Search

AbstractThe emergence of antibiotic resistance is one of the major global threats in healthcare. Metallo-β-Lactamases (MBL) are a class of enzymes in bacteria that cleave β-lactam antibiotics and confer resistance. MBLs are further divided into subclasses B1, B2 and B3. Of these, subclasses B1-MBLs (including NDM-1, VIM-2 and IMP-1) constitute the clinically prevalent lactamases conferring resistance. To date, no effective drugs are available clinically against MBLs. In this work, we aim to identify potent inhibitors for the B1 subclass of MBL from available marine metabolites in Comprehensive Marine Natural Product database through integrated in silicoapproaches. We have used two methods, namely, the high-throughput strategy and the pharmacophore-based strategy to identify potential inhibitors from marine metabolites. High-throughput virtual screening identified N-methyl mycosporine-Ser, which had the highest binding affinity to NDM-1. The pharmacophore-based approach based on co-crystallized ligands identified makaluvic acid and didymellamide with higher binding affinity across B1-MBLs. Taking into account of the advantage of a pharmacophore model-based approach with higher binding affinity, we conclude that both makaluvic acid and didymellamide show potential broad-spectrum effects by binding to all three B1-MBL receptors. The study also indicates the need to take multiple in silicoapproaches to screen and identify novel inhibitors. Together, our study reveals promising inhibitors that can be identified from marine systems.Communicated by Ramaswamy H. Sarma

Phase separation of proteins play key roles in cellular physiology including bacterial division, tumorigenesis etc. Consequently, understanding the molecular forces that drive phase separation has gained considerable attention and several factors including hydrophobicity, protein dynamics, etc., have been implicated in phase separation. Data-driven identification of new phase separating proteins can enable in-depth understanding of cellular physiology and may pave way towards developing novel methods of tackling disease progression. In this work, we exploit the existing wealth of data on phase separating proteins to develop sequence-based machine learning method for prediction of phase separating proteins. We use reduced alphabet schemes based on hydrophobicity and conformational similarity along with distributed representation of protein sequences and biochemical properties as input features to Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. We used both curated and balanced dataset for building the models. RF trained on balanced dataset with hydropathy, conformational similarity embeddings and biochemical properties achieved accuracy of 97%. Our work highlights the use of conformational similarity, a feature that reflects amino acid flexibility, and hydrophobicity for predicting phase separating proteins. Use of such “interpretable” features obtained from the ever-growing knowledgebase of phase separation is likely to improve prediction performances further.

AbstractThe increase in drug resistance over the last two decades is a big threat in health care settings. More importantly, the dissemination of carbapenem-resistant Enterobacteriaceae is the major threat to public health with an increase in morbidity and mortality. β-lactamase is known to confer enteric bacteria with nearly complete resistance to all β-lactam antibiotics including the late-generation carbapenems. The commercially available β-lactamase inhibitors, clavulanic acid, sulbactam, and tazobactam are being met with an increasing number of resistant phenotypes and are ineffective against pathogens harbouring New Delhi metallo-β-lactamase (NDM-1). Inhibition of New Delhi metallo-β-lactamase-1 activity is one potential way to treat metallo β-lactamase (MBL) producing multi drug resistant (MDR) pathogen. The present study focused on screening of Klebsiella pneumoniaeNew Delhi metallo-β-lactamase-1 (BLIs) from endophytic Streptomycesspp. using in vitroand in silicomethods. The study identified three potential inhibitors of New Delhi metallo-β-lactamase-1, namely dodecanoic acid, dl-alanyl-l-leucine and phenyl propanedioic acid. These molecules were found to bind to other MBLs namely, IMP-1 and VIM-2. To the best of our knowledge, this is the first kind of study reporting the binding mode of these molecules with New Delhi metallo-β-lactamase-1.Communicated by Ramaswamy H. Sarma

Hemolytic anemia is a blood disorder whose incidence is increasing in the world in recent years especially after the pandemic. Conventional treatments include use of steroids and immunosuppresants that are accompanied by numerous adverse effects. With growing interest in using complex multi-component formulations for multi-targeted therapy, the present study aims to investigate the therapeutic efficacy of a traditional Ayurvedicherbomineral preparation, Punarnavadi Mandura, which has been traditionally used as a supplement in iron-deficiency anemia, against phenylhydrazine-induced hemolytic anemia in rodent models.

Antifreeze proteins (AFP) observed in cold‐adapting organisms bind to ice crystals and prevent further ice growth. However, the molecular mechanism of AFP‐ice binding and AFP‐inhibited ice growth remains unclear. Here we report the interaction of the insect antifreeze protein (Tenebrio molitor, TmAFP) with ice crystal by molecular dynamics simulation studies. Two sets of simulations were carried out at 263 K by placing the protein near the primary prism plane (PP) and basal plane (BL) of the ice crystal. To delineate the effect of temperatures, both the PP and BL simulations were carried out at 253 K as well. The analyses revealed that the protein interacts strongly with the ice crystal in BL simulation than in PP simulation both at 263 K and 253 K. Further, it was observed that the interactions are primarily mediated through the interface waters. We also observed that as the temperature decreases, the interaction between the protein and the ice increases which can be attributed to the decreased flexibility and the increased structuring of the protein at low temperature. In essence, our study has shed light on the interaction mechanism between the TmAFP antifreeze protein and the ice crystal.