Evidence map›Paper›PMID 42582691›Full record

ArticleChemical science2026

A unified predictor of protein stability changes across all mutation types

Hong Tan, Shenggeng Lin, Yi Xiong

Abstract read
In one paragraph

Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hong TanState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University Shanghai China xiongyi@sjtu.edu.cn.ORCID https://orcid.org/0009-0008-8030-6670
Shenggeng LinState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University Shanghai China xiongyi@sjtu.edu.cn.
Yi XiongState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University Shanghai China xiongyi@sjtu.edu.cn.ORCID https://orcid.org/0000-0003-2910-6725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prediction of protein stability change caused by amino acid substitutions or indels (insertions/deletions) is crucial for protein engineering. While current models excel at single-point substitutions, they struggle with multi-point mutations and indels due to simplistic additivity assumptions and the inability to model backbone conformational changes. To address these limitations, we introduce UniStab, an end-to-end framework for predicting stability changes across all mutation types. By leveraging the implicit geometric reasoning of a pre-trained folding model, UniStab effectively captures non-additive epistatic interactions and local backbone rearrangements without the prohibitive cost of explicit structure generation. Evaluated on a comprehensive benchmark, UniStab demonstrates state-of-the-art performance, particularly in the challenging scenarios of multi-point mutations and indels. Beyond predictive accuracy, UniStab provides interpretable structural insights and effectively guides the design of stabilized variants, facilitating its potential utility in rational protein engineering.

Identifiers

PMID42582691
PMCPMC13458449

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.