AlphaFold 2, but not AlphaFold 3, predicts confident but unrealistic β-solenoid structures for repeat proteins by Olivia S. Pratt, Luc G. Elliott, Margaux Haon, Shahram Mesdaghi, Rebecca M. Price, Adam J. Simpkin, Daniel J. Rigden

“…AlphaFold 2 (AF2) has revolutionised protein structure prediction but, like any new tool, its performance on specific classes of targets, especially those potentially under-represented in its training data, merits attention. …”
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Leveraging AlphaFold for innovation and sustainable health research in Africa by Emmanuel Nji, Katharina C. Cramer, Nicolas V. Rüffin, Fatoumata G. Fofana, Walid Heiba, Safiétou Sankhe

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An outlook on structural biology after AlphaFold: tools, limits and perspectives by Serena Rosignoli, Maddalena Pacelli, Francesca Manganiello, Alessandro Paiardini

Subjects: “…AlphaFold…”
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Single-cell RNA sequencing and AlphaFold 3 insights into cytokine signaling and its role in uveal melanoma by Hongyan Sun, Cunzi Li, Zuhui Pu, Zuhui Pu, Ying Lu, Ying Lu, Zijing Wu, Zijing Wu, Lan Zhou, Lan Zhou, Hongzhan Lin, Yumo Wang, Tao Zi, Lisha Mou, Lisha Mou, Ming-ming Yang

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AlphaFold2 and ESMFold: A large-scale pairwise model comparison of human enzymes upon Pfam functional annotation by Matteo Manfredi, Gabriele Vazzana, Castrense Savojardo, Pier Luigi Martelli, Rita Casadio

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Structural engineering of stabilized, expanded epitope nanoparticle vaccines for HPV by Michaela Helble, Michaela Helble, Xizhou Zhu, Pratik S. Bhojnagarwala, Kevin Liaw, Yangcheng Gao, Amber Kim, Kelly Bayruns, Madison E. McCanna, Joyce Park, Kylie M. Konrath, Kylie M. Konrath, Sam Garfinkle, Sam Garfinkle, Taylor Brysgel, David B. Weiner, David B. Weiner, Daniel W. Kulp, Daniel W. Kulp

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Cross-species comparison of AlphaFold-derived G protein-coupled receptor structures reveals novel melatonin-related receptor in Neurospora crassa. by Cathryn S D Maienza, Guillaume Lamoureux, Kwangwon Lee

“…This proof-of-concept study underscores the potential of N. crassa as a model organism for circadian research and demonstrates the broader applicability of using AlphaFold2, especially when sequence similarity does not lead to candidate genes, for identifying novel receptors across different species.…”
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Structural bioinformatic study of human mitochondrial respiratory integral membrane megacomplex and its AlphaFold 3 predicted water-soluble QTY megacomplex analog by Edward Chen, Shuguang Zhang

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AlphaFold-based AI docking reveals AMPK/SIRT1-TFEB pathway modulation by traditional Chinese medicine in metabolic-associated fatty liver disease by Lulu Zhang, Yi Zheng, Mingyan Shao, Aiping Chen, Meiyi Liu, Wenlong Sun, Tianxing Li, Yini Fang, Yang Dong, Shipeng Zhao, Hui Luo, Juan Feng, Qi Wang, Lingru Li, Yanfei Zheng

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Structural bioinformatic study of six human olfactory receptors and their AlphaFold3 predicted water-soluble QTY variants and OR1A2 with an odorant octanoate and TAAR9 with spermidine by Finn Johnsson, Taner Karagöl, Alper Karagöl, Shuguang Zhang

“…Furthermore, we also used AlphaFold3 and molecular dynamics to study the odorant octanoate with OR1A2 and spermidine with TAAR9. …”
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Design of a humanized CD40 agonist antibody with specific properties using AlphaFold2 and development of an anti-PD-L1/CD40 bispecific antibody for cancer immunotherapy by Kun Du, He Huang

“…It also demonstrates that the current AlphaFold2(AlphaFold2-Multimer) can predict antibody-antigen complexes. …”
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Theoretical Study of Sphingomyelinases from <i>Entamoeba histolytica</i> and <i>Trichomonas vaginalis</i> Sheds Light on the Evolution of Enzymes Needed for Survival and Colonizati... by Fátima Berenice Ramírez-Montiel, Sairy Yarely Andrade-Guillen, Ana Laura Medina-Nieto, Ángeles Rangel-Serrano, José A. Martínez-Álvarez, Javier de la Mora, Naurú Idalia Vargas-Maya, Claudia Leticia Mendoza-Macías, Felipe Padilla-Vaca, Bernardo Franco

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Common themes in architecture and interactions of prokaryotic PolB2 and Pol V mutasomes inferred from in silico studies by Kęstutis Timinskas, Albertas Timinskas, Česlovas Venclovas

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Predicting the impact of missense mutations on an unresolved protein’s stability, structure, and function: A case study of Alzheimer’s disease-associated TREM2 R47H variant by Joshua Pillai, Kijung Sung, Chengbiao Wu

Subjects: “…AlphaFold2…”
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Direct coupling analysis and the attention mechanism by Francesco Caredda, Andrea Pagnani

“…For this reason, being able to predict their structure from the amino acid sequence has been and still is a phenomenal computational challenge that the introduction of AlphaFold solved with unprecedented accuracy. However, the inherent complexity of AlphaFold’s architectures makes it challenging to understand the rules that ultimately shape the protein’s predicted structure. …”
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Physical-aware model accuracy estimation for protein complex using deep learning method by Haodong Wang, Meng Sun, Lei Xie, Dong Liu, Guijun Zhang

“…With the breakthrough of AlphaFold2 on monomers, the research focus of structure prediction has shifted to protein complexes, driving the continued development of new methods for multimer structure prediction. …”
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A fiducial-assisted strategy compatible with resolving small MFS transporter structures in multiple conformations using cryo-EM by Pujun Xie, Yan Li, Gaëlle Lamon, Huihui Kuang, Da-Neng Wang, Nathaniel J. Traaseth

“…The latter was enabled through AlphaFold2 predictions, which minimized guesswork and reduced the need for screening several constructs. …”
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Severe deviation in protein fold prediction by advanced AI: a case study by Jacinto López-Sagaseta, Alejandro Urdiciain

“…Abstract Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences. …”
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Protein identification using Cryo-EM and artificial intelligence guides improved sample purification by Kenneth D. Carr, Dane Evan D. Zambrano, Connor Weidle, Alex Goodson, Helen E. Eisenach, Harley Pyles, Alexis Courbet, Neil P. King, Andrew J. Borst

“…This identification was validated through comparisons with DLST structures in the Protein Data Bank, AlphaFold 3 predictions based on the DLST sequence from our E. coli expression vector, and traditional biochemical methods. …”
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AI, big data, and robots for the evolution of biotechnology by Haseong Kim

“…In the biosciences field in Korea, such technologies have become known to the local public with the introduction of the AI doctor Watson in large number of hospitals. Additionally, AlphaFold, a technology resembling the AI AlphaGo for the game Go, has surpassed the limit on protein folding predictions—the most challenging problems in the field of protein biology. …”
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