Prediction and validation of nanowire proteins in <i>Oleidesulfovibrio alaskensis</i> G20 using machine learning and feature engineering.

ABSTRACT: The type-IV bacterial pili and multiheme c-type cytochrome protein family have gained significant attention due to their role in extracellular electron transfer (EET), which defines their electrogenic properties. These electrogenic behaviors play a crucial role in interspecies microbial communication, essential for microbial biofilm formation and the development of robust technologies such as biosensors. Given the technological and ecological significance of electron transfer mechanisms, this study presents NanowireML (NWML), a 2-staged machine learning (ML) system to identify and analyze nanowire (NW) proteins. Stage 1 predicts NW proteins using minimal features, and Stage 2 leverages graphical knowledge representation to predict the most relevant NW mechanism governing candidates in spec

SUBMITTER: Raya D 

PROVIDER: S-EPMC12088768 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature