Direct coupling analysis and the attention mechanism by Francesco Caredda, Andrea Pagnani

Subjects: “…Protein structure prediction…”
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The protein structurome of Orthornavirae and its dark matter by Pascal Mutz, Antonio Pedro Camargo, Harutyun Sahakyan, Uri Neri, Anamarija Butkovic, Yuri I. Wolf, Mart Krupovic, Valerian V. Dolja, Eugene V. Koonin

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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

Subjects: “…protein structure prediction…”
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Computer simulation of the spatial structure of MUC 1 peptides capable of inhibiting apoptosis by N. V. Ivanisenko, I. N. Lavrik, V. A. Ivanisenko

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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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Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction. by Akash Bahai, Chee Keong Kwoh, Yuguang Mu, Yinghui Li

“…Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, have shown some promise for RNA structure prediction as well, but face unique challenges. …”
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Modify possibilities of the secondary structures prediction method by Alvydas Špokas, Albertas Timinskas

“… It was analyzed dependence of the average accuracy of secondary protein structure prediction on various GOR algorithm modifications. …”
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NOVEL MUTATIONS IN THE NON-STRUCTURE PROTEIN 2 OF SARS-CoV-2 by Mohsen Nakhaei, Zohreh-Al-Sadat Ghoreshi, Mohammad Rezaei Zadeh Rukerd, Hedyeh Askarpour, Nasir

“…Bioinformatics analysis was conducted to investigate the effect of mutations on protein structure, prediction of homology models, phylogeny tree. …”
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An outlook on structural biology after AlphaFold: tools, limits and perspectives by Serena Rosignoli, Maddalena Pacelli, Francesca Manganiello, Alessandro Paiardini

“…AlphaFold and similar groundbreaking, AI‐based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab‐initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. …”
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Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers by Mohammed Abdul Lateef Junaid

“…AI-driven tools like AlphaFold, which have significantly advanced protein structure prediction, are evaluated alongside AI’s role in expediting drug discovery. …”
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Exploration of predictive markers associated with primary angle-closure glaucoma risk in companion dogs using genomic information by Ji Hyun Jun, Gwang Hyeon Lee, Zultsetseg Byambasuren, Hong Sik Kong

“…To assess the impact of non-synonymous SNP (nsSNPs), functional analysis of candidate genes, Hazard Assessment, and protein structure prediction were conducted. Sequencing for SNP validation involved samples from 95 dogs of ten breeds with reported domestic and international glaucoma cases. …”
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Global atlas of predicted functional domains in Legionella pneumophila Dot/Icm translocated effectors by Deepak T Patel, Peter J Stogios, Lukasz Jaroszewski, Malene L Urbanus, Mayya Sedova, Cameron Semper, Cathy Le, Abraham Takkouche, Keita Ichii, Julie Innabi, Dhruvin H Patel, Alexander W Ensminger, Adam Godzik, Alexei Savchenko

“…By leveraging advancements in 3D protein structure prediction, we provide a comprehensive structural analysis of 368 L. pneumophila effectors, representing a global atlas of predicted functional domains summarized in a database ( https://pathogens3d.org/legionella-pneumophila ). …”
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Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization by Jiwei Liu, Lei Wu, Anqi Xie, Weici Liu, Zhao He, Yuan Wan, Wenjun Mao

“…AI’s exceptional performance in protein structure prediction and molecular interaction simulation has introduced novel perspectives and tools for Nb design and optimization. …”
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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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Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis by Zhongli Wang, Zhongli Wang, Gaopei Zhu, Shixue Li, Shixue Li

“…Citation analysis highlighted two major breakthroughs: AlphaFold’s protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). …”
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Tat-fimbriae (“tafi”): An unusual type of haloarchaeal surface structure depending on the twin-arginine translocation pathway by Anna V. Galeva, Dahe Zhao, Alexey S. Syutkin, Marina Yu Topilina, Sergei Yu Shchyogolev, Elena Yu Pavlova, Olga M. Selivanova, Igor I. Kireev, Alexey K. Surin, Gennady L. Burygin, Jingfang Liu, Hua Xiang, Mikhail G. Pyatibratov

“…Molecular genetic evidence demonstrates TafA was transported through the twin-arginine translocation pathway (Tat-pathway). Based on protein structure prediction (including AlphaFold 3), tafi exhibits a linear structure: TafC at the tip, TafE acting as an adapter, TafA forming the core filament, and they link the fourth subunit TafF, anchoring tafi to the cell wall. …”
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Characterization of the C-terminal tail of the Arc protein. by Melissa Boldridge, Jody Shimabukuro, Keith Nakamatsu, Christian Won, Chad Jansen, Helen Turner, Lei Wang

“…We compared the sequences of Arc proteins in different mammal species and found that the amino-acid composition in the C-terminal tail region has a significantly higher degree of variation rate than the rest of the protein. Structural prediction programs suggested that the C-terminal tail is structurally disordered. …”
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Deep learning–based assessment of missense variants in the COG4 gene presented with bilateral congenital cataract by Shaohua Zhang, Yang Sun, Yinghong Ji, Li Ning, Binghe Xiao, Maierdanjiang Ainiwaer, Houyi Liu, Yingying Hong

“…Objective We compared the protein structure and pathogenicity of clinically relevant variants of the COG4 gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time.Methods and analysis The sequences of clinically relevant Cog4 missense variants (one novel identified p.Y714F and three pre-existing p.G512R, p.R729W and p.L769R from Uniprot Q9H9E3) were imported into AF2 for protein structural prediction, and the pathogenicity was estimated using AM and ThermoMPNN. …”
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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

“…Over the last decade, countless in-silico methods have been developed to predict the pathogenicity of point mutations on resolved structures, but no studies have evaluated their capabilities on unresolved protein structures predicted by AF2. Herein, we investigated Alzheimer's disease (AD)-causing coding variants of the triggering receptor expressed on myeloid cells 2 (TREM2) receptor using in-silico mutagenesis techniques on the AF2-predicted structure. …”
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