@article {46119, title = {Mechanistic insights into enzymatic catalysis by trehalase from the insect gut endosymbiont Enterobacter cloacae}, journal = {FEBS Journal}, volume = {286}, year = {2019}, month = {MAY}, pages = {1700-1716}, type = {Article}, abstract = {

Energy metabolism in the diamondback moth Plutella xylostella is facilitated by trehalase, an enzyme which assists in trehalose hydrolysis, from the predominant gut bacterium Enterobacter cloacae. We report the biochemical and structural characterization of recombinant trehalase from E. cloacae (Px_EclTre). Px_EclTre showed K-M of 1.47 (+/- 0.05) mm, k(cat) of 6254.72 min(-1) and V-max 0.2 (+/- 0.002) mm center dot min(-1) at 55 degrees C and acidic pH. Crystal structures of Px_EclTre were determined in the ligand-free form and bound to the inhibitor Validoxylamine A. The crystal structure of the ligand-free form, unavailable until now for any other bacterial trehalases, enabled us to delineate the conformational changes accompanying ligand binding in trehalases. Multiple salt bridges were identified that potentially facilitated closure of a hood over the substrate-binding site. A cluster of five tryptophans lined the -1 substrate-binding subsite, interacted with crucial active site residues and contributed to both trehalase activity and stability. The importance of these residues in enzyme activity was further validated by mutagenesis studies. Many of these identified residues form part of signature motifs and other conserved sequences in trehalases. The structure analysis thus led to the assignment of the functional role to these conserved residues. This information can be further explored for the design of effective inhibitors against trehalases.

}, keywords = {Enterobacter cloacae, Plutella xylostella, structural analysis, trehalase, validoxylamine A}, issn = {1742-464X}, doi = {10.1111/febs.14760}, author = {Adhav, Anmol and Harne, Shrikant and Bhide, Amey and Giri, Ashok and Gayathri, Pananghat and Joshi, Rakesh} } @article {48236, title = {Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys}, journal = {Computers in Biology and Medicine}, volume = {146}, year = {2022}, month = {JUL}, pages = {105419}, type = {Article}, abstract = {

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8\% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6\%-80.2\%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

}, keywords = {BBV152, Covaxin, COVID-19, Ensemble methods, Infection, machine learning, SARS-CoV-2}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2022.105419}, author = {Singh, Prateek and Ujjainiya, Rajat and Prakash, Satyartha and Naushin, Salwa and Sardana, Viren and Bhatheja, Nitin and Singh, Ajay Pratap and Barman, Joydeb and Kumar, Kartik and Gayali, Saurabh and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Lahiri, Amit and Kar, Susanta and Bhosale, Vivek and Srivastava, Mrigank and Mugale, Madhav Nilakanth and Pandey, C. P. and Khan, Shaziya and Katiyar, Shivani and Raj, Desh and Ishteyaque, Sharmeen and Khanka, Sonu and Rani, Ankita and Promila and Sharma, Jyotsna and Seth, Anuradha and Dutta, Mukul and Saurabh, Nishant and Veerapandian, Murugan and Venkatachalam, Ganesh and Bansal, Deepak and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Veeranna, Ravindra P. and Pal, Anirban and Singh, Ranvijay Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Rahman, Syed Nasar and Selvakumar, Gopika and Venkatesan, Subramanian and Karmakar, Malay Kumar and Sardana, Harish Kumar and Kothari, Anamika and Parihar, Devendra Singh and Thakur, Anupma and Saifi, Anas and Gupta, Naman and Singh, Yogita and Reddu, Ritu and Gautam, Rizul and Mishra, Anuj and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Padwad, Yogendra and Patial, Vikram and Hallan, Vipin and Singh, Damanpreet and Tirpude, Narendra and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sistla, Ramakrishna and Balthu, Narender Kumar and Kumar, Kiran A. and Ranjith, Siva and Kumar, B. Vijay and Jamwal, Piyush Singh and Wali, Anshu and Ahmed, Sajad and Chouhan, Rekha and Gandhi, Sumit G. and Sharma, Nancy and Rai, Garima and Irshad, Faisal and Jamwal, Vijay Lakshmi and Paddar, Masroor Ahmad and Khan, Sameer Ullah and Malik, Fayaz and Ghosh, Debashish and Thakkar, Ghanshyam and Barik, S. K. and Tripathi, Prabhanshu and Satija, Yatendra Kumar and Mohanty, Sneha and Khan, Md Tauseef and Subudhi, Umakanta and Sen, Pradip and Kumar, Rashmi and Bhardwaj, Anshu and Gupta, Pawan and Sharma, Deepak and Tuli, Amit and Chaudhuri, Saumya Ray and Krishnamurthi, Srinivasan and Prakash, L. and Rao, V. Ch and Singh, B. N. and Chaurasiya, Arvindkumar and Chaurasiya, Meera and Bhadange, Mayuri and Likhitkar, Bhagyashree and Mohite, Sharada and Patil, Yogita and Kulkarni, Mahesh and Joshi, Rakesh and Pandya, Vaibhav and Mahajan, Sachin and Patil, Amita and Samson, Rachel and Vare, Tejas and Dharne, Mahesh and Giri, Ashok and Mahajan, Sachin and Paranjape, Shilpa and Sastry, G. Narahari and Kalita, Jatin and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, RaviKumar and Dutta, Prachurjya and Singh, Moirangthem Goutam and Gogoi, Gayatri and Tapadar, Yasmin Begam and Babu, Elapavalooru V. S. S. K. and Sukumaran, Rajeev K. and Nair, Aishwarya R. and Puthiyamadam, Anoop and Valappil, Prajeesh Kooloth and Prasannakumari, Adrash Velayudhan Pillai and Chodankar, Kalpana and Damare, Samir and Agrawal, Ved Varun and Chaudhary, Kumardeep and Agrawal, Anurag and Sengupta, Shantanu and Dash, Debasis} } @article {47118, title = {Molecular elucidation of pancreatic elastase inhibition by baicalein}, journal = {Journal of Biomolecular Structure \& Dynamics}, volume = {40}, year = {2022}, month = {JUL}, pages = {5759-5768}, type = {Article}, abstract = {

The serine protease, elastase exists in various forms and plays diverse roles in the human body. Pharmacological inhibition of elastase has been investigated for its therapeutic role in managing conditions such as diabetes, pneumonia and arthritis. Sivelestat, a synthetic molecule, is the only elastase inhibitor to have been approved by any major drug regulatory authority (PMDA, in this case) - but still has failed to attain widespread clinical usage owing to its high price, cumbersome administration and obscure long-term safety profile. In order to find a relatively better-suited alternative, screening was conducted using plant flavonoids, which yielded baicalein, a molecule that showed robust inhibition against Pancreatic Elastase inhibition (IC50: 3.53 mu M). Other than having a considerably lower IC(50)than sivelestat, baicalein is also cheaper, safer and easier to administer. While MicroScale Thermophoresis validated baicalein-elastase interaction, enzyme-kinetic studies, molecular docking and molecular dynamic simulation revealed the mode of inhibition to be non-competitive. Baicalein exhibited binding to a distinct allosteric site on the enzyme. The current study demonstrates the elastase inhibition properties of baicalein in an in-vitro and in-silico environment.

}, keywords = {Baicalein, elastase, Enzyme inhibition, molecular interaction, sivelestat}, issn = {0739-1102}, doi = {10.1080/07391102.2021.1873189}, author = {Ghosh, Debajeet and Bansode, Sneha and Joshi, Rakesh and Kolte, Baban and Gacche, Rajesh} }