Journal Articles
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Item An exhaustive review of computational prediction techniques for PPI sites, protein locations, and protein functions(Springer, 2023) Bhat, P.; Patil, N.The field of proteomics encompasses a comprehensive examination of proteins, encompassing their structural properties, interactions with other biomolecules, subcellular localization, functional roles, interaction sites, regions of disorder, and exploring novel protein designs. Each of these domains interlinks, contributing valuable information to the study of each other part. Extensive research in most of these areas has given rise to many more challenges that require further exploration. This review mainly concentrates on prediction approaches for protein–protein interaction sites, protein subcellular locations, and protein functions. We provide an exhaustive collection of several latest works in the above three domains, along with a digest of their descriptions in the most recent times. We conclude the review by highlighting the existing challenges and emphasizing the need for a deeper exploration of the research gaps in these studies. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Item Expression of Bacillus licheniformis chitin deacetylase in E. coli pLysS: Sustainable production, purification and characterisation(Elsevier B.V., 2019) Bhat, P.; Pawaskar, G.-M.; Raval, R.; Cord-Landwehr, S.; Moerschbacher, B.; Raval, K.Chitosan obtained by enzymatic deacetylation of chitin using chitin deacetylase (CDA) holds promise primarily due to the possibility to yield chitosan with non-random patterns of acetylation and more environmentally friendly process compared to chemical deacetylation. In the present study, a sustainable bioprocess is reported for over-expression of a bacterial CDA in E. coli pLysS cells. A Bacillus licheniformis CDA gene is identified in the genome of the bacterium, cloned, and expressed, yielding enzymatically active recombinant protein. For enzyme production, a growth medium is formulated using carbon and nitrogen sources, which do not compete with the human food chain. The maximum enzyme activity of 320 ± 20 U/mL is achieved under optimized conditions. The CDA productivity is improved by about 23 times in shake flask culture by optimizing operating conditions and medium components. The CDA is purified and the enzyme kinetic values i.e. Km, Vmax and Kcat are reported. Also the effect of cofactors, temperature, and pH on the enzyme activity is reported. Further, economic yield is proposed for production of CDA through this bioprocess. © 2019 Elsevier B.V.Item Hydroxyapatite—a promising sunscreen filter(Springer, 2020) Pal, A.; Hadagalli, K.; Bhat, P.; Goel, V.; Mandal, S.Exposure to ultraviolet (UV) radiation has been known to cause skin cancer, erythema, and sunburn. Continuous efforts have been made to make sunscreens more efficient and non-toxic. Inorganic sunscreens like TiO2 and ZnO are continued to be used for a few decades, and they are efficient in giving protection against harmful UV radiation, but they are photochemically active as well. They generate free radicals upon irradiation, which leads to reactive oxygen species (ROS) generation which is harmful to the human skin. Hydroxyapatite (HA) is a biocompatible material as it has a composition the same as the mineral content of the human bone; therefore, it is suitable for the dermatological application. Though HA itself does not provide protection against UV, studies on doped HA with various ions showed excellent performance. Pure HA absorbs only between 200 and 340 nm, with an intense band below 247 nm. HA doped with bivalent Zn2+, Fe2+, and trivalent Fe3+ and Cr3+, showed absorbance in the entire UV region. TiO2 provides absorbance in the entire UV range, while ZnO does so only in UVA. Compared to HA (refractive index, n = 1.6), TiO2 (n = 2.6) and ZnO (n = 1.9) have higher refractive index, which gives unwanted whitening effect. Additional properties can be brought in HA composites by adding material while retaining their individual properties. As HA is not photocatalytic, it does not lead to a generation of free radicals. This paper throws light on several aspects of HA-based sunscreen filters as an emerging future cosmetic material, and brief analysis and conclusions. © 2019, Australian Ceramic Society.Item Masculinities, femininities, and the patriarchal family: a reading of The Great Indian Kitchen(Routledge, 2024) Karimpaniyil, R.; Bhat, P.This article seeks to examine the representation of masculinities and femininities in the renowned South Indian drama film The Great Indian Kitchen. The research construes the manner in which the two dominant genders promote and/or modify patriarchal norms within the institution of family. The functioning of women as ancillary members of patriarchy, the interplay between masculinities and femininities, their evolution in contemporary times, etc., are also critically engaged in the paper. The paper argues that the movie The Great Indian Kitchen not only illustrates different masculinities and femininities but also reconstructs the patriarchal family structure which institutionalises gender inequality. It further argues that the movie proposes an alternative image of the family based on gender equality, where men and women live with mutual respect and complementation. © 2023 Informa UK Limited, trading as Taylor & Francis Group.Item Detection of heart arrhythmia with electrocardiography(Springer, 2024) Jat, T.; Patil, N.; Bhat, P.Early detection of cardiac arrhythmia, a prevalent form of cardiovascular disease (CVD) impacting millions globally, is heavily reliant on the accurate analysis of heartbeats. Physicians often recommend that patients wear Holter monitors for 24 h or longer to observe concerning cardiac issues, resulting in the collection of substantial amounts of electrocardiogram (ECG) data. Consequently, there is a need to automate the process of interpreting ECGs to detect cardiac abnormalities efficiently. Current state-of-the-art studies rely on handcrafted feature extraction, which may not effectively capture the intricate temporal relationships inherent in ECG signal data. To address this limitation and facilitate the diagnosis of cardiac diseases, this study proposes a technique that converts electrocardiogram signals into images, subsequently training a deep learning model on the generated images. Image encoding techniques such as Gramian Angular Difference Field (GADF), Gramian Angular Summation Field (GASF) and Markov Transition Field (MTF) are employed to translate the ECG signals into images. The highest accuracy, 96.71%, was achieved by training the Convolutional Neural Network (CNN) model using the concatenation of these three image encoding techniques. The proposed approach is assessed using ECG recordings from the MIT-BIH Arrhythmia Database to detect heart arrhythmia, demonstrating the efficacy of the approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.Item Integrating Evolutionary and Structural Properties for Protein Interaction Site Prediction Using Graph and Temporal Convolutions(Institute of Electrical and Electronics Engineers Inc., 2025) Bhat, P.; Patil, N.Predicting protein interaction sites is crucial for tasks such as constructing protein interaction networks, analyzing protein functions, studying molecular-level pathology, and designing novel drugs. However, the restricted predictive performance of sequence-based computational approaches has led to the rise of structure-oriented approaches. Existing cutting-edge methods mostly focus on the secondary structural features, leaving significant scope for further performance improvement. This study incorporates additional structural features from a tertiary-level perspective to derive composite features using graph and temporal convolutions. A hybrid weighted loss function efficiently handles the class imbalance. A fully connected neural network generates the final predictions. The outlined model is tested on various publicly accessible datasets, showing a substantial improvement in performance over leading models. Comparative analysis with the best models from the literature reports enhancement in the Matthews Correlation Coefficient(MCC) and Area under the precision-recall curve (AUPRC) by 4.8% and 4.1% on the Test_60 dataset, 9.8% and 11.2% on the Test_315 dataset, 10.4% and 11.5% on the Dtestset72 dataset, 12.6% and 13.9% on the PDBtestset164 dataset and 10% and 13.1% on the Test_84 dataset. Finally, the statistical t-test showcases the significance of the proposed model in the protein interaction site prediction task. © 2025 IEEE.Item Class-Balanced Protein Interaction Site Prediction Using Global and Local Features with XGBoost and Deep Learning(Springer, 2025) Kulkarni, B.C.; Sai, B.S.; Kolagad, V.; Patil, N.; Bhat, P.Inter-protein interactions are critical in biological pathways. Determining the protein–protein interaction (PPI) sites is vital for comprehending protein behavior and designing medications. Traditional experimental protocols for pinpointing these sites are prolonged and costly, making computational approaches an efficient alternative. However, many computational methods fail to resolve the problem of class imbalance in PPI datasets and focus predominantly on local contextual features, ignoring global sequence information. In this work, we address class imbalance in PPI site prediction by applying a series of balancing techniques: selective thinning of the majority class, Tomek Links to remove noisy samples near the class boundary, and random augmentation of the minority class. We then further balance the data using Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Networks (GANs), with GANs showing a slight edge in improving data quality and reducing noise. Our approach incorporates four key features: secondary structure, raw protein sequence, Position-Specific Scoring Matrix (PSSM), and Relative Solvent Accessibility (RSA). We use both nearby contextual and holistic sequence features for training two models: XGBoost and a Deep Neural Network (DNN). The performance of the models was assessed using accuracy, Matthews correlation coefficient (MCC), precision, recall, and F-score. We correlate the impact of using balanced versus unbalanced datasets and measure the share of global features in enhancing model performance. The findings demonstrate that class balancing significantly upgrades prediction performance. The XGBoost model realized an accuracy of 0.831 and precision of 0.417, outperforming the DNN in these metrics. The DNN model attained a higher recall of 0.723 and an F-score of 0.485, exemplifying its effectiveness in accurately detecting true PPI sites. Both models showcased a good MCC of 0.30, corroborating the effectiveness of the introduced balancing strategies and the assimilation of global features in robust PPI site prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.Item Detection of Heart Abnormality with Stethoscope Sounds(Springer, 2025) Jat, T.; Bhat, P.; Patil, N.Cardiac rhythm assessment is a critical step in the early diagnosis of cardiac arrhythmia, which has been identified as a kind of cardiovascular disease (CVD) that affects millions of individuals worldwide. Although electrocardiography is the right test to confirm cardiovascular diseases, due to the time and cost involved in the test, an alternate solution like heart abnormality detection using a stethoscope test is needed. A stethoscope test is a facility that is relatively easily available in rural parts of the country and can aid in the early diagnosis of heart abnormalities. The central aim of this work is to build a deep learning model for classifying heartbeat sounds which are captured with the help of iStethoscope Pro iPhone app or using a digital stethoscope. The proposed methodology is implemented using the Heart Sounds Classification Challenge dataset from PASCAL and the 2016 Physionet Challenge dataset. To extract features from the recorded heart sounds, we employ Mel spectrograms, Mel-frequency cepstral coefficients (MFCC), and Chroma short-time Fourier transform (STFT). A key novelty of our approach lies in the use of the stacked Bidirectional and Unidirectional Long Short Term Memory (SBU-LSTM) and deep BiLSTM architectures, which, combined with the three feature types (Mel spectrograms, Chroma STFT, and MFCC), enhances model performance for the classification task. Additionally, we introduce the use of Mel spectrograms and Chroma STFT with the 2D Convolutional Neural Network (CNN) architecture, which, as far as we know, has not been investigated in prior research. Experimental results show that the best accuracy achieved is 72% for PASCAL’s Dataset-A, 66% for Dataset-B, 83% for Dataset A + B, and 89% for Physionet’s Dataset-C. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.