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Raju B, Narendra G, Verma H, Kumar M, Sapra B, Kaur G, jain SK, Silakari O. Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors. ACS OMEGA 2022; 7:31999-32013. [PMID: 36120033 PMCID: PMC9476183 DOI: 10.1021/acsomega.2c02983] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to find the possible CYP1B1 inhibitors using machine learning (ML) and structure-based virtual screening (SB-VS) approaches. Initially, three different ML models were developed such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN); subsequently, the best-selected ML model was employed for virtual screening of the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC50 range of 0.07-3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems.
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Affiliation(s)
- Baddipadige Raju
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Gera Narendra
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Himanshu Verma
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Manoj Kumar
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Bharti Sapra
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Gurleen Kaur
- Center
for Basic and Translational Research in Health Sciences, Guru Nanak Dev University, Amritsar 143005, India
| | - Subheet Kumar jain
- Center
for Basic and Translational Research in Health Sciences, Guru Nanak Dev University, Amritsar 143005, India
| | - Om Silakari
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
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Lu Z, Zhang Z, Yang M, Xiao M. Ubiquitin-specific protease 1 inhibition sensitizes hepatocellular carcinoma cells to doxorubicin by ubiquitinated proliferating cell nuclear antigen-mediated attenuation of stemness. Anticancer Drugs 2022; 33:622-631. [PMID: 35324534 DOI: 10.1097/cad.0000000000001311] [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: 12/24/2022]
Abstract
Currently, resistance to the chemotherapeutic agent doxorubicin (Dox) in hepatocellular carcinoma (HCC) cells is an obstacle in developing effective Dox-targeted clinical therapies. Ubiquitin-specific protease 1 (USP1) plays a crucial role in the progression of multiple cancers. In this study, the purpose was to investigate the effect of USP1 depletion with chemotherapeutant Dox on the HCC cells. Flow cytometry was used to detect the ratio of apoptosis. The expression levels of selected proteins were evaluated by western blotting. In addition, the expression of genes was quantitated by quantitative real-time PCR assay. Coimmunoprecipitation was performed to confirm the interaction between USP1 and proliferating cell nuclear antigen (PCNA). Sphere formation assay was carried out to investigate the cancer stemness. Subcutaneous xenograft and orthotopic liver tumor models were established to examine the growth of tumor. Knockdown of USP1 increased the rate of Dox-induced apoptosis in stem-like and nonstem-like HCC cells. The combination of Dox and the USP1 inhibitor SJB3-019A (SJB3) markedly enhanced apoptosis in the primary liver carcinoma/PRF/5 and MHCC-97H cell lines. Notably, Dox/SJB3-induced tumor inhibition was further determined in vivo using a xenograft and orthotopic liver tumor model. Mechanically, USP1 inhibition via SJB3 or short hairpin RNA significantly decreased cancer stemness, including sphere formation ability and the expression of Nanog, Sox2, and c-Myc. The sensitization of HCC to Dox by SJB3 is attributed to the upregulation of PCNA ubiquitylation. Thus, genetic or pharmacological inhibition of USP1 restored the sensitivity of HCC cells to Dox in vitro and in vivo , representing a new potential therapeutic strategy for HCC.
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Affiliation(s)
- Zhe Lu
- Clinical Laboratory, Women and Children's Health Care Center of Hainan Province and Departments of
| | | | - Min Yang
- Medical Oncology, Hainan Cancer Hospital, Haikou, P.R. China
| | - Meifang Xiao
- Clinical Laboratory, Women and Children's Health Care Center of Hainan Province and Departments of
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Raju B, Verma H, Narendra G, Sapra B, Silakari O. Multiple machine learning, molecular docking, and ADMET screening approach for identification of selective inhibitors of CYP1B1. J Biomol Struct Dyn 2021; 40:7975-7990. [PMID: 33769194 DOI: 10.1080/07391102.2021.1905552] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Cytochrome P4501B1 is a ubiquitous family protein that is majorly overexpressed in tumors and is responsible for biotransformation-based inactivation of anti-cancer drugs. This inactivation marks the cause of resistance to chemotherapeutics. In the present study, integrated in-silico approaches were utilized to identify selective CYP1B1 inhibitors. To achieve this objective, we initially developed different machine learning models corresponding to two isoforms of the CYP1 family i.e. CYP1A1 and CYP1B1. Subsequently, small molecule databases including ChemBridge, Maybridge, and natural compound library were screened from the selected models of CYP1B1 and CYP1A1. The obtained CYP1B1 inhibitors were further subjected to molecular docking and ADMET analysis. The selectivity of the obtained hits for CYP1B1 over the other isoforms was also judged with molecular docking analysis. Finally, two hits were found to be the most stable which retained key interactions within the active site of CYP1B1 after the molecular dynamics simulations. Novel compound with CYP-D9 and CYP-14 IDs were found to be the most selective CYP1B1 inhibitors which may address the issue of resistance. Moreover, these compounds can be considered as safe agents for further cell-based and animal model studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Baddipadige Raju
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, India
| | - Himanshu Verma
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, India
| | - Gera Narendra
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, India
| | - Bharti Sapra
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, India
| | - Om Silakari
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, India
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Jamal S, Grover A, Grover S. Machine Learning From Molecular Dynamics Trajectories to Predict Caspase-8 Inhibitors Against Alzheimer's Disease. Front Pharmacol 2019; 10:780. [PMID: 31354494 PMCID: PMC6639425 DOI: 10.3389/fphar.2019.00780] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 06/17/2019] [Indexed: 01/08/2023] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells takes place leading to loss of memory and decreased cognitive ability. AD is a leading cause of death worldwide and is progressive in nature with symptoms worsening over time. Machine learning–based computational predictive models based on 2D and 3D descriptors have been effective in identifying potential active compounds. However, the use of data from molecular dynamics (MD) trajectories for training machine learning models still needs to be explored. In the present study, descriptors have been extracted from the MD trajectories of caspase-8 ligand complexes to train models using artificial neural networks and random forest algorithms. Caspase-8 plays a key role in causing AD by cleaving amyloid precursor proteins during apoptosis leading to increased formation of the amyloid-beta peptide. A total of 43 ligands were docked using the glide module of Schrodinger software, and short MD simulations of 10 ns were performed for the calculation of MD descriptors. The MD descriptors were also combined with the 2D and 3D descriptors of chemical compounds, and individual descriptor based as well as combination models were generated. This study demonstrated that MD descriptors could be effectively used for the characterization of bioactive compounds along with lead prioritization and optimization.
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Affiliation(s)
- Salma Jamal
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Sonam Grover
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India
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Jamal S, Ali W, Nagpal P, Grover S, Grover A. Computational models for the prediction of adverse cardiovascular drug reactions. J Transl Med 2019; 17:171. [PMID: 31118067 PMCID: PMC6530172 DOI: 10.1186/s12967-019-1918-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 05/10/2019] [Indexed: 02/06/2023] Open
Abstract
Background Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. Methods In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. Results This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. Conclusions The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development. Electronic supplementary material The online version of this article (10.1186/s12967-019-1918-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Salma Jamal
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India
| | - Waseem Ali
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India
| | - Priya Nagpal
- Department of Biotechnology, Jamia Millia Islamia, New Delhi, India
| | - Sonam Grover
- JH-Institute of Molecular Medicine, Jamia Hamdard, New Delhi, India.
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.
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Aswathy L, Jisha RS, Masand VH, Gajbhiye JM, Shibi IG. Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction. In Silico Pharmacol 2018; 6:12. [PMID: 30607325 PMCID: PMC6314802 DOI: 10.1007/s40203-018-0049-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 06/07/2018] [Indexed: 02/03/2023] Open
Abstract
The inhibition of abnormal amyloid β (Aβ) aggregation has been regarded as a good target to control Alzheimer's disease. The present study adopted 2D-QSAR, HQSAR and 3D QSAR (CoMFA & CoMSIA) modeling approaches to identify the structural and physicochemical requirements for the potential Aβ aggregation inhibition. A structure-based molecular docking technique is utilized to approve the features that are obtained from the ligand-based techniques on 30 curcumin derivatives. The combined outputs were then used to screen the modified 10 compounds. The 2D QSAR model on curcumin derivatives gave statistical values R2 = 0.9086 and SEE = 0.1837. The model was further confirmed by Y-randomization test and Applicability domain analysis by the standardization approach. The HQSAR study (Q2 = 0.615, Rncv 2 = 0.931, Rpred 2 = 0.956) illustrated the important molecular fingerprints for inhibition. Contour maps of 3D QSAR models, CoMFA (Q2 = 0.687, Rncv 2 = 0.787, Rpred 2 = 0.731) and CoMSIA (Q2 = 0.743, Rncv 2 = 0.972, Rpred 2 = 0.713), depict that the models are robust and provide explanation of the important features, like steric, electrostatic and hydrogen bond acceptor, which play important role for interaction with the receptor site cavity. The molecular docking study of the curcumin derivatives elucidates the important interactions between the amino acid residues at the catalytic site of the receptor and the ligands, indicating the structural requirements of the inhibitors. The ligand-receptor interactions of top hits were analyzed to explore the pharmacophore features of Aβ aggregation inhibition. The Aβ aggregation inhibitory activities of novel chemical entities were then obtained through inverse QSAR. The newly designed molecules were further screened through machine learning, prediction of toxicity and nature of metabolism to get the proposed six lead compounds.
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Affiliation(s)
- Lilly Aswathy
- Department of Chemistry, Sree Narayana College, Chempazhanthy, Thiruvananthapuram, Kerala 695587 India
| | - Radhakrishnan S. Jisha
- Department of Chemistry, Sree Narayana College, Chempazhanthy, Thiruvananthapuram, Kerala 695587 India
| | - Vijay H. Masand
- Department of Chemistry, Vidya Bharati College, Camp, Amravati, Maharashtra 444 602 India
| | - Jayant M. Gajbhiye
- Division of Organic Chemistry, CSIR-National Chemical Laboratory, Pune, 411 008 India
| | - Indira G. Shibi
- Department of Chemistry, Sree Narayana College, Chempazhanthy, Thiruvananthapuram, Kerala 695587 India
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Molecular mechanism of the TP53-MDM2-AR-AKT signalling network regulation by USP12. Oncogene 2018; 37:4679-4691. [DOI: 10.1038/s41388-018-0283-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 12/20/2017] [Accepted: 03/23/2018] [Indexed: 11/08/2022]
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8
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Paul N, Carabet LA, Lallous N, Yamazaki T, Gleave ME, Rennie PS, Cherkasov A. Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor. J Chem Inf Model 2016; 56:2507-2516. [DOI: 10.1021/acs.jcim.6b00400] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Naman Paul
- Vancouver Prostate
Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6
| | - Lavinia A. Carabet
- Vancouver Prostate
Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6
| | - Nada Lallous
- Vancouver Prostate
Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6
| | - Takeshi Yamazaki
- Vancouver Prostate
Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6
| | - Martin E. Gleave
- Vancouver Prostate
Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6
| | - Paul S. Rennie
- Vancouver Prostate
Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6
| | - Artem Cherkasov
- Vancouver Prostate
Centre, Department of Urologic Sciences, Faculty of Medicine, The University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada V6H 3Z6
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9
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Liu J, Zhu H, Zhong N, Jiang Z, Xu L, Deng Y, Jiang Z, Wang H, Wang J. Gene silencing of USP1 by lentivirus effectively inhibits proliferation and invasion of human osteosarcoma cells. Int J Oncol 2016; 49:2549-2557. [PMID: 27840911 DOI: 10.3892/ijo.2016.3752] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 10/20/2016] [Indexed: 11/06/2022] Open
Abstract
Osteosarcoma is the most frequent malignant bone tumor, affecting the extremities of adolescents and young adults. Ubiquitin-specific protease 1 (USP1) plays a critical role in many cellular processes including proteasome degradation, chromatin remodeling and cell cycle regulation. In the present study, we discovered that USP1 was overexpressed in 26 out of 30 osteosarcoma tissues compared to cartilage tumor tissues and normal bone tissues. We then constructed a lentiviral vector mediating RNA interference (RNAi) targeting USP1 and demonstrated that it significantly suppressed the mRNA and protein expression of the USP1 gene in U2OS cells. Knockdown of USP1 inhibited the growth and colony-forming, as well as significantly reduced the invasiveness of U2OS cells. Western blot analysis indicated that suppression of USP1 downregulated the expression of many proteins including SIK2, MMP-2, GSK-3β, Bcl-2, Stat3, cyclin E1, Notch1, Wnt-1 and cyclin A1. Most of these proteins are associated with tumor genesis and development. RNAi of SIK2 significantly decreased SIK2 protein expression and inhibited the ability of forming colonies, as well as induced apoptosis and reduced the invasiveness of U2OS cells. Collectively, our results suggest that silencing USP1 inhibits cell proliferation and invasion in U2OS cells. Therefore, USP1 may provide a novel therapeutic target for the treatment of osteosarcoma.
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Affiliation(s)
- Jinbo Liu
- Department of Orthopaedics, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China
| | - Hongjun Zhu
- Department of Thoracic Surgery, The First People's Hospital of Shangqiu, Shangqiu, Henan 476100, P.R. China
| | - Ning Zhong
- Department of Thoracic Surgery, Kunshan First People's Hospital Affiliated to Jiangsu University, Kunshan, Jiangsu 215000, P.R. China
| | - Zifeng Jiang
- Clinical Laboratories, The University of Chicago Medical Center, Chicago, IL 60637, USA
| | - Lele Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215200, P.R. China
| | - Youping Deng
- Department of Medicine, Rush University Medical Center, Chicago, IL 60612, USA
| | - Zhenhuan Jiang
- Department of Orthopaedics, People's Hospital of Yixing City, Yixing, Jiangsu 214200, P.R. China
| | - Hongwei Wang
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jinzhi Wang
- Department of Cell Biology, School of Medicine, Soochow University, Suzhou, Jiangsu 215007, P.R. China
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Jamal S, Goyal S, Shanker A, Grover A. Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes. BMC Genomics 2016; 17:807. [PMID: 27756223 PMCID: PMC5070370 DOI: 10.1186/s12864-016-3108-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 09/20/2016] [Indexed: 01/01/2023] Open
Abstract
Background Alzheimer’s disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer’s is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer’s towards development of effective AD therapeutics. Results In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. Conclusions To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3108-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Salma Jamal
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.,Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India
| | - Sukriti Goyal
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.,Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India
| | - Asheesh Shanker
- Bioinformatics Programme, Centre for Biological Sciences, Central University of South Bihar, BIT Campus, Patna, Bihar, India
| | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
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Singh N, Shah P, Dwivedi H, Mishra S, Tripathi R, Sahasrabuddhe AA, Siddiqi MI. Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase. MOLECULAR BIOSYSTEMS 2016; 12:3711-3723. [DOI: 10.1039/c6mb00574h] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Integrated in silico approaches for the identification of antitrypanosomal inhibitors.
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Affiliation(s)
- Nidhi Singh
- Molecular and Structural Biology Division
- CSIR-Central Drug Research Institute
- Lucknow 226031
- India
| | - Priyanka Shah
- Molecular and Structural Biology Division
- CSIR-Central Drug Research Institute
- Lucknow 226031
- India
| | - Hemlata Dwivedi
- Division of Parasitology
- CSIR-Central Drug Research Institute
- Lucknow
- India
| | - Shikha Mishra
- Molecular and Structural Biology Division
- CSIR-Central Drug Research Institute
- Lucknow 226031
- India
| | - Renu Tripathi
- Division of Parasitology
- CSIR-Central Drug Research Institute
- Lucknow
- India
- Academy of Scientific and Innovative Research (AcSIR)
| | - Amogh A. Sahasrabuddhe
- Molecular and Structural Biology Division
- CSIR-Central Drug Research Institute
- Lucknow 226031
- India
- Academy of Scientific and Innovative Research (AcSIR)
| | - Mohammad Imran Siddiqi
- Molecular and Structural Biology Division
- CSIR-Central Drug Research Institute
- Lucknow 226031
- India
- Academy of Scientific and Innovative Research (AcSIR)
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