Zeitschriftenartikel: „AlphaFold2“

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Wheeler, Richard John. „A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure“. PLOS ONE 16, Nr. 11 (11.11.2021): 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.

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Stein, Richard A., und Hassane S. Mchaourab. „SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2“. PLOS Computational Biology 18, Nr. 8 (22.08.2022): e1010483. http://dx.doi.org/10.1371/journal.pcbi.1010483.

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The unprecedented performance of Deepmind’s Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold’s ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.

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Gordon, Catriona H., Emily Hendrix, Yi He und Mark C. Walker. „AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes“. Biomolecules 13, Nr. 8 (12.08.2023): 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.

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Nunes-Alves, Ariane, und Kenneth Merz. „AlphaFold2 in Molecular Discovery“. Journal of Chemical Information and Modeling 63, Nr. 19 (09.10.2023): 5947–49. http://dx.doi.org/10.1021/acs.jcim.3c01459.

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Tourlet, Sébastien, Ragousandirane Radjasandirane, Julien Diharce und Alexandre G. de Brevern. „AlphaFold2 Update and Perspectives“. BioMedInformatics 3, Nr. 2 (09.05.2023): 378–90. http://dx.doi.org/10.3390/biomedinformatics3020025.

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Access to the three-dimensional (3D) structural information of macromolecules is of major interest in both fundamental and applied research. Obtaining this experimental data can be complex, time consuming, and costly. Therefore, in silico computational approaches are an alternative of interest, and sometimes present a unique option. In this context, the Protein Structure Prediction method AlphaFold2 represented a revolutionary advance in structural bioinformatics. Named method of the year in 2021, and widely distributed by DeepMind and EBI, it was thought at this time that protein-folding issues had been resolved. However, the reality is slightly more complex. Due to a lack of input experimental data, related to crystallographic challenges, some targets have remained highly challenging or not feasible. This perspective exercise, dedicated to a non-expert audience, discusses and correctly places AlphaFold2 methodology in its context and, above all, highlights its use, limitations, and opportunities. After a review of the interest in the 3D structure and of the previous methods used in the field, AF2 is brought into its historical context. Its spatial interests are detailed before presenting precise quantifications showing some limitations of this approach and finishing with the perspectives in the field.

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Bollinger, Terry. „Why AlphaFold is Not Like AlphaGo“. Terry's Archive Online 2021, Nr. 02 (12.04.2021): 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.

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Ng, Tsz Kin, Jie Ji, Qingping Liu, Yao Yao, Wen-Ying Wang, Yingjie Cao, Chong-Bo Chen et al. „Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2“. Biomolecules 14, Nr. 1 (21.12.2023): 14. http://dx.doi.org/10.3390/biom14010014.

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Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.

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Wilson, Carter J., Wing-Yiu Choy und Mikko Karttunen. „AlphaFold2: A Role for Disordered Protein/Region Prediction?“ International Journal of Molecular Sciences 23, Nr. 9 (21.04.2022): 4591. http://dx.doi.org/10.3390/ijms23094591.

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The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).

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伏信, 進矢. „【用語解説】AlphaFold2“. Bulletin of Applied Glycoscience 13, Nr. 2 (20.08.2023): 136. http://dx.doi.org/10.5458/bag.13.2_136.

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Boland, Devon J., und Nicola M. Ayres. „Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums“. PLOS Computational Biology 20, Nr. 6 (27.06.2024): e1012123. http://dx.doi.org/10.1371/journal.pcbi.1012123.

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AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module’s focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms.

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Yang, Zhuoya. „AlphaFold2-based structure prediction and target study of PD-L1 protein“. Theoretical and Natural Science 3, Nr. 1 (28.04.2023): 1–10. http://dx.doi.org/10.54254/2753-8818/3/20220152.

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PD-L1 is an immune protein in human body that can play an important role in cancer immunotherapy. By binding to antibodies, the binding activity of PD-L1 and PD-1 is blocked, which in turn inhibits cancer cells. Thus the structure of PD-L1 is very important in studying the binding of antibodies to it. However, experimental methods to solve the structures of PD- L1 and numerous complexes are expensive and consuming. Thus, it is essential to exploit computational methods to help biologists figure out the structures and the underlying mechanisms. In this paper, we explore whether AlphaFold2 is able to accurately predict the structure of PD-L1 and whether we can use AlphaFold2 to capture the binding sites of PD-L1 when binding to different antibodies. Our results show that AlphaFold2 has high confident scores and accuracy in predicting the structure of PD-L1 and the binding sites with atezolizumab and durvalumab. For the interaction between PD-L1 and the antibodies, AlphaFold2 can capture most of the hydrogen bonds as well as the salt bridges. Our work suggests that AlphaFold2 can not only be used as a tool to predict the structure of proteins, but also serves as a useful tool for antibody discovery, e.g. providing high-quality predicted structures for downstreaming docking, which brings new hope for drug discovery.

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Tikhonov, Denis B., und Boris S. Zhorov. „P-Loop Channels: Experimental Structures, and Physics-Based and Neural Networks-Based Models“. Membranes 12, Nr. 2 (16.02.2022): 229. http://dx.doi.org/10.3390/membranes12020229.

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The superfamily of P-loop channels includes potassium, sodium, and calcium channels, as well as TRP channels and ionotropic glutamate receptors. A rapidly increasing number of crystal and cryo-EM structures have revealed conserved and variable elements of the channel structures. Intriguing differences are seen in transmembrane helices of channels, which may include π-helical bulges. The bulges reorient residues in the helices and thus strongly affect their intersegment contacts and patterns of ligand-sensing residues. Comparison of the experimental structures suggests that some π-bulges are dynamic: they may appear and disappear upon channel gating and ligand binding. The AlphaFold2 models represent a recent breakthrough in the computational prediction of protein structures. We compared some crystal and cryo-EM structures of P-loop channels with respective AlphaFold2 models. Folding of the regions, which are resolved experimentally, is generally similar to that predicted in the AlphaFold2 models. The models also reproduce some subtle but significant differences between various P-loop channels. However, patterns of π-bulges do not necessarily coincide in the experimental and AlphaFold2 structures. Given the importance of dynamic π-bulges, further studies involving experimental and theoretical approaches are necessary to understand the cause of the discrepancy.

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Sawhney, Aman, Jiefu Li und Li Liao. „Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features“. International Journal of Molecular Sciences 25, Nr. 10 (11.05.2024): 5247. http://dx.doi.org/10.3390/ijms25105247.

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Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2’s predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

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Fiorini, Giovana, Luana Luiza Bastos und Rafael Pereira Lemos. „ColabFold: uma ferramenta web para modelagem de proteínas“. BIOINFO 3, Nr. 1 (21.09.2023): 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.

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Qiu, Xinru, Han Li, Greg Ver Steeg und Adam Godzik. „Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development“. Biomolecules 14, Nr. 3 (12.03.2024): 339. http://dx.doi.org/10.3390/biom14030339.

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Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

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Finkelstein, Alexei V. „Protein 3D Structure Identification by AlphaFold: a Physics-Based Prediction or Recognition Using Huge Databases?“ Journal of Molecular Biology 6, Nr. 1 (20.03.2024): 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.

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Hartley, Sophia M., Kelly A. Tiernan, Gjina Ahmetaj, Adriana Cretu, Yan Zhuang und Marc Zimmer. „AlphaFold2 and RoseTTAFold predict posttranslational modifications. Chromophore formation in GFP-like proteins“. PLOS ONE 17, Nr. 6 (16.06.2022): e0267560. http://dx.doi.org/10.1371/journal.pone.0267560.

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AlphaFold2 and RoseTTAfold are able to predict, based solely on their sequence whether GFP-like proteins will post-translationally form a chromophore (the part of the protein responsible for fluorescence) or not. Their training has not only taught them protein structure and folding, but also chemistry. The structures of 21 sequences of GFP-like fluorescent proteins that will post-translationally form a chromophore and of 23 GFP-like non-fluorescent proteins that do not have the residues required to form a chromophore were determined by AlphaFold2 and RoseTTAfold. The resultant structures were mined for a series of geometric measurements that are crucial to chromophore formation. Statistical analysis of these measurements showed that both programs conclusively distinguished between chromophore forming and non-chromophore forming proteins. A clear distinction between sequences capable of forming a chromophore and those that do not have the residues required for chromophore formation can be obtained by examining a single measurement—the RMSD of the overlap of the central alpha helices of the crystal structure of S65T GFP and the AlphaFold2 determined structure. Only 10 of the 578 GFP-like proteins in the pdb have no chromophore, yet when AlphaFold2 and RoseTTAFold are presented with the sequences of 44 GFP-like proteins that are not in the pdb they fold the proteins in such a way that one can unequivocally distinguish between those that can and cannot form a chromophore.

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Cramer, Patrick. „AlphaFold2 and the future of structural biology“. Nature Structural & Molecular Biology 28, Nr. 9 (10.08.2021): 704–5. http://dx.doi.org/10.1038/s41594-021-00650-1.

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Jones, David T., und Janet M. Thornton. „The impact of AlphaFold2 one year on“. Nature Methods 19, Nr. 1 (Januar 2022): 15–20. http://dx.doi.org/10.1038/s41592-021-01365-3.

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Kosugi, Takatsugu, und Masahito Ohue. „Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold“. International Journal of Molecular Sciences 24, Nr. 17 (26.08.2023): 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.

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Wuyun, Qiqige, Yihan Chen, Yifeng Shen, Yang Cao, Gang Hu, Wei Cui, Jianzhao Gao und Wei Zheng. „Recent Progress of Protein Tertiary Structure Prediction“. Molecules 29, Nr. 4 (13.02.2024): 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.

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Aubel, Margaux, Lars Eicholt und Erich Bornberg-Bauer. „Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning“. F1000Research 12 (29.03.2023): 347. http://dx.doi.org/10.12688/f1000research.130443.1.

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Background: De novo protein coding genes emerge from scratch in the non-coding regions of the genome and have, per definition, no homology to other genes. Therefore, their encoded de novo proteins belong to the so-called "dark protein space". So far, only four de novo protein structures have been experimentally approximated. Low homology, presumed high disorder and limited structures result in low confidence structural predictions for de novo proteins in most cases. Here, we look at the most widely used structure and disorder predictors and assess their applicability for de novo emerged proteins. Since AlphaFold2 is based on the generation of multiple sequence alignments and was trained on solved structures of largely conserved and globular proteins, its performance on de novo proteins remains unknown. More recently, natural language models of proteins have been used for alignment-free structure predictions, potentially making them more suitable for de novo proteins than AlphaFold2. Methods: We applied different disorder predictors (IUPred3 short/long, flDPnn) and structure predictors, AlphaFold2 on the one hand and language-based models (Omegafold, ESMfold, RGN2) on the other hand, to four de novo proteins with experimental evidence on structure. We compared the resulting predictions between the different predictors as well as to the existing experimental evidence. Results: Results from IUPred, the most widely used disorder predictor, depend heavily on the choice of parameters and differ significantly from flDPnn which has been found to outperform most other predictors in a comparative assessment study recently. Similarly, different structure predictors yielded varying results and confidence scores for de novo proteins. Conclusions: We suggest that, while in some cases protein language model based approaches might be more accurate than AlphaFold2, the structure prediction of de novo emerged proteins remains a difficult task for any predictor, be it disorder or structure.

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Dominguez, Matthew J., Jon J. McCord und R. Bryan Sutton. „Redefining the architecture of ferlin proteins: Insights into multi-domain protein structure and function“. PLOS ONE 17, Nr. 7 (28.07.2022): e0270188. http://dx.doi.org/10.1371/journal.pone.0270188.

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Ferlins are complex, multi-domain proteins, involved in membrane trafficking, membrane repair, and exocytosis. The large size of ferlin proteins and the lack of consensus regarding domain boundaries have slowed progress in understanding molecular-level details of ferlin protein structure and function. However, in silico protein folding techniques have significantly enhanced our understanding of the complex ferlin family domain structure. We used RoseTTAFold to assemble full-length models for the six human ferlin proteins (dysferlin, myoferlin, otoferlin, Fer1L4, Fer1L5, and Fer1L6). Our full-length ferlin models were used to obtain objective domain boundaries, and these boundaries were supported by AlphaFold2 predictions. Despite the differences in amino acid sequence between the ferlin proteins, the domain ranges and distinct subdomains in the ferlin domains are remarkably consistent. Further, the RoseTTAFold/AlphaFold2 in silico boundary predictions allowed us to describe and characterize a previously unknown C2 domain, ubiquitous in all human ferlins, which we refer to as C2-FerA. At present, the ferlin domain-domain interactions implied by the full-length in silico models are predicted to have a low accuracy; however, the use of RoseTTAFold and AlphaFold2 as a domain finder has proven to be a powerful research tool for understanding ferlin structure.

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Osman, Sara. „Space exploration: finding new protein conformations using AlphaFold2“. Nature Structural & Molecular Biology 30, Nr. 12 (Dezember 2023): 1835. http://dx.doi.org/10.1038/s41594-023-01186-2.

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Borkakoti, Neera, und Janet M. Thornton. „AlphaFold2 protein structure prediction: Implications for drug discovery“. Current Opinion in Structural Biology 78 (Februar 2023): 102526. http://dx.doi.org/10.1016/j.sbi.2022.102526.

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Marchal, Iris. „OpenFold provides insights into AlphaFold2’s learning behavior“. Nature Biotechnology 42, Nr. 6 (Juni 2024): 847. http://dx.doi.org/10.1038/s41587-024-02290-4.

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Yang, Yacong, Yu Hu, Fengli Yao, Jinbo Yang, Leilei Ge, Peng Wang und Ximing Xu. „Virtual screening and activity evaluation of human uric acid transporter 1 (hURAT1) inhibitors“. RSC Advances 13, Nr. 6 (2023): 3474–86. http://dx.doi.org/10.1039/d2ra07193b.

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Paiardini, Alessandro. „Protein Structure Prediction in Drug Discovery“. Biomolecules 13, Nr. 8 (17.08.2023): 1258. http://dx.doi.org/10.3390/biom13081258.

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When the results of DeepMind’s AlphaFold2 at CASP were announced in 2020, the scientific world was so amazed by how effectively it performed that “it will change everything” became the motto for this revolution [...]

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Arantes, Pablo R., Lukasz Nierzwicki, Helen Belato, Alexandra M. D'Ordine, Gerwald Jogl, George Lisi und Giulia Palermo. „Assessing structure and dynamics of AlphaFold2 prediction of GeoCas9“. Biophysical Journal 121, Nr. 3 (Februar 2022): 45a. http://dx.doi.org/10.1016/j.bpj.2021.11.2474.

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Zhang, Heng, Daniel S. Zhu und Jieqing Zhu. „Family-wide analysis of integrin structures predicted by AlphaFold2“. Computational and Structural Biotechnology Journal 21 (2023): 4497–507. http://dx.doi.org/10.1016/j.csbj.2023.09.022.

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Wang, Lei, Zehua Wen, Shi-Wei Liu, Lihong Zhang, Cierra Finley, Ho-Jin Lee und Hua-Jun Shawn Fan. „Overview of AlphaFold2 and breakthroughs in overcoming its limitations“. Computers in Biology and Medicine 176 (Juni 2024): 108620. http://dx.doi.org/10.1016/j.compbiomed.2024.108620.

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Yao, Wenyi. „Protein structure prediction based on deep learning: HER2 in complex with a covalent inhibitor“. Advances in Engineering Innovation 6, Nr. 1 (20.02.2024): 13–20. http://dx.doi.org/10.54254/2977-3903/6/2024056.

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HER2 protein overexpression is associated with the malignant degree and poor prognosis of breast cancer. HER2 levels are elevated in 20% of breast tumors. Several covalent tyrosine kinase inhibitors have been found to reduce tumor cell survival and proliferation in vitro and inhibit downstream HER2 signaling. In the field of protein structure prediction, AlphaFold2, which achieved excellent results in CASP14, can periodically predict protein structures with atomic precision in the absence of similar protein structures. In this study, AlphaFold2 was used to predict the monomeric structure of the HER2 protein. This predicted structure was compared to the conformation of HER2 in complex with a covalent inhibitor, allowing for an examination of the conformational changes induced by the inhibitor. By combining the conformational changes of HER2 protein with the docking results of Protein-Ligand Interaction Profiler, other potential binding sites were identified, which could further reveal the mechanism of drug discovery.

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Tunyasuvunakool, Kathryn, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland et al. „Highly accurate protein structure prediction for the human proteome“. Nature 596, Nr. 7873 (22.07.2021): 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 residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

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Aderinwale, Tunde, Vijay Bharadwaj, Charles Christoffer, Genki Terashi, Zicong Zhang, Rashidedin Jahandideh, Yuki Kagaya und Daisuke Kihara. „Real-time structure search and structure classification for AlphaFold protein models“. Communications Biology 5, Nr. 1 (05.04.2022). 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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Baltzis, Athanasios, Leila Mansouri, Suzanne Jin, Björn E. Langer, Ionas Erb und Cedric Notredame. „Highly significant improvement of protein sequence alignments with AlphaFold2“. Bioinformatics, 21.09.2022. http://dx.doi.org/10.1093/bioinformatics/btac625.

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Abstract Motivation Protein sequence alignments are essential to structural, evolutionary and functional analysis, but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis. Results Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly closer to the structural reference than sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis. Availability and implementation All data, analyses and results are available on Zenodo (https://doi.org/10.5281/zenodo.7031286). The code and scripts have been deposited in GitHub (https://github.com/cbcrg/msa-af2-nf) and the various containers in (https://cloud.sylabs.io/library/athbaltzis/af2/alphafold, https://hub.docker.com/r/athbaltzis/pred). Supplementary information Supplementary data are available at Bioinformatics online.

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Alderson, T. Reid, Iva Pritišanac, Đesika Kolarić, Alan M. Moses und Julie D. Forman-Kay. „Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2“. Proceedings of the National Academy of Sciences 120, Nr. 44 (25.10.2023). http://dx.doi.org/10.1073/pnas.2304302120.

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The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed that these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly fivefold enriched in conditionally folded IDRs over IDRs in general and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.

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van der Weg, Karel, und Holger Gohlke. „TopEnzyme: A framework and database for structural coverage of the functional enzyme space“. Bioinformatics, 08.03.2023. http://dx.doi.org/10.1093/bioinformatics/btad116.

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Abstract Motivation TopEnzyme is a database of structural enzyme models created with TopModel and is linked to the SWISS-MODEL repository and AlphaFold Protein Structure Database to provide an overview of structural coverage of the functional enzyme space for over 200,000 enzyme models. It allows the user to quickly obtain representative structural models for 60% of all known enzyme functions. Results We assessed the models with TopScore and contributed 9039 good-quality and 1297 high-quality structures. Furthermore, we compared these models to AlphaFold2 models with TopScore and found that the TopScore differs only by 0.04 on average in favor of AlphaFold2. We tested TopModel and AlphaFold2 for targets not seen in the respective training databases and found that both methods create qualitatively similar structures. When no experimental structures are available, this database will facilitate quick access to structural models across the currently most extensive structural coverage of the functional enzyme space within Swiss-Prot. Availability We provide a full web interface to the database at https://cpclab.uni-duesseldorf.de/topenzyme/. Supplementary information Supplementary data are available at Bioinformatics online.

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Zhao, Kailong, Yuhao Xia, Fujin Zhang, Xiaogen Zhou, Stan Z. Li und Guijun Zhang. „Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader“. Communications Biology 6, Nr. 1 (04.03.2023). http://dx.doi.org/10.1038/s42003-023-04605-8.

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AbstractRecognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we propose a method, PAthreader, to recognize remote templates and explore folding pathways. Firstly, we design a three-track alignment between predicted distance profiles and structure profiles extracted from PDB and AlphaFold DB, to improve the recognition accuracy of remote templates. Secondly, we improve the performance of AlphaFold2 using the templates identified by PAthreader. Thirdly, we explore protein folding pathways based on our conjecture that dynamic folding information of protein is implicitly contained in its remote homologs. The results show that the average accuracy of PAthreader templates is 11.6% higher than that of HHsearch. In terms of structure modelling, PAthreader outperform AlphaFold2 and ranks first on the CAMEO blind test for the latest three months. Furthermore, we predict protein folding pathways for 37 proteins, in which the results of 7 proteins are almost consistent with those of biological experiments, and the other 30 human proteins have yet to be verified by biological experiments, revealing that folding information can be exploited from remote homologous structures.

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Peng, Zhenling, Wenkai Wang, Hong Wei, Xiaoge Li und Jianyi Yang. „Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15“. Proteins: Structure, Function, and Bioinformatics, 10.08.2023. 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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Lau, Andy M., Shaun M. Kandathil und David T. Jones. „Merizo: a rapid and accurate protein domain segmentation method using invariant point attention“. Nature Communications 14, Nr. 1 (19.12.2023). http://dx.doi.org/10.1038/s41467-023-43934-4.

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AbstractThe AlphaFold Protein Structure Database, containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the database is precluded by an urgent need for tools that allow the efficient traversal, discovery, and documentation of its contents. Identifying domain regions in the database is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on AlphaFold2 models via self-distillation, enabling it to be applied to both experimental and AlphaFold2 models. As proof of concept, we apply Merizo to the human proteome, identifying 40,818 putative domains that can be matched to CATH representative domains.

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Goulet, Adeline, und Christian Cambillau. „Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device“. Frontiers in Molecular Biosciences 9 (09.05.2022). http://dx.doi.org/10.3389/fmolb.2022.907452.

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In 2021, the release of AlphaFold2 - the DeepMind’s machine-learning protein structure prediction program - revolutionized structural biology. Results of the CASP14 contest were an immense surprise as AlphaFold2 successfully predicted 3D structures of nearly all submitted protein sequences. The AlphaFold2 craze has rapidly spread the life science community since structural biologists as well as untrained biologists have now the possibility to obtain high-confidence protein structures. This revolution is opening new avenues to address challenging biological questions. Moreover, AlphaFold2 is imposing itself as an essential step of any structural biology project, and requires us to revisit our structural biology workflows. On one hand, AlphaFold2 synergizes with experimental methods including X-ray crystallography and cryo-electron microscopy. On the other hand, it is, to date, the only method enabling structural analyses of large and flexible assemblies resistant to experimental approaches. We illustrate this valuable application of AlphaFold2 with the structure prediction of the whole host adhesion device from the Lactobacillus casei bacteriophage J-1. With the ongoing improvement of AlphaFold2 algorithms and notebooks, there is no doubt that AlphaFold2-driven biological stories will increasingly be reported, which questions the future directions of experimental structural biology.

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Rajpal, Simar, und Daniel Plymire. „Comparison Of 3D Structures Generated by AlphaFold2 to Experimental Structures In Oncogenic Proteins“. Journal of Student Research 12, Nr. 4 (30.11.2023). http://dx.doi.org/10.47611/jsrhs.v12i4.5532.

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AlphaFold2 is a machine-learning algorithm that can predict the 3D structure of proteins. 3D protein structures are essential for understanding the function of oncogenic proteins, which can potentially cause cancer. In this study, we compared the structure of 26 oncogenic proteins found experimentally and computationally using AlphaFold2. We used RMSD values to measure how well the AlphaFold2 model fit the experimentally derived protein structures. RMSD values for the oncogenic proteins ranged from 0.204 Å to 1.980 Å with an average of 0.633 Å, showing that AlphaFold2 was a promising tool for predicting the 3D structure of oncogenic proteins. However, we noted that AlphaFold2 has limitations in predicting the structure of highly disordered proteins, proteins with multiple conformations, mutated and artificial proteins, and proteins that are not well-studied experimentally. Our study suggests that AlphaFold2 could be used to identify new targets for cancer treatment and design drugs that can fit into the binding sites of oncogenic proteins. We also hypothesize that AlphaFold2 could be improved by increasing the amount of data available for training, improving the resolution of current data, and using it in conjunction with other protein structure models. We believe that AlphaFold2 is a powerful tool for predicting the structure of oncogenic proteins. Despite the current limitations, we are optimistic that future research will improve AlphaFold2, finding use in cancer research.

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Saldaño, Tadeo, Nahuel Escobedo, Julia Marchetti, Diego Javier Zea, Juan Mac Donagh, Ana Julia Velez Rueda, Eduardo Gonik et al. „Impact of protein conformational diversity on AlphaFold predictions“. Bioinformatics, 05.04.2022. http://dx.doi.org/10.1093/bioinformatics/btac202.

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Abstract Motivation After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here, we address the performance of AlphaFold2 predictions obtained through ColabFold under this ensemble paradigm. Results Using a curated collection of apo–holo pairs of conformers, we found that AlphaFold2 predicts the holo form of a protein in ∼70% of the cases, being unable to reproduce the observed conformational diversity with the same error for both conformers. More importantly, we found that AlphaFold2's performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo–holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions. Availability and implementation Data and code used in this manuscript are publicly available at https://gitlab.com/sbgunq/publications/af2confdiv-oct2021. Supplementary information Supplementary data are available at Bioinformatics online.

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Uzoeto, Henrietta Onyinye, Samuel Cosmas, Toluwalope Temitope Bakare und 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, Nr. 1 (17.05.2024). 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.

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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, 02.11.2023. 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.

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Crunkhorn, Sarah. „Assessing accuracy of AlphaFold2“. Nature Reviews Drug Discovery, 31.05.2024. http://dx.doi.org/10.1038/d41573-024-00090-8.

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Tsaban, Tomer, Julia K. Varga, Orly Avraham, Ziv Ben-Aharon, Alisa Khramushin und Ora Schueler-Furman. „Harnessing protein folding neural networks for peptide–protein docking“. Nature Communications 13, Nr. 1 (10.01.2022). http://dx.doi.org/10.1038/s41467-021-27838-9.

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AbstractHighly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide–protein interactions. Our simple implementation of AlphaFold2 generates peptide–protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide–protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

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Abanades, Brennan, Wing Ki Wong, Fergus Boyles, Guy Georges, Alexander Bujotzek und Charlotte M. Deane. „ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins“. Communications Biology 6, Nr. 1 (29.05.2023). http://dx.doi.org/10.1038/s42003-023-04927-7.

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AbstractImmune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of deep learning models trained to accurately predict the structure of antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) and T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state of the art accuracy while being far faster than AlphaFold2. For example, on a benchmark of 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.81Å, a 0.09Å improvement over AlphaFold-Multimer, while being over a hundred times faster. Similar results are also achieved for nanobodies, (NanoBodyBuilder2 predicts CDR-H3 loops with an average RMSD of 2.89Å, a 0.55Å improvement over AlphaFold2) and TCRs. By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction. ImmuneBuilder is made freely available, both to download (https://github.com/oxpig/ImmuneBuilder) and to use via our webserver (http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred). We also make available structural models for ~150 thousand non-redundant paired antibody sequences (https://doi.org/10.5281/zenodo.7258553).

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Xia, Yuhao, Kailong Zhao, Dong Liu, Xiaogen Zhou und Guijun Zhang. „Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning“. Communications Biology 6, Nr. 1 (01.12.2023). http://dx.doi.org/10.1038/s42003-023-05610-7.

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AbstractAccurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

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Zheng, Lingyan, Shuiyang Shi, Xiuna Sun, Mingkun Lu, Yang Liao, Sisi Zhu, Hongning Zhang et al. „MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics“. Briefings in Bioinformatics 25, Nr. 2 (22.01.2024). http://dx.doi.org/10.1093/bib/bbae006.

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Abstract Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled ‘MoDAFold’ was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

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