Jun 2026· Journal of Computer-Aided Molecular Design· Vol 40· 0 citations· 37 references
MedicineComputer Science
TL;DR
Prot-ΔΔG is introduced, a purely sequence-based deep learning framework that integrates large-scale pre-trained protein language models with a BiGRU-DBRNN encoder that effectively captures evolutionary and context-dependent patterns without relying on structural inputs.
It is shown that accurate ΔTm prediction does not require structural input features, but can achieve state-of-the-art results with a careful training design for large sequence-based protein language models, and choices such as loss function, pooling strategy, auxiliary supervision, and finetuning regime materially affe...
Daniel Siegismund, Mario Wieser, E. Natali et al.· bioRxiv· 0 citations
DeepPNI is a deep learning regression model that integrates sequence- and structure-based features to estimate mutation-induced changes in binding free energy in protein–nucleic acid complexes, developed using a comprehensive dataset of 1754 mutations spanning protein–DNA and protein–RNA complexes.
Multiple sequence alignment (MSA) Pairformer is presented, a protein language model that builds on AlphaFold2/3's bidirectional refinement between sequence and pairwise residue representations to accurately model the evolution of protein-protein interactions, despite training exclusively on individual chains.
Yo Akiyama, Zhidian Zhang, Olivia Tang et al.· Cell· 3 citations
iSCALE serves as an effective in silico tool for large-scale protein-RNA binding ΔΔG prediction, which pushes the border of understanding in mutation-induced pathological outcomes.
The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible...
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
A pipeline reformulating kinase-substrate modeling as a Bayesian inference problem is presented and it is revealed that the interaction types and distances to the catalytic pocket significantly influence pathogenicity scores.
Jinyuan Hu, Shimian Li, Yue Xue et al.· Journal of Chemical Informat...· 0 citations
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