A transformer-based ensemble framework for the prediction of protein–protein interaction sites
The identification of protein–protein interaction (PPI) sites is essential in the research of
protein function and the discovery of new drugs. So far, a variety of computational tools …
protein function and the discovery of new drugs. So far, a variety of computational tools …
Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry
Protein-based therapeutics have revolutionized the pharmaceutical industry and become
vital components in the development of future therapeutics. They offer several advantages …
vital components in the development of future therapeutics. They offer several advantages …
Growing ecosystem of deep learning methods for modeling protein–protein interactions
JR Rogers, G Nikolényi… - … Engineering, Design and …, 2023 - academic.oup.com
Numerous cellular functions rely on protein–protein interactions. Efforts to comprehensively
characterize them remain challenged however by the diversity of molecular recognition …
characterize them remain challenged however by the diversity of molecular recognition …
A knowledge distillation-guided equivariant graph neural network for improving protein interaction site prediction performance
S Chen, Z Tang, L You, CYC Chen - Knowledge-Based Systems, 2024 - Elsevier
Protein–protein interactions play crucial roles in biological systems. The precise
identification of protein interaction sites is essential for advancing our understanding of …
identification of protein interaction sites is essential for advancing our understanding of …
MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction
J Wang, Z Yang, C Chen, G Yao, X Wan… - Briefings in …, 2024 - academic.oup.com
Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and
target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme …
target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme …
Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning
Abstract The 3'untranslated regions (3'UTRs) of messenger RNAs contain many important
cis‐regulatory elements that are under functional and evolutionary constraints. It is …
cis‐regulatory elements that are under functional and evolutionary constraints. It is …
PDNAPred: Interpretable prediction of protein-DNA binding sites based on pre-trained protein language models
L Zhang, T Liu - International Journal of Biological Macromolecules, 2024 - Elsevier
Protein-DNA interactions play critical roles in various biological processes and are essential
for drug discovery. However, traditional experimental methods are labor-intensive and …
for drug discovery. However, traditional experimental methods are labor-intensive and …
[PDF][PDF] Diversification of the Rho transcription termination factor in bacteria
SM Moreira, T Chyou, JT Wade… - Nucleic acids …, 2024 - academic.oup.com
Correct termination of transcription is essential for gene expression. In bacteria, factor-
dependent termination relies on the Rho factor, that classically has three conserved …
dependent termination relies on the Rho factor, that classically has three conserved …
Deep learning in computational biology: Advancements, challenges, and future outlook
S Kumar, D Guruparan, P Aaron, P Telajan… - arxiv preprint arxiv …, 2023 - arxiv.org
Deep learning has become a powerful tool in computational biology, revolutionising the
analysis and interpretation of biological data over time. In our article review, we delve into …
analysis and interpretation of biological data over time. In our article review, we delve into …
Insights into the inner workings of transformer models for protein function prediction
M Wenzel, E Grüner, N Strodthoff - Bioinformatics, 2024 - academic.oup.com
Motivation We explored how explainable artificial intelligence (XAI) can help to shed light
into the inner workings of neural networks for protein function prediction, by extending the …
into the inner workings of neural networks for protein function prediction, by extending the …