Статті в журналах: "AlphaFold"

1

Finkelstein, Alexei V. "Protein 3D Structure Identification by AlphaFold: a Physics-Based Prediction or Recognition Using Huge Databases?" Journal of Molecular Biology 6, no. 1 (March 20, 2024): 1–10. http://dx.doi.org/10.52338/tjomb.2024.3935.

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The great success of AlphaFold programs poses the questions: (i) What is the main reason for this success? (ii) What AlphaFolds does: physics-based prediction of the spatial structure of a protein from its amino acid sequence or recognition of this structure from similarity of the target sequence to some parts of sequences with already known spatial structures? The answers given here are: (i) the main reason for the AlphaFold’s success is the usage of huge databases which already cover virtually all protein superfamilies existing in Nature; (ii) using these databases, multiple sequence alignments, and coevolutionary information – like correlations of amino acid residues of the contacting chain regions – AlphaFold recognizes a spatial structure by similarity of the target sequence (or its parts) to related sequence(s) with already known spatial structures. We emphasize that this does not diminish the merit and utility of AlphaFold but only explains the basis of its success.

2

Wheeler, Richard John. "A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure." PLOS ONE 16, no. 11 (November 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 parasites in an early-branching eukaryotic lineage. The cause appears to be due to poor sampling of this branch of life (Discoba) in the protein sequences databases used for the AlphaFold database and ColabFold. Here, by comprehensively gathering openly available protein sequence data for Discoba species, significant improvements to AlphaFold2 protein structure prediction over the AlphaFold database and ColabFold are demonstrated. This is made available as an easy-to-use tool for the parasitology community in the form of Colaboratory notebooks for generating multiple sequence alignments and AlphaFold2 predictions of protein structure for Trypanosoma, Leishmania and related species.

3

Wen, Haosheng, Wei-Hsiang Weng, and Marcos Sotomayor. "A curated AlphaFold 2/AlphaFill cadherinosome." Biophysical Journal 122, no. 3 (February 2023): 474a—475a. http://dx.doi.org/10.1016/j.bpj.2022.11.2544.

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4

Manabe, Noriyoshi. "AlphaFill: Docking Ligands and Cofactors into AlphaFold Models." Trends in Glycoscience and Glycotechnology 35, no. 206 (July 25, 2023): E61. http://dx.doi.org/10.4052/tigg.2316.6e.

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5

AlQuraishi, Mohammed. "AlphaFold at CASP13." Bioinformatics 35, no. 22 (May 22, 2019): 4862–65. http://dx.doi.org/10.1093/bioinformatics/btz422.

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Abstract Summary: Computational prediction of protein structure from sequence is broadly viewed as a foundational problem of biochemistry and one of the most difficult challenges in bioinformatics. Once every two years the Critical Assessment of protein Structure Prediction (CASP) experiments are held to assess the state of the art in the field in a blind fashion, by presenting predictor groups with protein sequences whose structures have been solved but have not yet been made publicly available. The first CASP was organized in 1994, and the latest, CASP13, took place last December, when for the first time the industrial laboratory DeepMind entered the competition. DeepMind's entry, AlphaFold, placed first in the Free Modeling (FM) category, which assesses methods on their ability to predict novel protein folds (the Zhang group placed first in the Template-Based Modeling (TBM) category, which assess methods on predicting proteins whose folds are related to ones already in the Protein Data Bank.) DeepMind's success generated significant public interest. Their approach builds on two ideas developed in the academic community during the preceding decade: (i) the use of co-evolutionary analysis to map residue co-variation in protein sequence to physical contact in protein structure, and (ii) the application of deep neural networks to robustly identify patterns in protein sequence and co-evolutionary couplings and convert them into contact maps. In this Letter, we contextualize the significance of DeepMind's entry within the broader history of CASP, relate AlphaFold's methodological advances to prior work, and speculate on the future of this important problem.

6

Varadi, Mihaly, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, et al. "AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models." Nucleic Acids Research 50, no. D1 (November 17, 2021): D439—D444. http://dx.doi.org/10.1093/nar/gkab1061.

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Abstract 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 predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

7

Pak, Marina A., Karina A. Markhieva, Mariia S. Novikova, Dmitry S. Petrov, Ilya S. Vorobyev, Ekaterina S. Maksimova, Fyodor A. Kondrashov, and Dmitry N. Ivankov. "Using AlphaFold to predict the impact of single mutations on protein stability and function." PLOS ONE 18, no. 3 (March 16, 2023): e0282689. http://dx.doi.org/10.1371/journal.pone.0282689.

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AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is “solved”. However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding.

8

Le Page, Michael. "Why AlphaFold is transformational." New Scientist 255, no. 3398 (August 2022): 11. http://dx.doi.org/10.1016/s0262-4079(22)01373-2.

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9

Laura Howes. "AlphaFold goes all atom." C&EN Global Enterprise 101, no. 37 (November 13, 2023): 6. http://dx.doi.org/10.1021/cen-10137-scicon3.

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10

Porta-Pardo, Eduard, Victoria Ruiz-Serra, Samuel Valentini, and Alfonso Valencia. "The structural coverage of the human proteome before and after AlphaFold." PLOS Computational Biology 18, no. 1 (January 24, 2022): e1009818. http://dx.doi.org/10.1371/journal.pcbi.1009818.

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The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions. At the same time the fraction of dark proteome is reduced from 26% to just 10% when AlphaFold models are considered. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (69% of Clinvar pathogenic mutations and 88% of oncogenic mutations), AlphaFold models still provide an additional coverage of 3% to 13% of these critically important sets of biomedical genes and mutations. Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.

11

Manabe, Noriyoshi. "AlphaFillはAlphaFoldモデルにリガンドやコファクターを補完する". Trends in Glycoscience and Glycotechnology 35, № 206 (25 липня 2023): J62. http://dx.doi.org/10.4052/tigg.2316.6j.

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12

Kosugi, Takatsugu, and Masahito Ohue. "Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold." International Journal of Molecular Sciences 24, no. 17 (August 26, 2023): 13257. http://dx.doi.org/10.3390/ijms241713257.

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More than 930,000 protein–protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein–cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein–peptide complexes and one protein–protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

13

Baselious, Fady, Sebastian Hilscher, Dina Robaa, Cyril Barinka, Mike Schutkowski, and Wolfgang Sippl. "Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor." International Journal of Molecular Sciences 25, no. 2 (January 22, 2024): 1358. http://dx.doi.org/10.3390/ijms25021358.

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HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

14

Wuyun, Qiqige, Yihan Chen, Yifeng Shen, Yang Cao, Gang Hu, Wei Cui, Jianzhao Gao, and Wei Zheng. "Recent Progress of Protein Tertiary Structure Prediction." Molecules 29, no. 4 (February 13, 2024): 832. http://dx.doi.org/10.3390/molecules29040832.

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The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

15

Gutnik, Daria, Peter Evseev, Konstantin Miroshnikov, and Mikhail Shneider. "Using AlphaFold Predictions in Viral Research." Current Issues in Molecular Biology 45, no. 4 (April 21, 2023): 3705–32. http://dx.doi.org/10.3390/cimb45040240.

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Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

16

Dabrowski-Tumanski, Pawel, and Andrzej Stasiak. "AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins’ Topology." Molecules 28, no. 22 (November 7, 2023): 7462. http://dx.doi.org/10.3390/molecules28227462.

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AlphaFold is a groundbreaking deep learning tool for protein structure prediction. It achieved remarkable accuracy in modeling many 3D structures while taking as the user input only the known amino acid sequence of proteins in question. Intriguingly though, in the early steps of each individual structure prediction procedure, AlphaFold does not respect topological barriers that, in real proteins, result from the reciprocal impermeability of polypeptide chains. This study aims to investigate how this failure to respect topological barriers affects AlphaFold predictions with respect to the topology of protein chains. We focus on such classes of proteins that, during their natural folding, reproducibly form the same knot type on their linear polypeptide chain, as revealed by their crystallographic analysis. We use partially artificial test constructs in which the mutual non-permeability of polypeptide chains should not permit the formation of complex composite knots during natural protein folding. We find that despite the formal impossibility that the protein folding process could produce such knots, AlphaFold predicts these proteins to form complex composite knots. Our study underscores the necessity for cautious interpretation and further validation of topological features in protein structures predicted by AlphaFold.

17

Tunyasuvunakool, Kathryn. "Assessing AlphaFold predictions." Acta Crystallographica Section A Foundations and Advances 78, a1 (July 29, 2022): a227. http://dx.doi.org/10.1107/s2053273322097728.

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18

Tong, Alexander B., Jason D. Burch, Daniel McKay, Carlos Bustamante, Michael A. Crackower, and Hao Wu. "Could AlphaFold revolutionize chemical therapeutics?" Nature Structural & Molecular Biology 28, no. 10 (September 24, 2021): 771–72. http://dx.doi.org/10.1038/s41594-021-00670-x.

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19

Wei, Guo-Wei. "Protein structure prediction beyond AlphaFold." Nature Machine Intelligence 1, no. 8 (August 2019): 336–37. http://dx.doi.org/10.1038/s42256-019-0086-4.

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20

Edwards, Chris. "AlphaFold Spreads through Protein Science." Communications of the ACM 66, no. 5 (April 21, 2023): 10–12. http://dx.doi.org/10.1145/3586582.

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21

Terwilliger, Thomas C., Dorothee Liebschner, Tristan I. Croll, Christopher J. Williams, Airlie J. McCoy, Billy K. Poon, Pavel V. Afonine, et al. "Alphafold changes everything (and nothing)." Acta Crystallographica Section A Foundations and Advances 79, a2 (August 22, 2023): C1. http://dx.doi.org/10.1107/s2053273323096079.

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22

Efraimidis, Evangelos, Marios G. Krokidis, Themis P. Exarchos, Tamas Lazar, and Panagiotis Vlamos. "In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer’s Disease." International Journal of Molecular Sciences 24, no. 17 (August 31, 2023): 13543. http://dx.doi.org/10.3390/ijms241713543.

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Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold’s performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer’s disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). Α higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

23

Azzaz, Fodil, Nouara Yahi, Henri Chahinian, and Jacques Fantini. "The Epigenetic Dimension of Protein Structure Is an Intrinsic Weakness of the AlphaFold Program." Biomolecules 12, no. 10 (October 20, 2022): 1527. http://dx.doi.org/10.3390/biom12101527.

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One of the most important lessons we have learned from sequencing the human genome is that not all proteins have a 3D structure. In fact, a large part of the human proteome is made up of intrinsically disordered proteins (IDPs) which can adopt multiple structures, and therefore, multiple functions, depending on the ligands with which they interact. Under these conditions, one can wonder about the value of algorithms developed for predicting the structure of proteins, in particular AlphaFold, an AI which claims to have solved the problem of protein structure. In a recent study, we highlighted a particular weakness of AlphaFold for membrane proteins. Based on this observation, we have proposed a paradigm, referred to as “Epigenetic Dimension of Protein Structure” (EDPS), which takes into account all environmental parameters that control the structure of a protein beyond the amino acid sequence (hence “epigenetic”). In this new study, we compare the reliability of the AlphaFold and Robetta algorithms’ predictions for a new set of membrane proteins involved in human pathologies. We found that Robetta was generally more accurate than AlphaFold for ascribing a membrane-compatible topology. Raft lipids (e.g., gangliosides), which control the structural dynamics of membrane protein structure through chaperone effects, were identified as major actors of the EDPS paradigm. We conclude that the epigenetic dimension of a protein structure is an intrinsic weakness of AI-based protein structure prediction, especially AlphaFold, which warrants further development.

24

Gordon, Catriona H., Emily Hendrix, Yi He, and Mark C. Walker. "AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes." Biomolecules 13, no. 8 (August 12, 2023): 1243. http://dx.doi.org/10.3390/biom13081243.

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Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a growing class of natural products biosynthesized from a genetically encoded precursor peptide. The enzymes that install the post-translational modifications on these peptides have the potential to be useful catalysts in the production of natural-product-like compounds and can install non-proteogenic amino acids in peptides and proteins. However, engineering these enzymes has been somewhat limited, due in part to limited structural information on enzymes in the same families that nonetheless exhibit different substrate selectivities. Despite AlphaFold2’s superior performance in single-chain protein structure prediction, its multimer version lacks accuracy and requires high-end GPUs, which are not typically available to most research groups. Additionally, the default parameters of AlphaFold2 may not be optimal for predicting complex structures like RiPP biosynthetic enzymes, due to their dynamic binding and substrate-modifying mechanisms. This study assessed the efficacy of the structure prediction program ColabFold (a variant of AlphaFold2) in modeling RiPP biosynthetic enzymes in both monomeric and dimeric forms. After extensive benchmarking, it was found that there were no statistically significant differences in the accuracy of the predicted structures, regardless of the various possible prediction parameters that were examined, and that with the default parameters, ColabFold was able to produce accurate models. We then generated additional structural predictions for select RiPP biosynthetic enzymes from multiple protein families and biosynthetic pathways. Our findings can serve as a reference for future enzyme engineering complemented by AlphaFold-related tools.

25

Fiorini, Giovana, Luana Luiza Bastos, and Rafael Pereira Lemos. "ColabFold: uma ferramenta web para modelagem de proteínas." BIOINFO 3, no. 1 (September 21, 2023): 22. http://dx.doi.org/10.51780/bioinfo-03-22.

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A modelagem de proteínas é um desafio da biologia molecular que ficou em aberto por mais de 50 anos. Recentemente, estratégias computacionais obtiveram bastante sucesso em modelar tridimensionalmente a estrutura de macromoléculas. O AlphaFold é um software que utiliza aprendizado profundo para predizer estruturas de proteínas. Entretanto, sua instalação e uso ainda pode ser complexo para boa parte dos potenciais usuários. Em 2022, Milot Mirdita e colaboradores propuseram a ferramenta ColabFold: um programa rápido e fácil de usar para a previsão de estruturas de proteínas e complexos, que funciona por meio de um navegador de internet. Neste artigo, você irá conhecer um pouco das funcionalidades do ColabFold. A ferramenta está disponível em: https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb.

26

Rhoades, Raina, Brianna Henry, Dominique Prichett, Yayin Fang, and Shaolei Teng. "Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure." Genes 13, no. 5 (April 28, 2022): 789. http://dx.doi.org/10.3390/genes13050789.

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Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental Bos taurus structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures.

27

Bollinger, Terry. "Why AlphaFold is Not Like AlphaGo." Terry's Archive Online 2021, no. 02 (April 12, 2021): 0206. http://dx.doi.org/10.48034/20210206.

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AlphaFold2 is the second major iteration of a protein structure predictor by Google-owned DeepMind Lab. DeepMind is famous for creating AlphaGo Zero, the first game-playing system to transcend the rules taught by human trainers. When AlphaFold2 made a significant leap in protein prediction accuracy in the fourteenth annual CASP competition, even reserved publications like Nature were noticeably breathless in their praise of the results. It was not just the impressive and well-proven leap in prediction accuracy that made AlphaFold2 notable, but also its association with the DeepMind brand and implicitly with the beyond-human learning successes of AlphaGo Zero. But is this latter component of its notoriety and acclaim justified? That is, beyond superficial name similarities, is the design of AlphaFold2 sufficiently like that of AlphaGo Zero to enable a similar leap ahead of human knowledge and expertise? An analysis of the underlying designs says no. In contrast to the fully virtualized, faster-than-human learning speeds of AlphaGo Zero, the learning speed of AlphaFold2 remains firmly attached to and limited by human experimental time. AlphFold2 thus is inherently incapable of the trans-human leaps in learning speed demonstrated by AlphaGo Zero.

28

Otto, Claudia. "3D-Proteinstrukturvorhersage mittels KI-System AlphaFold." Recht Innovativ 5, no. 1 (December 2021): 80–91. http://dx.doi.org/10.1007/s43442-021-0070-4.

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29

Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. "Applying and improving AlphaFold at CASP14." Proteins: Structure, Function, and Bioinformatics 89, no. 12 (November 24, 2021): 1711–21. http://dx.doi.org/10.1002/prot.26257.

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30

Krissinel, E., R. Keegan, C. Ballard, A. Lebedev, and V. Uski. "AlphaFold-2 revolution for crystallographic software." Acta Crystallographica Section A Foundations and Advances 78, a2 (August 23, 2022): a80. http://dx.doi.org/10.1107/s2053273322095985.

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31

Campbell, Elizabeth A., Helen Walden, Johannes C. Walter, Arun K. Shukla, Martin Beck, Lori A. Passmore, and H. Eric Xu. "AlphaFold: Research accelerator and hypothesis generator." Molecular Cell 84, no. 3 (February 2024): 404–8. http://dx.doi.org/10.1016/j.molcel.2023.12.035.

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32

Evseev, Peter, Daria Gutnik, Mikhail Shneider, and Konstantin Miroshnikov. "Use of an Integrated Approach Involving AlphaFold Predictions for the Evolutionary Taxonomy of Duplodnaviria Viruses." Biomolecules 13, no. 1 (January 5, 2023): 110. http://dx.doi.org/10.3390/biom13010110.

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The evaluation of the evolutionary relationships is exceptionally important for the taxonomy of viruses, which is a rapidly expanding area of research. The classification of viral groups belonging to the realm Duplodnaviria, which include tailed bacteriophages, head-tailed archaeal viruses and herpesviruses, has undergone many changes in recent years and continues to improve. One of the challenging tasks of Duplodnaviria taxonomy is the classification of high-ranked taxa, including families and orders. At the moment, only 17 of 50 families have been assigned to orders. The evaluation of the evolutionary relationships between viruses is complicated by the high level of divergence of viral proteins. However, the development of structure prediction algorithms, including the award-winning AlphaFold, encourages the use of the results of structural predictions to clarify the evolutionary history of viral proteins. In this study, the evolutionary relationships of two conserved viral proteins, the major capsid protein and terminase, representing different viruses, including all classified Duplodnaviria families, have been analysed using AlphaFold modelling. This analysis has been undertaken using structural comparisons and different phylogenetic methods. The results of the analyses mainly indicated the high quality of AlphaFold modelling and the possibility of using the AlphaFold predictions, together with other methods, for the reconstruction of the evolutionary relationships between distant viral groups. Based on the results of this integrated approach, assumptions have been made about refining the taxonomic classification of bacterial and archaeal Duplodnaviria groups, and problems relating to the taxonomic classification of Duplodnaviria have been discussed.

33

Terwilliger, Thomas. "AlphaFold changes everything (and nothing)." Acta Crystallographica Section A Foundations and Advances 78, a1 (July 29, 2022): a57. http://dx.doi.org/10.1107/s2053273322099429.

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34

Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. "Highly accurate protein structure prediction with AlphaFold." Nature 596, no. 7873 (July 15, 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 three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our 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. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

35

Laura Howes. "Move over AlphaFold? Here comes Meta AI." C&EN Global Enterprise 100, no. 40 (November 14, 2022): 4. http://dx.doi.org/10.1021/cen-10040-scicon2.

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36

Yoon, Tae-Sung. "Sweet protein crystallography in post-AlphaFold era." Acta Crystallographica Section A Foundations and Advances 79, a1 (July 7, 2023): a179. http://dx.doi.org/10.1107/s2053273323098200.

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37

Sala, D., F. Engelberger, H. S. Mchaourab, and J. Meiler. "Modeling conformational states of proteins with AlphaFold." Current Opinion in Structural Biology 81 (August 2023): 102645. http://dx.doi.org/10.1016/j.sbi.2023.102645.

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38

Chai, Lawrence, Ping Zhu, Jin Chai, Changxu Pang, Babak Andi, Sean McSweeney, John Shanklin, and Qun Liu. "AlphaFold Protein Structure Database for Sequence-Independent Molecular Replacement." Crystals 11, no. 10 (October 12, 2021): 1227. http://dx.doi.org/10.3390/cryst11101227.

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Crystallographic phasing recovers the phase information that is lost during a diffraction experiment. Molecular replacement is a commonly used phasing method for crystal structures in the protein data bank. In one form it uses a protein sequence to search a structure database to find suitable templates for phasing. However, sequence information is not always available, such as when proteins are crystallized with unknown binding partner proteins or when the crystal is of a contaminant. The recent development of AlphaFold published the predicted protein structures for every protein from twenty distinct species. In this work, we tested whether AlphaFold-predicted E. coli protein structures were accurate enough to enable sequence-independent phasing of diffraction data from two crystallization contaminants of unknown sequence. Using each of more than 4000 predicted structures as a search model, robust molecular replacement solutions were obtained, which allowed the identification and structure determination of YncE and YadF. Our results demonstrate the general utility of the AlphaFold-predicted structure database with respect to sequence-independent crystallographic phasing.

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El Badaoui, Lina, and Alastair J. Barr. "Analysis of Receptor-Type Protein Tyrosine Phosphatase Extracellular Regions with Insights from AlphaFold." International Journal of Molecular Sciences 25, no. 2 (January 9, 2024): 820. http://dx.doi.org/10.3390/ijms25020820.

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The receptor-type protein tyrosine phosphatases (RPTPs) are involved in a wide variety of physiological functions which are mediated via their diverse extracellular regions. They play key roles in cell–cell contacts, bind various ligands and are regulated by dimerization and other processes. Depending on the subgroup, they have been described as everything from ‘rigid rods’ to ‘floppy tentacles’. Here, we review current experimental structural knowledge on the extracellular region of RPTPs and draw on AlphaFold structural predictions to provide further insights into structure and function of these cellular signalling molecules, which are often mutated in disease and are recognised as drug targets. In agreement with experimental data, AlphaFold predicted structures for extracellular regions of R1, and R2B subgroup RPTPs have an extended conformation, whereas R2B RPTPs are twisted, reflecting their high flexibility. For the R3 PTPs, AlphaFold predicts that members of this subgroup adopt an extended conformation while others are twisted, and that certain members, such as CD148, have one or more large, disordered loop regions in place of fibronectin type 3 domains suggested by sequence analysis.

40

Fu, Zheng-Qing, Hansen L. Sha, and Bingdong Sha. "AI-Based Protein Interaction Screening and Identification (AISID)." International Journal of Molecular Sciences 23, no. 19 (October 2, 2022): 11685. http://dx.doi.org/10.3390/ijms231911685.

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In this study, we presented an AISID method extending AlphaFold-Multimer’s success in structure prediction towards identifying specific protein interactions with an optimized AISIDscore. The method was tested to identify the binding proteins in 18 human TNFSF (Tumor Necrosis Factor superfamily) members for each of 27 human TNFRSF (TNF receptor superfamily) members. For each TNFRSF member, we ranked the AISIDscore among the 18 TNFSF members. The correct pairing resulted in the highest AISIDscore for 13 out of 24 TNFRSF members which have known interactions with TNFSF members. Out of the 33 correct pairing between TNFSF and TNFRSF members, 28 pairs could be found in the top five (including 25 pairs in the top three) seats in the AISIDscore ranking. Surprisingly, the specific interactions between TNFSF10 (TNF-related apoptosis-inducing ligand, TRAIL) and its decoy receptors DcR1 and DcR2 gave the highest AISIDscore in the list, while the structures of DcR1 and DcR2 are unknown. The data strongly suggests that AlphaFold-Multimer might be a useful computational screening tool to find novel specific protein bindings. This AISID method may have broad applications in protein biochemistry, extending the application of AlphaFold far beyond structure predictions.

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Garrido-Rodríguez, Pedro, Miguel Carmena-Bargueño, María Eugenia de la Morena-Barrio, Carlos Bravo-Pérez, Belén de la Morena-Barrio, Rosa Cifuentes-Riquelme, María Luisa Lozano, Horacio Pérez-Sánchez, and Javier Corral. "Analysis of AlphaFold and molecular dynamics structure predictions of mutations in serpins." PLOS ONE 19, no. 7 (July 5, 2024): e0304451. http://dx.doi.org/10.1371/journal.pone.0304451.

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Serine protease inhibitors (serpins) include thousands of structurally conserved proteins playing key roles in many organisms. Mutations affecting serpins may disturb their conformation, leading to inactive forms. Unfortunately, conformational consequences of serpin mutations are difficult to predict. In this study, we integrate experimental data of patients with mutations affecting one serpin with the predictions obtained by AlphaFold and molecular dynamics. Five SERPINC1 mutations causing antithrombin deficiency, the strongest congenital thrombophilia were selected from a cohort of 350 unrelated patients based on functional, biochemical, and crystallographic evidence supporting a folding defect. AlphaFold gave an accurate prediction for the wild-type structure. However, it also produced native structures for all variants, regardless of complexity or conformational consequences in vivo. Similarly, molecular dynamics of up to 1000 ns at temperatures causing conformational transitions did not show significant changes in the native structure of wild-type and variants. In conclusion, AlphaFold and molecular dynamics force predictions into the native conformation at conditions with experimental evidence supporting a conformational change to other structures. It is necessary to improve predictive strategies for serpins that consider the conformational sensitivity of these molecules.

42

Varadi, Mihaly, Damian Bertoni, Paulyna Magana, Urmila Paramval, Ivanna Pidruchna, Malarvizhi Radhakrishnan, Maxim Tsenkov, et al. "AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences." Nucleic Acids Research, November 2, 2023. http://dx.doi.org/10.1093/nar/gkad1011.

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Abstract 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 into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.

43

Yin, Rui, and Brian G. Pierce. "Evaluation of AlphaFold Antibody‐Antigen Modeling with Implications for Improving Predictive Accuracy." Protein Science, December 10, 2023. http://dx.doi.org/10.1002/pro.4865.

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AbstractHigh resolution antibody‐antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody‐antigen complexes. Initial benchmarking showed that despite overall success in modeling protein‐protein complexes, AlphaFold and AlphaFold‐Multimer have limited success in modeling antibody‐antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody‐antigen modeling performance on 427 nonredundant antibody‐antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near‐native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody‐antigen models in many cases, while additional training or other optimization may further improve performance.This article is protected by copyright. All rights reserved.

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Uzoeto, Henrietta Onyinye, Samuel Cosmas, Toluwalope Temitope Bakare, and Olanrewaju Ayodeji Durojaye. "AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements." Beni-Suef University Journal of Basic and Applied Sciences 13, no. 1 (May 17, 2024). http://dx.doi.org/10.1186/s43088-024-00503-y.

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AbstractBreakthrough achievements in protein structure prediction have occurred recently, mostly due to the advent of sophisticated machine learning methods and significant advancements in algorithmic approaches. The most recent version of the AlphaFold model, known as “AlphaFold-latest,” which expands the functionalities of the groundbreaking AlphaFold2, is the subject of this article. The goal of this novel model is to predict the three-dimensional structures of various biomolecules, such as ions, proteins, nucleic acids, small molecules, and non-standard residues. We demonstrate notable gains in precision, surpassing specialized tools across multiple domains, including protein–ligand interactions, protein–nucleic acid interactions, and antibody–antigen predictions. In conclusion, this AlphaFold framework has the ability to yield atomically-accurate structural predictions for a variety of biomolecular interactions, hence facilitating advancements in drug discovery.

45

Hekkelman, Maarten L., Ida de Vries, Robbie P. Joosten, and Anastassis Perrakis. "AlphaFill: enriching AlphaFold models with ligands and cofactors." Nature Methods, November 24, 2022. http://dx.doi.org/10.1038/s41592-022-01685-y.

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AbstractArtificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecules, essential for molecular structure or function: hemoglobin lacks bound heme; zinc-finger motifs lack zinc ions essential for structural integrity and metalloproteases lack metal ions needed for catalysis. Ligands important for biological function are absent too; no ADP or ATP is bound to any of the ATPases or kinases. Here we present AlphaFill, an algorithm that uses sequence and structure similarity to ‘transplant’ such ‘missing’ small molecules and ions from experimentally determined structures to predicted protein models. The algorithm was successfully validated against experimental structures. A total of 12,029,789 transplants were performed on 995,411 AlphaFold models and are available together with associated validation metrics in the alphafill.eu databank, a resource to help scientists make new hypotheses and design targeted experiments.

46

Tejero, Roberto, Yuanpeng Janet Huang, Theresa A. Ramelot, and Gaetano T. Montelione. "AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures." Frontiers in Molecular Biosciences 9 (June 13, 2022). http://dx.doi.org/10.3389/fmolb.2022.877000.

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Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

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Aderinwale, Tunde, Vijay Bharadwaj, Charles Christoffer, Genki Terashi, Zicong Zhang, Rashidedin Jahandideh, Yuki Kagaya, and Daisuke Kihara. "Real-time structure search and structure classification for AlphaFold protein models." Communications Biology 5, no. 1 (April 5, 2022). http://dx.doi.org/10.1038/s42003-022-03261-8.

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AbstractLast year saw a breakthrough in protein structure prediction, where the AlphaFold2 method showed a substantial improvement in the modeling accuracy. Following the software release of AlphaFold2, predicted structures by AlphaFold2 for proteins in 21 species were made publicly available via the AlphaFold Database. Here, to facilitate structural analysis and application of AlphaFold2 models, we provide the infrastructure, 3D-AF-Surfer, which allows real-time structure-based search for the AlphaFold2 models. In 3D-AF-Surfer, structures are represented with 3D Zernike descriptors (3DZD), which is a rotationally invariant, mathematical representation of 3D shapes. We developed a neural network that takes 3DZDs of proteins as input and retrieves proteins of the same fold more accurately than direct comparison of 3DZDs. Using 3D-AF-Surfer, we report structure classifications of AlphaFold2 models and discuss the correlation between confidence levels of AlphaFold2 models and intrinsic disordered regions.

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Peng, Zhenling, Wenkai Wang, Hong Wei, Xiaoge Li, and Jianyi Yang. "Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15." Proteins: Structure, Function, and Bioinformatics, August 10, 2023. http://dx.doi.org/10.1002/prot.26570.

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AbstractWe present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high‐quality multiple sequence alignments (MSAs). Top MSAs were then selected for the subsequent step of structure prediction. We utilized trRosettaX2 and AlphaFold2 for monomer structure prediction (group name Yang‐Server), and AlphaFold‐Multimer for multimer structure prediction (group name Yang‐Multimer). Yang‐Server and Yang‐Multimer are ranked at the top and the fourth, respectively, for monomer and multimer structure prediction. For 94 monomers, the average TM‐score of the predicted structure models by Yang‐Server is 0.876, compared to 0.798 by the default AlphaFold2 (i.e., the group NBIS‐AF2‐standard). For 42 multimers, the average DockQ score of the predicted structure models by Yang‐Multimer is 0.464, compared to 0.389 by the default AlphaFold‐Multimer (i.e., the group NBIS‐AF2‐multimer). Detailed analysis of the results shows that several factors contribute to the improvement, including improved MSAs, iterated modeling for large targets, interplay between monomer and multimer structure prediction for intertwined structures, etc. However, the structure predictions for orphan proteins and multimers remain challenging, and breakthroughs in this area are anticipated in the future.

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"AlphaFold and beyond." Nature Methods 20, no. 2 (February 2023): 163. http://dx.doi.org/10.1038/s41592-023-01790-6.

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50

Wallner, Björn. "AFsample: Improving Multimer Prediction with AlphaFold using Massive Sampling." Bioinformatics, September 15, 2023. http://dx.doi.org/10.1093/bioinformatics/btad573.

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Abstract The AlphaFold2 neural network model has revolutionized structural biology with unprecedented performance. We demonstrate that by stochastically perturbing the neural network by enabling dropout at inference combined with massive sampling, it is possible to improve the quality of the generated models. We generated around 6,000 models per target compared to 25 default for AlphaFold-Multimer, with v1 and v2 multimer network models, with and without templates, and increased the number of recycles within the network. The method was benchmarked in CASP15, and compared to AlphaFold-Multimer v2 it improved the average DockQ from 0.41 to 0.55 using identical input and was ranked at the very top in the protein assembly category when compared to all other groups participating in CASP15. The simplicity of the method should facilitate the adaptation by the field, and the method should be useful for anyone interested in modelling multimeric structures, alternate conformations or flexible structures. Availability AFsample is available online at http://wallnerlab.org/AFsample. Supplementary information Supplementary data are available at Bioinformatics online.

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