---
title: "Protein Structure Prediction"
slug: "protein-structure"
discipline: "Biochemistry / AI"
description: "Computational protein biology. AlphaFold, protein design, structure prediction, drug discovery through molecular simulation, and de novo protein engineering."
icon: "🔬"
url: "https://science-database.com/technology/protein-structure"
api: "https://science-database.com/api/v1/technology/protein-structure"
llms_txt: "https://science-database.com/technology/protein-structure/llms.txt"
articles_indexed: 27
last_updated: "2026-09-08T05:37:12.720Z"
search_terms:
  - "protein structure prediction AlphaFold"
  - "de novo protein design"
  - "molecular dynamics drug discovery"
source: "science-database.com"
license: "metadata CC0, abstracts belong to respective publishers"
---

# Protein Structure Prediction

Computational protein biology. AlphaFold, protein design, structure prediction, drug discovery through molecular simulation, and de novo protein engineering.

**Discipline:** Biochemistry / AI  
**Indexed Papers:** 27  
**Last Updated:** 2026-09-08

## Top Publications

Ranked by citation impact across Semantic Scholar, OpenAlex & arXiv.

### Highly accurate protein structure prediction with AlphaFold

- **Authors:** J. Jumper, Richard Evans, A. Pritzel, Tim Green, Michael Figurnov, O. Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, A. Cowie, B. Romera-Paredes, Stanislav Nikolov, Rishub Jain, J. Adler, T. Back, Stig Petersen, D. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, O. Vinyals, A. Senior, K. Kavukcuoglu, Pushmeet Kohli, D. Hassabis
- **Journal:** Nature
- **Published:** 2021
- **DOI:** [10.1038/s41586-021-03819-2](https://doi.org/10.1038/s41586-021-03819-2)
- **Citations:** 38,861
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://www.nature.com/articles/s41586-021-03819-2.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-dc32a984b651256a8ec282be52310e6bd33d9815/llms.txt)

> This work validated an entirely redesigned version of the neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods.

### Accurate structure prediction of biomolecular interactions with AlphaFold 3

- **Authors:** Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, A. Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, Sebastian Bodenstein, David A Evans, Chia-Chun Hung, Michael O’Neill, D. Reiman, Kathryn Tunyasuvunakool, Zachary Wu, Akvile Zemgulyte, Eirini Arvaniti, Charles Beattie, Ottavia Bertolli, Alex Bridgland, Alexey Cherepanov, Miles Congreve, A. Cowen-Rivers, Andrew Cowie, Michael Figurnov, Fabian B Fuchs, Hannah Gladman, Rishub Jain, Yousuf A. Khan, Caroline M R Low, Kuba Perlin, Anna Potapenko, Pascal Savy, Sukhdeep Singh, A. Stecula, Ashok Thillaisundaram, Catherine Tong, Sergei Yakneen, Ellen D. Zhong, Michal Zielinski, Augustin Žídek, V. Bapst, Pushmeet Kohli, Max Jaderberg, D. Hassabis, J. Jumper
- **Journal:** Nature
- **Published:** 2024
- **DOI:** [10.1038/s41586-024-07487-w](https://doi.org/10.1038/s41586-024-07487-w)
- **Citations:** 13,574
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.1038/s41586-024-07487-w)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-7572ba7f604ef95d7acdd657ebac458106bd35df/llms.txt)

> The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein–ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein–nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody–antigen prediction accuracy.

### ColabFold: making protein folding accessible to all

- **Authors:** Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, Martin Steinegger
- **Journal:** Nature Methods
- **Published:** 2022-05-30
- **DOI:** [10.1038/s41592-022-01488-1](https://doi.org/10.1038/s41592-022-01488-1)
- **Citations:** 10,045
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://www.nature.com/articles/s41592-022-01488-1.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4281790889/llms.txt)

> ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessib...

### The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest

- **Authors:** Damian Szklarczyk, Rebecca Kirsch, Mikaela Koutrouli, Katerina Nastou, Farrokh Mehryary, Radja Hachilif, Annika L. Gable, Tao Fang, Nadezhda T. Doncheva, Sampo Pyysalo, Peer Bork, Lars Juhl Jensen, Christian von Mering
- **Journal:** Nucleic Acids Research
- **Published:** 2022-10-19
- **DOI:** [10.1093/nar/gkac1000](https://doi.org/10.1093/nar/gkac1000)
- **Citations:** 9,568
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://academic.oup.com/nar/article-pdf/51/D1/D638/48440966/gkac1000.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4308834893/llms.txt)

> Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically ...

### AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

- **Authors:** Mihály Váradi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yu Yuan, Oana Stroe, Gemma Wood, Agata Laydon, Augustin Žídek, Tim Green, Kathryn Tunyasuvunakool, Stig Petersen, John Jumper, Ellen Clancy, Richard Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowie, Nicole Hobbs, Pushmeet Kohli, Gerard J. Kleywegt, Ewan Birney, Demis Hassabis, Sameer Velankar
- **Journal:** Nucleic Acids Research
- **Published:** 2021-10-19
- **DOI:** [10.1093/nar/gkab1061](https://doi.org/10.1093/nar/gkab1061)
- **Citations:** 8,528
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.1093/nar/gkab1061)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3211795435/llms.txt)

> The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of ...

### Accurate prediction of protein structures and interactions using a three-track neural network

- **Authors:** Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, J.H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov‐Keller, Manoj Kumar Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher García, Nick V. Grishin, Paul D. Adams, Randy J. Read, David Baker
- **Journal:** Science
- **Published:** 2021-07-15
- **DOI:** [10.1126/science.abj8754](https://doi.org/10.1126/science.abj8754)
- **Citations:** 5,913
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.1126/science.abj8754)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3186179742/llms.txt)

> DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transf...

### Protein complex prediction with AlphaFold-Multimer

- **Authors:** Richard Evans, M. E. O’Neill, Alexander Pritzel, Н. В. Антропова, Andrew Senior, Tim Green, Augustin Žídek, Russ Bates, Sam Blackwell, Jason Yim, Olaf Ronneberger, Sebastian W. Bodenstein, Michał Zieliński, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, Rishub Jain, Ellen Clancy, Pushmeet Kohli, John Jumper, Demis Hassabis
- **Journal:** bioRxiv (Cold Spring Harbor Laboratory)
- **Published:** 2021-10-04
- **DOI:** [10.1101/2021.10.04.463034](https://doi.org/10.1101/2021.10.04.463034)
- **Citations:** 4,167
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3202105508/llms.txt)

> While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increas...

### Improved protein structure prediction using potentials from deep learning

- **Authors:** Andrew Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis
- **Journal:** Nature
- **Published:** 2020-01-15
- **DOI:** [10.1038/s41586-019-1923-7](https://doi.org/10.1038/s41586-019-1923-7)
- **Citations:** 3,601
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://discovery.ucl.ac.uk/10089234/1/343019_3_art_0_py4t4l_convrt.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W2999044305/llms.txt)

### Highly accurate protein structure prediction for the human proteome

- **Authors:** Kathryn Tunyasuvunakool, J. Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, A. Cowie, Clemens Meyer, Agata Laydon, S. Velankar, G. Kleywegt, A. Bateman, R. Evans, A. Pritzel, Michael Figurnov, O. Ronneberger, Russ Bates, Simon A A Kohl, Anna Potapenko, A. Ballard, B. Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, D. Reiman, Stig Petersen, A. Senior, K. Kavukcuoglu, E. Birney, Pushmeet Kohli, J. Jumper, D. Hassabis
- **Journal:** Nature
- **Published:** 2021
- **DOI:** [10.1038/s41586-021-03828-1](https://doi.org/10.1038/s41586-021-03828-1)
- **Citations:** 2,584
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://www.nature.com/articles/s41586-021-03828-1.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-1fb3ad9969245795f268636eff9a145337144718/llms.txt)

> The state-of-the-art machine learning method, AlphaFold, is applied at a scale that covers almost the entire human proteome, and the resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence.

### AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

- **Authors:** Mihály Váradi, Damian Bertoni, Paulyna Magaña, Urmila Paramval, Ivanna Pidruchna, Malarvizhi Radhakrishnan, Maxim Tsenkov, Sreenath Nair, Milot Mirdita, Jingi Yeo, Oleg Kovalevskiy, Kathryn Tunyasuvunakool, Agata Laydon, Augustin Žídek, Hamish Tomlinson, Dhavanthi Hariharan, Josh Abrahamson, Tim Green, John Jumper, Ewan Birney, Martin Steinegger, Demis Hassabis, Sameer Velankar
- **Journal:** Nucleic Acids Research
- **Published:** 2023-10-18
- **DOI:** [10.1093/nar/gkad1011](https://doi.org/10.1093/nar/gkad1011)
- **Citations:** 2,096
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkad1011/52777135/gkad1011.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4388464011/llms.txt)

> The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated ...

### Robust deep learning–based protein sequence design using ProteinMPNN

- **Authors:** Justas Dauparas, Ivan Anishchenko, Nathaniel R. Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville P. Bethel, Philip J. Y. Leung, Timothy F. Huddy, Samuel J. Pellock, Doug Tischer, F. Chan, Brian Koepnick, Hannah Nguyen, Alex Kang, Banumathi Sankaran, Asim K. Bera, Neil P. King, David Baker
- **Journal:** Science
- **Published:** 2022-09-15
- **DOI:** [10.1126/science.add2187](https://doi.org/10.1126/science.add2187)
- **Citations:** 2,057
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://research.wur.nl/en/publications/robust-deep-learning-based-protein-sequence-design-using-proteinm)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4296032638/llms.txt)

> Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, Prot...

### AlphaFold and Implications for Intrinsically Disordered Proteins

- **Authors:** Kiersten M. Ruff, Rohit V. Pappu
- **Journal:** Journal of Molecular Biology
- **Published:** 2021-08-18
- **DOI:** [10.1016/j.jmb.2021.167208](https://doi.org/10.1016/j.jmb.2021.167208)
- **Citations:** 686
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://www.sciencedirect.com/science/article/pii/S0022283621004411/pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3195375135/llms.txt)

> Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structural bioinformatics was the structural annotation of over 98% of protein sequences in the human prote...

### Protein structure predictions to atomic accuracy with AlphaFold

- **Authors:** John Jumper, Demis Hassabis
- **Journal:** Nature Methods
- **Published:** 2022-01-01
- **DOI:** [10.1038/s41592-021-01362-6](https://doi.org/10.1038/s41592-021-01362-6)
- **Citations:** 294
- **Source:** OpenAlex
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4206563428/llms.txt)

### De novo protein design by inversion of the AlphaFold structure prediction network

- **Authors:** Casper A. Goverde, Benedict Wolf, Hamed Khakzad, Stéphane Rosset, Bruno E. Correia
- **Journal:** Protein Science
- **Published:** 2023-05-11
- **DOI:** [10.1002/pro.4653](https://doi.org/10.1002/pro.4653)
- **Citations:** 97
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pro.4653)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4376131109/llms.txt)

> De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. Alph...

### Protein structure prediction beyond AlphaFold

- **Authors:** Guo‐Wei Wei
- **Journal:** Nature Machine Intelligence
- **Published:** 2019-08-09
- **DOI:** [10.1038/s42256-019-0086-4](https://doi.org/10.1038/s42256-019-0086-4)
- **Citations:** 84
- **Source:** OpenAlex
- **Access:** Open Access
- **PDF:** [Download](https://pmc.ncbi.nlm.nih.gov/articles/PMC10956386/pdf/nihms-1972073.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W2967175367/llms.txt)

### Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction

- **Authors:** Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter
- **Journal:** Current opinion in structural biology
- **Published:** 2025
- **DOI:** [10.1016/j.sbi.2024.102973](https://doi.org/10.1016/j.sbi.2024.102973)
- **Citations:** 42
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.1016/j.sbi.2024.102973)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-78c0348f9dd5117b8494dceca73d7a7ad9252cde/llms.txt)

> Three blind spots that alternative conformations reveal about AF-based protein structure prediction are reviewed and suggested approaches to predict alternative folds more reliably are suggested.

### Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives

- **Authors:** Yuktika Malhotra, Jerry John, Deepika Yadav, D. Sharma, Vanshika, Kamal Rawal, Vaibhav Mishra, Navaneet Chaturvedi
- **Journal:** Computers in biology and medicine
- **Published:** 2025
- **DOI:** [10.1016/j.compbiomed.2025.109842](https://doi.org/10.1016/j.compbiomed.2025.109842)
- **Citations:** 33
- **Source:** Semantic Scholar
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-956a714e0cd9b6ddf7c0bb35d50c3dc8a4c6e6b6/llms.txt)

> This review provides a comprehensive analysis of AlphaFold and its derivatives (AF2 and AF3) in protein structure prediction, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques.

### Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.

- **Authors:** Jue Wang, Joseph L. Watson, S. Lisanza
- **Journal:** Cold Spring Harbor perspectives in biology
- **Published:** 2024
- **DOI:** [10.1101/cshperspect.a041472](https://doi.org/10.1101/cshperspect.a041472)
- **Citations:** 32
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](http://cshperspectives.cshlp.org/content/early/2024/03/01/cshperspect.a041472.full.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-327990f4276d1d533214c8d2673a1ea97968353a/llms.txt)

> This work reviews recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion, and suggests that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.

### Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction

- **Authors:** Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter
- **Journal:** ArXiv
- **Published:** 2024
- **Citations:** 31
- **Source:** Semantic Scholar
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-a5f27b945ba3236d1b2d3896f8a47deb793551b4/llms.txt)

> Three blind spots that alternative conformations reveal about AF-based protein structure prediction are reviewed and suggested approaches to predict alternative folds more reliably are suggested.

### Emerging frontiers in protein structure prediction following the AlphaFold revolution

- **Authors:** M. Rennie, Michael R. Oliver
- **Journal:** Journal of the Royal Society Interface
- **Published:** 2025
- **DOI:** [10.1098/rsif.2024.0886](https://doi.org/10.1098/rsif.2024.0886)
- **Citations:** 21
- **Source:** Semantic Scholar
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-021a5bdde19ddeb7eaa9458ed5cb7d041a5d4903/llms.txt)

> This review focuses on the application of state-of-the-art protein structure prediction to these advanced applications of deep learning, and suggests a set of guidelines for reporting AlphaFold predictions.

### AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements

- **Authors:** H. Uzoeto, S. Cosmas, Toluwalope Temitope Bakare, Olanrewaju Ayodeji Durojaye
- **Journal:** Beni-Suef University Journal of Basic and Applied Sciences
- **Published:** 2024
- **DOI:** [10.1186/s43088-024-00503-y](https://doi.org/10.1186/s43088-024-00503-y)
- **Citations:** 18
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.1186/s43088-024-00503-y)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-83224d8d0fa3ee44cc796b419ccaa02c33f89456/llms.txt)

> This AlphaFold framework has the ability to yield atomically-accurate structural predictions for a variety of biomolecular interactions, hence facilitating advancements in drug discovery.

### Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16

- **Authors:** Jian Liu, P. Neupane, Jianlin Cheng
- **Journal:** bioRxiv
- **Published:** 2025
- **DOI:** [10.1101/2025.06.06.658338](https://doi.org/10.1101/2025.06.06.658338)
- **Citations:** 9
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.1101/2025.06.06.658338)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-8ca8c93e121a606271cdde62ab38bea9c1e764fa/llms.txt)

> The results show that MSA engineering through the use of different protein sequence databases, alignment tools, and domain segmentation as well as extensive model sampling are the key to generate accurate and correct structural models.

### From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.

- **Authors:** Jad F. Abbass
- **Journal:** Current protein & peptide science
- **Published:** 2025
- **DOI:** [10.2174/0113892037374986250711152300](https://doi.org/10.2174/0113892037374986250711152300)
- **Citations:** 8
- **Source:** Semantic Scholar
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-f704dd8ddb4dc79c5dacf2d755266502ab8299a3/llms.txt)

> This review will begin by revisiting DeepMind's early efforts in CASP13, detailing the architecture and the remarkable progress that led to their breakthrough of AlphaFold2 in CASP14 (2020), and delve into two main areas: (1) AlphaFold's contributions to the scientific community across various fields over the past four years, and (2) the latest improvements, enhancements, and achievements by DeepMind.

### Receptor- ligand interactions in plant inmate immunity revealed by AlphaFold protein structure prediction

- **Authors:** Li Wang, Yulin Jia, Aron Osakina, K. Olsen, Yixiao Huang, M. Jia, S. Ponniah, Rodrigo Pedrozo, C. Nicolli, Jeremy D. Edwards
- **Journal:** bioRxiv
- **Published:** 2024
- **DOI:** [10.1101/2024.06.12.598632](https://doi.org/10.1101/2024.06.12.598632)
- **Citations:** 5
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://www.biorxiv.org/content/biorxiv/early/2024/06/12/2024.06.12.598632.full.pdf)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-0d0993595529b13284dd294d86221b1be849ef0e/llms.txt)

> Detecting interaction of both Ptr and Pi39(t) with AVR-Pita, and Pi-9 with both AVR-Pi9 and AVR-Pik, revealed a new insight into recognition of pathogen signaling molecules by these host R genes in triggering plant innate immunity.

### Beyond Current Boundaries: Integrating Deep Learning and AlphaFold for Enhanced Protein Structure Prediction from Low-Resolution Cryo-EM Maps

- **Authors:** Xin Ma, Dong Si
- **Journal:** Computational biology and chemistry
- **Published:** 2024
- **DOI:** [10.48550/arXiv.2410.23321](https://doi.org/10.48550/arXiv.2410.23321)
- **Citations:** 4
- **Source:** Semantic Scholar
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-c31940bc89efb1960c32cd266e8608c845218862/llms.txt)

> This study introduces DeepTracer-LowResEnhance, an innovative computational framework that uniquely integrates structural predictions from AlphaFold with a deep-learning-based map refinement strategy specifically tailored to enhance low-resolution maps.

### Revolutionizing structural biology: AI-driven protein structure prediction from AlphaFold to next-generation innovations.

- **Authors:** Mowna Sundari Thangamalai, Deepali Desai, Chandrabose Selvaraj
- **Journal:** Advances in protein chemistry and structural biology
- **Published:** 2025
- **DOI:** [10.1016/bs.apcsb.2025.04.002](https://doi.org/10.1016/bs.apcsb.2025.04.002)
- **Citations:** 3
- **Source:** Semantic Scholar
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-58d23e732b1c84b906013912a8d34f09eb6c3cde/llms.txt)

> This research emphasizes AI's importance in structural biology and envisions a future in which predictive tools will provide comprehensive insights into protein function, dynamics, and therapeutic potential.

### AlphaFold as a Prior: Guiding Protein Structure Prediction Using Experimental Data with ROCKET

- **Authors:** A. Fadini, Minhuan Li, A. McCoy, Thomas C. Terwilliger, R. J. Read, D. Hekstra, M. Alquraishi
- **Journal:** Structural Dynamics
- **Published:** 2025
- **DOI:** [10.1063/4.0000860](https://doi.org/10.1063/4.0000860)
- **Citations:** 2
- **Source:** Semantic Scholar
- **Access:** Open Access
- **PDF:** [Download](https://doi.org/10.1063/4.0000860)
- **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/s2-5397a0669b36a2131be7b9784697b05cb057279a/llms.txt)

> An augmentation of AlphaFold2, ROCKET, is presented that refines its predictions using cryo-EM, cryo-ET, and X-ray crystallography data, and it is demonstrated that this approach captures biologically important structural variation that AlphaFold2 does not.

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