Mycobacterium Time-Series Genome Analysis Identifies AAC2′ as a Potential Drug Target with Naloxone Showing Potential Bait Drug Synergism

Niranjan, Vidya; Uttarkar, Akshay; Murali, Keerthana; Niranjan, Swarna; Jayalatha, G.; Kumar, Jitendra

doi:10.3390/molecules27196150

Cited by 9 publications

(3 citation statements)

References 72 publications

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“…For a path of d s = 4, the closest DT pair is aac–fas linked by accD1 / accE5 / Rv0200 proteins. Aminoglycoside 2’- N -acetyltransferase ( aac ) modifies aminoglycosides through co-enzyme A-dependent acetylation of the 2- hydroxyl or amino group, conferring resistance to the DT pair [ 47 ]. The linking proteins accD1 and accE5 are acetyl-CoA carboxylases (ACC) that catalyze the α-carboxylation of acetyl-CoA to produce malonyl-CoA, which serves as a building block for fatty acid biosynthesis [ 48 ], whereas Rv0200 is a possible transmembrane protein.…”

Section: Resultsmentioning

confidence: 99%

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network

Zahra,

Vagiona,

Uddin

et al. 2023

IJMS

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Tuberculosis remains the leading cause of death from a single pathogen. On the other hand, antimicrobial resistance (AMR) makes it increasingly difficult to deal with this disease. We present the hyperbolic embedding of the Mycobacterium tuberculosis protein interaction network (mtbPIN) of resistant strain (MTB XDR1219) to determine the biological relevance of its latent geometry. In this hypermap, proteins with similar interacting partners occupy close positions. An analysis of the hypermap of available drug targets (DTs) and their direct and intermediate interactors was used to identify potentially useful drug combinations and drug targets. We identify rpsA and rpsL as close DTs targeted by different drugs (pyrazinamide and aminoglycosides, respectively) and propose that the combination of these drugs could have a synergistic effect. We also used the hypermap to explain the effects of drugs that affect multiple DTs, for example, forcing the bacteria to deal with multiple stresses like ethambutol, which affects the synthesis of both arabinogalactan and lipoarabinomannan. Our strategy uncovers novel potential DTs, such as dprE1 and dnaK proteins, which interact with two close DT pairs: arabinosyltransferases (embC and embB), Ser/Thr protein kinase (pknB) and RNA polymerase (rpoB), respectively. Our approach provides mechanistic explanations for existing drugs and suggests new DTs. This strategy can also be applied to the study of other resistant strains.

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Section: Resultsmentioning

confidence: 99%

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network

Zahra,

Vagiona,

Uddin

et al. 2023

IJMS

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“…The Poisson-Boltzmann Finite (PBF) model was chosen for solvation with water as a solvent because it yields higher energy than other PCM models. A detailed methodology is provided in publication [45].…”

Section: Quantum Convergence and Single Point Energy Calculationsmentioning

confidence: 99%

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein

Niranjan,

Uttarkar,

Ramakrishnan

et al. 2023

CIMB

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The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of −6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

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“…In the current study, we have used AAC2 from is an aminoglycoside 2'-N-acetyltransferase, which means that it can add an acetyl group to the 2'-hydroxyl group of aminoglycoside antibiotics [19]. This modification makes the antibiotics inactive, which can lead to resistance to treatment [20]. The aac(2')-Ic gene is found in all strains of M. tuberculosis, and it is one of the most common genes associated with drug resistance.…”

Section: Identification Of Active Site/ligand Binding Sitementioning

confidence: 99%

Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS

Niranjan¹,

Rao²,

Uttarkar³

et al. 2023

PLoS ONE

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Coarse-grained simulations have emerged as a valuable tool in the study of large and complex biomolecular systems. These simulations, which use simplified models to represent complex biomolecules, reduce the computational cost of simulations and enable the study of larger systems for longer periods of time than traditional atomistic simulations. GROMACS is a widely used software package for performing coarse-grained simulations of biomolecules, and several force fields have been developed specifically for this purpose. In this protocol paper, we explore the advantages of using coarse-grained simulations in the study of biomolecular systems, focusing specifically on simulations performed using GROMACS. We discuss the force fields required for these simulations and the types of research questions that can be addressed using coarse-grained simulations. We also highlight the potential benefits of coarse-grained simulations for the development of new force fields and simulation methodologies. We then discuss the expected results from coarse-grained simulations using GROMACS and the various techniques that can be used to analyze these results. We explore the use of trajectory analysis tools, as well as thermodynamic and structural analysis techniques, to gain insight into the behavior of biomolecular systems.

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Mycobacterium Time-Series Genome Analysis Identifies AAC2′ as a Potential Drug Target with Naloxone Showing Potential Bait Drug Synergism

Cited by 9 publications

References 72 publications

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein

Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS

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