Biology subjects

Akhter, S.

Publications and source records attributed to Akhter, S..

3 recordsLinked to original sources

bioRxiv2024

Mechanistic Studies of Small Molecule Ligands Selective to RNA Single G Bulges

Small-molecule RNA binders have emerged as an important pharmacological modality. A profound understanding of the ligand selectivity, binding mode, and influential factors governing ligand engagement with RNA targets is the foundation for rational ligand design. Here, we report a novel class of coumarin derivatives exhibiting selective binding affinity towards single G RNA bulges. Harnessing the computational power of all-atom Gaussian accelerated Molecular Dynamics (GaMD) simulations, we unveiled a rare minor groove binding mode of the ligand with a key interaction between the coumarin moiety and the G bulge. This predicted binding mode is consistent with results obtained from structure-activity-relationship (SAR) studies and transverse relaxation measurements by NMR spectroscopy. We further generated 444 molecular descriptors from 69 coumarin derivatives and identified key contributors to the binding events, such as charge state and planarity, by lasso (least absolute shrinkage and selection operator) regression. Strikingly, small structure perturbations on these key contributors, such as the addition of a methyl group that disrupts the planarity of the ligand resulted in > 100-fold reduction in the binding affinity. Our work deepened the understanding of RNA-small molecule interactions and integrated a new generalizable platform for the rational design of selective small-molecule RNA binders.

biochemistry↗

bioRxiv2024

Characterization of natural product inhibitors of quorum sensing in Pseudomonas aeruginosa reveals competitive inhibition of RhlR by ortho-vanillin

Quorum sensing (QS) is a cell-cell signaling system that enables bacteria to coordinate population density-dependent changes in behavior. This chemical communication pathway is mediated by diffusible N-acyl L-homoserine lactone signals and cytoplasmic signal-responsive LuxR-type receptors in Gram-negative bacteria. As many common pathogenic bacteria use QS to regulate virulence, there is significant interest in disrupting QS as a potential therapeutic strategy. Prior studies have implicated the natural products salicylic acid, cinnamaldehyde and other related benzaldehyde derivatives as inhibitors of QS in the opportunistic pathogen Pseudomonas aeruginosa, yet we lack an understanding of the mechanisms by which these compounds function. Herein, we evaluate the activity of a set of benzaldehyde derivatives using heterologous reporters of the P. aeruginosa LasR and RhlR QS signal receptors. We find that most tested benzaldehyde derivatives can antagonize LasR or RhlR reporter activation at micromolar concentrations, although certain molecules also caused mild growth defects and nonspecific reporter antagonism. Notably, several compounds showed promising RhlR or LasR specific inhibitory activities over a range of concentrations below that causing toxicity. ortho-Vanillin, a previously untested compound, was the most promising within this set. Competition experiments against the native ligands for LasR and RhlR revealed that ortho-vanillin can interact competitively with RhlR but not with LasR. Overall, these studies expand our understanding of benzaldehyde activities in the LasR and RhlR receptors and reveal potentially promising effects of ortho-vanillin as a small molecule QS modulator against RhlR. IMPORTANCE Quorum sensing (QS) regulates many aspects of pathogenesis in bacteria and has attracted interest as a target for anti-virulence therapies. As QS is regulated by low molecular weight chemical signals, the development of chemical strategies that can interfere with this cell-cell communication pathway has seen considerable scrutiny over the past 25 years. Much of this research has focused on common human pathogens, including the LasR and RhlR QS receptors in Pseudomonas aeruginosa. Potent and selective chemical agents capable of blocking the activity of these receptors remain relatively scarce, however. Natural products have provided a bounty of chemical scaffolds with anti-QS activities, but their molecular mechanisms are poorly characterized. The current study serves to fill this void by examining the activity of an important and wide-spread class of natural product QS modulators, benzaldehydes and related derivatives, in LasR and RhlR. We demonstrate that ortho-vanillin can act as a competitive inhibitor of RhlR, a receptor that has emerged and may supplant LasR in certain settings as a target for QS control in P. aeruginosa. The results and insights provided herein will advance the design of chemical tools to study QS with improved activities and selectivities.

microbiology↗

bioRxiv2022

Optimal feature selection and software tool development for bacteriocin prediction

Antibiotic resistance is a major public health concern around the globe. As a result, researchers always look for new compounds to develop new antibiotic drugs for combating antibiotic-resistant bacteria. Bacteriocin becomes a promising antimicrobial agent to fight against antibiotic resistance, due to its narrow killing spectrum. Sequence matching methods are widely used to identify bacteriocins by comparing them with the known bacteriocin sequences; however, these methods often fail to detect new bacteriocin sequences due to sequences high diversity. The ability to use a machine learning approach can help find new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. The aim of this work is to identify optimal sets of features and develop a machine learning-based software tool for predicting bacteriocin protein sequences with high accuracy. We extracted potential features from known bacteriocin and non-bacteriocin sequences by considering the physicochemical and structural properties of the protein sequences. Then we reduced the feature set using statistical justifications and recursive feature elimination technique. Finally, we built support vector machine (SVM) and random forest (RF) models using the selected features and our models can achieve accuracy up to 95.54%. We compared the performance of our method with a popular sequence matching-based approach and a deep learning-based method. We also developed a software tool called Bacteriocin Prediction (BacPred) that implements the prediction model using the optimal set of features obtained from this study. The software package and its user manual are available at https://github.com/suraiya14/ML_bacteriocins/BacPred.

bioinformatics↗