Evidence map›Paper›PMID 35507262›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Turning Failures into Applications: The Problem of Protein ΔΔG Prediction.

Rita Casadio, Castrense Savojardo, Piero Fariselli, Emidio Capriotti, Pier Luigi Martelli

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Stability and expression of SARS-CoV-2 spike-protein mutations.Molecular and cellular biochemistry · 2023
    Article
  3. Article
  4. Review
  5. A Glance into MTHFR Deficiency at a Molecular Level.International journal of molecular sciences · 2021
    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.

Rita CasadioBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy. rita.casadio@unibo.It.
Castrense SavojardoBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Piero FariselliDepartment of Medical Sciences, University of Torino, Turin, Italy.
Emidio CapriottiBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Pier Luigi MartelliBiocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After nearly two decades of research in the field of computational methods based on machine learning and knowledge-based potentials for ΔG and ΔΔG prediction upon variations, we now realize that all the approaches are poorly performing when tested on specific cases and that there is large space for improvement. Why this is so? Is it wrong the underlying assumption that experimental protein thermodynamics in solution reflects the thermodynamics of a single protein? Both machine learning and knowledge-based computational methods are rigorous and we know the solid theory behind. We are now in a critical situation, which suggests that predictions of protein instability upon variation should be considered with care. In the following, we will show how to cope with the problem of understanding which protein positions may be of interest for biotechnological and biomedical purposes. By applying a consensus procedure, we indicate possible strategies for the result interpretation.

Indexed as

Machine LearningProteinsThermodynamicsProteinsBenchmarking ΔΔG predictionCAGI experimentFrataxin instabilityMachine learningProtein instabilityΔG predictionΔΔG prediction

Identifiers

What OpenQuestion holds

Textmetadata
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.