iProt-Sub: a comprehensive package for accurately map** and predicting protease-specific substrates and cleavage sites

J Song, Y Wang, F Li, T Akutsu… - Briefings in …, 2019 - academic.oup.com
Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The
key to better understanding the mechanisms that control this process is to identify the …

A review of methods available to estimate solvent-accessible surface areas of soluble proteins in the folded and unfolded states

S Ausaf Ali, M Imtaiyaz Hassan, A Islam… - Current Protein and …, 2014 - benthamdirect.com
Solvent accessible surface area (SASA) of proteins has always been considered as a
decisive factor in protein folding and stability studies. It is defined as the surface …

ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction

J Tubiana, D Schneidman-Duhovny, HJ Wolfson - Nature Methods, 2022 - nature.com
Predicting the functional sites of a protein from its structure, such as the binding sites of small
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone …

R Heffernan, Y Yang, K Paliwal, Y Zhou - Bioinformatics, 2017 - academic.oup.com
Motivation The accuracy of predicting protein local and global structural properties such as
secondary structure and solvent accessible surface area has been stagnant for many years …

PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites

J Song, H Tan, AJ Perry, T Akutsu, GI Webb… - PloS one, 2012 - journals.plos.org
The ability to catalytically cleave protein substrates after synthesis is fundamental for all
forms of life. Accordingly, site-specific proteolysis is one of the most important post …

iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets

Z Chen, X Liu, P Zhao, C Li, Y Wang, F Li… - Nucleic acids …, 2022 - academic.oup.com
The rapid accumulation of molecular data motivates development of innovative approaches
to computationally characterize sequences, structures and functions of biological and …

[HTML][HTML] Protein representations: Encoding biological information for machine learning in biocatalysis

D Harding-Larsen, J Funk, NG Madsen… - Biotechnology …, 2024 - Elsevier
Enzymes offer a more environmentally friendly and low-impact solution to conventional
chemistry, but they often require additional engineering for their application in industrial …

PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework

J Song, F Li, K Takemoto, G Haffari, T Akutsu… - Journal of theoretical …, 2018 - Elsevier
Determining the catalytic residues in an enzyme is critical to our understanding the
relationship between protein sequence, structure, function, and enhancing our ability to …

Single‐sequence‐based prediction of protein secondary structures and solvent accessibility by deep whole‐sequence learning

R Heffernan, K Paliwal, J Lyons, J Singh… - Journal of …, 2018 - Wiley Online Library
Predicting protein structure from sequence alone is challenging. Thus, the majority of
methods for protein structure prediction rely on evolutionary information from multiple …

Enhanced prediction of hot spots at protein-protein interfaces using extreme gradient boosting

H Wang, C Liu, L Deng - Scientific reports, 2018 - nature.com
Identification of hot spots, a small portion of protein-protein interface residues that contribute
the majority of the binding free energy, can provide crucial information for understanding the …