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

Subjects: “…Protein structure prediction…”
Get full text

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

Subjects: Get full text

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…”
Get full text

Computer simulation of the spatial structure of MUC 1 peptides capable of inhibiting apoptosis by N. V. Ivanisenko, I. N. Lavrik, V. A. Ivanisenko

Subjects: Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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). …”
Get full text

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. …”
Get full text

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. …”
Get full text