Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Prot-ΔΔG: Prediction of protein-protein binding affinity changes upon mutations with pre-training strategies.

Journal of computer-aided molecular design - 25 Jun 2026

Zhou Han, Wang Yuxiang, Shi Xiumin, Ma Yongfeng

Abstract excerpt

Protein-protein interactions are essential for diverse biological activities, but amino acid mutations can disrupt these interactions, leading to dysfunction and disease. Mutation impacts can be quantified via change in protein-protein binding affinity (ΔΔG) before and after mutation. Accurate prediction of ΔΔG is critical for understanding disease mechanisms, guiding drug discovery, and advancing protein...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Amino Acid Sequence
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Mutation

Share to Protein Binding

Share to Proteins

Source

Identifiers

PMID: 42350672
DOI: 10.1007/s10822-026-00867-6

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prot-ΔΔG: Prediction of protein-protein binding affinity changes upon mutations with pre-training strategies.PMID 42350672Read ↗

3D-ΔΔG: A Dual-Channel Prediction Model for Protein-Protein Binding Affinity Changes Following Mutation Based on Protein 3D Structures.Semantic proximity Read ↗

Sequence-aware Prediction of Point Mutation-induced Effects on Protein-Protein Binding Affinity using Deep LearningSemantic proximity Read ↗

MuLAN: Mutation-driven Light Attention Networks for investigating protein-protein interactions from sequencesSemantic proximity Read ↗

ProBASS – a language model with sequence and structural features for predicting the effect of mutations on binding affinitySemantic proximity Read ↗

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Semantic proximity Read ↗

Mutation effect estimation on protein-protein interactions using deep contextualized representation learningSemantic proximity Read ↗

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.Semantic proximity Read ↗

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.Semantic proximity Read ↗

USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions.Semantic proximity Read ↗

Predicting Protein-RNA Binding Affinity Changes via Spatial Coupling-Aware State Space ModelingSemantic proximity Read ↗

MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Semantic proximity Read ↗

DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.