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: 4 complete abstracts (655 words), 10 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
The characteristic 3-dimensional shape of a protein, including the secondary, supersecondary (motifs), tertiary (domains) and quaternary structure of the peptide chain. protein structure, quaternary describes the conformation assumed by multimeric proteins (aggregates of more than one polypeptide chain).
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: Conformation, Protein; Conformations, Protein; Protein Conformations.
Where it fits in the MeSH classification
- Broader topics
- Molecular Conformation
- Narrower topics
- Protein Structural Elements; Protein Structure, Quaternary; Protein Structure, Secondary; Protein Structure, Tertiary; Structural Homology, Protein
Related concepts
- Protein Structure
- Quaternary
Research landscape
PubMed indexes 428,913 records tagged with the MeSH heading “Protein Conformation” (counts retrieved 2026-10-29). 44,925 of them (10.5%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 149,816 records.
Reviews account for 37,469 records (8.7%) and meta-analyses for 71. Randomized controlled trials number 63 (0.0%) 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 | 87,280 |
| 2000-2009 | 146,892 |
| 2010-2019 | 149,816 |
| 2020 onward | 44,925 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 37,469 | 8.7% |
| Meta-analyses | 71 | 0.0% |
| Randomized controlled trials | 63 | 0.0% |
Method: PubMed query "Protein Conformation"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in molecular biology
Molecular biology establishes mechanisms through controlled experiments, often in simplified systems such as purified proteins, cultured cells or model organisms. Such work can show what is possible in a defined system, but whether a pathway matters in a whole organism or in human disease needs further evidence. Check the experimental system, whether findings were reproduced by independent groups, and whether the authors tested the mechanism directly or inferred it from correlation.
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 |
|---|---|---|---|---|
| Exploring structural diversity across the protein universe with The Encyclopedia of Domains | 2024 | Research article Design not recorded in structured metadata | 128 | cc by PMID 39480926 · DOI · PMC7618865 |
| Site-saturation mutagenesis of 500 human protein domains | 2025 | Research article Design not recorded in structured metadata | 125 | cc by PMID 39779847 · DOI · PMC11754108 |
| actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions | 2025 | Research article Design not recorded in structured metadata | 67 | cc by PMID 40080667 · DOI · PMC11925850 |
| A resource of RNA-binding protein motifs across eukaryotes reveals evolutionary dynamics and gene-regulatory function | 2026 | Research article Design not recorded in structured metadata | 24 | cc by PMID 40715457 · DOI · PMC12990809 |
Exploring structural diversity across the protein universe with The Encyclopedia of Domains [1]
Abstract reading summary — source excerpts
- Opening excerpt
- The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts.
- Closing excerpt
- We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.
- 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 (130 words)
The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enable many functional and evolutionary analyses but has remained challenging because of the sheer scale of the data. Using deep learning methods, we have detected and classified every domain in the AFDB, producing The Encyclopedia of Domains. We detected nearly 365 million domains, over 100 million more than can be found by sequence methods, covering more than 1 million taxa. Reassuringly, 77% of the nonredundant domains are similar to known superfamilies, greatly expanding representation of their domain space. We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39480926 · DOI · PMC7618865
Site-saturation mutagenesis of 500 human protein domains [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Missense variants that change the amino acid sequences of proteins cause one-third of human genetic diseases1.
- Closing excerpt
- Our data demonstrate the feasibility of assaying human protein variants at scale and provides a large consistent reference dataset for clinical variant interpretation and training and benchmarking of computational methods.
- 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 (175 words)
Missense variants that change the amino acid sequences of proteins cause one-third of human genetic diseases1. Tens of millions of missense variants exist in the current human population, and the vast majority of these have unknown functional consequences. Here we present a large-scale experimental analysis of human missense variants across many different proteins. Using DNA synthesis and cellular selection experiments we quantify the effect of more than 500,000 variants on the abundance of more than 500 human protein domains. This dataset reveals that 60% of pathogenic missense variants reduce protein stability. The contribution of stability to protein fitness varies across proteins and diseases and is particularly important in recessive disorders. We combine stability measurements with protein language models to annotate functional sites across proteins. Mutational effects on stability are largely conserved in homologous domains, enabling accurate stability prediction across entire protein families using energy models. Our data demonstrate the feasibility of assaying human protein variants at scale and provides a large consistent reference dataset for clinical variant interpretation and training and benchmarking of computational methods.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39779847 · DOI · PMC11754108
actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions [3]
Abstract reading summary — source excerpts
- Opening excerpt
- SummaryOne of the main advantages of deep learning models of protein structure, such as Alphafold2, is their ability to accurately estimate the confidence of a generated structural model, which allows us to focus on highly confident predictions.
- Closing excerpt
- com/sokrypton/ColabFold) repository, installable both locally or usable with Colab notebook.
- 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 (191 words)
SummaryOne of the main advantages of deep learning models of protein structure, such as Alphafold2, is their ability to accurately estimate the confidence of a generated structural model, which allows us to focus on highly confident predictions. The ipTM score provides a confidence estimate of interchain contacts in protein-protein interactions. However, interactions, in particular motif-mediated interactions, often also contain regions that remain flexible upon binding. These noninteracting flanking regions are assigned low confidence values and will affect ipTM, as it considers all interchain residue-residue pairs, and two models of the same motif-domain interaction, but differing in the length of their flanking regions, would be assigned very different values. Here, we propose actual interface pTM (actifpTM), a modified ipTM measure, that focuses on the residues participating in the interaction, resulting in a more robust measure of interaction confidence. Besides, actifpTM is calculated both for the full complex as well as for each pair of chains, making it well-suited for evaluating multi-chain complexes with a particularly critical binding interface, such as antibody-antigen interactions.Availability and implementationThe method is available as part of the ColabFold (https://github.com/sokrypton/ColabFold) repository, installable both locally or usable with Colab notebook.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40080667 · DOI · PMC11925850
A resource of RNA-binding protein motifs across eukaryotes reveals evolutionary dynamics and gene-regulatory function [4]
Abstract reading summary — source excerpts
- Opening excerpt
- RNA-binding proteins (RBPs) are key regulators of gene expression; however, their RNA-binding specificities, that is, motifs, have not been comprehensively determined.
- Closing excerpt
- We demonstrate the utility of EuPRI for inferring post-transcriptional function and evolutionary relationships by identifying rapid, recent evolution of post-transcriptional regulatory networks in worms and plants, in contrast to the vertebrate RNA motif set, which has remained relatively stable after a large expansion between the metazoan and vertebrate ancestors.
- 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 (159 words)
RNA-binding proteins (RBPs) are key regulators of gene expression; however, their RNA-binding specificities, that is, motifs, have not been comprehensively determined. Here we introduce Eukaryotic Protein-RNA Interactions (EuPRI), a freely available resource of RNA motifs for 34,746 RBPs from 690 eukaryotes. EuPRI includes in vitro binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of predicted motifs. We predict these motifs with an algorithm, Joint Protein-Ligand Embedding, which can detect distant homology relationships and map specificity-determining peptides. EuPRI quadruples the number of available RBP motifs, expanding the motif repertoire across all major eukaryotic clades and assigning motifs to the majority of human RBPs. We demonstrate the utility of EuPRI for inferring post-transcriptional function and evolutionary relationships by identifying rapid, recent evolution of post-transcriptional regulatory networks in worms and plants, in contrast to the vertebrate RNA motif set, which has remained relatively stable after a large expansion between the metazoan and vertebrate ancestors.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40715457 · DOI · PMC12990809
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 Bromodomain biology and drug discovery [5]
- Research article Structure- and mechanism-guided design of single fluorescent protein-based biosensors [6]
- Research article Adipocyte lipolysis: from molecular mechanisms of regulation to disease and therapeutics [7]
- Research article Protein-Nanoparticle Interaction: Corona Formation and Conformational Changes in Proteins on Nanoparticles [8]
- Research article Structure and Aggregation Mechanisms in Amyloids [9]
- Research article Lipoprotein(a) beyond the kringle IV repeat polymorphism: The complexity of genetic variation in the LPA gene [10]
References
- Lau AM, Bordin N, Kandathil SM, Sillitoe I, Waman VP, Wells J, et al.. Exploring structural diversity across the protein universe with The Encyclopedia of Domains. . 2024. PMID 39480926 · DOI · PMC7618865
- Beltran A, Jiang X, Shen Y, Lehner B. Site-saturation mutagenesis of 500 human protein domains. . 2025. PMID 39779847 · DOI · PMC11754108
- Varga JK, Ovchinnikov S, Schueler-Furman O. actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions. . 2025. PMID 40080667 · DOI · PMC11925850
- Sasse A, Ray D, Laverty KU, Tam CL, Albu M, Zheng H, et al.. A resource of RNA-binding protein motifs across eukaryotes reveals evolutionary dynamics and gene-regulatory function. . 2026. PMID 40715457 · DOI · PMC12990809
- Zaware N, Zhou MM. Bromodomain biology and drug discovery. Nat Struct Mol Biol. 2019. PMID 31582847 · DOI · PMC6984398
- Nasu Y, Shen Y, Kramer L, Campbell RE. Structure- and mechanism-guided design of single fluorescent protein-based biosensors. Nat Chem Biol. 2021. PMID 33558715 · DOI
- Yang A, Mottillo EP. Adipocyte lipolysis: from molecular mechanisms of regulation to disease and therapeutics. Biochem J. 2020. PMID 32168372 · DOI · PMC7187988
- Park SJ. Protein-Nanoparticle Interaction: Corona Formation and Conformational Changes in Proteins on Nanoparticles. Int J Nanomedicine. 2020. PMID 32821101 · DOI · PMC7418457
- Almeida ZL, Brito RMM. Structure and Aggregation Mechanisms in Amyloids. Molecules. 2020. PMID 32155822 · DOI · PMC7179426
- Coassin S, Kronenberg F. Lipoprotein(a) beyond the kringle IV repeat polymorphism: The complexity of genetic variation in the LPA gene. Atherosclerosis. 2022. PMID 35606073 · DOI · PMC7613587
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.