Evidence map›Paper›PMID 38288671›Full record

ArticleProtein engineering, design & selection : PEDS2024

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks.

Leonardo V Castorina, Suleyman Mert Ünal, Kartic Subr, Christopher W Wood

Abstract read
In one paragraph

Article in Protein engineering, design & selection : PEDS, 2024. 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. Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026
    Article
  3. Article
  4. Article
  5. Review
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

4 authors.

Leonardo V CastorinaSchool of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB United Kingdom.ORCID 0000-0002-4247-4883
Suleyman Mert ÜnalSchool of Biological Sciences, University of Edinburgh, Roger Land Building, Edinburgh EH9 3FF, United Kingdom.
Kartic SubrSchool of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB United Kingdom.
Christopher W WoodSchool of Biological Sciences, University of Edinburgh, Roger Land Building, Edinburgh EH9 3FF, United Kingdom.ORCID 0000-0003-1243-3105

Funding

Biotechnology and Biological Sciences Research Council BB/W013320/1Wellcome Trust
6 · The paper itself

Abstract

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.

Indexed as

Neural Networks, ComputerProteinsAmino Acid SequenceSoftwareProteinsAlphaFold 2Convolutional Neural Networks (CNNs)protein sequence designuser interface (UI)

Identifiers

PMID38288671
PMCPMC10939383

What OpenQuestion holds

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LicenceCC BY
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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.