Department of Biological Sciences

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    pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
    (Elsevier, 2025-09) Basu, Sushmita
    Interactions between proteins and lipids are crucial for numerous cellular processes. Some of the lipid interacting segments in protein sequences are intrinsically disordered regions (IDRs), which may gain secondary structures upon binding. We collected experimentally annotated lipid-interacting IDRs, named membrane molecular recognition features (MemMoRFs). We used this dataset to develop and test an accurate and relatively fast sequence-based MemMoRF predictor, pLMMoRF, thereby supporting tedious and costly experimental identification of MemMoRFs. Our predictor utilizes a protein language model (pLM) which we processed to generate inputs to a deep convolutional neural network. We considered various pLMs (ESM-2, ProstT5, ProtT5 and Ankh) and applied feature selection to reduce their outputs, creating a more compact neural network model. pLMMoRF leverages the Ankh-based model, selected for its higher accuracy compared to our other models. Tests on low similarity test datasets demonstrate that pLMMoRF is more accurate than the sole current predictor of MemMoRFs, CoMemMoRFPred. Moreover, pLMMoRF has a relatively small computational footprint because of the compact network size and use of dedicated GPU nodes. This allowed us to make MemMoRF predictions for the human proteome. We analyzed these predictions and made them publicly available, facilitating an improved understanding of functions of membrane-coupled proteins. Our work underscores the importance of selecting key embedding features to enhance predictive performance and reduce computational footprint of sequence-based predictors of protein functions. The web server for the pLMMoRF predictor and the predictions for human proteins
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    ML-based technologies in sustainable agro-food production and beyond: Tapping the (semi) arid landscape for bioactives-based product development
    (Elsevier, 2024-08) Joshi, Mukul; Deepa, P.R.; Sharma, Pankaj Kumar; Mahapatra, Tanmaya
    The current era of rapid climate change necessitates greater emphasis on wild, often underutilized yet sturdy, edible plants that are capable of growing in harsh arid lands. When compared to more popular crops like rice, these are often of traditional significance and more region-specific; but needing less chemical fertilizers, pesticides and irrigation water, they can not only provide food and nutrition in a sustainable manner but also medicinally valuable compounds (nutraceuticals) to target various communicable and non-communicable diseases. These bioactive metabolites could also serve as markers for in-process quality control of herbal formulations and as metabolic biomarkers. Of late, a few of the common food crops across the world have benefited from the use of technological interventions, employing various Internet of Things (IoT) devices and sensors to collect data on the farm and conduct agro-food specific analytics. Machine Learning (ML) and deep learning (DL) have found application in numerous facets of agriculture, particularly in tasks such as yield prediction, disease detection, weed detection, crop recognition, and assessing crop quality at pre-harvest, harvest, and post-harvest stages. ML technology also has shown potential to be effectively employed at various stages of bioactives discovery, encompassing target identification, compound screening, lead discovery, as well as pre-clinical and clinical development phases. However, the usage of these modern technologies has been less explored in the desert plants of the world. The current article reviews a few available examples and highlights the potential of employing ML and DL technologies in edible plants of the world, with a focus on sustainable desert flora, for achievement of multidisciplinary objectives, that is, agro-food production, food safety and bioactives discovery.