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Article ; Online: Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism.

Sandhu, Hardeep / Garg, Prabha

2023 Volume 36, Issue 12, Page(s) 1876–1890

Abstract: Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for ...

Abstract	Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for the toxicities associated with the drugs. Identification of reactive metabolites of drug candidates can be very helpful in the initial stages of drug discovery. Quinones are soft electrophiles that are generated as reactive intermediates during metabolism. Quinones make up more than 40% of the reactive metabolites. In this work, a reliable data set of 510 molecules was used to develop machine learning and deep learning-based predictive models to predict the formation of quinone-type metabolites. For representing molecules, two-dimensional (2D) descriptors, PubChem fingerprints, electro-topological state (E-state) fingerprints, and metabolic reactivity-based descriptors were used. Developed models were compared to the existing Xenosite web server using the untouched test set of 102 molecules. The best model achieved an accuracy of 86.27%, while the Xenosite server could achieve an accuracy of only 52.94% on the test set. Descriptor analysis revealed that the presence of greater numbers of polar moieties in a molecule can prevent the formation of quinone-type metabolites. In addition, the presence of a nitrogen atom in an aromatic ring and the presence of metabolophores V51, V52, and V53 (SMARTCyp descriptors) decrease the probability of quinone formation. Finally, a tool based on the best machine learning models was developed, which is accessible at http://14.139.57.41/quinonepred/.
MeSH term(s)	Humans ; Benzoquinones/metabolism ; Machine Learning ; Quinones/metabolism
Chemical Substances	quinone (3T006GV98U) ; Benzoquinones ; Quinones
Language	English
Publishing date	2023-10-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639353-6
ISSN	1520-5010 ; 0893-228X
ISSN (online)	1520-5010
ISSN	0893-228X
DOI	10.1021/acs.chemrestox.3c00162
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Article: CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm.

Gahlawat, Anuj / Singh, Anjali / Sandhu, Hardeep / Garg, Prabha

Journal of cheminformatics

2024 Volume 16, Issue 1, Page(s) 12

Abstract: Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and ... ...

Abstract	Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.
Language	English
Publishing date	2024-01-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2486539-4
ISSN	1758-2946
ISSN	1758-2946
DOI	10.1186/s13321-024-00803-6
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Article ; Online: Probing the Molecular Basis of Cofactor Affinity and Conformational Dynamics of

Kumar, Navneet / Garg, Prabha

The journal of physical chemistry. B

2022 Volume 126, Issue 7, Page(s) 1447–1461

Abstract: The emergence of multidrug-resistant and extensively drug-resistant tuberculosis strains is the reason that the infectious tuberculosis pathogen is still the most common cause of death. The quest for new antitubercular drugs that can fit into multidrug ... ...

Abstract	The emergence of multidrug-resistant and extensively drug-resistant tuberculosis strains is the reason that the infectious tuberculosis pathogen is still the most common cause of death. The quest for new antitubercular drugs that can fit into multidrug regimens, function swiftly, and overcome the ever-increasing prevalence of drug resistance continues. The crucial role of
MeSH term(s)	Binding Sites ; Escherichia coli/metabolism ; Guanosine Diphosphate/chemistry ; Guanosine Triphosphate/chemistry ; Molecular Dynamics Simulation ; Mycobacterium tuberculosis/metabolism ; Peptide Elongation Factor Tu/chemistry ; Peptide Elongation Factor Tu/metabolism ; Peptide Elongation Factors
Chemical Substances	Peptide Elongation Factors ; Guanosine Diphosphate (146-91-8) ; Guanosine Triphosphate (86-01-1) ; Peptide Elongation Factor Tu (EC 3.6.1.-)
Language	English
Publishing date	2022-02-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1520-5207
ISSN (online)	1520-5207
DOI	10.1021/acs.jpcb.1c09438
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Article: Unlocking translational machinery for antitubercular drug development.

Kumar, Navneet / Wani, Mushtaq Ahmad / Raje, Chaaya Iyengar / Garg, Prabha

Trends in biochemical sciences

2024 Volume 49, Issue 3, Page(s) 195–198

Abstract: Targeting translational factor proteins (TFPs) presents significant promise for the development of innovative antitubercular drugs. Previous insights from antibiotic binding mechanisms and recently solved 3D crystal structures of Mycobacterium ... ...

Abstract	Targeting translational factor proteins (TFPs) presents significant promise for the development of innovative antitubercular drugs. Previous insights from antibiotic binding mechanisms and recently solved 3D crystal structures of Mycobacterium tuberculosis (Mtb) elongation factor thermo unstable-GDP (EF-Tu-GDP), elongation factor thermo stable-EF-Tu (EF-Ts-EF-Tu), and elongation factor G-GDP (EF-G-GDP) have opened up new avenues for the design and development of potent antituberculosis (anti-TB) therapies.
MeSH term(s)	Guanosine Diphosphate/chemistry ; Guanosine Diphosphate/metabolism ; Peptide Elongation Factor Tu/chemistry ; Peptide Elongation Factor Tu/metabolism ; Antitubercular Agents/pharmacology ; Antitubercular Agents/therapeutic use ; Peptide Elongation Factors/chemistry ; Peptide Elongation Factors/metabolism ; Proteins/metabolism
Chemical Substances	Guanosine Diphosphate (146-91-8) ; Peptide Elongation Factor Tu (EC 3.6.1.-) ; Antitubercular Agents ; Peptide Elongation Factors ; Proteins
Language	English
Publishing date	2024-01-08
Publishing country	England
Document type	Journal Article
ZDB-ID	194216-5
ISSN	1362-4326 ; 0968-0004 ; 0376-5067
ISSN (online)	1362-4326
ISSN	0968-0004 ; 0376-5067
DOI	10.1016/j.tibs.2023.12.008
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Article: Probing the Molecular Basis of Cofactor Affinity and Conformational Dynamics of Mycobacterium tuberculosis Elongation Factor Tu: An Integrated Approach Employing Steered Molecular Dynamics and Umbrella Sampling Simulations

Kumar, Navneet / Garg, Prabha

Journal of physical chemistry. 2022 Feb. 15, v. 126, no. 7

2022

Abstract	The emergence of multidrug-resistant and extensively drug-resistant tuberculosis strains is the reason that the infectious tuberculosis pathogen is still the most common cause of death. The quest for new antitubercular drugs that can fit into multidrug regimens, function swiftly, and overcome the ever-increasing prevalence of drug resistance continues. The crucial role of MtbEF-Tu in translation and trans-translation processes makes it an excellent target for antitubercular drug design. In this study, the primary sequence of MtbEF-Tu was used to model the three-dimensional structures of MtbEF-Tu in the presence of GDP (“off” state) and GTP (“on” state). The binding free energy computed using both the molecular mechanics/Poisson–Boltzmann surface area and umbrella sampling approaches shows that GDP binds to MtbEF-Tu with an ∼2-fold affinity compared to GTP. The steered molecular dynamics (SMD) and umbrella sampling simulation also shows that the dissociation of GDP from MtbEF-Tu in the presence of Mg²⁺ is a thermodynamically intensive process, while in the absence of Mg²⁺, the destabilized GDP dissociates very easily from the MtbEF-Tu. Naturally, the dissociation of Mg²⁺ from the MtbEF-Tu is facilitated by the nucleotide exchange factor EF-Ts, and this prior release of magnesium makes the dissociation process of destabilized GDP easy, similar to that observed in the umbrella sampling and SMD study. The MD simulations of MtbEF-Tu’s “on” state conformation in the presence of GTP reveal that the secondary structure of switch-I and Mg²⁺ coordination network remains similar to its template despite the absence of identity in the conserved region of switch-I. On the other hand, the secondary structure in the conserved region of the switch-I of MtbEF-Tu unwinds from a helix to a loop in the presence of GDP. The major conformational changes observed in switch-I and the movement of Thr64 away from Mg²⁺ mainly reflect essential conformational changes to make the shift of MtbEF-Tu’s “on” state to the “off” state in the presence of GDP. These obtained structural and functional insights into MtbEF-Tu are pivotal for a better understanding of structural–functional linkages of MtbEF-Tu, and these findings may serve as a basis for the design and development of MtbEF-Tu-specific inhibitors.
Keywords	Gibbs free energy ; Mycobacterium tuberculosis ; antibiotics ; death ; dissociation ; drug design ; magnesium ; molecular dynamics ; multiple drug resistance ; pathogens ; peptide elongation factors ; surface area ; tuberculosis
Language	English
Dates of publication	2022-0215
Size	p. 1447-1461.
Publishing place	American Chemical Society
Document type	Article
ISSN	1520-5207
DOI	10.1021/acs.jpcb.1c09438
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Article ; Online: AndroPred: an artificial intelligence-based model for predicting androgen receptor inhibitors.

Gagare, Rohit / Sharma, Anju / Garg, Prabha

Journal of biomolecular structure & dynamics

2023 , Page(s) 1–9

Abstract: Androgen receptor (AR), a steroid receptor, plays a pivotal role in the pathogenesis of prostate cancer (PCa). AR controls the transcription of genes that help cells avoid apoptosis and proliferate, thereby contributing to the development of PCa. ... ...

Abstract	Androgen receptor (AR), a steroid receptor, plays a pivotal role in the pathogenesis of prostate cancer (PCa). AR controls the transcription of genes that help cells avoid apoptosis and proliferate, thereby contributing to the development of PCa. Understanding AR molecular mechanisms has led to the development of newer drugs that inhibit androgen production enzymes or block ARs. The FDA has approved a small number of AR-inhibiting drugs for use in PCa thus far, as the identification of novel AR inhibitors is difficult, expensive, time-consuming, and labor-intensive. To accelerate the process, artificial intelligence (AI) algorithms were employed to predict AR inhibitors using a dataset of 2242 compounds. Four machine learning (ML) and deep learning (DL) algorithms were used to train different prediction models based on molecular descriptors (1D, 2D, and molecular fingerprints). The DL-based prediction model outperformed the other trained models with accuracies of 92.18% and 93.05% on the training and test datasets, respectively. Our findings highlight the potential of DL, particularly the DNN model, as an effective approach for predicting AR inhibitors, which could significantly streamline the process of identifying novel AR inhibitors in PCa drug discovery. Further validation of these models using experimental assays and prospective testing of newly designed compounds would be valuable to confirm their predictive power and applicability in practical drug discovery settings.Communicated by Ramaswamy H. Sarma.
Language	English
Publishing date	2023-07-26
Publishing country	England
Document type	Journal Article
ZDB-ID	49157-3
ISSN	1538-0254 ; 0739-1102
ISSN (online)	1538-0254
ISSN	0739-1102
DOI	10.1080/07391102.2023.2239935
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Article ; Online: Artificial intelligence in intestinal polyp and colorectal cancer prediction.

Sharma, Anju / Kumar, Rajnish / Yadav, Garima / Garg, Prabha

Cancer letters

2023 Volume 565, Page(s) 216238

Abstract: Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic ... ...

Abstract	Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic analysis to diagnose intestinal cancers, premalignant polyps, gastrointestinal inflammatory lesions, and bleeding. Patients' responses to treatments and prognoses have both been predicted using AI by combining multiple algorithms. In this review, we explored the recent applications of AI algorithms in the identification and characterization of intestinal polyps and colorectal cancer predictions. AI-based prediction models have the potential to help medical practitioners diagnose, establish prognoses, and find accurate conclusions for the treatment of patients. With the understanding that rigorous validation of AI approaches using randomized controlled studies is solicited before widespread clinical use by health authorities, the article also discusses the limitations and challenges associated with deploying AI systems to diagnose intestinal malignancies and premalignant lesions.
MeSH term(s)	Humans ; Artificial Intelligence ; Intestinal Polyps ; Algorithms ; Colorectal Neoplasms/diagnosis
Language	English
Publishing date	2023-05-19
Publishing country	Ireland
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	195674-7
ISSN	1872-7980 ; 0304-3835
ISSN (online)	1872-7980
ISSN	0304-3835
DOI	10.1016/j.canlet.2023.216238
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Article ; Online: Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images.

Sharma, Anju / Kumar, Rajnish / Garg, Prabha

International journal of medical informatics

2023 Volume 177, Page(s) 105142

Abstract: Background: Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of ... ...

Abstract	Background: Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of time and effort for doctors to visualize a large number of images, and diagnosis is prone to human error. As a result, developing automated artificial intelligence (AI) based GI disease diagnosis methods is a crucial and emerging research area. AI-based prediction models may lead to improvements in the early diagnosis of gastrointestinal disorders, assessing severity, and healthcare systems for the benefit of patients as well as clinicians. The focus of this research is on the early diagnosis of gastrointestinal diseases using a convolution neural network (CNN) to enhance diagnosis accuracy. Methods: Various CNN models (baseline model and using transfer learning (VGG16, InceptionV3, and ResNet50)) were trained on a benchmark image dataset, KVASIR, containing images from inside the GI tract using n-fold cross-validation. The dataset comprises images of three disease states-polyps, ulcerative colitis, and esophagitis-as well as images of the healthy colon. Data augmentation strategies together with statistical measures were used to improve and evaluate the model's performance. Additionally, the test set comprising 1200 images was used to evaluate the model's accuracy and robustness. Results: The CNN model using the weights of the ResNet50 pre-trained model achieved the highest average accuracy of approximately 99.80% on the training set (100% precision and approximately 99% recall) and accuracies of 99.50% and 99.16% on the validation and additional test set, respectively, while diagnosing GI diseases. When compared to other existing systems, the proposed ResNet50 model outperforms them all. Conclusion: The findings of this study indicate that AI-based prediction models using CNNs, specifically ResNet50, can improve diagnostic accuracy for detecting gastrointestinal polyps, ulcerative colitis, and esophagitis. The prediction model is available at https://github.com/anjus02/GI-disease-classification.git.
MeSH term(s)	Humans ; Colitis, Ulcerative ; Artificial Intelligence ; Deep Learning ; Gastrointestinal Diseases/diagnostic imaging ; Endoscopy ; Esophagitis
Language	English
Publishing date	2023-07-05
Publishing country	Ireland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1466296-6
ISSN	1872-8243 ; 1386-5056
ISSN (online)	1872-8243
ISSN	1386-5056
DOI	10.1016/j.ijmedinf.2023.105142
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Article ; Online: Structural-functional analysis of drug target aspartate semialdehyde dehydrogenase.

Kumar, Rajender / R, Rajkumar / Diwakar, Vineet / Khan, Nazam / Kumar Meghwanshi, Gautam / Garg, Prabha

Drug discovery today

2024 Volume 29, Issue 3, Page(s) 103908

Abstract: Aspartate β-semialdehyde dehydrogenase (ASADH) is a key enzyme in the biosynthesis of essential amino acids in microorganisms and some plants. Inhibition of ASADHs can be a potential drug target for developing novel antimicrobial and herbicidal compounds. ...

Abstract	Aspartate β-semialdehyde dehydrogenase (ASADH) is a key enzyme in the biosynthesis of essential amino acids in microorganisms and some plants. Inhibition of ASADHs can be a potential drug target for developing novel antimicrobial and herbicidal compounds. This review covers up-to-date information about sequence diversity, ligand/inhibitor-bound 3D structures, potential inhibitors, and key pharmacophoric features of ASADH useful in designing novel and target-specific inhibitors of ASADH. Most reported ASADH inhibitors have two highly electronegative functional groups that interact with two key arginyl residues present in the active site of ASADHs. The structural information, active site binding modes, and key interactions between the enzyme and inhibitors serve as the basis for designing new and potent inhibitors against the ASADH family.
MeSH term(s)	Aspartate-Semialdehyde Dehydrogenase/chemistry ; Aspartate-Semialdehyde Dehydrogenase/metabolism ; Catalytic Domain ; Enzyme Inhibitors/pharmacology ; Enzyme Inhibitors/chemistry
Chemical Substances	Aspartate-Semialdehyde Dehydrogenase (EC 1.2.1.11) ; Enzyme Inhibitors
Language	English
Publishing date	2024-01-30
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1324988-5
ISSN	1878-5832 ; 1359-6446
ISSN (online)	1878-5832
ISSN	1359-6446
DOI	10.1016/j.drudis.2024.103908
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Zs.A 4725: Show issues

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Article ; Online: TNF

Prabha, Niharika K / Sharma, Anju / Sandhu, Hardeep / Garg, Prabha

Molecular diversity

2023

Abstract: Rheumatoid arthritis (RA), characterized by severe inflammation in the joint lining, is a progressive, chronic, autoimmune disorder with high morbidity and mortality rates. There are several mechanisms responsible for joint damage, but overproduction of ... ...

Abstract	Rheumatoid arthritis (RA), characterized by severe inflammation in the joint lining, is a progressive, chronic, autoimmune disorder with high morbidity and mortality rates. There are several mechanisms responsible for joint damage, but overproduction of TNF-α is a significant mechanism that results in excess swelling and pain. Drugs acting on TNF-α are known to significantly reduce the disease progression and improve the quality of life in many RA patients. Hence, inhibiting TNF-α is considered one of the most effective treatments for RA. Currently, there are only a few FDA-approved TNF-α inhibitors, which are mainly monoclonal antibodies, fusion proteins, or biosimilars with disadvantages such as poor stability, difficulty in route of administration (often given as injection or infusion), cost-prohibitive large-scale production, and increased side effects. There are just a handful of small compounds known to have TNF- inhibitory capabilities. Thus, there is a dire need for new drugs, especially small molecules in the market, such as TNF-α inhibitors. The conventional method of identifying TNF-α inhibitors is expensive, labor, and time intensive. Machine learning (ML) can be used to solve existing drug discovery and development problems. In this study, four classification algorithms-naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and support vector machine (SVM)-were used to train ML models for classifying TNF-α inhibitors based on three sets of features. The performance of the RF model was found to be best when using 1D, 2D, and fingerprints as features, with an accuracy of 87.96 and a sensitivity of 86.17. To our knowledge, this is the first ML model for TNF-α inhibitor prediction. The model is available at http://14.139.57.41/tnfipred/.
Language	English
Publishing date	2023-07-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1376507-3
ISSN	1573-501X ; 1381-1991
ISSN (online)	1573-501X
ISSN	1381-1991
DOI	10.1007/s11030-023-10685-9
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