Biology subjects

Mahbub, M. M.

Publications and source records attributed to Mahbub, M. M..

3 recordsLinked to original sources

bioRxiv2024

High-Throughput Screening Reveals Potential Inhibitors Targeting Trimethoprim-Resistant DfrA1 Protein in Klebsiella pneumoniae and Escherichia coli

The DfrA1 protein provides trimethoprim resistance in bacteria, especially Klebsiella pneumoniae and Escherichia coli, by modifying dihydrofolate reductase, which reduces the binding efficacy of the antibiotic. Thus, this study aimed to identify inhibitors of the trimethoprim-resistant DfrA1 protein through high-throughput computational screening of 3,601 newly synthesized chemical compounds sourced from the ChemDiv database. We conducted high-throughput computational optimization and screening of a library containing 3,601 compounds against the DfrA1 protein from K. pneumoniae and E. coli to identify potential drug candidates (DCs). Through this extensive approach, we identified six promising DCs, labeled DC1 to DC6, as potential inhibitors of DfrA1. Each DC demonstrated strong initial binding affinity and favorable chemical interactions with the DfrA1 binding sites when compared to the effective drug Iclaprim (effective antibiotic against DfrA1), used as a control. To validate these findings, we further investigated the molecular mechanisms of inhibition, focusing on the thermodynamic properties of the promising DCs. Furthermore, molecular dynamics simulation (MDS) validated the inhibitory efficacy of these six DCs against the DfrA1 protein. Our results showed that DC4 (an organoflourinated compound) and DC6 (a benzimidazol compound) showed superior efficacy against the DfrA1 protein than the control drug, particularly regarding stability, solvent-accessible surface area, solvent exposure, polarity, and binding site interactions, which influence their residence time and efficacy. Overall, findings of this study suggest that DC4 and DC6 have the potential to act as inhibitors against the DfrA1, offering promising prospects for the treatment and management of infections caused by trimethoprim-resistant K. pneumoniae and E. coli in both humans and animals.

bioinformatics↗

bioRxiv2024

In silico Identification of Novel Common Drug Targets Against Four Infectious Acinetobacter Species

The global emergence of multidrug-resistant Acinetobacter species has become a major concern in the management of hospital-acquired infections. Moreover, misdiagnosis of one species of Acinetobacter with another has been reported, making it difficult to choose appropriate treatment. World Health Organization emphasizes the urgent need to develop new antibiotics to combat Acinetobacter infections. This study aimed to discover novel common drug targets that will be effective against Acinetobacter nosocomialis, Acinetobacter baumannii, Acinetobacter pittii, and Acinetobacter haemolyticus. We utilized a cluster-based subtractive genomics approach to identify potential drug targets. The main focus was to find drug targets that are absent in humans and essential for pathogens. We also performed metabolic pathway and subcellular localization analyses. Furthermore, protein structure-based studies and druggability analyses were conducted to identify viable therapeutic options. Out of 1245 protein clusters (minimum 4 proteins/cluster), 204 clusters were human non-homologous and essential for bacteria. Among them, 39 clusters were cytoplasmic and involved in unique metabolic pathways which are specific to the pathogens. After analyzing the drug target sequences of DrugBank database, 12 clusters were found to be novel drug targets. Eventually, proteins of one cluster were identified as advantageous drug targets having drug-binding pockets at very similar regions with high druggability scores. These proteins can be inhibited to disrupt the Lysine/DAP biosynthetic pathway of Acinetobacter. Our research might open up a new possibility for drug discovery against these pathogens.

bioinformatics↗

bioRxiv2024

A Framework for Accurate Prediction of Plastic-Degrading Enzymes using Convolutional Neural Networks

The growing accumulation of plastic waste presents a significant environmental challenge, necessitating innovative approaches to mitigate its impact. Enzymatic degradation has emerged as a promising solution for addressing plastic pollution. However, the isolation and characterization of plastic-degrading enzymes (PDEs) through laboratory experiments are costly, time-consuming, and often complicated by nonculturable microorganisms. Consequently, accurate in silico identification of PDEs is desirable to explore the diversity of natural enzymes and harness their potential for combating plastic pollution. This study introduces a novel feature extraction strategy for identifying plastic-degrading enzymes, incorporating Autocorrelation (AAutoCor), Composition of k-spaced Amino Acid Pairs (KSAP), Dipeptide Deviation from Expected Mean (DDE), Composition/Transition/Distribution (C/T/D), Conjoint Triad, and Secondary Structure. A combination of ANOVA and XGBoost, feature selection methods, was applied to optimize the feature dimensions for improved performance. Seven supervised machine learning models were employed to evaluate the dataset: Convolutional Neural Network, Random Forest Classifier, Feedforward Neural Network, Logistic Regression, Naive Bayes Classifier, K-nearest Neighbor, and XGBoost Classifier. Among these models, the CNN model demonstrated the best performance, achieving an accuracy of 0.96, an F1 score of 0.80, and an ROC-AUC score of 0.96. These findings underscore the potential of the proposed system as an accurate predictor of plastic-degrading enzymes from environmental sequences. This approach significantly enhances efforts to develop sustainable solutions to plastic waste by accelerating the discovery of novel PDEs.

bioinformatics↗