Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (591 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Procedures by which protein structure and function are changed or created in vitro by altering existing or synthesizing new structural genes that direct the synthesis of proteins with sought-after properties. Such procedures may include the design of molecular models of proteins using computer graphics or other molecular modeling techniques; site-specific mutagenesis (mutagenesis, site-specific) of existing genes; and directed molecular evolution techniques to create new genes.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Engineering, Protein; Genetic Engineering of Proteins; Genetic Engineering, Protein; Engineering, Protein Genetic; Protein Genetic Engineering; Proteins, Genetic Engineering.
Where it fits in the MeSH classification
- Broader topics
- Genetic Engineering
- Narrower topics
- Amino Acid Substitution; Cell Surface Display Techniques; Mutagenesis, Insertional; Mutagenesis, Site-Directed
- See also
- Mutagenesis, Site-Directed; Directed Molecular Evolution
Related concepts
- Molecular Models
- Computer Graphics
- Mutagenesis
- Site-Specific
- Directed Molecular Evolution
Research landscape
PubMed indexes 135,903 records tagged with the MeSH heading “Protein Engineering” (counts retrieved 2026-10-29). 11,544 of them (8.5%) were published from 2020 onward, and the busiest period so far is 2000-2009 with 52,259 records.
Reviews account for 7,644 records (5.6%) and meta-analyses for 317. Randomized controlled trials number 137 (0.1%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 29,538 |
| 2000-2009 | 52,259 |
| 2010-2019 | 42,562 |
| 2020 onward | 11,544 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 7,644 | 5.6% |
| Meta-analyses | 317 | 0.2% |
| Randomized controlled trials | 137 | 0.1% |
Method: PubMed query "Protein Engineering"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in genetics and genomics
Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Atomically accurate de novo design of antibodies with RFdiffusion | 2026 | Research article Design not recorded in structured metadata | 160 | cc by PMID 41193805 · DOI · PMC12727541 |
| Biophysics-based protein language models for protein engineering | 2025 | Research article Design not recorded in structured metadata | 39 | cc by PMID 40935922 · DOI · PMC12446067 |
| Computational enzyme design by catalytic motif scaffolding | 2026 | Research article Design not recorded in structured metadata | 34 | cc by PMID 41339546 · DOI · PMC12727513 |
Atomically accurate de novo design of antibodies with RFdiffusion [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Despite the central role of antibodies in modern medicine, no method currently exists to design novel, epitope-specific antibodies entirely in silico.
- Closing excerpt
- Our approach establishes a framework for the computational design, screening and characterization of fully de novo antibodies with atomic-level precision in both structure and epitope targeting.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (233 words)
Despite the central role of antibodies in modern medicine, no method currently exists to design novel, epitope-specific antibodies entirely in silico. Instead, antibody discovery currently relies on immunization, random library screening or the isolation of antibodies directly from patients1. Here we demonstrate that combining computational protein design using a fine-tuned RFdiffusion2 network with yeast display screening enables the de novo generation of antibody variable heavy chains (VHHs), single-chain variable fragments (scFvs) and full antibodies that bind to user-specified epitopes with atomic-level precision. We experimentally characterize VHH binders to four disease-relevant epitopes. Cryo-electron microscopy confirms the binding pose of designed VHHs targeting influenza haemagglutinin and Clostridium difficile toxin B (TcdB). A high-resolution structure of the influenza-targeting VHH confirms atomic accuracy of the designed complementarity-determining regions (CDRs). Although initial computational designs exhibit modest affinity (tens to hundreds of nanomolar Kd), affinity maturation using OrthoRep3 enables production of single-digit nanomolar binders that maintain the intended epitope selectivity. We further demonstrate the de novo design of scFvs to TcdB and a PHOX2B peptide-MHC complex by combining designed heavy-chain and light-chain CDRs. Cryo-electron microscopy confirms the binding pose for two distinct TcdB scFvs, with high-resolution data for one design verifying the atomically accurate design of the conformations of all six CDR loops. Our approach establishes a framework for the computational design, screening and characterization of fully de novo antibodies with atomic-level precision in both structure and epitope targeting.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41193805 · DOI · PMC12727541
Biophysics-based protein language models for protein engineering [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure and function.
- Closing excerpt
- We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (162 words)
Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose mutational effect transfer learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics. We fine-tune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40935922 · DOI · PMC12446067
Computational enzyme design by catalytic motif scaffolding [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Enzymes find broad use as biocatalysts in industry and medicine owing to their exquisite selectivity, efficiency and mild reaction conditions.
- Closing excerpt
- These findings lay the basis for practical applicability of de novo protein catalysts in synthesis and describe fundamental principles of protein design and enzyme catalysis.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (196 words)
Enzymes find broad use as biocatalysts in industry and medicine owing to their exquisite selectivity, efficiency and mild reaction conditions. Custom-designed enzymes can produce tailor-made biocatalysts with potential applications that extend beyond natural reactions. However, current design methods require testing a large number of designs and mostly produce de novo enzymes with low catalytic activities1-3. As a result, they require costly experimental optimization and high-throughput screening to be industrially viable4,5. Here we present rotamer inverted fragment finder-diffusion (Riff-Diff), a hybrid machine learning and atomistic modelling strategy for scaffolding catalytic arrays in de novo proteins. We highlight the general applicability of Riff-Diff by designing enzymes for two mechanistically distinct chemical transformations, the retro-aldol reaction and the Morita-Baylis-Hillman reaction. We show that in both cases, it is possible to generate catalysts that exhibit activities rivalling those optimized by in vitro evolution, along with exquisite stereoselectivity. High-resolution structures of six of the designs revealed near-atomic active site design precision. The design strategy can, in principle, be applied to any catalytically competent amino acid array. These findings lay the basis for practical applicability of de novo protein catalysts in synthesis and describe fundamental principles of protein design and enzyme catalysis.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41339546 · DOI · PMC12727513
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Immunogenicity risk assessment and mitigation for engineered antibody and protein therapeutics [4]
- Research article Biocatalysis: landmark discoveries and applications in chemical synthesis [5]
- Research article Automated in vivo enzyme engineering accelerates biocatalyst optimization [6]
- Research article Unlocking the potential of enzyme engineering via rational computational design strategies [7]
- Research article Engineered Protein Hydrogels as Biomimetic Cellular Scaffolds [8]
- Research article Yeast surface display technology: Mechanisms, applications, and perspectives [9]
References
- Bennett NR, Watson JL, Ragotte RJ, Borst AJ, See DL, Weidle C, et al.. Atomically accurate de novo design of antibodies with RFdiffusion. . 2026. PMID 41193805 · DOI · PMC12727541
- Gelman S, Johnson B, Freschlin CR, Sharma A, D'Costa S, Peters J, et al.. Biophysics-based protein language models for protein engineering. . 2025. PMID 40935922 · DOI · PMC12446067
- Braun M, Tripp A, Chakatok M, Kaltenbrunner S, Fischer C, Stoll D, et al.. Computational enzyme design by catalytic motif scaffolding. . 2026. PMID 41339546 · DOI · PMC12727513
- Carter PJ, Quarmby V. Immunogenicity risk assessment and mitigation for engineered antibody and protein therapeutics. Nat Rev Drug Discov. 2024. PMID 39424922 · DOI
- O'Connell A, Barry A, Burke AJ, Hutton AE, Bell EL, Green AP, et al.. Biocatalysis: landmark discoveries and applications in chemical synthesis. Chem Soc Rev. 2024. PMID 38407834 · DOI
- Orsi E, Schada von Borzyskowski L, Noack S, Nikel PI, Lindner SN. Automated in vivo enzyme engineering accelerates biocatalyst optimization. Nat Commun. 2024. PMID 38658554 · DOI · PMC11043082
- Zhou L, Tao C, Shen X, Sun X, Wang J, Yuan Q. Unlocking the potential of enzyme engineering via rational computational design strategies. Biotechnol Adv. 2024. PMID 38740355 · DOI
- Liu Y, Gilchrist AE, Heilshorn SC. Engineered Protein Hydrogels as Biomimetic Cellular Scaffolds. Adv Mater. 2024. PMID 39233559 · DOI · PMC11573243
- Li Y, Wang X, Zhou NY, Ding J. Yeast surface display technology: Mechanisms, applications, and perspectives. Biotechnol Adv. 2024. PMID 39117125 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.