); (S.L.); (B.W.); (F.L.); (S.X.)
- Jiangsu Province Engineering Research Center of Eco-Cultivation and High-Value Utilization of Chines Medicinal Materials, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
| |
Collapse
|
5
|
Freitas de Sousa FJ, Nunes Azevedo FF, Santos de Oliveira FL, Vieira Carletti J, Freire VN, Zanatta G. Quantum biochemistry description of PI3Kα enzyme bound to selective inhibitors. J Biomol Struct Dyn 2023:1-11. [PMID: 37632299 DOI: 10.1080/07391102.2023.2251063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
The PI3K class I is composed of four PI3K isoforms that serve as regulatory enzymes governing cellular metabolism, proliferation, and survival. The hyperactivation of PI3Kα is observed in various types of cancer and is linked to poor prognosis. Unfortunately, the development inhibitors selectively targeting one of the isoforms remains challenging, with only few agents in clinical use. The main difficulty arises from the high conservation among residues at the ATP-binding pocket across isoforms, which also serves as target pocket for inhibitors. In this work, molecular dynamics and quantum calculations were performed to investigate the molecular features guiding the binding of selective inhibitors, alpelisib and GDC-0326, into the ATP-binding pocket of PI3Kα. While molecular dynamics allowed crystallographic coordinates to relax, the interaction eergy between each amino acid residues and inhibitors was obtained by combining the Molecular Fractionation with Conjugated Caps scheme with Density Functional Theory calculations. In addition, the atomic charge of ligands in the bound and unbound (free) was calculated. Results indicated that the most relevant residues for the binding of alpelisib are Ile932, Glu859, Val851, Val850, Tyr836, Met922, Ile800, and Ile848, while the most important residues for the binding of GDC-0326 are Ile848, Ile800, Ile932, Gln859, Glu849, and Met922. In addition, residues Trp780, Ile800, Tyr836, Ile848, Gln859 Val850, Val851, Ile932 and Met922 are common hotspots for both inhibitors. Overall, the results from this work contribute to improving the understanding of the molecular mechanisms controlling selectivity and highlight important interactions to be considered during the rational design of new agents.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
| | | | | | | | | | - Geancarlo Zanatta
- Department of Biochemistry and Molecular Biology, Federal University of Ceará, Fortaleza, Brazil
- Department of Biophysics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| |
Collapse
|
6
|
Liu J, Wan J, Ren Y, Shao X, Xu X, Rao L. DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2023; 63:4850-4863. [PMID: 37539963 DOI: 10.1021/acs.jcim.3c00776] [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: 08/05/2023]
Abstract
Accurate prediction of the protein-ligand binding affinity (PLBA) with an affordable cost is one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a great challenge in the computational and theoretical chemistry. Herein, we have systematically addressed the complicated solvation and desolvation effects on the PLBA brought by the difference of the explicit water in the protein cavity before and after ligands bind to the protein-binding site. Based on the new solvation model, a nonfitting method at the first-principles level for the PLBA prediction was developed by taking the bridging and displaced water (BDW) molecules into account simultaneously. The newly developed method, DOX_BDW, was validated against a total of 765 noncovalent and covalent protein-ligand binding pairs, including the CASF2016 core set, Cov_2022 covalent binding testing set, and six testing sets for the hit and lead compound optimization (HLO) simulation. In all of the testing sets, the DOX_BDW method was able to produce PLBA predictions that were strongly correlated with the corresponding experimental data (R = 0.66-0.85). The overall performance of DOX_BDW is better than the current empirical scoring functions that are heavily parameterized. DOX_BDW is particularly outstanding for the covalent binding situation, implying the need for considering an electronic structure in covalent drug design. Furthermore, the method is especially recommended to be used in the HLO scenario of SBDD, where hundreds of similar derivatives need to be screened and refined. The computational cost of DOX_BDW is affordable, and its accuracy is remarkable.
Collapse
Affiliation(s)
- Jiaqi Liu
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Jian Wan
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Yanliang Ren
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Xubo Shao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| | - Xin Xu
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education (MOE) Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, People's Republic of China
| | - Li Rao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, College of Chemistry, Central China Normal University, Wuhan 43009, People's Republic of China
| |
Collapse
|
7
|
Vasile S, Roos K. Understanding the Structure-Activity Relationship through Density Functional Theory: A Simple Method Predicts Relative Binding Free Energies of Metalloenzyme Fragment-like Inhibitors. ACS OMEGA 2023; 8:21438-21449. [PMID: 37360476 PMCID: PMC10285960 DOI: 10.1021/acsomega.2c08156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/29/2023] [Indexed: 06/28/2023]
Abstract
Despite being involved in several human diseases, metalloenzymes are targeted by a small percentage of FDA-approved drugs. Development of novel and efficient inhibitors is required, as the chemical space of metal binding groups (MBGs) is currently limited to four main classes. The use of computational chemistry methods in drug discovery has gained momentum thanks to accurate estimates of binding modes and binding free energies of ligands to receptors. However, exact predictions of binding free energies in metalloenzymes are challenging due to the occurrence of nonclassical phenomena and interactions that common force field-based methods are unable to correctly describe. In this regard, we applied density functional theory (DFT) to predict the binding free energies and to understand the structure-activity relationship of metalloenzyme fragment-like inhibitors. We tested this method on a set of small-molecule inhibitors with different electronic properties and coordinating two Mn2+ ions in the binding site of the influenza RNA polymerase PAN endonuclease. We modeled the binding site using only atoms from the first coordination shell, hence reducing the computational cost. Thanks to the explicit treatment of electrons by DFT, we highlighted the main contributions to the binding free energies and the electronic features differentiating strong and weak inhibitors, achieving good qualitative correlation with the experimentally determined affinities. By introducing automated docking, we explored alternative ways to coordinate the metal centers and we identified 70% of the highest affinity inhibitors. This methodology provides a fast and predictive tool for the identification of key features of metalloenzyme MBGs, which can be useful for the design of new and efficient drugs targeting these ubiquitous proteins.
Collapse
|
8
|
Guareschi R, Lukac I, Gilbert IH, Zuccotto F. SophosQM: Accurate Binding Affinity Prediction in Compound Optimization. ACS OMEGA 2023; 8:15083-15098. [PMID: 37151542 PMCID: PMC10157843 DOI: 10.1021/acsomega.2c08132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/07/2023] [Indexed: 05/09/2023]
Abstract
The optimization of compounds' binding affinity for a biological target is a crucial aspect of the drug development process. Being able to accurately predict binding energies in advance of synthesizing compounds would have a massive impact on the speed of the drug discovery process. The ideal binding affinity prediction method should combine accuracy, reliability, and speed. In this paper, we present SophosQM, a quantum mechanics (QM)-based approach, which can accurately predict the binding affinities of compounds to proteins. The binding affinity predictive models generated by SophosQM are based on the fragment molecular orbital (FMO) method to estimate the enthalpic component of the binding free energy, and a macroscopic descriptor, clog P, is used as an approximation of the entropic component. The affinity prediction is performed using multilinear regression, fitting the experimental values against the FMO-computed enthalpic term and clog P. The quality of the prediction can be assessed in terms of the correlation coefficient between experimental and predicted values. In this work, the method's reliability and accuracy are exemplified by applying SophosQM to 70 compounds binding to six different targets of pharmaceutical relevance. Overall, the results show a very satisfactory performance with a global correlation coefficient in the order of 0.9. Our predictions also show a satisfactory performance compared to data based on free energy perturbation. Finally, SophosQM can also be applied in high-throughput mode by using semiempirical QM methods to evaluate large portions of chemical space, while retaining a good level of accuracy, but decreasing the computing time to just a few seconds per compound.
Collapse
|
9
|
Nguyen TM, Quinn TP, Nguyen T, Tran T. Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1020-1029. [PMID: 35820003 DOI: 10.1109/tcbb.2022.3190266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and allows to distill biological knowledge from the models. Counterfactual explanation is one popular approach to explaining the behaviour of a deep neural network, which works by systematically answering the question "How would the model output change if the inputs were changed in this way?". We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex. Our proposed framework provides human-interpretable counterfactual instances while optimizing both the input drug and target for counterfactual generation at the same time. We benchmark the proposed MACDA framework using the Davis and PDBBind dataset and find that our framework produces more parsimonious explanations with no loss in explanation validity, as measured by encoding similarity. We then present a case study involving ABL1 and Nilotinib to demonstrate how MACDA can explain the behaviour of a DTA model in the underlying substructure interaction between inputs in its prediction, revealing mechanisms that align with prior domain knowledge.
Collapse
|
10
|
Prediction of drug protein interactions based on variable scale characteristic pyramid convolution network. Methods 2023; 211:42-47. [PMID: 36804213 DOI: 10.1016/j.ymeth.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/31/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
MOTIVATION In the process of drug screening, it is significant to improve the accuracy of drug-target binding affinity prediction. A multilayer convolutional neural network is one of the most popular existing methods for predicting affinity based on deep learning. It uses multiple convolution layers to extract features from the simplified molecular input system (SMILES) strings of the compounds and amino acid sequences of proteins and then performs affinity prediction analysis. However, the semantic information contained in low-level features can gradually be lost due to the increasing network depth, which affects the prediction performance. RESULT We propose a novel method called the Pyramid Network Convolution Drug-Target Binding Affinity (PCNN-DTA) method for drug-target binding affinity prediction. The PCNN-DTA method, which is based on a feature pyramid network (FPN), fuses the features extracted from each layer of a multilayer convolution network to retain more low-level feature information, thus improving the prediction accuracy. PCNN-DTA is compared with other typical algorithms on three benchmark datasets, namely, the KIBA, Davis, and Binding DB datasets. Experimental results show that the PCNN-DTA method is superior to existing regression prediction methods using convolutional neural networks, which further demonstrates its effectiveness.
Collapse
|
11
|
Xia C, Feng SH, Xia Y, Pan X, Shen HB. Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network. Brief Bioinform 2023; 24:6982728. [PMID: 36627113 DOI: 10.1093/bib/bbac603] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/01/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein-ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein-ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability.
Collapse
Affiliation(s)
- Chunqiu Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Shi-Hao Feng
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| |
Collapse
|
12
|
The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27249041. [PMID: 36558174 PMCID: PMC9788237 DOI: 10.3390/molecules27249041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
The modern development of computer technology and different in silico methods have had an increasing impact on the discovery and development of new drugs. Different molecular docking techniques most widely used in silico methods in drug discovery. Currently, the time and financial costs for the initial hit identification can be significantly reduced due to the ability to perform high-throughput virtual screening of large compound libraries in a short time. However, the selection of potential hit compounds still remains more of a random process, because there is still no consensus on what the binding energy and ligand efficiency (LE) of a potentially active compound should be. In the best cases, only 20-30% of compounds identified by molecular docking are active in biological tests. In this work, we evaluated the impact of the docking software used as well as the type of the target protein on the molecular docking results and their accuracy using an example of the three most popular programs and five target proteins related to neurodegenerative diseases. In addition, we attempted to determine the "reliable range" of the binding energy and LE that would allow selecting compounds with biological activity in the desired concentration range.
Collapse
|
13
|
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
|
14
|
Liu W, Liu Z, Liu H, Westerhoff LM, Zheng Z. Free Energy Calculations Using the Movable Type Method with Molecular Dynamics Driven Protein–Ligand Sampling. J Chem Inf Model 2022; 62:5645-5665. [PMID: 36282990 PMCID: PMC9709919 DOI: 10.1021/acs.jcim.2c00278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Fast and accurate biomolecular free energy estimation has been a significant interest for decades, and with recent advances in computer hardware, interest in new method development in this field has even grown. Thorough configurational state sampling using molecular dynamics (MD) simulations has long been applied to the estimation of the free energy change corresponding to the receptor-ligand complexing process. However, performing large-scale simulation is still a computational burden for the high-throughput hit screening. Among molecular modeling tools, docking and scoring methods are widely used during the early stages of the drug discovery process in that they can rapidly generate discrete receptor-ligand binding modes and their individual binding affinities. Unfortunately, the lack of thorough conformational sampling in docking and scoring protocols leads to difficulty discovering global minimum binding modes on a complicated energy landscape. The Movable Type (MT) method is a novel absolute binding free energy approach which has demonstrated itself to be robust across a wide range of targets and ligands. Traditionally, the MT method is used with protein-ligand binding modes generated with rigid-receptor or flexible-receptor (induced fit) docking protocols; however, these protocols are by their nature less likely to be effective with more highly flexible targets or with those situations in which binding involves multiple step pathways. In these situations, more thorough samplings are required to better explain the free energy of binding. Therefore, to explore the prediction capability and computational efficiency of the MT method when using more thorough protein-ligand conformational sampling protocols, in the present work, we introduced a series of binding mode modeling protocols ranging from conventional docking routines to single-trajectory conventional molecular dynamics (cMD) and parallel Monte Carlo molecular dynamics (MCMD). Through validation against several structurally and mechanistically diverse protein-ligand test sets, we explore the performance of the MT method as a virtual screening tool to work with the docking protocols and as an MD simulation-based binding free energy tool.
Collapse
Affiliation(s)
- Wenlang Liu
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
| | - Zhenhao Liu
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
| | - Hao Liu
- School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
| | | | - Zheng Zheng
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan430070, PR China
- QuantumBio Inc., State College, Pennsylvania16801, United States
| |
Collapse
|
15
|
Adekoya OC, Adekoya GJ, Sadiku ER, Hamam Y, Ray SS. Application of DFT Calculations in Designing Polymer-Based Drug Delivery Systems: An Overview. Pharmaceutics 2022; 14:1972. [PMID: 36145719 PMCID: PMC9505803 DOI: 10.3390/pharmaceutics14091972] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 01/18/2023] Open
Abstract
Drug delivery systems transfer medications to target locations throughout the body. These systems are often made up of biodegradable and bioabsorbable polymers acting as delivery components. The introduction of density functional theory (DFT) has tremendously aided the application of computational material science in the design and development of drug delivery materials. The use of DFT and other computational approaches avoids time-consuming empirical processes. Therefore, this review explored how the DFT computation may be utilized to explain some of the features of polymer-based drug delivery systems. First, we went through the key aspects of DFT and provided some context. Then we looked at the essential characteristics of a polymer-based drug delivery system that DFT simulations could predict. We observed that the Gaussian software had been extensively employed by researchers, particularly with the B3LYP functional and 6-31G(d, p) basic sets for polymer-based drug delivery systems. However, to give researchers a choice of basis set for modelling complicated organic systems, such as polymer-drug complexes, we then offered possible resources and presented the future trend.
Collapse
Affiliation(s)
- Oluwasegun Chijioke Adekoya
- Department of Chemical, Metallurgical and Materials Engineering, Faculty of Engineering and the Built Environment, Institute of NanoEngineering Research (INER), Tshwane University of Technology, Pretoria 0183, South Africa
| | - Gbolahan Joseph Adekoya
- Department of Chemical, Metallurgical and Materials Engineering, Faculty of Engineering and the Built Environment, Institute of NanoEngineering Research (INER), Tshwane University of Technology, Pretoria 0183, South Africa
- Department of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0001, South Africa
| | - Emmanuel Rotimi Sadiku
- Department of Chemical, Metallurgical and Materials Engineering, Faculty of Engineering and the Built Environment, Institute of NanoEngineering Research (INER), Tshwane University of Technology, Pretoria 0183, South Africa
| | - Yskandar Hamam
- Department of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0001, South Africa
- École Supérieure d’Ingénieurs en Électrotechnique et Électronique, Cité Descartes, 2 Boulevard Blaise Pascal, Noisy-le-Grand, 93160 Paris, France
| | - Suprakas Sinha Ray
- Centre for Nanostructures and Advanced Materials, DSI-CSIR Nanotechnology Innovation Centre, Council for Scientific and Industrial Research, CSIR, Pretoria 0001, South Africa
- Department of Chemical Sciences, University of Johannesburg, Doornforntein, Johannesburg 2028, South Africa
| |
Collapse
|
16
|
Zapata-Acevedo CA, Guevara-Vela JM, Popelier PLA, Rocha Rinza T. Binding Energy Partition of Promising IRAK-4 Inhibitor (Zimlovisertib) for the Treatment of COVID-19 Pneumonia. Chemphyschem 2022; 23:e202200455. [PMID: 36044560 PMCID: PMC9538207 DOI: 10.1002/cphc.202200455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/19/2022] [Indexed: 01/05/2023]
Abstract
The technique of Fragment-Based Drug Design (FBDD) considers the interactions of different moieties of molecules with biological targets for the rational construction of potential drugs. One basic assumption of FBDD is that the different functional groups of a ligand interact with a biological target in an approximately additive, that is, independent manner. We investigated the interactions of different fragments of ligands and Interleukin-1 Receptor-Associated Kinase 4 (IRAK-4) throughout the FBDD design of Zimlovisertib, a promising anti-inflammatory, currently in trials to be used for the treatment of COVID-19 pneumonia. We utilised state-of-the-art methods of wave function analyses mainly the Interacting Quantum Atoms (IQA) energy partition for this purpose. By means of IQA, we assessed the suitability of every change to the ligand in the five stages of FBDD which led to Zimlovisertib on a quantitative basis. We determined the energetics of the interaction of different functional groups in the ligands with the IRAK-4 protein target and thereby demonstrated the adequacy (or lack thereof) of the changes made across the design of this drug. This analysis permits to verify whether a given alteration of a prospective drug leads to the intended tuning of non-covalent interactions with its protein objective. Overall, we expect that the methods exploited in this paper will prove valuable in the understanding and control of chemical modifications across FBDD processes.
Collapse
Affiliation(s)
- César Arturo Zapata-Acevedo
- Tecnologico de Monterrey: Instituto Tecnologico y de Estudios Superiores de MonterreyChemistryAv. Carlos Lazo 100Santa Fe, La Loma01389Álvaro ObregónMEXICO
| | | | - Paul L. A. Popelier
- UoM: The University of ManchesterChemistryOxford RoadM13 9PLManchesterUNITED KINGDOM
| | - Tomás Rocha Rinza
- Institute Of Chemistry, National Autonomous University of MexicoDepartment of Physical ChemistryCircuito Exterior, Ciudad Universitaria04510Mexico CityMEXICO
| |
Collapse
|
17
|
Veríssimo GC, Serafim MSM, Kronenberger T, Ferreira RS, Honorio KM, Maltarollo VG. Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. Expert Opin Drug Discov 2022; 17:929-947. [PMID: 35983695 DOI: 10.1080/17460441.2022.2114451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.
Collapse
Affiliation(s)
- Gabriel C Veríssimo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Mateus Sá M Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Medical Oncology and Pneumology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Rafaela S Ferreira
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia M Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
| | - Vinícius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| |
Collapse
|
18
|
Monteleone S, Fedorov DG, Townsend-Nicholson A, Southey M, Bodkin M, Heifetz A. Hotspot Identification and Drug Design of Protein-Protein Interaction Modulators Using the Fragment Molecular Orbital Method. J Chem Inf Model 2022; 62:3784-3799. [PMID: 35939049 DOI: 10.1021/acs.jcim.2c00457] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions (PPIs) are essential for the function of many proteins. Aberrant PPIs have the potential to lead to disease, making PPIs promising targets for drug discovery. There are over 64,000 PPIs in the human interactome reference database; however, to date, very few PPI modulators have been approved for clinical use. Further development of PPI-specific therapeutics is highly dependent on the availability of structural data and the existence of reliable computational tools to explore the interface between two interacting proteins. The fragment molecular orbital (FMO) quantum mechanics method offers comprehensive and computationally inexpensive means of identifying the strength (in kcal/mol) and the chemical nature (electrostatic or hydrophobic) of the molecular interactions taking place at the protein-protein interface. We have integrated FMO and PPI exploration (FMO-PPI) to identify the residues that are critical for protein-protein binding (hotspots). To validate this approach, we have applied FMO-PPI to a dataset of protein-protein complexes representing several different protein subfamilies and obtained FMO-PPI results that are in agreement with published mutagenesis data. We observed that critical PPIs can be divided into three major categories: interactions between residues of two proteins (intermolecular), interactions between residues within the same protein (intramolecular), and interactions between residues of two proteins that are mediated by water molecules (water bridges). We extended our findings by demonstrating how this information obtained by FMO-PPI can be utilized to support the structure-based drug design of PPI modulators (SBDD-PPI).
Collapse
Affiliation(s)
- Stefania Monteleone
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Dmitri G Fedorov
- Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
| | - Andrea Townsend-Nicholson
- Institute of Structural & Molecular Biology, Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Michelle Southey
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Michael Bodkin
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Alexander Heifetz
- Evotec UK Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| |
Collapse
|
19
|
Manathunga M, Götz AW, Merz KM. Computer-aided drug design, quantum-mechanical methods for biological problems. Curr Opin Struct Biol 2022; 75:102417. [PMID: 35779437 DOI: 10.1016/j.sbi.2022.102417] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/14/2022] [Accepted: 05/16/2022] [Indexed: 11/28/2022]
Abstract
Quantum chemistry enables to study systems with chemical accuracy (<1 kcal/mol from experiment) but is restricted to a handful of atoms due to its computational expense. This has led to ongoing interest to optimize and simplify these methods while retaining accuracy. Implementing quantum mechanical (QM) methods on modern hardware such as multiple-GPUs is one example of how the field is optimizing performance. Multiscale approaches like the so-called QM/molecular mechanical method are gaining popularity in drug discovery because they focus the application of QM methods on the region of choice (e.g., the binding site), while using efficient MM models to represent less relevant areas. The creation of simplified QM methods is another example, including the use of machine learning to create ultra-fast and accurate QM models. Herein, we summarize recent advancements in the development of optimized QM methods that enhance our ability to use these methods in computer aided drug discovery.
Collapse
Affiliation(s)
- Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States. https://twitter.com/@MaduManathunga
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, United States. https://twitter.com/@awgoetz
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States.
| |
Collapse
|
20
|
Jongkon N, Seaho B, Tayana N, Prateeptongkum S, Duangdee N, Jaiyong P. Computational Analysis and Biological Activities of Oxyresveratrol Analogues, the Putative Cyclooxygenase-2 Inhibitors. Molecules 2022; 27:molecules27072346. [PMID: 35408774 PMCID: PMC9000610 DOI: 10.3390/molecules27072346] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/27/2022] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Polyphenols are a large family of naturally occurring phytochemicals. Herein, oxyresveratrol was isolated from ethanolic crude extracts of Artocarpus lacucha Buch.-Ham., and chemically modified to derive its lipophilic analogues. Biological screening assays showed their inhibitory potency against cyclooxygenase-2 (COX-2) with very low cytotoxicity to the MRC-5 normal cell lines. At the catalytic site of COX-2, docking protocols with ChemPLP, GoldScore and AutoDock scoring functions were carried out to reveal hydrogen bonding interactions with key polar contacts and hydrophobic pi-interactions. For more accurate binding energetics, COX-2/ligand complexes at the binding region were computed in vacuo and implicit aqueous solvation using M06-2X density functional with 6-31G+(d,p) basis set. Our computational results confirmed that dihydrooxyresveratrol (4) is the putative inhibitor of human COX-2 with the highest inhibitory activity (IC50 of 11.50 ± 1.54 µM) among studied non-fluorinated analogues for further lead optimization. Selective substitution of fluorine provides a stronger binding affinity; however, lowering the cytotoxicity of a fluorinated analogue to a normal cell is challenging. The consensus among biological activities, ChemPLP docking score and the binding energies computed at the quantum mechanical level is obviously helpful for identification of oxyresveratrol analogues as a putative anti-inflammatory agent.
Collapse
Affiliation(s)
- Nathjanan Jongkon
- Department of Social and Applied Science, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand;
| | - Boonwiset Seaho
- Department of Chemistry, Faculty of Science and Technology, Thammasat University, Pathum Thani 12120, Thailand; (B.S.); (S.P.)
| | - Ngampuk Tayana
- Drug Discovery and Development Center, Office of Advance Science and Technology, Thammasat University, Pathum Thani 12120, Thailand;
| | - Saisuree Prateeptongkum
- Department of Chemistry, Faculty of Science and Technology, Thammasat University, Pathum Thani 12120, Thailand; (B.S.); (S.P.)
| | - Nongnaphat Duangdee
- Drug Discovery and Development Center, Office of Advance Science and Technology, Thammasat University, Pathum Thani 12120, Thailand;
- Correspondence: (N.D.); (P.J.)
| | - Panichakorn Jaiyong
- Department of Chemistry, Faculty of Science and Technology, Thammasat University, Pathum Thani 12120, Thailand; (B.S.); (S.P.)
- Correspondence: (N.D.); (P.J.)
| |
Collapse
|
21
|
Wei L, Chen Y, Liu J, Rao L, Ren Y, Xu X, Wan J. Cov_DOX: A Method for Structure Prediction of Covalent Protein-Ligand Bindings. J Med Chem 2022; 65:5528-5538. [PMID: 35353519 DOI: 10.1021/acs.jmedchem.1c02007] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A handful of molecular docking tools have been extended to enable a covalent docking. However, all of them face the challenge brought by the covalent bond between proteins and ligands. Many covalent drug design scenarios still heavily rely on demanding crystallographic experiments for accurate binding structures. Aiming at filling the gap between covalent dockings and crystallographic experiments, we develop and validate a hybrid method, dubbed as Cov_DOX, in this work. Cov_DOX achieves an overall success rate of 81% with RMSD < 2 Å for the Top 1 pose prediction in the validation against a test set including 405 crystal structures for covalent protein-ligand complexes, covering various types of the warhead chemistry and receptors. Such accuracy is not far from the much more demanding crystallographic experiments, in sharp contrast to the performance of the covalent docking front runners (success rate: 40-60%).
Collapse
Affiliation(s)
- Lin Wei
- Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 43009, China
| | - Yaru Chen
- Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 43009, China
| | - Jiaqi Liu
- Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 43009, China
| | - Li Rao
- Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 43009, China
| | - Yanliang Ren
- Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 43009, China
| | - Xin Xu
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education (MOE) Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, People's Republic of China
| | - Jian Wan
- Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 43009, China
| |
Collapse
|
22
|
Nguyen TM, Nguyen T, Le TM, Tran T. GEFA: Early Fusion Approach in Drug-Target Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:718-728. [PMID: 34197324 DOI: 10.1109/tcbb.2021.3094217] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.
Collapse
|
23
|
Suay-García B, Bueso-Bordils JI, Falcó A, Antón-Fos GM, Alemán-López PA. Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design. Int J Mol Sci 2022; 23:ijms23031620. [PMID: 35163543 PMCID: PMC8836228 DOI: 10.3390/ijms23031620] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.
Collapse
Affiliation(s)
- Beatriz Suay-García
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
- Correspondence:
| | - Jose I. Bueso-Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Antonio Falcó
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
| | - Gerardo M. Antón-Fos
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Pedro A. Alemán-López
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| |
Collapse
|
24
|
Abstract
Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques that take into account the structure of the macromolecular target to predict compounds that are likely to establish optimal interactions with the binding site. The current interest in machine learning algorithms based on deep neural networks encouraged the application of deep learning to SBDD related problems. This chapter covers selected works in this active area of research.
Collapse
|
25
|
de Jesus JPA, Assis LC, de Castro AA, da Cunha EFF, Nepovimova E, Kuca K, de Castro Ramalho T, de Almeida La Porta F. Effect of drug metabolism in the treatment of SARS-CoV-2 from an entirely computational perspective. Sci Rep 2021; 11:19998. [PMID: 34620963 PMCID: PMC8497625 DOI: 10.1038/s41598-021-99451-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/01/2021] [Indexed: 12/25/2022] Open
Abstract
Understanding the effects of metabolism on the rational design of novel and more effective drugs is still a considerable challenge. To the best of our knowledge, there are no entirely computational strategies that make it possible to predict these effects. From this perspective, the development of such methodologies could contribute to significantly reduce the side effects of medicines, leading to the emergence of more effective and safer drugs. Thereby, in this study, our strategy is based on simulating the electron ionization mass spectrometry (EI-MS) fragmentation of the drug molecules and combined with molecular docking and ADMET models in two different situations. In the first model, the drug is docked without considering the possible metabolic effects. In the second model, each of the intermediates from the EI-MS results is docked, and metabolism occurs before the drug accesses the biological target. As a proof of concept, in this work, we investigate the main antiviral drugs used in clinical research to treat COVID-19. As a result, our strategy made it possible to assess the biological activity and toxicity of all potential by-products. We believed that our findings provide new chemical insights that can benefit the rational development of novel drugs in the future.
Collapse
Affiliation(s)
- João Paulo Almirão de Jesus
- Laboratory of Nanotechnology and Computational Chemistry, Federal Technological University of Paraná, Avenida dos Pioneiros 3131, Londrina, Paraná, CEP 86036-370, Brazil
| | - Letícia Cristina Assis
- Department of Chemistry, Federal University of Lavras, Lavras, Minas Gerais, CEP 37200-000, Brazil
| | | | | | - Eugenie Nepovimova
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic
| | - Kamil Kuca
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic.
| | - Teodorico de Castro Ramalho
- Department of Chemistry, Federal University of Lavras, Lavras, Minas Gerais, CEP 37200-000, Brazil
- Department of Chemistry, Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic
| | - Felipe de Almeida La Porta
- Laboratory of Nanotechnology and Computational Chemistry, Federal Technological University of Paraná, Avenida dos Pioneiros 3131, Londrina, Paraná, CEP 86036-370, Brazil.
| |
Collapse
|
26
|
Hisama K, Orimoto Y, Pomogaeva A, Nakatani K, Aoki Y. Ab initio multi-level layered elongation method and its application to local interaction analysis between DNA bulge and ligand molecules. J Chem Phys 2021; 155:044110. [PMID: 34340364 DOI: 10.1063/5.0050096] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
A multi-level layered elongation method was developed for efficiently analyzing the electronic states of local structures in large bio/nano-systems at the full ab initio level of theory. The original elongation method developed during the last three decades in our group has focused on the system in one direction from one terminal to the other terminal to sequentially construct the electronic states of a polymer, called a theoretical synthesis of polymers. In this study, an important region termed the central (C) part is targeted in a large polymer and the remainder are terminal (T) parts. The electronic structures along with polymer elongation are calculated repeatedly from both end T parts to the C central part at the same time. The important C part is treated with large basis sets (high level) and the other regions are treated with small basis sets (low level) in the ab initio theoretical framework. The electronic structures besides the C part can be reused for other systems with different structures at the C part, which renders the method computationally efficient. This multi-level layered elongation method was applied to the investigation on DNA single bulge recognition of small molecules (ligands). The reliability and validity of our approach were examined in comparison with the results obtained by direct calculations using a conventional quantum chemical method for the entire system. Furthermore, stabilization energies by the formation of the complex of bulge DNA and a ligand were estimated with basis set superposition error corrections incorporated into the elongation method.
Collapse
Affiliation(s)
- Keisuke Hisama
- Department of Interdisciplinary Engineering Sciences, Chemistry and Materials Science, Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasuga-Park, Fukuoka 816-8580, Japan
| | - Yuuichi Orimoto
- Department of Material Sciences, Faculty of Engineering Sciences, Kyushu University, 6-1 Kasuga-Park, Fukuoka 816-8580, Japan
| | - Anna Pomogaeva
- Department of Material Sciences, Faculty of Engineering Sciences, Kyushu University, 6-1 Kasuga-Park, Fukuoka 816-8580, Japan
| | - Kazuhiko Nakatani
- Department of Regulatory Bioorganic Chemistry, The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
| | - Yuriko Aoki
- Department of Material Sciences, Faculty of Engineering Sciences, Kyushu University, 6-1 Kasuga-Park, Fukuoka 816-8580, Japan
| |
Collapse
|
27
|
Muratov EN, Amaro R, Andrade CH, Brown N, Ekins S, Fourches D, Isayev O, Kozakov D, Medina-Franco JL, Merz KM, Oprea TI, Poroikov V, Schneider G, Todd MH, Varnek A, Winkler DA, Zakharov AV, Cherkasov A, Tropsha A. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem Soc Rev 2021; 50:9121-9151. [PMID: 34212944 PMCID: PMC8371861 DOI: 10.1039/d0cs01065k] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Indexed: 01/18/2023]
Abstract
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.
Collapse
Affiliation(s)
- Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
| | - Rommie Amaro
- University of California in San DiegoSan DiegoCAUSA
| | | | | | - Sean Ekins
- Collaborations PharmaceuticalsRaleighNCUSA
| | - Denis Fourches
- Department of Chemistry, North Carolina State UniversityRaleighNCUSA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Melon UniversityPittsburghPAUSA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookNYUSA
| | | | - Kenneth M. Merz
- Department of Chemistry, Michigan State UniversityEast LansingMIUSA
| | - Tudor I. Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, University of New Mexico, AlbuquerqueNMUSA
- Department of Rheumatology and Inflammation Research, Gothenburg UniversitySweden
- Novo Nordisk Foundation Center for Protein Research, University of CopenhagenDenmark
| | | | - Gisbert Schneider
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of TechnologyZurichSwitzerland
| | | | - Alexandre Varnek
- Department of Chemistry, University of StrasbourgStrasbourgFrance
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido UniversitySapporoJapan
| | - David A. Winkler
- Monash Institute of Pharmaceutical Sciences, Monash UniversityMelbourneVICAustralia
- School of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe UniversityBundooraAustralia
- School of Pharmacy, University of NottinghamNottinghamUK
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, University of British ColumbiaVancouverBCCanada
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
| |
Collapse
|
28
|
Borbulevych OY, Martin RI, Westerhoff LM. The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design. J Comput Aided Mol Des 2021; 35:433-451. [PMID: 33108589 PMCID: PMC8018927 DOI: 10.1007/s10822-020-00354-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 10/12/2020] [Indexed: 12/29/2022]
Abstract
Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule-along with any bound ligand(s)-within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method-coupled with our XModeScore method for experimental tautomer/protomer state determination-to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.
Collapse
Affiliation(s)
- Oleg Y Borbulevych
- QuantumBio Inc, 2790 West College Ave, Suite 900, State College, PA, 16801, USA
| | - Roger I Martin
- QuantumBio Inc, 2790 West College Ave, Suite 900, State College, PA, 16801, USA
| | - Lance M Westerhoff
- QuantumBio Inc, 2790 West College Ave, Suite 900, State College, PA, 16801, USA.
| |
Collapse
|
29
|
Sessa F, Olsson M, Söderberg F, Wang F, Rahm M. Experimental Quantum Chemistry: A Hammett-inspired Fingerprinting of Substituent Effects. Chemphyschem 2021; 22:569-576. [PMID: 33502056 PMCID: PMC8049055 DOI: 10.1002/cphc.202001053] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/27/2021] [Indexed: 01/20/2023]
Abstract
The quantum mechanically calculable Q descriptor is shown to be a potent quantifier of chemical reactivity in complex molecules - it shows a strong correlation to experimentally derived field effects in non-aromatic substrates and Hammett σm and σp parameters. Models for predicting substituent effects from Q are presented and applied, including on the elusive pentazolyl substituent. The presented approach enables fast computational estimation of substituent effects, and, in extension, medium-throughput screening of molecules and compound design. An experimental dataset is suggested as a candidate benchmark for aiding the general development and comparison of electronic structure analyses. It is here used to evaluate the experimental quantum chemistry (EQC) framework for chemical bonding analysis in larger molecules.
Collapse
Affiliation(s)
- Francesco Sessa
- Department of Chemistry and Chemical EngineeringChalmers University of TechnologySE-412 96GothenburgSweden
| | - Martina Olsson
- Department of Chemistry and Chemical EngineeringChalmers University of TechnologySE-412 96GothenburgSweden
| | - Fredrik Söderberg
- Department of Chemistry and Chemical EngineeringChalmers University of TechnologySE-412 96GothenburgSweden
| | - Fang Wang
- Department of ChemistryUniversity of Rhode Island140 Flagg RoadKingstonRhode Island02881USA
| | - Martin Rahm
- Department of Chemistry and Chemical EngineeringChalmers University of TechnologySE-412 96GothenburgSweden
| |
Collapse
|
30
|
Toviwek B, Gleeson D, Gleeson MP. QM/MM and molecular dynamics investigation of the mechanism of covalent inhibition of TAK1 kinase. Org Biomol Chem 2021; 19:1412-1425. [PMID: 33501482 DOI: 10.1039/d0ob02273j] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
TAK1 is a serine/threonine kinase which is involved in the moderation of cell survival and death via the TNFα signalling pathway. It is also implicated in a range of cancer and anti-inflammatory diseases. Drug discovery efforts on this target have focused on both traditional reversible ATP-binding site inhibitors and increasingly popular irreversible covalent binding inhibitors. Irreversible inhibitors can offer benefits in terms of potency, selectivity and PK/PD meaning they are increasingly pursued where the strategy exists. TAK1 kinase differs from the better-known kinase EGFR in that the reactive cysteine nucleophile targeted by electrophilic inhibitors is located towards the back of the ATP binding site, not at its mouth. While a wealth of structural and computational effort has been spent exploring EGFR, only limited studies on TAK1 have been reported. In this work we report the first QM/MM study on TAK1 aiming to better understand aspects of covalent adduct formation. Our goal is to identify the general base in the catalytic reaction, whether the process proceeds via a stepwise or concerted pathway, and how the highly flexible G-loop and A-loop affect the catalytic cysteine located nearby.
Collapse
Affiliation(s)
- Borvornwat Toviwek
- Department of Chemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
| | | | | |
Collapse
|
31
|
Anighoro A. Underappreciated Chemical Interactions in Protein-Ligand Complexes. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2114:75-86. [PMID: 32016887 DOI: 10.1007/978-1-0716-0282-9_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Non-covalent interactions lie at the bases of the molecular recognition process. In medicinal chemistry, understanding how bioactive molecules interact with their target can help to explain structure-activity relationships (SAR) and improve potency of lead compounds. In particular, computational analysis of protein-ligand complexes can help to unravel key interactions and guide structure-based drug design.The literature describing protein-ligand complexes is typically focused on few types of non-covalent interactions (e.g., hydrophobic contacts, hydrogen bonds, and salt bridges). Stacking interactions involving aromatic rings are also relatively well known to medicinal chemistry practitioners. Potency optimization efforts are often focused on targeting these interactions. However, a variety of underappreciated interactions were shown to have a relevant effect on the stabilization of protein-ligand complexes. This chapter aims at listing selected non-covalent interactions and discuss some examples on how they can impact drug design.
Collapse
|
32
|
Abstract
The understanding of binding interactions between a protein and a small molecule plays a key role in the rationalization of potency and selectivity and in design of new ideas. However, even when a target of interest is structurally enabled, visual inspection and force field-based molecular mechanics calculations cannot always explain the full complexity of the molecular interactions that are critical in drug design. Quantum mechanical methods have the potential to address this shortcoming, but traditionally, computational expense has made the application of these calculations impractical. The fragment molecular orbital (FMO) method offers a solution that combines accuracy, speed, and the ability to characterize important interactions (i.e. its strength in kcal/mol and chemical nature: hydrophobic, electrostatic, etc) that would otherwise be hard to detect. In this chapter, we describe the FMO method and illustrate its application in the discovery of the benzothiazole (BZT) series as novel tyrosine kinase ITK inhibitors for treatment of allergic asthma.
Collapse
|
33
|
Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods Mol Biol 2021. [PMID: 32016893 DOI: 10.1007/978-1-0716-0282-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanics (QM) approaches are often too computationally expensive to be of practical use in time-sensitive situations, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed, and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule toward ligand binding, including an analysis of their chemical nature. Such information is essential for an efficient structure-based drug design (SBDD) process. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions.
Collapse
|
34
|
Ugbaja SC, Sanusi ZK, Appiah-Kubi P, Lawal MM, Kumalo HM. Computational modelling of potent β-secretase (BACE1) inhibitors towards Alzheimer's disease treatment. Biophys Chem 2020; 270:106536. [PMID: 33387910 DOI: 10.1016/j.bpc.2020.106536] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/20/2020] [Accepted: 12/21/2020] [Indexed: 12/28/2022]
Abstract
Researchers have identified the β-amyloid precursor protein cleaving enzyme 1 (BACE1) in the multifactorial pathway of Alzheimer's disease (AD) as a drug target. The design and development of molecules to inhibit BACE1 as a potential cure for AD thus remained significant. Herein, we simulated two potent BACE1 inhibitors (AM-6494 and CNP-520) to understand their binding affinity at the atomistic level. AM-6494 is a newly reported potent BACE1 inhibitor with an IC50 value of 0.4 nM in vivo and now picked for preclinical considerations. Umibecestat (CNP-520), which was discontinued at human trials lately, was considered to enable a reasonable evaluation of our results. Using density functional theory (DFT) and Our Own N-layered Integrated molecular Orbital and Molecular Mechanics (ONIOM), we achieved the aim of this investigation. These computational approaches enabled the prediction of the electronic properties of AM-6494 and CNP-520 plus their binding energies when complexed with BACE1. For AM-6494 and CNP-520 interaction with protonated BACE1, the ONIOM calculation gave binding free energy of -62.849 and -33.463 kcal/mol, respectively. In the unprotonated model, we observed binding free energy of -59.758 kcal/mol in AM-6494. Taken together thermochemistry of the process and molecular interaction plot, AM-6494 is more favourable than CNP-520 towards the inhibition of BACE1. The protonated model gave slightly better binding energy than the unprotonated form. However, both models could sufficiently describe ligand binding to BACE1 at the atomistic level. Understanding the detailed molecular interaction of these inhibitors could serve as a basis for pharmacophore exploration towards improved inhibitor design.
Collapse
Affiliation(s)
- Samuel C Ugbaja
- Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Zainab K Sanusi
- Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Patrick Appiah-Kubi
- Molecular Bio-computational and Drug Design Research Group, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Monsurat M Lawal
- Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4001, South Africa.
| | - Hezekiel M Kumalo
- Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4001, South Africa.
| |
Collapse
|
35
|
Adeowo FY, Ejalonibu MA, Elrashedy AA, Lawal MM, Kumalo HM. Multi-target approach for Alzheimer's disease treatment: computational biomolecular modeling of cholinesterase enzymes with a novel 4- N-phenylaminoquinoline derivative reveal promising potentials. J Biomol Struct Dyn 2020; 39:3825-3841. [PMID: 33030113 DOI: 10.1080/07391102.2020.1826129] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The identification of dual inhibitors targeting the active sites of the cholinesterase enzymes, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE), have lately surfaced as a multi-approach towards Alzheimer treatment. More recently, a novel series of 4-N-phenylaminoquinolines was synthesized and evaluated against AChE and BuChE in which one of the compounds displayed appreciable inhibition compared to the standard compound, galantamine. To provide a clearer picture of the inhibition mechanism of this potent compound at the molecular level, computational biomolecular modeling was carried out. The investigation was initiated with the exploration of the chemical properties of the identified compound 11 b and reference drug, galantamine. Density functional theory (DFT) calculations reveal some conceptual parameters that provide information on the stability and reactivity of the compounds as potential inhibitors. To unveil the binding mechanism, energetics and enzyme-ligand interactions, molecular dynamics (MD) simulations of six different systems were executed over a period. Calculated binding free energy values are in the same order with experimental IC50 data. Identification of the main residues driving optimum binding of the active compound 11 b to the binding region of both AChE and BuChE showed Trp81 and Trp110 as the most important, respectively. It was proposed that the studied compound could serve as a dual inhibitor for AChE and BuChE, therefore, would potentially be a promising moiety in a multi-target approach for the treatment of Alzheimer's disorder.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Fatima Y Adeowo
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa
| | - Murtala A Ejalonibu
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa
| | - Ahmed A Elrashedy
- Molecular Bio-computational and Drug Design Research Group, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Monsurat M Lawal
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa
| | - Hezekiel M Kumalo
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa
| |
Collapse
|
36
|
Akinpelu OI, Lawal MM, Kumalo HM, Mhlongo NN. Computational studies of the properties and activities of selected trisubstituted benzimidazoles as potential antitubercular drugs inhibiting MTB-FtsZ polymerization. J Biomol Struct Dyn 2020; 40:1558-1570. [PMID: 33021149 DOI: 10.1080/07391102.2020.1830176] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Trisubstituted benzimidazoles (trisbenz) are significantly active against nonreplicating Mycobacterium tuberculosis (MTB) by inhibiting the polymerization of Filamentous Temperature Sensitive Mutant Z (FtsZ), an essential bacteria cell division protein. In-depth in-silico study of 5 of the most active trisubstituted benzimidazoles; trisbenz 1, 2, 3, 4 and 5, giving insight into their properties, such as stability, bioavailability, interactions with residues at the binding site of MTB-FtsZ and their influence on structural dynamics of the protein have been conducted. This was achieved through the application of in-silico methods including density functional theory (DFT) calculations, ADME properties calculations, molecular docking and molecular dynamics simulations. A DFT approach was applied to predict reactivity properties of potent FtsZ inhibitors, and the results reveal the relative reactivity of these inhibitors as bioactive moieties. The estimated ADME properties predicted all 5 compounds to be bioavailable and suitable for oral administration. Molecular docking, binding free energy, RMSD, RMSF, and hydrogen bond analysis confirmed these 5 compounds as potent MTB-FtsZ inhibitors. Although analyses proved these compounds to be bioactive and potent MTB-FtsZ inhibitors, however, trisbenz 1 appeared to be the most active against this protein while trisbenz 5 was the least active. This study further confirms the experimental study while also giving insight on the compounds mechanism of action and presents their bioavailability properties.
Collapse
Affiliation(s)
- Olayinka I Akinpelu
- Biomolecular Modelling Research Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa.,School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Monsurat M Lawal
- Biomolecular Modelling Research Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa
| | - Hezekiel M Kumalo
- Biomolecular Modelling Research Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa
| | - Ndumiso N Mhlongo
- Biomolecular Modelling Research Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban, South Africa
| |
Collapse
|
37
|
Zheng Z, Borbulevych OY, Liu H, Deng J, Martin RI, Westerhoff LM. MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment. J Chem Inf Model 2020; 60:5437-5456. [PMID: 32791826 PMCID: PMC7781189 DOI: 10.1021/acs.jcim.0c00618] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
![]()
For decades, the
complicated energy surfaces found in macromolecular
protein:ligand structures, which require large amounts of computational
time and resources for energy state sampling, have been an inherent
obstacle to fast, routine free energy estimation in industrial drug
discovery efforts. Beginning in 2013, the Merz research group addressed
this cost with the introduction of a novel sampling methodology termed
“Movable Type” (MT). Using numerical integration methods,
the MT method reduces the computational expense for energy state sampling
by independently calculating each atomic partition function from an
initial molecular conformation in order to estimate the molecular
free energy using ensembles of the atomic partition functions. In
this work, we report a software package, the DivCon Discovery Suite
with the MovableType module from QuantumBio Inc., that performs this
MT free energy estimation protocol in a fast, fully encapsulated manner.
We discuss the computational procedures and improvements to the original
work, and we detail the corresponding settings for this software package.
Finally, we introduce two validation benchmarks to evaluate the overall
robustness of the method against a broad range of protein:ligand structural
cases. With these publicly available benchmarks, we show that the
method can use a variety of input types and parameters and exhibits
comparable predictability whether the method is presented with “expensive”
X-ray structures or “inexpensively docked” theoretical
models. We also explore some next steps for the method. The MovableType
software is available at http://www.quantumbioinc.com/
Collapse
Affiliation(s)
- Zheng Zheng
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States.,School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Oleg Y Borbulevych
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
| | - Hao Liu
- School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Jianpeng Deng
- School of Chemistry, Chemical Engineering and Life Science, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, P. R. China
| | - Roger I Martin
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
| | - Lance M Westerhoff
- QuantumBio Inc., 2790 West College Avenue, Suite 900, State College, Pennsylvania 16801, United States
| |
Collapse
|
38
|
Hassanzadeh P. Towards the quantum-enabled technologies for development of drugs or delivery systems. J Control Release 2020; 324:260-279. [DOI: 10.1016/j.jconrel.2020.04.050] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 04/28/2020] [Accepted: 04/29/2020] [Indexed: 12/20/2022]
|
39
|
Pecina A, Eyrilmez SM, Köprülüoğlu C, Miriyala VM, Lepšík M, Fanfrlík J, Řezáč J, Hobza P. SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design. Chempluschem 2020; 85:2362-2371. [PMID: 32609421 DOI: 10.1002/cplu.202000120] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/27/2020] [Indexed: 12/17/2022]
Abstract
Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.
Collapse
Affiliation(s)
- Adam Pecina
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Saltuk M Eyrilmez
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
| | - Cemal Köprülüoğlu
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
| | - Vijay Madhav Miriyala
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Martin Lepšík
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Jindřich Fanfrlík
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Jan Řezáč
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic
| | - Pavel Hobza
- Institute of Organic Chemistry, and Biochemistry of Czech Academy of Sciences, Flemingovo namesti 2, 166 10, Prague, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Palacky University, 771 46, Olomouc, Czech Republic
| |
Collapse
|
40
|
Bera I, Payghan PV. Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery. Curr Pharm Des 2020; 25:3339-3349. [PMID: 31480998 DOI: 10.2174/1381612825666190903153043] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 09/01/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. OBJECTIVE The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. METHOD This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. RESULTS This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. CONCLUSION The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.
Collapse
Affiliation(s)
- Indrani Bera
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, United States
| | - Pavan V Payghan
- Structural Biology and Bioinformatics Department, CSIR-IICB, Kolkata, India.,Department of Pharmaceutical Sciences, Washington State University College of Pharmacy and Pharmaceutical Sciences, Spokane, WA, United States
| |
Collapse
|
41
|
Bagheri S, Behnejad H, Firouzi R, Karimi-Jafari MH. Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2. Mol Inform 2020; 39:e2000036. [PMID: 32485047 DOI: 10.1002/minf.202000036] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 05/28/2020] [Indexed: 11/12/2022]
Abstract
In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6-D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin-dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry-corrected heavy-atom RMSD and the fraction of recovered ligand-protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.
Collapse
Affiliation(s)
- Saleh Bagheri
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran
| | - Hassan Behnejad
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran
| | - Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
| | | |
Collapse
|
42
|
Liu J, He X. Fragment-based quantum mechanical approach to biomolecules, molecular clusters, molecular crystals and liquids. Phys Chem Chem Phys 2020; 22:12341-12367. [PMID: 32459230 DOI: 10.1039/d0cp01095b] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
To study large molecular systems beyond the system size that the current state-of-the-art ab initio electronic structure methods could handle, fragment-based quantum mechanical (QM) approaches have been developed over the past years, and proved to be efficient in dealing with large molecular systems at various ab initio levels. According to the fragmentation approach, a large molecular system can be divided into subsystems (fragments), and subsequently the property of the whole system can be approximately obtained by taking a proper combination of the corresponding terms of individual fragments. Therefore, the standard QM calculation of a large system could be circumvented by carrying out a series of calculations on small fragments, which significantly promotes computational efficiency. The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method is one of the fragment-based QM approaches which has been developed by our research group in recent years. This Perspective presents the theoretical framework of this fragmentation method and its applications in biomolecules, molecular clusters, molecular crystals and liquids, including total energy calculation, protein-ligand/protein binding affinity prediction, geometry optimization, vibrational spectrum simulation, ab initio molecular dynamics simulation, and prediction of excited-state properties.
Collapse
Affiliation(s)
- Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | | |
Collapse
|
43
|
Lawal MM, Lawal IA, Klink MJ, Tolufashe GF, Ndagi U, Kumalo HM. Density functional theory study of gold(III)-dithiocarbamate complexes with characteristic anticancer potentials. J Inorg Biochem 2020; 206:111044. [DOI: 10.1016/j.jinorgbio.2020.111044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 01/29/2020] [Accepted: 02/13/2020] [Indexed: 01/12/2023]
|
44
|
Heifetz A, Morao I, Babu MM, James T, Southey MWY, Fedorov DG, Aldeghi M, Bodkin MJ, Townsend-Nicholson A. Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method. J Chem Theory Comput 2020; 16:2814-2824. [PMID: 32096994 PMCID: PMC7161079 DOI: 10.1021/acs.jctc.9b01136] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information. Using the fragment molecular orbital (FMO) quantum mechanics method to analyze 35 crystal structures representing different branches of the class A GPCR family, we have identified 69 topologically equivalent TM residues that form a consensus network of 51 inter-TM interactions, providing novel results that are consistent with and help to rationalize experimental data. This discovery establishes a comprehensive picture of how defined molecular forces govern specific interhelical interactions which, in turn, support the structural stability, ligand binding, and activation of GPCRs.
Collapse
Affiliation(s)
- Alexander Heifetz
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
- Institute
of Structural & Molecular Biology, Research Department of Structural
& Molecular Biology, Division of Biosciences, University College London, London, WC1E 6BT, United Kingdom
- E-mail: (A.H.)
| | - Inaki Morao
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
- E-mail: (I.M.)
| | - M. Madan Babu
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Tim James
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
| | | | - Dmitri G. Fedorov
- CD-FMat,
National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
| | - Matteo Aldeghi
- Department
of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany
| | - Michael J. Bodkin
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
| | - Andrea Townsend-Nicholson
- Institute
of Structural & Molecular Biology, Research Department of Structural
& Molecular Biology, Division of Biosciences, University College London, London, WC1E 6BT, United Kingdom
| |
Collapse
|
45
|
Magwenyane AM, Mhlongo NN, Lawal MM, Amoako DG, Somboro AM, Sosibo SC, Shunmugam L, Khan RB, Kumalo HM. Understanding the Hsp90 N-terminal Dynamics: Structural and Molecular Insights into the Therapeutic Activities of Anticancer Inhibitors Radicicol (RD) and Radicicol Derivative (NVP-YUA922). Molecules 2020; 25:E1785. [PMID: 32295059 PMCID: PMC7221724 DOI: 10.3390/molecules25081785] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/03/2020] [Accepted: 04/08/2020] [Indexed: 11/23/2022] Open
Abstract
Heat shock protein 90 (Hsp90) is a crucial component in carcinogenesis and serves as a molecular chaperone that facilitates protein maturation whilst protecting cells against temperature-induced stress. The function of Hsp90 is highly dependent on adenosine triphosphate (ATP) binding to the N-terminal domain of the protein. Thus, inhibition through displacement of ATP by means of competitive binding with a suitable organic molecule is considered an attractive topic in cancer research. Radicicol (RD) and its derivative, resorcinylic isoxazole amine NVP-AUY922 (NVP), have shown promising pharmacodynamics against Hsp90 activity. To date, the underlying binding mechanism of RD and NVP has not yet been investigated. In this study, we provide a comprehensive understanding of the binding mechanism of RD and NVP, from an atomistic perspective. Density functional theory (DFT) calculations enabled the analyses of the compounds' electronic properties and results obtained proved to be significant in which NVP was predicted to be more favorable with solvation free energy value of -23.3 kcal/mol and highest stability energy of 75.5 kcal/mol for a major atomic delocalization. Molecular dynamic (MD) analysis revealed NVP bound to Hsp90 (NT-NVP) is more stable in comparison to RD (NT-RD). The Hsp90 protein exhibited a greater binding affinity for NT-NVP (-49.4 ± 3.9 kcal/mol) relative to NT-RD (-28.9 ± 4.5 kcal/mol). The key residues influential in this interaction are Gly 97, Asp 93 and Thr 184. These findings provide valuable insights into the Hsp90 dynamics and will serve as a guide for the design of potent novel inhibitors for cancer treatment.
Collapse
Affiliation(s)
- Ayanda M. Magwenyane
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
| | - Ndumiso N. Mhlongo
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
| | - Monsurat M. Lawal
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
| | - Daniel G. Amoako
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
- Biomedical Resource Unit, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa
| | - Anou M. Somboro
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
- Biomedical Resource Unit, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa
| | - Sphelele C. Sosibo
- School of Physical and Chemical Sciences, Department of Chemistry, North West University, Mafikeng Campus, Mmabatho 2790, South Africa;
| | - Letitia Shunmugam
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
| | - Rene B. Khan
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
| | - Hezekiel M. Kumalo
- Drug Research and Innovation Unit, Discipline of Medical Biochemistry, School of Laboratory Medicine and Medical Science, University of KwaZulu-Natal, Durban 4000, South Africa; (A.M.M.); (N.N.M.); (M.M.L.); (D.G.A.); (A.M.S.); (L.S.); (R.B.K.)
| |
Collapse
|
46
|
Drug repurposing: Fusidic acid as a potential inhibitor of M. tuberculosis FtsZ polymerization – Insight from DFT calculations, molecular docking and molecular dynamics simulations. Tuberculosis (Edinb) 2020; 121:101920. [DOI: 10.1016/j.tube.2020.101920] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/02/2020] [Accepted: 03/03/2020] [Indexed: 11/23/2022]
|
47
|
QM Implementation in Drug Design: Does It Really Help? Methods Mol Biol 2020. [PMID: 32016884 DOI: 10.1007/978-1-0716-0282-9_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Abstract
Computational chemistry allows one to characterize the structure, dynamics, and energetics of protein-ligand interactions, which makes it a valuable tool in drug discovery in both academic research and pharmaceutical industry. Molecular mechanics (MM)-based approaches are widely utilized to assist the discovery of new drug candidates. However, the complexity of protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. Aiming to provide better accuracy in the description of protein-ligand interactions, quantum mechanics (QM)-based approaches are becoming increasingly explored. In principle, QM calculation includes all contributions to the energy, accounting for terms usually missing in empirical force fields, and provides a greater degree of transferability. The usefulness of QM in drug design cannot be overemphasized. In this chapter, we present recent developments and applications of fragment-based QM method in studying the protein-ligand and protein-protein interactions. We critically discuss the performance of the fragment-based QM method at different ab initio levels while trying to answer a critical question: do QM-based methods really help in drug design?
Collapse
|
48
|
Understanding the potency of malarial ligand (D44) in plasmodium FKBP35 and modelled halogen atom (Br, Cl, F) functional groups. J Mol Graph Model 2020; 97:107553. [PMID: 32035313 DOI: 10.1016/j.jmgm.2020.107553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/10/2020] [Accepted: 01/27/2020] [Indexed: 11/21/2022]
Abstract
The present study clearly depicts the understanding of the D44 in Plasmodium FKBP35 around the hinge region. To analyse the binding stability of D44 ligand and to understand the role of halogen bond, hydrogen bond interaction formed between the hinge region amino acids: Isoleucine (Ile74), Phenylalanine (Phe54), Aspartic acid (Asp55) Phenylalanine (Phe64),Tyrosine (Tyr100), Tryptophan (TRP 77) and ligand D44 was portrayed specifically through interaction energy calculations at HF, M062X, MP2 level of theories for different basis set (6-311G**, 6-31+G*, LANL2DZ). The investigation will provide an apparent picture regarding the non-covalent interaction that hold the contact of ligand and amino acids in the hinge region and the implication of modelled functional groups (Br, Cl, F, OSO and NH2) on ligand, which will help chemist in synthesizing new novel ligands. HOMO, LUMO chart calculated for D44 ligands reveals graphic illustration of orbital's that stimulate for contact. The aim and natural bond orbital analysis identified key contribution of individual hydrogen/halogen bonds that contribute for the binding strength through stabilization energy, ρ and ∇2ρ values. Overall this study finds out that the Stability of D44 in Plasmodium FKBP35 was enhanced by the Halogen atom (Br, Cl, F) functional groups; which provide an innovative pathway for the selection of functional groups that opt for the hinge region side chains on the ligand.
Collapse
|
49
|
Grillo IB, Urquiza-Carvalho GA, Bachega JFR, Rocha GB. Elucidating Enzymatic Catalysis Using Fast Quantum Chemical Descriptors. J Chem Inf Model 2020; 60:578-591. [PMID: 31895567 DOI: 10.1021/acs.jcim.9b00860] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In general, computational simulations of enzymatic catalysis processes are thermodynamic and structural surveys to complement experimental studies, requiring high level computational methods to match accurate energy values. In the present work, we propose the usage of reactivity descriptors, theoretical quantities calculated from the electronic structure, to characterize enzymatic catalysis outlining its reaction profile using low-level computational methods, such as semiempirical Hamiltonians. We simulate three enzymatic reactions paths, one containing two reaction coordinates and without prior computational study performed, and calculate the reactivity descriptors for all obtained structures. We observed that the active site local hardness does not change substantially, even more so for the amino-acid residues that are said to stabilize the reaction structures. This corroborates with the theory that activation energy lowering is caused by the electrostatic environment of the active sites. Also, for the quantities describing the atom electrophilicity and nucleophilicity, we observed abrupt changes along the reaction coordinates, which also shows the enzyme participation as a reactant in the catalyzed reaction. We expect that such electronic structure analysis allows the expedient proposition and/or prediction of new mechanisms, providing chemical characterization of the enzyme active sites, thus hastening the process of transforming the resolved protein three-dimensional structures in catalytic information.
Collapse
Affiliation(s)
- Igor Barden Grillo
- Department of Chemistry , Federal University of Paraíba , Cidade Universitária, João Pessoa , Paraíba 58051-085 , Brazil
| | - Gabriel A Urquiza-Carvalho
- Department of Fundamental Chemistry , Federal University of Pernambuco , Cidade Universitária, Recife , Pernambuco 50670-901 , Brazil
| | - José Fernando Ruggiero Bachega
- Department of Pharmacosciences , Federal University of Health Sciences of Porto Alegre , Centro Histórico, Porto Alegre , Rio Grande do Sul 90050-170 , Brazil
| | - Gerd Bruno Rocha
- Department of Chemistry , Federal University of Paraíba , Cidade Universitária, João Pessoa , Paraíba 58051-085 , Brazil
| |
Collapse
|
50
|
Morao I, Heifetz A, Fedorov DG. Accurate Scoring in Seconds with the Fragment Molecular Orbital and Density-Functional Tight-Binding Methods. Methods Mol Biol 2020; 2114:143-148. [PMID: 32016891 DOI: 10.1007/978-1-0716-0282-9_9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The accurate evaluation of receptor-ligand interactions is an essential part of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been combined with the density-functional tight-binding (DFTB) method to compute energy calculations of biological systems in seconds. FMO-DFTB outperformed GBVI/WSA in identifying a set of 10 binders versus a background of 500 decoys applied to human k-opioid receptor. The significant increase in the speed and the high accuracy achieved with FMO-DFTB calculations allows FMO to be applied in areas of drug discovery that were not previously accessible to traditional QM methodologies. For the first time, it is now possible to perform FMO calculations in a high-throughput manner.
Collapse
Affiliation(s)
- Inaki Morao
- Evotec (UK) Ltd., Abingdon, Oxfordshire, UK.
| | | | - Dmitri G Fedorov
- Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
| |
Collapse
|