Dr. Mushtaq Ahmad Wani
@niper.irins.org
Senior Research Fellow
Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar - 160062, Punjab (INDIA)
His research work focused on computational modelling for the design and optimization of novel small molecules as potential next-generation inhibitors of mycobacterium cytochrome bc1-aa3 super complex. His research interests include the development of machine learning models, artificial intelligence, molecular modelling, and Pharmacophore mapping.
EDUCATION
(2017-2023) Doctor of Philosophy (Pharmacoinformatics) at National Institute of Pharmaceutical Education & Research, Kolkata, West Bengal, India
(2015-2017) Masters of pharmacy (Pharmacoinformatics) from National Institute of Pharmaceutical Education and Research, S.A.S Nagar, Punjab, India
(2010-2015) Bachelor of Pharmacy from University of Kashmir: Srinagar, Jammu, and Kashmir, India
RESEARCH, TEACHING, or OTHER INTERESTS
Pharmacy, Molecular Biology, Multidisciplinary, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Navneet Kumar, Mushtaq Ahmad Wani, Chaaya Iyengar Raje, and Prabha Garg
Elsevier BV
Lakshmi Mounika Kelam, Mushtaq Ahmad Wani, and Devendra K. Dhaked
Elsevier BV
Mushtaq Ahmad Wani and Devendra Kumar Dhaked
Springer Science and Business Media LLC
The terminal oxidases of the oxidative phosphorylation pathway play a significant role in the survival and growth of M. tuberculosis, targeting these components lead to inhibition of M. tuberculosis. Many drug candidates targeting various components of the electron transport chain in M. tuberculosis have recently been discovered. The cytochrome bc1-aa3 supercomplex is one of the most important components of the electron transport chain in M. tuberculosis, and it has emerged as the novel target for several promising candidates. There are two cryo-electron microscopy structures (PDB IDs: 6ADQ and 6HWH) of the cytochrome bc1-aa3 supercomplex that aid in the development of effective and potent inhibitors for M. tuberculosis. In recent years, a number of potential candidates targeting the QcrB subunit of the cytochrome bc1 complex have been developed. In this review, we describe the recently identified inhibitors that target the electron transport chain's terminal oxidase enzyme in M. tuberculosis, specifically the QcrB subunit of the cytochrome bc1 complex.
Mushtaq Ahmad Wani and Kuldeep K. Roy
Springer Science and Business Media LLC
Tuberculosis (TB) is an infectious disease and the leading cause of death globally. The rapidly emerging cases of drug resistance among pathogenic mycobacteria have been a global threat urging the need of new drug discovery and development. However, considering the fact that the new drug discovery and development is commonly lengthy and costly processes, strategic use of the cutting-edge machine learning (ML) algorithms may be very supportive in reducing both the cost and time involved. Considering the urgency of new drugs for TB, herein, we have attempted to develop predictive ML algorithms-based models useful in the selection of novel potential small molecules for subsequent in vitro validation. For this purpose, we used the GlaxoSmithKline (GSK) TCAMS TB dataset comprising a total of 776 hits that were made publicly available to the wider scientific community through the ChEMBL Neglected Tropical Diseases (ChEMBL-NTD) database. After exploring the different ML classifiers, viz. decision trees (DT), support vector machine (SVM), random forest (RF), Bernoulli Naive Bayes (BNB), K-nearest neighbors (k-NN), and linear logistic regression (LLR), and ensemble learning models (bagging and Adaboost) for training the model using the GSK dataset, we concluded with three best models, viz. Adaboost decision tree (ABDT), RF classifier, and k-NN models that gave the top prediction results for both the training and test sets. However, during the prediction of the external set of known anti-tubercular compounds/drugs, it was realized that each of these models had some limitations. The ABDT model correctly predicted 22 molecules as actives, while both the RF and k-NN models predicted 18 molecules correctly as actives; a number of molecules were predicted as actives by two of these models, while the third model predicted these compounds as inactives. Therefore, we concluded that while deciding the anti-tubercular potential of a new molecule, one should rely on the use of consensus predictions using these three models; it may lessen the attrition rate during the in vitro validation. We believe that this study may assist the wider anti-tuberculosis research community by providing a platform for predicting small molecules with subsequent validation for drug discovery and development.
Mushtaq Ahmad Wani, Prabha Garg, and Kuldeep K. Roy
Springer Science and Business Media LLC
The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700), we have explored a variety of machine learning classifiers to build in silico models for AMP prediction, including Random Forest (RF), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), and ensemble learning. Among the various models generated, the RF classifier-based model top-performed in both the internal [Accuracy: 91.40%, Precision: 89.37%, Sensitivity: 90.05%, and Specificity: 92.36%] and external validations [Accuracy: 89.43%, Precision: 88.92%, Sensitivity: 85.21%, and Specificity: 92.43%]. In addition, the RF classifier-based model correctly predicted the known AMPs and non-AMPs; those kept aside as an additional external validation set. The performance assessment revealed three features viz. ChargeD2001, PAAC12 (pseudo amino acid composition), and polarity T13 that are likely to play vital roles in the antimicrobial activity of AMPs. The developed RF-based classification model may further be useful in the design and prediction of the novel potential AMPs.
Supriya Sharma, Sharad Wakode, Anjali Sharma, Nisha Nair, Mahaveer Dhobi, Mushtaq Ahmad Wani, and Faheem Hyder Pottoo
Springer Science and Business Media LLC
In the last few years, neurodegenerative diseases like Alzheimer’s disease (AD) and Parkinson’s disease (PD) have attracted attention due to their high prevalence worldwide. Environmental factors may be one of the biggest reasons for these diseases related to neuronal dysfunctions. Most of neuronal disorders are strongly associated with pre- and postnatal exposure to environmental toxins released from industries. Some of the neurotoxic metals such as lead, aluminum, mercury, manganese, cadmium, and arsenic as well as some pesticides and metal-based nanoparticles have been involved in AD and PD due to their ability to produce senile/amyloid plaques and NFTs which are the main feature of these neuronal dysfunctions. Exposure to solvents is also majorly responsible for neurodegenerative disorders. The present review highlights the impact of omnipresent heavy metals with some other neurotoxins on human health and how they give rise to neuronal dysfunctions which in turn causes socio-economic consequences due to increasing pollution worldwide. Graphical abstract Graphical abstract
Zubair Shanib Bhat, Muzafar Ahmad Rather, Hafiz Ul Lah, Aehtesham Hussain, Mubashir Maqbool, Syed Khalid Yousuf, Zuhra Jabeen, Mushtaq Ahmad Wani, and Zahoor Ahmad
Elsevier BV
Ashis Roy, Mrinalkanti Kundu, Pranab Dhar, Arnish Chakraborty, Soumen Mukherjee, Jayatri Naskar, Chhanda Rarhi, Rajib Barik, Susanta Kumar Mondal, Mushtaq Ahmad Wani,et al.
Wiley
Kuldeep K. Roy and Mushtaq Ahmad Wani
Informa UK Limited
ABSTRACT Introduction: Tuberculosis (TB) is a leading infectious disease worldwide whose chemotherapy is challenged by the continued rise of drug resistance. This epidemic urges the need to discover anti-TB drugs with novel modes of action. Areas covered: The mycobacterial electron transport chain (ETC) pathway represents a hub of anti-TB drug targets. Herein, the authors highlight the various targets within the mycobacterial ETC and highlight some of the promising ETC-targeted drugs and clinical candidates that have been discovered or repurposed. Furthermore, recent breakthroughs in the availability of X-ray and/or cryo-EM structures of some targets are discussed, and various opportunities of exploiting these structures for the discovery of new anti-TB drugs are emphasized. Expert opinion: The drug discovery efforts targeting the ETC pathway have led to the FDA approval of bedaquiline, a FOF1-ATP synthase inhibitor, and the discovery of Q203, a clinical candidate drug targeting the mycobacterial cytochrome bcc-aa3 supercomplex. Moreover, clofazimine, a proposed prodrug competing with menaquinone for its reduction by mycobacterial NADH dehydrogenase 2, has been repurposed for TB treatment. Recently available structures of the mycobacterial ATP synthase C9 rotary ring and the cytochrome bcc-aa3 supercomplex represent further opportunities for the structure-based drug design (SBDD) of the next-generation of inhibitors against Mycobacterium tuberculosis.
Faizana Fayaz, Faheem H. Pottoo, Sadat Shafi, Mushtaq A. Wani, Sharad Wakode, and Anjali Sharma
Bentham Science Publishers Ltd.
Medicinal Chemistry has played a critical role in evolving new products, resources and processes which inexorably correspond to our high standards of living. Unfortunately, this has also caused deterioration of human health and threats to the global environment, even deaths when highly exposed to certain chemicals, whether due to improper use, mishandling or disposal. There are chemicals, which apart from being carcinogens, endocrine disruptors or neurotoxins, are also responsible for climate change and ozone depletion. Certain chemicals are known to cause neurotoxicity and are having tendencies to damage the central and peripheral nervous system or brain by damaging neurons or cells which are responsible for transmitting and processing of signals. This has raised serious concerns for the use and handling of such chemicals and has given growth to a relatively new emerging field known as Green Chemistry that strives to achieve sustainability at the molecular level and has an ability to harness chemicals to meet environmental and economic goals. It has been reported in the literature that apart from family history in the aetiology of Amyotrophic lateral Sclerosis (ALS), also termed as “Lou Gehrig’s disease”, a neurological disorder, environmental factors, heavy metals, particularly selenium, lead, mercury, cadmium, formaldehyde, pesticides and certain herbicides are known to cause ALS. ALS, a progressive neurodegenerative disease affects the motor cortex, brain stem and spinal cord, causing muscular weakness, spasticity, and hyperreflexia. In this article we are aiming to discuss and summarize the various corroborations and findings supporting the undesirable role of chemical substance/herbicides/pesticides in ALS aetiology and its mitigation by adopting green chemistry.
Bikram Singh Datta, Ghulam Hassan, Syed Manzoor Kadri, Waseem Qureshi, Mustadiq Ahmad Kamili, Hardeep Singh, Ahmad Manzoor, Mushtaq Ahmad Wani, Shamas u Din, and Natasha Thakur
Journal of Infection in Developing Countries
Background: To study the profile of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) in tertiary care hospital setting, representing almost the whole affected population in Kashmir valley of India. Methodology: A total of 910 cases of pulmonary tuberculosis were enrolled over four years. Among these, cases of MDR-TB and XDR-TB were meticulously studied for drug susceptibility, treatment, adverse effects profile and overall survival. Results: Fifty-two (5.7%) cases of MDR-TB were identified, among which eight (15.3%) were diagnosed as XDR-TB on the basis of drug susceptibility testing, using the prescribed definition. The cases were sensitive to 2, 3, 4, 5 and more than 5 drugs in almost equal proportions. Thirty-seven (71.1%) cases were successfully cured; eleven (21.1%) patients died; and only four (7.6%) cases defaulted, indicating overall satisfactory adherence to treatment. Conclusion: For effective treatment of MDR-TB and XDR-TB, early case detection, improved laboratory facilities, availability of appropriate treatment regimens, and financial assistance in resource-limited settings through effective political intervention are necessary for better patient adherence and overall cure.