Evidence map›Paper›PMID 36582438›Full record

ArticleComputational and structural biotechnology journal2023

Prediction of protein stability changes upon single-point variant using 3D structure profile.

Jianting Gong, Juexin Wang, Xizeng Zong, Zhiqiang Ma, Dong Xu

Open access · goldAbstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
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

12 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Epileptic EncephalopathyBiomolecules · 2023
    Article
  12. Article
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

5 authors at 4 institutions in 2 countries.

Jianting GongSchool of Information Science and Technology, and Institution of Computational Biology, Northeast Normal University, Changchun 130117, China.
Juexin WangDepartment of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA.
Xizeng ZongSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130117, China.
Zhiqiang MaSchool of Information Science and Technology, and Institution of Computational Biology, Northeast Normal University, Changchun 130117, China.
Dong XuDepartment of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
Northeast Normal University · CNChangchun University of TechnologyIndiana University – Purdue University Indianapolis · USUniversity of Missouri · US

Funding

Multi-view self-supervised deep learning for biological sequences and beyondR35GM126985 · NIGMS · UNIVERSITY OF SOUTH FLORIDA · PI DONG XU · 2018 to 2026
$3.8M
NIGMS NIH HHS R35 GM126985
6 · The paper itself

Abstract

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

Identifiers

PMID36582438
PMCPMC9791599
OpenAlexW4313339887

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.