Molecular Cloning and AlphaFold Modeling of Thyrotropin (ag-TSH) From the Amazonian Fish Pirarucu () by Renan Passos Freire, Jorge Enrique Hernandez-Gonzalez, Eliana Rosa Lima, Miriam Fussae Suzuki, João Ezequiel de Oliveira, Lucas Simon Torati, Paolo Bartolini, Carlos Roberto Jorge Soares

“…The Arapaima gigas thyrotropin (ag-TSH) model, solved by AlphaFold, was used in molecular dynamics simulations with Scleropages formosus receptor, providing similar values of free energy ΔG bind and ΔG PMF in comparison with Homo sapiens model. …”
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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms by Nicola Bordin, Ian Sillitoe, Vamsi Nallapareddy, Clemens Rauer, Su Datt Lam, Vaishali P. Waman, Neeladri Sen, Michael Heinzinger, Maria Littmann, Stephanie Kim, Sameer Velankar, Martin Steinegger, Burkhard Rost, Christine Orengo

“…A new protein domain classification protocol incorporating deep learning strategies for detecting sequence and structure similarities between domain is used to systematically study and analyse the predicted AlphaFold2 structural models for proteins of 21 organisms.…”
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Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps by Maytha Alshammari, Willy Wriggers, Jiangwen Sun, Jing He

Subjects: “…AlphaFold2…”
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Leveraging AlphaFold 2 as a general-purpose architecture for small molecule conformer prediction by Lesman, D

“…The major advantage of deep learning methods is their relatively high speeds and low costs. Here, we build on AlphaFold2’s architecture for the purpose of MCG. We explore how various architectures and loss functions modify performance for MCG. …”

AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes by Catriona H. Gordon, Emily Hendrix, Yi He, Mark C. Walker

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Use of an Integrated Approach Involving AlphaFold Predictions for the Evolutionary Taxonomy of <i>Duplodnaviria</i> Viruses by Peter Evseev, Daria Gutnik, Mikhail Shneider, Konstantin Miroshnikov

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AlphaFold-Predicted Structures of KCTD Proteins Unravel Previously Undetected Relationships among the Members of the Family by Luciana Esposito, Nicole Balasco, Giovanni Smaldone, Rita Berisio, Alessia Ruggiero, Luigi Vitagliano

“…This scenario is rapidly changing due to the release of the protein three-dimensional models predicted by AlphaFold (AF). Here, we exploited the structural information contained in the AF database to gain insights into the relationships among the members of the KCTD family with the aim of facilitating the definition of the structural and molecular basis of key roles that these proteins play in many biological processes. …”
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SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures by Oz Kilim, Anikó Mentes, Balázs Pál, István Csabai, Ákos Gellért

“…Abstract Leveraging recent advances in computational modeling of proteins with AlphaFold2 (AF2) we provide a complete curated data set of all single mutations from each of the 7 main SARS-CoV-2 lineages spike protein receptor binding domain (RBD) resulting in 3819X7 = 26733 PDB structures. …”
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Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction by Zhe Liu, Weihao Pan, Weihao Li, Xuyang Zhen, Jisheng Liang, Wenxiang Cai, Fei Xu, Kai Yuan, Guan Ning Lin

Subjects: “…AlphaFold2…”
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pLDDT Values in AlphaFold2 Protein Models Are Unrelated to Globular Protein Local Flexibility by Oliviero Carugo

Subjects: “…AlphaFold…”
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Discovering deaminases using AlphaFold2: a strategy to search for tool proteins for gene editing by Yongye Huang, Jianxin Jiang, Min Wu

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AlphaFold2 and RoseTTAFold predict posttranslational modifications. Chromophore formation in GFP-like proteins by Sophia M. Hartley, Kelly A. Tiernan, Gjina Ahmetaj, Adriana Cretu, Yan Zhuang, Marc Zimmer

“…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. …”
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AlphaFold2 and RoseTTAFold predict posttranslational modifications. Chromophore formation in GFP-like proteins. by Sophia M Hartley, Kelly A Tiernan, Gjina Ahmetaj, Adriana Cretu, Yan Zhuang, Marc Zimmer

“…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. …”
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New insights into the pathogenicity of TMEM165 variants using structural modeling based on AlphaFold 2 predictions by Dominique Legrand, Mélissandre Herbaut, Zoé Durin, Guillaume Brysbaert, Muriel Bardor, Marc F. Lensink, François Foulquier

“…Until recently, the classical membrane protein topology prediction methods were unable to provide a clear picture of the organization of TMEM165 inside the cell membrane, or to explain in a convincing manner the impact of patient and experimentally-generated mutations on the transporter function of TMEM165. In this study, AlphaFold 2 was used to build a TMEM165 model that was then refined by molecular dynamics simulation with membrane lipids and water. …”
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Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold by Tiphanie Pruvost, Magali Mathieu, Steven Dubois, Bernard Maillère, Emmanuelle Vigne, Hervé Nozach

“…As human LAMP-1 structure has not yet been solved, we used the AlphaFold predicted structure of the full-length protein to combine with DMS data and ultimately finely map antibody epitopes. …”
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AlphaFold2-multimer guided high-accuracy prediction of typical and atypical ATG8-binding motifs by Tarhan Ibrahim, Virendrasinh Khandare, Federico Gabriel Mirkin, Yasin Tumtas, Doryen Bubeck, Tolga O. Bozkurt

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Exploring Structural Diversity among Adhesion Devices Encoded by Lactococcal P335 Phages with AlphaFold2 by Adeline Goulet, Jennifer Mahony, Christian Cambillau, Douwe van Sinderen

“…The protein structure prediction program AlphaFold2 is exquisitely adapted to unveil structural and functional details of such molecular machineries. …”
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A novel view of the insulin signaling pathway based on prediction of protein structure by the AI platform AlphaFold by Kyosuke Sato, Kenji Sugawara, Wataru Ogawa

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How AlphaFold2 Predicts Conditionally Folding Regions Annotated in an Intrinsically Disordered Protein Database, IDEAL by Hiroto Anbo, Koya Sakuma, Satoshi Fukuchi, Motonori Ota

“…AlphaFold2 (AF2) is a protein structure prediction program which provides accurate models. …”
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Structural Insights into the Dimeric Form of <i>Bacillus subtilis</i> RNase Y Using NMR and AlphaFold by Nelly Morellet, Pierre Hardouin, Nadine Assrir, Carine van Heijenoort, Béatrice Golinelli-Pimpaneau

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