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Karasev DA, Sobolev BN, Filimonov DA, Lagunin A. Prediction of viral protease inhibitors using proteochemometrics approach. Comput Biol Chem 2024; 110:108061. [PMID: 38574417 DOI: 10.1016/j.compbiolchem.2024.108061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/06/2024]
Abstract
Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands. We have developed an original protocol allowing us to collect a comprehensive set of 15 protein sequences and more than 9000 ligands from the ChEMBL database. The N-grams were derived from the 3D-based alignment, accurately superposing ligand-binding regions. In testing the ligand set in SAR mode with MNA descriptors, an accuracy above 0.95 was determined that shows the perspective of the antiviral drug search in virtual chemical libraries. The effective PCM models were built with the TLMNA descriptor. The strong validation procedure with pair exclusion simulated the prediction of interactions between the new ligands and new targets, resulting in accuracy estimation up to 0.89. The PCM approach shows slightly lower accuracy caused by more uncertainty compared with SAR, but it overcomes the problem of data incompleteness.
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Affiliation(s)
- Dmitry A Karasev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.
| | - Boris N Sobolev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow 117997, Russia
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2
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Gorostiola González M, Rakers PRJ, Jespers W, IJzerman AP, Heitman LH, van Westen GJP. Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities. Int J Mol Sci 2024; 25:3698. [PMID: 38612509 PMCID: PMC11011372 DOI: 10.3390/ijms25073698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.
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Affiliation(s)
- Marina Gorostiola González
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
- Oncode Institute, 2333 CC Leiden, The Netherlands
| | - Pepijn R. J. Rakers
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
| | - Willem Jespers
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
| | - Adriaan P. IJzerman
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
| | - Laura H. Heitman
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
- Oncode Institute, 2333 CC Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
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3
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Grandits M, Ecker GF. Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling. Curr Drug Res Rev 2024; 16:81-93. [PMID: 37157206 PMCID: PMC11340286 DOI: 10.2174/2589977515666230508123041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 05/10/2023]
Abstract
The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.
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Affiliation(s)
- Melanie Grandits
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
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4
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Haider S, Mushtaq M, Nur-E-Alam M, Ahmed A, Ul-Haq Z. Identification of novel small molecule inhibitors for solute carrier SGLT1; a computational exploration. J Biomol Struct Dyn 2023:1-11. [PMID: 37855364 DOI: 10.1080/07391102.2023.2270708] [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: 08/07/2023] [Accepted: 10/08/2023] [Indexed: 10/20/2023]
Abstract
Diabetes results in substantial disabilities, diminished quality of life, and mortality that imposes a huge economic burden on societies and governments worldwide. Despite the absence of specific oral therapies at present, there exists an urgent requirement to develop a novel drug for the treatment of diabetes mellitus. The membrane protein sodium glucose co-transporters (SGLT1) present a captivating therapeutic target for diabetes, given its pivotal role in facilitating glucose absorption in the small intestine, offering immense promise for potential therapeutic intervention. In this connection, the present study is aimed at identifying potential inhibitors of SGLT1 from a small molecule database, including compounds from both natural as well as synthetic origins. A comprehensive approach was employed, by integrating homology modeling, ligand-based pharmacophore modeling, virtual screening, and molecular docking simulation. The process resulted in the identification of 16 new compounds, featuring similar attributes as observed for the documented actives. In a systematic screening procedure, five potential virtual hits were selected for simulation studies followed by subsequent binding free energy calculations, providing deeper insight into the time-dependent behavior of protein-ligand complexes in a dynamic state. In conclusion, our findings demonstrated that the identified compounds, particularly compounds 81 and 91, exhibit enhanced stability and favorable binding affinities with the target protein, marking them promising candidates for further investigations.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sajjad Haider
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Mamona Mushtaq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Mohammad Nur-E-Alam
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Aftab Ahmed
- Chapman University School of pharmacy, Irvine, CA, USA
| | - Zaheer Ul-Haq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
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5
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Kong X, Lin K, Wu G, Tao X, Zhai X, Lv L, Dong D, Zhu Y, Yang S. Machine Learning Techniques Applied to the Study of Drug Transporters. Molecules 2023; 28:5936. [PMID: 37630188 PMCID: PMC10459831 DOI: 10.3390/molecules28165936] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/27/2023] [Accepted: 08/02/2023] [Indexed: 08/27/2023] Open
Abstract
With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters have important roles in influencing drug resistance, drug-drug interactions, and tissue-specific drug targeting. The investigation of drug transporter substrates and inhibitors is a crucial aspect of pharmaceutical development. However, long duration and high expenses pose significant challenges in the investigation of drug transporters. In this review, we discuss the present situation and challenges encountered in applying machine learning techniques to investigate drug transporters. The transporters involved include ABC transporters (P-gp, BCRP, MRPs, and BSEP) and SLC transporters (OAT, OATP, OCT, MATE1,2-K, and NET). The aim is to offer a point of reference for and assistance with the progression of drug transporter research, as well as the advancement of more efficient computer technology. Machine learning methods are valuable and attractive for helping with the study of drug transporter substrates and inhibitors, but continuous efforts are still needed to develop more accurate and reliable predictive models and to apply them in the screening process of drug development to improve efficiency and success rates.
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Affiliation(s)
- Xiaorui Kong
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
| | - Kexin Lin
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
| | - Gaolei Wu
- Department of Pharmacy, Dalian Women and Children’s Medical Group, Dalian 116024, China;
| | - Xufeng Tao
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
| | - Xiaohan Zhai
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
| | - Linlin Lv
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
| | - Deshi Dong
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
| | - Yanna Zhu
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
| | - Shilei Yang
- Department of Pharmacy, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.K.); (K.L.); (X.T.); (X.Z.); (L.L.); (D.D.)
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6
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Bongers BJ, Sijben HJ, Hartog PBR, Tarnovskiy A, IJzerman AP, Heitman LH, van Westen GJP. Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors. J Chem Inf Model 2023; 63:1745-1755. [PMID: 36926886 PMCID: PMC10052348 DOI: 10.1021/acs.jcim.2c01645] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Solute carriers (SLCs) are relatively underexplored compared to other prominent protein families such as kinases and G protein-coupled receptors. However, proteins from the SLC family play an essential role in various diseases. One such SLC is the high-affinity norepinephrine transporter (NET/SLC6A2). In contrast to most other SLCs, the NET has been relatively well studied. However, the chemical space of known ligands has a low chemical diversity, making it challenging to identify chemically novel ligands. Here, a computational screening pipeline was developed to find new NET inhibitors. The approach increases the chemical space to model for NETs using the chemical space of related proteins that were selected utilizing similarity networks. Prior proteochemometric models added data from related proteins, but here we use a data-driven approach to select the optimal proteins to add to the modeled data set. After optimizing the data set, the proteochemometric model was optimized using stepwise feature selection. The final model was created using a two-step approach combining several proteochemometric machine learning models through stacking. This model was applied to the extensive virtual compound database of Enamine, from which the top predicted 22,000 of the 600 million virtual compounds were clustered to end up with 46 chemically diverse candidates. A subselection of 32 candidates was synthesized and subsequently tested using an impedance-based assay. There were five hit compounds identified (hit rate 16%) with sub-micromolar inhibitory potencies toward NET, which are promising for follow-up experimental research. This study demonstrates a data-driven approach to diversify known chemical space to identify novel ligands and is to our knowledge the first to select this set based on the sequence similarity of related targets.
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Affiliation(s)
- Brandon J Bongers
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Huub J Sijben
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Peter B R Hartog
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | | | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Laura H Heitman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands.,Oncode Institute, Jaarbeursplein 6, Utrecht 3521 AL, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden 2333 CC, The Netherlands
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7
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Ehrlich A, Ioannidis K, Nasar M, Abu Alkian I, Daskal Y, Atari N, Kliker L, Rainy N, Hofree M, Shafran Tikva S, Houri I, Cicero A, Pavanello C, Sirtori CR, Cohen JB, Chirinos JA, Deutsch L, Cohen M, Gottlieb A, Bar-Chaim A, Shibolet O, Mandelboim M, Maayan SL, Nahmias Y. Efficacy and safety of metabolic interventions for the treatment of severe COVID-19: in vitro, observational, and non-randomized open-label interventional study. eLife 2023; 12:e79946. [PMID: 36705566 PMCID: PMC9937660 DOI: 10.7554/elife.79946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 01/26/2023] [Indexed: 01/28/2023] Open
Abstract
Background Viral infection is associated with a significant rewire of the host metabolic pathways, presenting attractive metabolic targets for intervention. Methods We chart the metabolic response of lung epithelial cells to SARS-CoV-2 infection in primary cultures and COVID-19 patient samples and perform in vitro metabolism-focused drug screen on primary lung epithelial cells infected with different strains of the virus. We perform observational analysis of Israeli patients hospitalized due to COVID-19 and comparative epidemiological analysis from cohorts in Italy and the Veteran's Health Administration in the United States. In addition, we perform a prospective non-randomized interventional open-label study in which 15 patients hospitalized with severe COVID-19 were given 145 mg/day of nanocrystallized fenofibrate added to the standard of care. Results SARS-CoV-2 infection produced transcriptional changes associated with increased glycolysis and lipid accumulation. Metabolism-focused drug screen showed that fenofibrate reversed lipid accumulation and blocked SARS-CoV-2 replication through a PPARα-dependent mechanism in both alpha and delta variants. Analysis of 3233 Israeli patients hospitalized due to COVID-19 supported in vitro findings. Patients taking fibrates showed significantly lower markers of immunoinflammation and faster recovery. Additional corroboration was received by comparative epidemiological analysis from cohorts in Europe and the United States. A subsequent prospective non-randomized interventional open-label study was carried out on 15 patients hospitalized with severe COVID-19. The patients were treated with 145 mg/day of nanocrystallized fenofibrate in addition to standard-of-care. Patients receiving fenofibrate demonstrated a rapid reduction in inflammation and a significantly faster recovery compared to patients admitted during the same period. Conclusions Taken together, our data suggest that pharmacological modulation of PPARα should be strongly considered as a potential therapeutic approach for SARS-CoV-2 infection and emphasizes the need to complete the study of fenofibrate in large randomized controlled clinical trials. Funding Funding was provided by European Research Council Consolidator Grants OCLD (project no. 681870) and generous gifts from the Nikoh Foundation and the Sam and Rina Frankel Foundation (YN). The interventional study was supported by Abbott (project FENOC0003). Clinical trial number NCT04661930.
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Affiliation(s)
- Avner Ehrlich
- Grass Center for Bioengineering, Benin School of Computer Science and EngineeringJerusalemIsrael
- Department of Cell and Developmental Biology, Silberman Institute of Life SciencesJerusalemIsrael
| | - Konstantinos Ioannidis
- Grass Center for Bioengineering, Benin School of Computer Science and EngineeringJerusalemIsrael
- Department of Cell and Developmental Biology, Silberman Institute of Life SciencesJerusalemIsrael
| | - Makram Nasar
- Division of Infectious Diseases, Barzilai Medical CenterAshkelonIsrael
| | | | - Yuval Daskal
- Grass Center for Bioengineering, Benin School of Computer Science and EngineeringJerusalemIsrael
- Department of Cell and Developmental Biology, Silberman Institute of Life SciencesJerusalemIsrael
| | - Nofar Atari
- Central Virology Laboratory, Public Health Services, Ministry of Health and Sheba Medical CenterTel HashomerIsrael
| | - Limor Kliker
- Central Virology Laboratory, Public Health Services, Ministry of Health and Sheba Medical CenterTel HashomerIsrael
| | - Nir Rainy
- Laboratory Division, Shamir (Assaf Harofeh) Medical CenterZerifinItaly
| | - Matan Hofree
- Klarman Cell Observatory, The Broad Institute of Harvard and MITCambridgeUnited States
| | - Sigal Shafran Tikva
- Laboratory Division, Shamir (Assaf Harofeh) Medical CenterZerifinItaly
- Hadassah Research and Innovation CenterJerusalemIsrael
- Department of Nursing, Faculty of School of Life and Health Sciences, The Jerusalem College of Technology Lev Academic CenterJerusalemIsrael
| | - Inbal Houri
- Department of Gastroenterology, Sourasky Medical CenterTel AvivIsrael
| | - Arrigo Cicero
- IRCSS S.Orsola-Malpighi University HospitalBolognaItaly
| | - Chiara Pavanello
- Centro Grossi Paoletti, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di MilanoMilanoItaly
- Centro Dislipidemie, Niguarda HospitalMilanoItaly
| | | | - Jordana B Cohen
- Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Julio A Chirinos
- Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | | | - Merav Cohen
- Grass Center for Bioengineering, Benin School of Computer Science and EngineeringJerusalemIsrael
- Department of Cell and Developmental Biology, Silberman Institute of Life SciencesJerusalemIsrael
| | - Amichai Gottlieb
- Division of Infectious Diseases, Barzilai Medical CenterAshkelonIsrael
| | - Adina Bar-Chaim
- Laboratory Division, Shamir (Assaf Harofeh) Medical CenterZerifinItaly
| | - Oren Shibolet
- Sackler Faculty of Medicine, Tel Aviv UniversityTel AvivIsrael
| | | | - Shlomo L Maayan
- Division of Infectious Diseases, Barzilai Medical CenterAshkelonIsrael
| | - Yaakov Nahmias
- Grass Center for Bioengineering, Benin School of Computer Science and EngineeringJerusalemIsrael
- Department of Cell and Developmental Biology, Silberman Institute of Life SciencesJerusalemIsrael
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Fenanir F, Semmeq A, Benguerba Y, Badawi M, Dziurla MA, Amira S, Laouer H. In silico investigations of some Cyperus rotundus compounds as potential anti-inflammatory inhibitors of 5-LO and LTA4H enzymes. J Biomol Struct Dyn 2022; 40:11571-11586. [PMID: 34355673 DOI: 10.1080/07391102.2021.1960197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The present study aimed to experimentally identify the essential oil of Algerian Cyperus rotundus L. and to model the interaction of some known anti-inflammatory molecules with two key enzymes involved in inflammation, 5-Lypoxygenase (5-LO) and leukotriene A4 hydrolase (LTA4H). Gas chromatography/gas chromatography-mass spectrometry (GC/GC-MS) revealed that 92.7% of the essential oil contains 35 compounds, including oxygenated sesquiterpenes (44.2%), oxygenated monoterpenes (30.2%), monoterpene hydrocarbons (11.8%) and sesquiterpene hydrocarbons (6.5%). The major identified oxygenated terpenes are humulene oxide II, caryophyllene oxide, khusinol, agarospirol, spathulinol and trans-pinocarveol Myrtenol and α-terpineol are known to exhibit anti-inflammatory activities. Several complexes obtained after docking the natural terpenes with 5-LO and LTA4H have shown strong hydrogen bonding interactions. The best docking energies were found with α-terpineol, Myrtenol and khusinol. The interaction between the natural products and amino-acid residues HIS367, ILE673 and GLN363 appears to be critical for 5-LO inhibition, while the interaction with residues GLU271, HIS295, TYR383, TYR378, GLU318, GLU296 and ASP375 is critical for LTA4H inhibition. Molecular dynamics (MD) trajectories of the selected docked complexes showed stable backbone root mean square deviation (RMSD), supporting the stability of the natural product-enzyme interaction.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Fares Fenanir
- Laboratory of Valorization of Natural and biological Resources, University Ferhat Abbas, Sétif, Algeria
| | - Abderrahmane Semmeq
- Laboratoire de Physique et Chimie Théoriques (UMR 7019), CNRS-Université de Lorraine, Saint-Avold, France
| | - Yacine Benguerba
- Laboratoire des Matériaux Polymères Multiphasiques, LMPMP, Université Ferhat ABBAS, Sétif, Algeria
| | - Michael Badawi
- Laboratoire de Physique et Chimie Théoriques (UMR 7019), CNRS-Université de Lorraine, Saint-Avold, France.,IUT de Moselle-Est, Université de Lorraine, Saint-Avold, France
| | | | - Smain Amira
- Laboratory of Phytotherapy Applied to Chroniques Diseases, University Ferhat Abbas, Sétif, Algeria
| | - Hocine Laouer
- Laboratory of Valorization of Natural and biological Resources, University Ferhat Abbas, Sétif, Algeria
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9
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Deep Learning Based-Virtual Screening Using 2D Pharmacophore Fingerprint in Drug Discovery. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10879-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Zhang C, Mou M, Zhou Y, Zhang W, Lian X, Shi S, Lu M, Sun H, Li F, Wang Y, Zeng Z, Li Z, Zhang B, Qiu Y, Zhu F, Gao J. Biological activities of drug inactive ingredients. Brief Bioinform 2022; 23:6582006. [PMID: 35524477 DOI: 10.1093/bib/bbac160] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/01/2022] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.
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Affiliation(s)
- Chenyang Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
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11
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Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, Maojo V, Pazos A, Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021; 19:4538-4558. [PMID: 34471498 PMCID: PMC8387781 DOI: 10.1016/j.csbj.2021.08.011] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 12/30/2022] Open
Abstract
Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
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Key Words
- ADMET, Absorption, distribution, metabolism, elimination and toxicity
- ADR, Adverse Drug Reaction
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APFP, Atom Pairs 2d FingerPrint
- AUC, Area under the Curve
- BBB, Blood–Brain barrier
- CDK, Chemical Development Kit
- CNN, Convolutional Neural Networks
- CNS, Central Nervous System
- CPI, Compound-protein interaction
- CV, Cross Validation
- Cheminformatics
- DL, Deep Learning
- DNA, Deoxyribonucleic acid
- Deep Learning
- Drug Discovery
- ECFP, Extended Connectivity Fingerprints
- FDA, Food and Drug Administration
- FNN, Fully Connected Neural Networks
- FP, Fringerprints
- FS, Feature Selection
- GCN, Graph Convolutional Networks
- GEO, Gene Expression Omnibus
- GNN, Graph Neural Networks
- GO, Gene Ontology
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- MACCS, Molecular ACCess System
- MCC, Matthews correlation coefficient
- MD, Molecular Descriptors
- MKL, Multiple Kernel Learning
- ML, Machine Learning
- Machine Learning
- Molecular Descriptors
- NB, Naive Bayes
- OOB, Out of Bag
- PCA, Principal Component Analyisis
- QSAR
- QSAR, Quantitative structure–activity relationship
- RF, Random Forest
- RNA, Ribonucleic Acid
- SMILES, simplified molecular-input line-entry system
- SVM, Support Vector Machines
- TCGA, The Cancer Genome Atlas
- WHO, World Health Organization
- t-SNE, t-Distributed Stochastic Neighbor Embedding
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Jose Liñares-Blanco
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
| | - Nereida Rodríguez-Fernández
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco Cedrón
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco J. Novoa
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Adrian Carballal
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Victor Maojo
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, Madrid 28660, Spain
| | - Alejandro Pazos
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
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12
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Ali Shah SM, Taju SW, Ho QT, Nguyen TTD, Ou YY. GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models. Comput Biol Med 2021; 131:104259. [PMID: 33581474 DOI: 10.1016/j.compbiomed.2021.104259] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 02/04/2021] [Accepted: 02/04/2021] [Indexed: 12/14/2022]
Abstract
Recently, language representation models have drawn a lot of attention in the field of natural language processing (NLP) due to their remarkable results. Among them, BERT (Bidirectional Encoder Representations from Transformers) has proven to be a simple, yet powerful language model that has achieved novel state-of-the-art performance. BERT adopted the concept of contextualized word embeddings to capture the semantics and context in which words appear. We utilized pre-trained BERT models to extract features from protein sequences for discriminating three families of glucose transporters: the major facilitator superfamily of glucose transporters (GLUTs), the sodium-glucose linked transporters (SGLTs), and the sugars will eventually be exported transporters (SWEETs). We treated protein sequences as sentences and transformed them into fixed-length meaningful vectors where a 768- or 1024-dimensional vector represents each amino acid. We observed that BERT-Base and BERT-Large models improved the performance by more than 4% in terms of average sensitivity and Matthews correlation coefficient (MCC), indicating the efficiency of this approach. We also developed a bidirectional transformer-based protein model (TransportersBERT) for comparison with existing pre-trained BERT models.
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Affiliation(s)
- Syed Muazzam Ali Shah
- Department of Computer Science & Engineering, Yuan Ze University, Chungli, 32003, Taiwan
| | - Semmy Wellem Taju
- Department of Computer Science & Engineering, Yuan Ze University, Chungli, 32003, Taiwan
| | - Quang-Thai Ho
- Department of Computer Science & Engineering, Yuan Ze University, Chungli, 32003, Taiwan
| | | | - Yu-Yen Ou
- Department of Computer Science & Engineering, Yuan Ze University, Chungli, 32003, Taiwan.
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13
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Burggraaff L, van Veen A, Lam CC, van Vlijmen HWT, IJzerman AP, van Westen GJP. Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs. J Chem Inf Model 2020; 60:4664-4672. [PMID: 32931270 PMCID: PMC7592116 DOI: 10.1021/acs.jcim.0c00695] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Indexed: 02/06/2023]
Abstract
Proteins often have both orthosteric and allosteric binding sites. Endogenous ligands, such as hormones and neurotransmitters, bind to the orthosteric site, while synthetic ligands may bind to orthosteric or allosteric sites, which has become a focal point in drug discovery. Usually, such allosteric modulators bind to a protein noncompetitively with its endogenous ligand or substrate. The growing interest in allosteric modulators has resulted in a substantial increase of these entities and their features such as binding data in chemical libraries and databases. Although this data surge fuels research focused on allosteric modulators, binding data is unfortunately not always clearly indicated as being allosteric or orthosteric. Therefore, allosteric binding data is difficult to retrieve from databases that contain a mixture of allosteric and orthosteric compounds. This decreases model performance when statistical methods, such as machine learning models, are applied. In previous work we generated an allosteric data subset of ChEMBL release 14. In the current study an improved text mining approach is used to retrieve the allosteric and orthosteric binding types from the literature in ChEMBL release 22. Moreover, convolutional deep neural networks were constructed to predict the binding types of compounds for class A G protein-coupled receptors (GPCRs). Temporal split validation showed the model predictiveness with Matthews correlation coefficient (MCC) = 0.54, sensitivity allosteric = 0.54, and sensitivity orthosteric = 0.94. Finally, this study shows that the inclusion of accurate binding types increases binding predictions by including them as descriptor (MCC = 0.27 improved to MCC = 0.34; validated for class A GPCRs, trained on all GPCRs). Although the focus of this study is mainly on class A GPCRs, binding types for all protein classes in ChEMBL were obtained and explored. The data set is included as a supplement to this study, allowing the reader to select the compounds and binding types of interest.
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Affiliation(s)
- Lindsey Burggraaff
- Division
of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333
CC, Leiden, The Netherlands
| | - Amber van Veen
- Division
of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333
CC, Leiden, The Netherlands
| | - Chi Chung Lam
- Division
of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333
CC, Leiden, The Netherlands
| | - Herman W. T. van Vlijmen
- Division
of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333
CC, Leiden, The Netherlands
- Janssen
Research & Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division
of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333
CC, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division
of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333
CC, Leiden, The Netherlands
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14
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Burggraaff L, Lenselink EB, Jespers W, van Engelen J, Bongers BJ, González MG, Liu R, Hoos HH, van Vlijmen HWT, IJzerman AP, van Westen GJP. Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors. J Chem Inf Model 2020; 60:4283-4295. [PMID: 32343143 PMCID: PMC7525794 DOI: 10.1021/acs.jcim.9b01204] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
![]()
Kinases are frequently
studied in the context of anticancer drugs.
Their involvement in cell responses, such as proliferation, differentiation,
and apoptosis, makes them interesting subjects in multitarget drug
design. In this study, a workflow is presented that models the bioactivity
spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC,
and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and
PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding
inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based
models were included, which were thoroughly benchmarked and optimized.
A virtual screening was performed to test the workflow for one of
the main targets, RET kinase. This resulted in 5 novel and chemically
dissimilar RET inhibitors with remaining RET activity of <60% (at
a concentration of 10 μM) and similarities with known RET inhibitors
from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors
were assessed in a concentration range and proved to be modestly active
with a pIC50 value of 5.1 for the most active compound.
The experimental validation of inhibitors for RET strongly indicates
that the multitarget workflow is able to detect novel inhibitors for
kinases, and hence, this workflow can potentially be applied in polypharmacology
modeling. We conclude that this approach can identify new chemical
matter for existing targets. Moreover, this workflow can easily be
applied to other targets as well.
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Affiliation(s)
- Lindsey Burggraaff
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Eelke B Lenselink
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Willem Jespers
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.,Department of Cell and Molecular Biology, Uppsala University, Uppsala 75124, Sweden
| | - Jesper van Engelen
- Department of Computer Science, Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
| | - Brandon J Bongers
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Marina Gorostiola González
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Rongfang Liu
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Holger H Hoos
- Department of Computer Science, Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
| | - Herman W T van Vlijmen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.,Janssen Research & Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Adriaan P IJzerman
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
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15
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Burggraaff L, van Vlijmen HWT, IJzerman AP, van Westen GJP. Quantitative prediction of selectivity between the A 1 and A 2A adenosine receptors. J Cheminform 2020; 12:33. [PMID: 33431012 PMCID: PMC7222572 DOI: 10.1186/s13321-020-00438-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 05/04/2020] [Indexed: 11/10/2022] Open
Abstract
The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (individual machine learning models). Here we show that modeling selectivity directly, by using the affinity difference between two drug targets as output value, leads to more accurate selectivity predictions. We test multiple approaches on a dataset consisting of ligands for the A1 and A2A adenosine receptors (among others classification, regression, and we define different selectivity classes). Finally, we present a regression model that predicts selectivity between these two drug targets by directly training on the difference in bioactivity, modeling the selectivity-window. The quality of this model was good as shown by the performances for fivefold cross-validation: ROC A1AR-selective 0.88 ± 0.04 and ROC A2AAR-selective 0.80 ± 0.07. To increase the accuracy of this selectivity model even further, inactive compounds were identified and removed prior to selectivity prediction by a combination of statistical models and structure-based docking. As a result, selectivity between the A1 and A2A adenosine receptors was predicted effectively using the selectivity-window model. The approach presented here can be readily applied to other selectivity cases.
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Affiliation(s)
- Lindsey Burggraaff
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Herman W T van Vlijmen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.,Janssen Research & Development, Turnhoutseweg 30, Beerse, Belgium
| | - Adriaan P IJzerman
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
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16
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Bongers BJ, IJzerman AP, Van Westen GJP. Proteochemometrics - recent developments in bioactivity and selectivity modeling. DRUG DISCOVERY TODAY. TECHNOLOGIES 2019; 32-33:89-98. [PMID: 33386099 DOI: 10.1016/j.ddtec.2020.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/18/2020] [Accepted: 08/28/2020] [Indexed: 06/12/2023]
Abstract
Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.
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Affiliation(s)
- Brandon J Bongers
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J P Van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.
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17
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Moumbock AF, Li J, Mishra P, Gao M, Günther S. Current computational methods for predicting protein interactions of natural products. Comput Struct Biotechnol J 2019; 17:1367-1376. [PMID: 31762960 PMCID: PMC6861622 DOI: 10.1016/j.csbj.2019.08.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 08/09/2019] [Accepted: 08/23/2019] [Indexed: 01/08/2023] Open
Abstract
Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.
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Affiliation(s)
| | | | | | | | - Stefan Günther
- Institute of Pharmaceutical Sciences, Research Group Pharmaceutical Bioinformatics, Albert-Ludwigs-Universität Freiburg, Germany
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18
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Oranje P, Gouka R, Burggraaff L, Vermeer M, Chalet C, Duchateau G, van der Pijl P, Geldof M, de Roo N, Clauwaert F, Vanpaeschen T, Nicolaï J, de Bruyn T, Annaert P, IJzerman AP, van Westen GJP. Novel natural and synthetic inhibitors of solute carriers SGLT1 and SGLT2. Pharmacol Res Perspect 2019; 7:e00504. [PMID: 31384471 PMCID: PMC6664820 DOI: 10.1002/prp2.504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 06/05/2019] [Accepted: 06/06/2019] [Indexed: 12/12/2022] Open
Abstract
Selective analogs of the natural glycoside phloridzin are marketed drugs that reduce hyperglycemia in diabetes by inhibiting the active sodium glucose cotransporter SGLT2 in the kidneys. In addition, intestinal SGLT1 is now recognized as a target for glycemic control. To expand available type 2 diabetes remedies, we aimed to find novel SGLT1 inhibitors beyond the chemical space of glycosides. We screened a bioactive compound library for SGLT1 inhibitors and tested primary hits and additional structurally similar molecules on SGLT1 and SGLT2 (SGLT1/2). Novel SGLT1/2 inhibitors were discovered in separate chemical clusters of natural and synthetic compounds. These have IC50-values in the 10-100 μmol/L range. The most potent identified novel inhibitors from different chemical clusters are (SGLT1-IC50 Mean ± SD, SGLT2-IC50 Mean ± SD): (+)-pteryxin (12 ± 2 μmol/L, 9 ± 4 μmol/L), (+)-ε-viniferin (58 ± 18 μmol/L, 110 μmol/L), quinidine (62 μmol/L, 56 μmol/L), cloperastine (9 ± 3 μmol/L, 9 ± 7 μmol/L), bepridil (10 ± 5 μmol/L, 14 ± 12 μmol/L), trihexyphenidyl (12 ± 1 μmol/L, 20 ± 13 μmol/L) and bupivacaine (23 ± 14 μmol/L, 43 ± 29 μmol/L). The discovered natural inhibitors may be further investigated as new potential (prophylactic) agents for controlling dietary glucose uptake. The new diverse structure activity data can provide a starting point for the optimization of novel SGLT1/2 inhibitors and support the development of virtual SGLT1/2 inhibitor screening models.
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Affiliation(s)
- Paul Oranje
- Unilever Research & DevelopmentVlaardingenThe Netherlands
| | - Robin Gouka
- Unilever Research & DevelopmentVlaardingenThe Netherlands
| | - Lindsey Burggraaff
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Mario Vermeer
- Unilever Research & DevelopmentVlaardingenThe Netherlands
| | - Clément Chalet
- Unilever Research & DevelopmentVlaardingenThe Netherlands
| | - Guus Duchateau
- Unilever Research & DevelopmentVlaardingenThe Netherlands
| | | | - Marian Geldof
- Unilever Research & DevelopmentVlaardingenThe Netherlands
| | - Niels de Roo
- Unilever Research & DevelopmentVlaardingenThe Netherlands
| | - Fenja Clauwaert
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological SciencesKU LeuvenLeuvenBelgium
| | - Toon Vanpaeschen
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological SciencesKU LeuvenLeuvenBelgium
| | - Johan Nicolaï
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological SciencesKU LeuvenLeuvenBelgium
| | - Tom de Bruyn
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological SciencesKU LeuvenLeuvenBelgium
| | - Pieter Annaert
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological SciencesKU LeuvenLeuvenBelgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery & Safety, Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
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