AllesTM: predicting multiple structural features of transmembrane proteins.

ABSTRACT:

Background

This study is motivated by the following three considerations: a) the physico-chemical properties of transmembrane (TM) proteins are distinctly different from those of globular proteins, necessitating the development of specialized structure prediction techniques, b) for many structural features no specialized predictors for TM proteins are available at all, and c) deep learning algorithms allow to automate the feature engineering process and thus facilitate the development of multi-target methods for predicting several protein properties at once.

Results

We present AllesTM, an integrated tool to predict almost all structural features of transmembrane proteins that can be extracted from atomic coordinate data. It blends several machine learning algorithms: random

SUBMITTER: Honigschmid P 

PROVIDER: S-EPMC7291640 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature