IDP-Seq2Seq – Identification of Intrinsically Disordered Proteins and Regions based on Sequence to Sequence Learning

IDP-Seq2Seq

:: DESCRIPTION

IDP-Seq2Seq applied the Sequence to Sequence Learning (Seq2Seq) derived from natural language processing (NLP) to map protein sequences to “semantic space” to reflect the structure patterns with the help of predicted Residue-Residue Contacts (CCMs) and other sequence-based features. Furthermore, the Attention mechanism was employed to capture the global associations between all residue pairs in the proteins.

::DEVELOPER

Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Tang YJ, Pang YH, Liu B.
IDP-Seq2Seq: identification of intrinsically disordered regions based on sequence to sequence learning.
Bioinformatics. 2021 Jan 29;36(21):5177-5186. doi: 10.1093/bioinformatics/btaa667. PMID: 32702119.

Enzymatic! 1.0 – Enzyme Learning Game

Enzymatic! 1.0

:: DESCRIPTION

Enzymatic! is an enzyme learning game.ENZYMES are extremely important in Biology! Essentially EVERY life process relies for EVERY living thing relies on ENZYMES! So… it might be important to understand them (at least a little bit…) In this amazing interactive experience, you will learn about enzymes by playing games, performing virtual experiments, and solving puzzles!

::DEVELOPER

BioMan Biology

:: SCREENSHOTS

:: REQUIREMENTS

Web browser / iPad/ iPhone

:: DOWNLOAD

Enzymatic!

:: MORE INFORMATION

EnTrans-Chlo – Transductive Learning for Protein Subchloroplast Localization

EnTrans-Chlo

:: DESCRIPTION

EnTrans-Chlo is an efficient multi-label predictor which is based on a transductive model for predicting single- and multi-location chloroplast proteins. EnTrans-Chlo represents using a TRANSductive-learning based algorithm to exploit ENsemble features of both sequence-based and evolutionary-based information to predictor protein subCHLOroplast localization.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

IEEE/ACM Trans Comput Biol Bioinform. 2016 Feb 8.
Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction.
Wan S, Mak MW, Kung SY.

ELASPIC – Ensemble Learning Approach for Stability Prediction of Interface and Core Mutations

ELASPIC

:: DESCRIPTION

ELASPIC constructs homology models of domains and domain-domain interactions, and uses those models, together with sequential and other features, to predict the energetic impact of a mutation on the stability of a single domain or the affinity between two domains.

::DEVELOPER

Kim Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS