Biology subjects

Peta, V. J.

Publications and source records attributed to Peta, V. J..

2 recordsLinked to original sources

bioRxiv2023

CREID- A ChemoReceptor-Effector Interaction Database

The ChemoReceptor-Effector Interaction Database (CREID) is a collection of bacterial chemoreceptor and effector protein and interaction data to understand the process that chemoreceptors and effectors play in various environments. Our website includes terms associated with chemosensory pathways to educate users and those involved in collaborative research to help them understand this complex biological network. It includes 2,440 proteins involved in chemoreceptor and effector systems from 7 different bacterial families with 1,996 chemoeffector interactions. It is available at https://react-creid.bicbioeng.org. Key HighlightsO_LICREID links bacterial chemoreceptors with their associated effectors. C_LIO_LIResearchers interested in what attracts or repels bacteria can use CREID as a comprehensive source for information. C_LIO_LIBiosensor developers can leverage CREID to discover better interactions for their applications. C_LIO_LICREID reveals knowledge gaps in chemoreceptor-effector interactions for both model and non-model organisms. C_LI

bioengineering↗

bioRxiv2023

Classification of bacterial nanowire proteins using Machine Learning and Feature Engineering model

Nanowires (NW) have been extensively studied for Shewanella spp. and Geobacter spp. and are mostly produced by Type IV pili or multiheme c-type cytochrome. Electron transfer via NW is the most studied mechanism in microbially induced corrosion, with recent interest in application in bioelectronics and biosensor. In this study, a machine learning (ML) based tool was developed to classify NW proteins. A manually curated 999 protein collection was developed as an NW protein dataset. Gene ontology analysis of the dataset revealed microbial NW is part of membranal proteins with metal ion binding motifs and plays a central role in electron transfer activity. Random Forest (RF), support vector machine (SVM), and extreme gradient boost (XGBoost) models were implemented in the prediction model and were observed to identify target proteins based on functional, structural, and physicochemical properties with 89.33%, 95.6%, and 99.99% accuracy. Dipetide amino acid composition, transition, and distribution protein features of NW are key important features aiding in the models high performance.

systems biology↗