A review on metallothionein research in marine and estuarine realms: past paradigms and future vistas - Adnan H. Gora; M. P. Sreeram; Saima Rehman; Qurat Ul Ain; Kajal Chakraborty; D. Prema; R. Lavanya; Prabhugouda Siriyappagouder; P. S. Asha (Frontiers in Marine Science, 2025)
Oceans and coastal waters, vital for human survival and climate regulation, are increasingly threatened by heavy metal pollution due to human activities including industrialization and urbanization. Heavy metals including tin, zinc, mercury, copper, nickel, cadmium, cobalt, vanadium, strontium, titanium, arsenic, lead, molybdenum, and chromium are persistent pollutants that can accumulate in marine organisms, posing significant risks to ecosystems and human health. While some heavy metals are essential in trace amounts, they can exert undesirable biological effects at higher concentrations and even become toxic. In response to such toxic exposure, terrestrial and aquatic plants and animals are known to have evolved inherent mechanisms to subdue heavy metal toxicity. These responses often involve the activation of various stress-related proteins, including heat shock proteins, antioxidant enzymes, and metal-binding molecules that work collectively to restore cellular homeostasis. One of the mechanisms to mitigate metal toxicity is through the activity of metallothionein proteins. Metallothioneins, low molecular weight, cysteine-rich proteins, play a crucial role in mitigating the toxic effects of heavy metals. These proteins bind heavy metals, aiding in detoxification, protecting the cells from their undesirable effects, and maintaining metal homeostasis. Thus, metallothionein expression and activity also serve as valuable biomarkers for assessing heavy metal pollution, providing insights into the biological impact of these contaminants. The present review explores the role of bacterial metallothioneins in detoxification and their potential in environmental risk assessment, focusing on their importance in marine species exposed to heavy metal pollution. We explore the studies that report heavy metal contamination in the coastal waters, followed by elucidating the effects of heavy metal exposure on metallothionein activity and expression in marine fish, crustaceans and mollusks. Finally, we provide possible future perspectives of how bacterial metallothioneins can be employed for mitigating ecological damage caused by heavy metals. By understanding the interactions between heavy metals and metallothioneins, we can develop more effective strategies for monitoring and mitigating the effects of heavy metal contamination in marine environments.

Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model - M. Sailaja; M. Prema Kumar; B. Swarna Jyothi; G. L. Narasamba Vanguri; S. Manjula; D. Divya Priya (Remote Sensing in Earth Systems Sciences, 2024)

Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins - Sofia D’Souza; K. V. Prema; S. Balaji; Ronak Shah (Interdisciplinary Sciences: Computational Life Sciences, 2023)
Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target effects of proteins against therapeutic candidates. This study aims to predict unknown ligand–target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs. We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that our deep learning model, DeepPS is computationally more efficient than the deep learning model trained on full-length raw sequences of proteins. We conclude that a beneficial research approach would be to integrate structural information of proteins for modeling drug-target interaction prediction of large datasets for more interpretability, high throughput, and broad applicability. Graphical abstract

Assessment of ecosystem health of a micro-level Ramsar coastal zone in the Vembanad Lake, Kerala, India - Shelton Padua; V. Kripa; D. Prema; K. S. Mohamed; R. Jeyabaskaran; P. Kaladharan; Lavanya Ratheesh; B. Jenny; G. Shylaja; Reena V. Joseph; M. P. Shyamala; P. Vysakhan; et al. (Environmental Monitoring and Assessment, 2023)

Overfishing and Climate Drives Changes in Biology and Recruitment of the Indian Oil Sardine Sardinella longiceps in Southeastern Arabian Sea - Vasant Kripa; Kolliyil S. Mohamed; K. P. Said Koya; R. Jeyabaskaran; D. Prema; Shelton Padua; Somy Kuriakose; P. S. Anilkumar; Preetha G. Nair; T. V. Ambrose; A. M. Dhanya; K. S. Abhilash; et al. (Frontiers in Marine Science, 2018)

An experimental study on usage of plastic oil and B20 algae biodiesel blend as substitute fuel to diesel engine - D. K. Ramesha; G. Prema Kumara; Lalsaheb; Aamir V. T. Mohammed; Haseeb A. Mohammad; Mufteeb Ain Kasma (Environmental Science and Pollution Research, 2016)

DNA cleavage and antimicrobial investigation of Co(II), Ni(II), and Cu(II) complexes with triazole Schiff bases: synthesis and spectral characterization - Ajaykumar D. Kulkarni; Sangamesh A. Patil; Vinod H. Naik; Prema S. Badami (Medicinal Chemistry Research, 2011)