Evidence map›Paper›PMID 40160862›Full record

ArticleComputational and structural biotechnology journal2025

VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance.

Shiho Ohno, Chika Ogura, Akane Yabuki, Kazuyoshi Itoh, Noriyoshi Manabe, Kiyohiko Angata, Akira Togayachi, Kiyoko Aoki-Kinoshita, Jun-Ichi Furukawa, Kei-Ichiro Inamori and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Neuromuscular Defects in aBiomolecules · 2025
    Article
  2. Review
  3. AlphaFold in protein structure and function prediction.Computational and structural biotechnology journal · 2025
    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

14 authors.

Shiho OhnoDivision of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Miyagi 981-8558, Japan.
Chika OguraDepartment of Science and Engineering for Sustainable Innovation, Faculty of Science and Engineering, Soka University, Japan.
Akane YabukiDepartment of Biosciences, Graduate School of Science and Engineering, Soka University, Japan.
Kazuyoshi ItohGlycan and Life Systems Integration Center (GaLSIC), Soka University, Hachioji 192-8577, Japan.
Noriyoshi ManabeDivision of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Miyagi 981-8558, Japan.
Kiyohiko AngataGlycan and Life Systems Integration Center (GaLSIC), Soka University, Hachioji 192-8577, Japan.
Akira TogayachiGlycan and Life Systems Integration Center (GaLSIC), Soka University, Hachioji 192-8577, Japan.
Kiyoko Aoki-KinoshitaDepartment of Biosciences, Graduate School of Science and Engineering, Soka University, Japan.
Jun-Ichi FurukawaInstitute for Glyco-Core Research (iGCORE), Nagoya University, Nagoya 466-8601, Japan.
Kei-Ichiro InamoriDivision of Glycopathology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Miyagi 981-8558, Japan.
Jin-Ichi InokuchiForefront Research Center, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan.
Tadashi KanameDepartment of Genome Medicine, National Center for Child Health and Development, Tokyo 157-0074, Japan.
Shoko NishiharaDepartment of Biosciences, Graduate School of Science and Engineering, Soka University, Japan.
Yoshiki YamaguchiDivision of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Miyagi 981-8558, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various computational methods have been developed to predict the pathogenicity of missense variants, which is crucial for diagnosing rare diseases. Recently, we introduced VarMeter, a diagnostic tool for predicting variant pathogenicity based on normalized solvent-accessible surface area (nSASA) and mutation energy calculated from AlphaFold 3D models, and validated it on arylsulfatase L. To evaluate the broader applicability of VarMeter and enhance its predictive accuracy, here we analyzed 296 pathogenic and 240 benign variants extracted from the ClinVar database. By comparing structural features including nSASA, mutation energy, and predicted local distance difference test (pLDDT) score, we identified distinct characteristics between pathogenic and benign variants. These features were used to develop VarMeter2, which classifies variants based on Mahalanobis distance. VarMeter2 achieved a prediction accuracy of 82 % for the ClinVar dataset, a marked improvement over the original VarMeter (74 %), and 84 % for published missense variants of

Indexed as

Missense variantMutation energyN-sulphoglucosamine sulphohydrolasePathogenicitypLDDTSolvent accessible surface area

Identifiers

PMID40160862
PMCPMC11952791

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Registered trials

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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.