Jisna V A
@iiitk.ac.in
Assistant Professor and Computer Science Engineering
IIITDM Kurnool/Assistant Professor
EDUCATION
Doctor of Philosophy
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Health Informatics, Molecular Biology, Structural Biology
Scopus Publications
Scopus Publications
Jisna Vellara Antony, Roosafeed Koya, Pulinthanathu Narayanan Pournami, Gopakumar Gopalakrishnan Nair, and Jayaraj Pottekkattuvalappil Balakrishnan
Springer Science and Business Media LLC
V. A. Jisna and P. B. Jayaraj
World Scientific Pub Co Pte Ltd
Protein secondary structure assignment, a subdiscipline of computational chemistry, is yet to be explored using deep learning techniques. Protein secondary structure elements are assigned to support structural analysis and prediction. Algorithms like DSSP, generally regarded as the gold standard for assigning the secondary structure of proteins, need full atom information to label protein coordinates. The PDB database has been the major repository for data on the 3D structures of proteins, nucleic acids, and other complex assemblies since 1971. However, a significant fraction of protein structures contains missing atoms. As a result, new approaches to reliably and consistently assigning secondary structures based on coarse-grained atomic coordinates are needed. While deep learning architectures have an unparalleled track record in applications such as protein structure prediction, there are only a few known deep learning solutions for structure assignment problems. While the gold standard methods are based on bonding information and other geometric characteristics, deep learning methods extract features themselves without human intervention. The benefit that standardised datasets provide to the effectiveness of deep learning systems in multiple domains motivated us to create a labeled dataset for protein structure assignment tasks. Additionally, a deep learning model, named PSSADL, solely based on [Formula: see text] coordinates was trained on the generated dataset to validate its potency. The proposed method, which combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)/Bidirectional LSTM networks, has been compared to the established standards and more recent techniques. The model achieved an accuracy of [Formula: see text] on the benchmark and individual test sets. The results show that deep learning techniques have a promising future in protein structure analysis, implying that the dataset developed as part of our work will be a valuable resource for further protein structure research.
Jisna Vellara Antony, Prayagh Madhu, Jayaraj Pottekkattuvalappil Balakrishnan, and Hemant Yadav
Springer Science and Business Media LLC
V. A. Jisna and P. B. Jayaraj
Springer Science and Business Media LLC
Jisna Antony, Akhil Penikalapati, J. Vinod Kumar Reddy, P. N. Pournami, and P. B. Jayaraj
Springer Singapore
Jisna Antony, Vishnu Sreenivas, and P.B. Jayaraj
IEEE
Biological activities of a human body are significantly influenced by proteins. The function of a protein can be identified by its structure. So, it is necessary to develop computational tools to predict the structure of a given protein, as experimental methods are costly and laborious. Many computational methods are available these days to predict the structure of proteins. Template-based protein structure modeling is one among such methods for protein structure prediction. Based on the assumption that similar sequences exhibit similar protein folds, template-based protein structure prediction is expected to be an accurate method to predict the structure of any protein sequence. It is a multi-step process that involves template selection, template-target sequence alignment, gap modeling and finally refining the model. Gap modeling can be effectively done now using fragment libraries. The quality of a predicted structure greatly depends on the quality of the fragment library generated. Since it has been proved that smaller length fragments model proteins more accurately than larger length fragments, the experiment was conducted with fragments of length two. In this work, a coordinate clustering approach has been introduced to reduce the conformational search space. The proposed method has been implemented and the results show a close resemblance with structures predicted by other servers. The source code of the work can be seen at: https://github.com/jisnava/Protein-Structure-Prediction
V.A. Jisna and C.C. Sobin
Institution of Engineering and Technology
Steganography is the art and science of writing hidden messages such that the existence of a secret communication is known only to the sender and receiver. For hiding messages different types of media are used. Audio steganography uses audio as the cover media. This paper makes a brief discussion on different audio steganography techniques. Among the techniques studied wavelet domain shows high hiding capacity and transparency. In wavelet domain different techniques are applied on the wavelet coefficients to increase the hiding capacity and perceptual transparency. The paper mainly concentrates on a survey on audio steganography in wavelet domain. Based on this survey, a proposal is made to improve the quality of retrieved data at the receiver side.