ArticleMechanobiology in medicine2024
From sequence to mechanobiology? Promises and challenges for AlphaFold 3.
Article in Mechanobiology in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Potential for the Terminal SKP1 Glycosyltransferase to Exert Non-Enzymatic Control of SKP1 inbioRxiv : the preprint server for biology · 2026Article
- Assessing Computational Strategies for the Evaluation of Antibody Binding Affinities.Journal of chemical theory and computation · 2025Article
- AlphaFold3: An Overview of Applications and Performance Insights.International journal of molecular sciences · 2025Review
- Challenges in simulating whole virus particles and how to fix them with the SIRAH force field.Biophysical reviews · 2025Review
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Predicting the impact of missense mutations on an unresolved protein's stability, structure, and function: A case study of Alzheimer's disease-associated TREM2 R47H variant.Computational and structural biotechnology journal · 2025Article
- Harnessing mechanobiology to enhance cell therapy.Mechanobiology in medicine · 2024Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Interactions between macromolecules orchestrate many mechanobiology processes. However, progress in the field has often been hindered by the monetary and time costs of obtaining reliable experimental structures. In recent years, deep-learning methods, such as AlphaFold, have democratized access to high-quality predictions of the structural properties of proteins and other macromolecules. The newest implementation, AlphaFold 3, significantly expands the applications of its predecessor, AlphaFold 2, by incorporating reliable models for small molecules and nucleic acids and enhancing the prediction of macromolecular complexes. While several limitations still exist, the continuous improvement of machine learning methods like AlphaFold is producing a significant revolution in the field. The possibility of easily accessing structural predictions of biomolecular complexes may create substantial impacts in mechanobiology. Indeed, structural studies are at the basis of several applications in the field, such as drug discovery for mechanosensing proteins, development of mechanotherapy, understanding the mechanotransduction mechanisms and the mechanistic basis of diseases, or designing biomaterials for tissue engineering.
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Registered trials
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.