Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Volume 42 Issue 2, February 2024

Focus on protein engineering

Artistic impression of the three data types key to machine learning for functional protein design: structure, sequence and labels. The structure of carbonic anhydrase is shown in front of a background composed of amino acid letters representing sequence data. The hue overlay represents a fitness landscape that experimentally acquired labels help to map.

See Notin et al.

Image: Nathan Rollins, Pascal Notin. Cover Design: Erin Dewalt.

Volume 42 Issue 2

Subscribe

Editorial

  • New methods for protein design speed up workflows, but issues of training data availability and method optimization remain.

    Editorial

    Advertisement

Top of page ⤴

News

Top of page ⤴

Correspondence

Top of page ⤴

Q&A

  • Following the publication of AlphaFold2 and RoseTTAFold in 2021, the field of protein structure prediction has moved quickly to incorporate these advances into protein engineering.

    • Anne Doerr
    Q&A
Top of page ⤴

Features

Top of page ⤴

Research Highlights

Top of page ⤴

News & Views

Top of page ⤴

Research Briefings

  • Most microbiome studies measure relative abundance of the various microorganisms in a sample. This study demonstrates the feasibility of large-scale measurements of absolute microbial concentrations. Moreover, it demonstrates how sample handling and storage methods can alter microbial measurements, potentially introducing bias in conclusions drawn about microbiome–host relationships.

    Research Briefing
Top of page ⤴

Primer

  • Models like ChatGPT and DALL-E2 generate text and images in response to a text prompt. Despite different data and goals, how can generative models be useful for protein engineering?

    • Chloe Hsu
    • Clara Fannjiang
    • Jennifer Listgarten
    Primer

  • Protein language models learn from diverse sequences spanning the evolutionary tree and have proven to be powerful tools for sequence design, variant effect prediction and structure prediction. What are the foundations of protein language models, and how are they applied in protein engineering?

    • Jeffrey A. Ruffolo
    • Ali Madani
    Primer
Top of page ⤴

Research

Top of page ⤴

Careers & Recruitment

  • Career Feature

    • Stanford’s SPARK program has established a process that advances academic discoveries to industry partnerships or investigator-initiated clinical trials and provides training that enhances career opportunities for its graduates.

      • Jeewon Sylvia Kim
      • Stephen Kargotich
      • Daria Mochly-Rosen
      Career Feature

  • People

    • Recent moves of note in and around the biotech and pharma industries.

      People
Top of page ⤴

Search

Advanced search

Quick links