Introduction
AlphaFold 2 changed how scientists predict protein structures. But the development didn't end there. AlphaFold 3 extended the system to cover interactions with DNA, RNA, and small molecules, even though it was not fully released to the public. Then came AlphaFold 4, which offers more accurate predictions, better performance with protein complexes, and easier integration with other tools.
In this article, we will briefly review what’s new in AlphaFold 4, how it evolved from earlier versions, and how it is being used by many groups in biology and biomedical research.
From AlphaFold 2 to AlphaFold 4: What Has Evolved?
1. A Short Mention of AlphaFold 3: AlphaFold 3 was introduced in 2023. It was not fully open-source, but it demonstrated the possibility of modeling interactions between proteins and other molecules like RNA or ligands. AlphaFold 4 continues this direction and improves usability and prediction quality, with more community-accessible features.
2. Complex Prediction Becomes More Reliable: Compared to AlphaFold 2, the new version is better at handling protein complexes. Both homo-oligomers and hetero-oligomers show improved accuracy. The model also provides clearer confidence scores, helping researchers decide which parts of the prediction are trustworthy.
3. Better Loop and Disorder Region Handling: Disordered parts of proteins and long loops used to be difficult to predict. AlphaFold 4 has improved performance in these regions, likely due to re-training and architecture tuning. These improvements are especially useful when modeling flexible regions.
4. More Flexible and Easier to Use in Pipelines: The new AlphaFold 4 is more modular and supports APIs and custom workflows. JSON-based input and output formats make it easier to use in local pipelines, cloud servers, or high-throughput jobs.
5. Less Dependent on Templates: In the past, if similar protein structures were not available, prediction quality was reduced. Now AlphaFold 4 can still give reliable models even without good templates. This is helpful for novel or engineered proteins.
6. Improved Confidence Metrics: The new model provides refined pLDDT and PAE scores, and introduces some new metrics. Together, they allow better judgment of prediction quality, especially for complex or novel sequences.
Five Ways to Use AlphaFold (2025 Edition)
| Method | Use Case | Cost | Customization | Accuracy |
|---|---|---|---|---|
| AlphaFold DB | Look up known UniProt entries | Free | No | High |
| Homology Search | Find similar protein structures | Free | No | Medium |
| AlphaFold Colab | Predict small proteins | Free / $13.99 | Limited | Moderate |
| ColabFold | Predict faster, support complex | Free | Limited | Medium-High |
| Local Install | Research use, full flexibility | High | Full | Highest |
Some labs also developed pipelines for batch prediction of whole families or environmental samples. These are useful for high-throughput or annotation work.
Beyond Prediction: Real Use Cases
It is also common to apply post-processing tools such as Rosetta (for minimization), GROMACS (for dynamics), or visualization in PyMOL to refine AlphaFold output.
Known Limitations and Common Fixes
To fix these, many researchers use:
Looking Around: Other Tools Worth Mentioning
Final Words
AlphaFold 4 is not just a tool. It is part of a new way of thinking in molecular biology. More researchers are including it in their daily work, and more applications are being developed.
When used together with other structural or bioinformatics tools, AlphaFold can help answer many types of biological questions. As the community continues to test and adapt it, the model will become more useful and reliable. We will continue to watch how this field grows and update this page from time to time.
About the Author: Justin T. Li received his Ph.D. in Neurobiology from the University of Wisconsin-Madison in 2000 and a M.S. in Computer Science from the University of Houston in 2001. Between 2004 and 2009, he served as an Assistant Professor in the Medical School at the University of Minnesota Twin Cities campus. From 2009 to 2013, he was the Chief Bioinformatics Officer at LC Sciences in Houston. In June 2013, Justin joined AccuraScience as a Lead Bioinformatician. He has published over 50 research articles in bioinformatics, computational biology, and related fields. More information about Justin can be found at https://www.accurascience.com/our_team.html.
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