ArticleiScience2026
An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) are fundamental to vast biological processes. Accurate prediction of mutation-induced changes in PPIs is crucial for protein engineering and drug discovery. Most existing PPI prediction approaches rely on refined physicochemical features or computationally intensive structural sampling. Here, we present delta-G message-passing-neural-network (DGMPNN), a message-passing neural-network framework for predicting the effects of mutations on PPIs. DGMPNN is pre-trained on an inverse protein folding (IPF) task and employs an adaptive residue-level attention pooling mechanism to enable effective transfer learning for predicting mutation-induced changes in protein binding affinity. The model achieves state-of-the-art performance on multiple benchmark datasets and deep mutational scanning (DMS) datasets. Experimental validation using computationally selected multi-site variants of the SARS-CoV-2 RBD-hACE2 complex showed strong agreement between predicted and experimentally measured binding affinities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.