A perspective on the prospective use of AI in protein structure prediction
R Versini, S Sritharan, B Aykac Fas… - Journal of Chemical …, 2023 - ACS Publications
AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as
highly reliable and effective methods for predicting protein structures. This article explores …
highly reliable and effective methods for predicting protein structures. This article explores …
Recent advances and challenges in protein structure prediction
Artificial intelligence has made significant advances in the field of protein structure prediction
in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated …
in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated …
Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning
Accurately capturing domain-domain interactions is key to understanding protein function
and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on …
and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on …
UniTmp: unified resources for transmembrane proteins
Abstract The UNIfied database of TransMembrane Proteins (UniTmp) is a comprehensive
and freely accessible resource of transmembrane protein structural information at different …
and freely accessible resource of transmembrane protein structural information at different …
PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants
Abstract N 6-methyladenosine (m6A), which is the mostly prevalent modification in
eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism …
eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism …
Endowing protein language models with structural knowledge
Understanding the relationships between protein sequence, structure and function is a long-
standing biological challenge with manifold implications from drug design to our …
standing biological challenge with manifold implications from drug design to our …
[HTML][HTML] Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors into Peptide Identification
Rescoring of peptide spectrum matches originating from database search engines enabled
by peptide property predictors is exceeding the performance of peptide identification from …
by peptide property predictors is exceeding the performance of peptide identification from …
Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes
M Zhang, Y Deng, Q Zhou, J Gao, D Zhang… - … Science: Processes & …, 2025 - pubs.rsc.org
The nano-self-assembly of natural organic matter (NOM) profoundly influences the
occurrence and fate of NOM and pollutants in large-scale complex environments. Machine …
occurrence and fate of NOM and pollutants in large-scale complex environments. Machine …
Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches
Protein–protein interactions play crucial roles in many biological processes. Traditionally,
protein complex structures are normally built by protein–protein docking. With the rapid …
protein complex structures are normally built by protein–protein docking. With the rapid …
NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide–human leukocyte antigen …
D Jiang, B Xi, W Tan, Z Chen, J Wei, M Hu, X Lu… - …, 2024 - academic.oup.com
Motivation Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor
immune responses when presented to autologous T cells by human leukocyte antigen …
immune responses when presented to autologous T cells by human leukocyte antigen …