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Journal articles on the topic 'Protein structure prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Cheng, Kaihui, Ce Liu, Qingkun Su, et al. "4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 93–101. https://doi.org/10.1609/aaai.v39i1.31984.

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Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the fo

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2

Deng, Haiyou, Ya Jia, and Yang Zhang. "Protein structure prediction." International Journal of Modern Physics B 32, no. 18 (2018): 1840009. http://dx.doi.org/10.1142/s021797921840009x.

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Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used prediction methods and the community-wide critical assessment of protein structure prediction (CASP) experiments.

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3

Dr., Pankaj Malik, Sharma Anmol, Anand Anoushka, Baliyan Anmol, Raj Amisha, and Singh Jasleen. "Enhancing Alpha Fold Predictions with Transfer Learning: A Comprehensive Analysis and Benchmarking." Enhancing Alpha Fold Predictions with Transfer Learning: A Comprehensive Analysis and Benchmarking 8, no. 12 (2024): 7. https://doi.org/10.5281/zenodo.10499711.

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Protein structure prediction is a critical facet of molecular biology, with profound implications for understanding cellular processes and advancing drug discovery. AlphaFold, a state-of-the-art deep learning model, has demonstrated groundbreaking success in predicting protein structures. However, challenges persist, particularly in scenarios with limited data for specific protein families. This research investigates the augmentation of AlphaFold predictions through the application of transfer learning techniques, leveraging knowledge gained from one set of proteins to enhance predictions for

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4

Kazm, Ammar, Aida Ali, and Haslina Hashim. "Transformer Encoder with Protein Language Model for Protein Secondary Structure Prediction." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13124–32. http://dx.doi.org/10.48084/etasr.6855.

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In bioinformatics, protein secondary structure prediction plays a significant role in understanding protein function and interactions. This study presents the TE_SS approach, which uses a transformer encoder-based model and the Ankh protein language model to predict protein secondary structures. The research focuses on the prediction of nine classes of structures, according to the Dictionary of Secondary Structure of Proteins (DSSP) version 4. The model's performance was rigorously evaluated using various datasets. Additionally, this study compares the model with the state-of-the-art methods i

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5

Natalia Garavano, Francisca Sadosky, and Facundo Bulgheroni. "Protein Structure Prediction Tools and Computational Approaches." Fusion of Multidisciplinary Research, An International Journal 4, no. 2 (2023): 498–509. https://doi.org/10.63995/mwcu4408.

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Protein structure prediction is a critical aspect of bioinformatics, aimed at determining the three-dimensional configuration of proteins from their amino acid sequences. With the advent of sophisticated computational approaches, this field has seen significant advancements. Methods like homology modeling, which relies on the similarity between the target protein and known structures, and ab initio modeling, which predicts structures from scratch, have become fundamental tools. Additionally, molecular dynamics simulations and machine learning techniques, such as AlphaFold, have revolutionized

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6

El Hefnawi, Mahmoud M., Mohamed E. Hasan, Amal Mahmoud, et al. "Prediction and Analysis of Three-Dimensional Structure of the p7- Transactivated Protein1 of Hepatitis C Virus." Infectious Disorders - Drug Targets 19, no. 1 (2019): 55–66. http://dx.doi.org/10.2174/1871526518666171215123214.

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Background:The p7-transactivated protein1 of Hepatitis C virus is a small integral membrane protein of 127 amino acids, which is crucial for assembly and release of infectious virions. Ab initio or comparative modelling, is an essential tool to solve the problem of protein structure prediction and to comprehend the physicochemical fundamental of how proteins fold in nature.Results:Only one domain (1-127) of p7-transactivated protein1 has been predicted using the systematic in silico approach, ThreaDom. I-TASSER was ranked as the best server for full-length 3-D protein structural predictions of

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7

Haque, Neshatul, Jessica B. Wagenknecht, Brian D. Ratnasinghe, and Michael T. Zimmermann. "Systematic analysis of the relationship between fold-dependent flexibility and artificial intelligence protein structure prediction." PLOS ONE 19, no. 11 (2024): e0313308. http://dx.doi.org/10.1371/journal.pone.0313308.

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Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about complicated biological problems and empowered a new generation of structure-based hypothesis testing. It is well-recognized that proteins have a modular organization according to archetypal folds. However, it is yet to be determined if predicted structures are tuned to one conformation of flexible proteins or if they represent average con

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8

PALOPOLI, LUIGI, and GIORGIO TERRACINA. "CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES." Journal of Bioinformatics and Computational Biology 02, no. 03 (2004): 471–95. http://dx.doi.org/10.1142/s0219720004000697.

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Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different predicti

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9

Deng Hai-You, Jia Ya, and Zhang Yang. "Protein structure prediction." Acta Physica Sinica 65, no. 17 (2016): 178701. http://dx.doi.org/10.7498/aps.65.178701.

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10

Benner, Steven A., Dietlind L. Geroff, and J. David Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (1996): 1448–49. http://dx.doi.org/10.1126/science.274.5292.1448.b.

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11

Benner, Steven A., Dietlind L. Geroff, and J. David Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (1996): 1448–49. http://dx.doi.org/10.1126/science.274.5292.1448-b.

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12

Barton, Geoffrey J., and Robert B. Russell. "Protein structure prediction." Nature 361, no. 6412 (1993): 505–6. http://dx.doi.org/10.1038/361505b0.

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13

Robson, Barry, and Jean Gamier. "Protein structure prediction." Nature 361, no. 6412 (1993): 506. http://dx.doi.org/10.1038/361506a0.

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14

Al-Lazikani, Bissan, Joon Jung, Zhexin Xiang, and Barry Honig. "Protein structure prediction." Current Opinion in Chemical Biology 5, no. 1 (2001): 51–56. http://dx.doi.org/10.1016/s1367-5931(00)00164-2.

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15

Westhead, David R., and Janet M. Thornton. "Protein structure prediction." Current Opinion in Biotechnology 9, no. 4 (1998): 383–89. http://dx.doi.org/10.1016/s0958-1669(98)80012-8.

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16

Benner, S. A., D. L. Geroff, and J. David Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (1996): 1447b—1451. http://dx.doi.org/10.1126/science.274.5292.1447b.

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17

Benner, S. A., D. L. Geroff, and J. D. Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (1996): 1448b—1449b. http://dx.doi.org/10.1126/science.274.5292.1448b.

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18

Garnier, J. "Protein structure prediction." Biochimie 72, no. 8 (1990): 513–24. http://dx.doi.org/10.1016/0300-9084(90)90115-w.

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19

Moult, John. "Protein structure prediction." Journal of Molecular Graphics and Modelling 18, no. 4-5 (2000): 553. http://dx.doi.org/10.1016/s1093-3263(00)80125-4.

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20

Haas, Jürgen, Alessandro Barbato, Tobias Schmidt, et al. "Expanding our knowledge of the protein universe: Modelling of protein structures." Acta Crystallographica Section A Foundations and Advances 70, a1 (2014): C491. http://dx.doi.org/10.1107/s2053273314095084.

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Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing goal in structural biology. Over the last two decades, a paradigm shift has occurred: starting from a large "knowledge gap" between the huge number of protein sequences compared to a small number of experimentally known structures, today, some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Methods for structure modeling and prediction have ma

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21

Baker, David. "Protein folding, structure prediction and design." Biochemical Society Transactions 42, no. 2 (2014): 225–29. http://dx.doi.org/10.1042/bst20130055.

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I describe how experimental studies of protein folding have led to advances in protein structure prediction and protein design. I describe the finding that protein sequences are not optimized for rapid folding, the contact order–protein folding rate correlation, the incorporation of experimental insights into protein folding into the Rosetta protein structure production methodology and the use of this methodology to determine structures from sparse experimental data. I then describe the inverse problem (protein design) and give an overview of recent work on designing proteins with new structur

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22

Susanty, Meredita, Tati Erawati Rajab, and Rukman Hertadi. "A Review of Protein Structure Prediction using Deep Learning." BIO Web of Conferences 41 (2021): 04003. http://dx.doi.org/10.1051/bioconf/20214104003.

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Proteins are macromolecules composed of 20 types of amino acids in a specific order. Understanding how proteins fold is vital because its 3-dimensional structure determines the function of a protein. Prediction of protein structure based on amino acid strands and evolutionary information becomes the basis for other studies such as predicting the function, property or behaviour of a protein and modifying or designing new proteins to perform certain desired functions. Machine learning advances, particularly deep learning, are igniting a paradigm shift in scientific study. In this review, we summ

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23

Huang, Yufei, Siyuan Li, Lirong Wu, et al. "Protein 3D Graph Structure Learning for Robust Structure-Based Protein Property Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12662–70. http://dx.doi.org/10.1609/aaai.v38i11.29161.

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Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena

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24

De, Meutter Joëlle, and Erik Goormaghtigh. "Evaluation of protein secondary structure from FTIR spectra improved after partial deuteration." European Biophysics Journal 53 (February 3, 2021): 613–28. https://doi.org/10.1007/s00249-021-01502-y.

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FTIR spectroscopy has become a major tool to determine protein secondary structure. One of the identified obstacle for reaching better predictions is the strong overlap of bands assigned to different secondary structures. Yet, while for instance disordered structures and α-helical structures absorb almost at the same wavenumber, the absorbance bands are differentially shifted upon deuteration, in part because exchange is much faster for disordered structures. We recorded the FTIR spectra of 85 proteins at different stages of hydrogen/deuterium exchange process using p

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25

Bernhofer, Michael, Christian Dallago, Tim Karl, et al. "PredictProtein - Predicting Protein Structure and Function for 29 Years." Nucleic Acids Research 49, W1 (2021): W535—W540. http://dx.doi.org/10.1093/nar/gkab354.

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Abstract Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disord

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26

Liu, Wei-min, and Kou-Chen Chou. "Prediction of protein secondary structure content." Protein Engineering, Design and Selection 12, no. 12 (1999): 1041–50. http://dx.doi.org/10.1093/protein/12.12.1041.

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27

Yang, Jianyi, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, and David Baker. "Improved protein structure prediction using predicted interresidue orientations." Proceedings of the National Academy of Sciences 117, no. 3 (2020): 1496–503. http://dx.doi.org/10.1073/pnas.1914677117.

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The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model

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28

Jumper, John, Richard Evans, Alexander Pritzel, et al. "Highly accurate protein structure prediction with AlphaFold." Nature 596, no. 7873 (2021): 583–89. http://dx.doi.org/10.1038/s41586-021-03819-2.

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AbstractProteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the

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29

Wheeler, Richard John. "A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure." PLOS ONE 16, no. 11 (2021): e0259871. http://dx.doi.org/10.1371/journal.pone.0259871.

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AlphaFold2 and RoseTTAfold represent a transformative advance for predicting protein structure. They are able to make very high-quality predictions given a high-quality alignment of the protein sequence with related proteins. These predictions are now readily available via the AlphaFold database of predicted structures and AlphaFold or RoseTTAfold Colaboratory notebooks for custom predictions. However, predictions for some species tend to be lower confidence than model organisms. Problematic species include Trypanosoma cruzi and Leishmania infantum: important unicellular eukaryotic human paras

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30

Bouatta, Nazim, Peter Sorger, and Mohammed AlQuraishi. "Protein structure prediction by AlphaFold2: are attention and symmetries all you need?" Acta Crystallographica Section D Structural Biology 77, no. 8 (2021): 982–91. http://dx.doi.org/10.1107/s2059798321007531.

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The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is comp

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31

Chen, Ye. "Advancements and Applications of Protein Structure Prediction Algorithms." Theoretical and Natural Science 74, no. 1 (2024): 119–27. https://doi.org/10.54254/2753-8818/2024.la18791.

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Protein structure prediction serves as a foundational aspect of molecular biology, where computational advancements have recently propelled significant increases in prediction accuracy. This paper evaluates traditional protein structure prediction methods, including homology modeling, threading, and Ab Initio techniques, emphasizing the inherent challenges these methods face in accurately modeling novel and highly flexible proteins. With the advent of AI-based models, particularly AlphaFold, the landscape of protein structure prediction has undergone a transformative shift. AlphaFold integrate

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32

Agarwal, Tejas. "Protein Structure Prediction, Structural Bioinformatics and Deep Learning." International Journal of Current Microbiology and Applied Sciences 13, no. 8 (2024): 180–86. https://doi.org/10.20546/ijcmas.2024.1308.023.

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Protein structure prediction is essential for understanding protein stability, and interactions. It holds immense potential for drug discovery and protein engineering. However, despite advancements in structural bioinformatics and artificial intelligence, a standardised model for structure prediction still needs to be worked out. Even prominent models like AlphaFold often undergo architectural changes. To address this gap, a comprehensive detail of recent progress and challenges in deep learning-based protein structure prediction has been presented. Additionally, a benchmark system for structu

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33

Jin, Shikai, Vinicius G. Contessoto, Mingchen Chen, et al. "AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes." Nucleic Acids Research 48, W1 (2020): W25—W30. http://dx.doi.org/10.1093/nar/gkaa356.

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Abstract The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-ba

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34

Cretin, Gabriel, Tatiana Galochkina, Alexandre G. de Brevern, and Jean-Christophe Gelly. "PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction." International Journal of Molecular Sciences 22, no. 16 (2021): 8831. http://dx.doi.org/10.3390/ijms22168831.

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Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a dee

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35

Tunyasuvunakool, Kathryn, Jonas Adler, Zachary Wu, et al. "Highly accurate protein structure prediction for the human proteome." Nature 596, no. 7873 (2021): 590–96. http://dx.doi.org/10.1038/s41586-021-03828-1.

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AbstractProtein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residue

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36

Vangaveti, Sweta, Thom Vreven, Yang Zhang, and Zhiping Weng. "Integrating ab initio and template-based algorithms for protein–protein complex structure prediction." Bioinformatics 36, no. 3 (2019): 751–57. http://dx.doi.org/10.1093/bioinformatics/btz623.

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Abstract Motivation Template-based and template-free methods have both been widely used in predicting the structures of protein–protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein–protein complex structure prediction. Results Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-f

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37

Yi, Wenjing, Ao Sun, Manman Liu, Xiaoqing Liu, Wei Zhang, and Qi Dai. "Comparative Study on Feature Selection in Protein Structure and Function Prediction." Computational and Mathematical Methods in Medicine 2022 (October 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/1650693.

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Many effective methods extract and fuse different protein features to study the relationship between protein sequence, structure, and function, but different methods have preferences in solving the research of protein structure and function, which requires selecting valuable and contributing features to design more effective prediction methods. This work mainly focused on the feature selection methods in the study of protein structure and function, and systematically compared and analyzed the efficiency of different feature selection methods in the prediction of protein structures, protein dis

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38

Kondo, Hiroko X., Hiroyuki Iizuka, Gen Masumoto, Yuichi Kabaya, Yusuke Kanematsu, and Yu Takano. "Prediction of Protein Function from Tertiary Structure of the Active Site in Heme Proteins by Convolutional Neural Network." Biomolecules 13, no. 1 (2023): 137. http://dx.doi.org/10.3390/biom13010137.

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Structure–function relationships in proteins have been one of the crucial scientific topics in recent research. Heme proteins have diverse and pivotal biological functions. Therefore, clarifying their structure–function correlation is significant to understand their functional mechanism and is informative for various fields of science. In this study, we constructed convolutional neural network models for predicting protein functions from the tertiary structures of heme-binding sites (active sites) of heme proteins to examine the structure–function correlation. As a result, we succeeded in the

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39

Rychlewski, L., and A. Godzik. "Secondary structure prediction using segment similarity." Protein Engineering Design and Selection 10, no. 10 (1997): 1143–53. http://dx.doi.org/10.1093/protein/10.10.1143.

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40

Feng, Yonge, and Liaofu Luo. "Using long-range contact number information for protein secondary structure prediction." International Journal of Biomathematics 07, no. 05 (2014): 1450052. http://dx.doi.org/10.1142/s1793524514500521.

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In this paper, we first combine tetra-peptide structural words with contact number for protein secondary structure prediction. We used the method of increment of diversity combined with quadratic discriminant analysis to predict the structure of central residue for a sequence fragment. The method is used tetra-peptide structural words and long-range contact number as information resources. The accuracy of Q3 is over 83% in 194 proteins. The accuracies of predicted secondary structures for 20 amino acid residues are ranged from 81% to 88%. Moreover, we have introduced the residue long-range con

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41

Lin, Zeming, Halil Akin, Roshan Rao, et al. "Evolutionary-scale prediction of atomic-level protein structure with a language model." Science 379, no. 6637 (2023): 1123–30. http://dx.doi.org/10.1126/science.ade2574.

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Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins

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42

De Meutter, Joëlle, and Erik Goormaghtigh. "Evaluation of protein secondary structure from FTIR spectra improved after partial deuteration." European Biophysics Journal 50, no. 3-4 (2021): 613–28. http://dx.doi.org/10.1007/s00249-021-01502-y.

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AbstractFTIR spectroscopy has become a major tool to determine protein secondary structure. One of the identified obstacle for reaching better predictions is the strong overlap of bands assigned to different secondary structures. Yet, while for instance disordered structures and α-helical structures absorb almost at the same wavenumber, the absorbance bands are differentially shifted upon deuteration, in part because exchange is much faster for disordered structures. We recorded the FTIR spectra of 85 proteins at different stages of hydrogen/deuterium exchange process using protein microarrays

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43

AlQuraishi, Mohammed. "Protein-structure prediction revolutionized." Nature 596, no. 7873 (2021): 487–88. http://dx.doi.org/10.1038/d41586-021-02265-4.

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44

Shortle, David. "Prediction of protein structure." Current Biology 10, no. 2 (2000): R49—R51. http://dx.doi.org/10.1016/s0960-9822(00)00290-6.

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45

Bowie, James U., and David Eisenberg. "Inverted protein structure prediction." Current Opinion in Structural Biology 3, no. 3 (1993): 437–44. http://dx.doi.org/10.1016/s0959-440x(05)80118-6.

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46

von Heijne, Gunnar. "Membrane protein structure prediction." Journal of Molecular Biology 225, no. 2 (1992): 487–94. http://dx.doi.org/10.1016/0022-2836(92)90934-c.

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47

Barton, Geoffrey J. "Protein secondary structure prediction." Current Opinion in Structural Biology 5, no. 3 (1995): 372–76. http://dx.doi.org/10.1016/0959-440x(95)80099-9.

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48

Marti-Renom, Marc A., Bozidar Yerkovich, and Andrej Sali. "Comparative Protein Structure Prediction." Current Protocols in Protein Science 28, no. 1 (2002): 2.9.1–2.9.22. http://dx.doi.org/10.1002/0471140864.ps0209s28.

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49

Strack, Rita. "Protein–ligand structure prediction." Nature Methods 21, no. 4 (2024): 549. http://dx.doi.org/10.1038/s41592-024-02249-y.

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50

Chen, Lingtao, Qiaomu Li, Kazi Fahim Ahmad Nasif, et al. "AI-Driven Deep Learning Techniques in Protein Structure Prediction." International Journal of Molecular Sciences 25, no. 15 (2024): 8426. http://dx.doi.org/10.3390/ijms25158426.

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Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some

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