Prediction of protein structures, functions and interactions using the IntFOLD7, MultiFOLD and ModFOLDdock servers

McGuffin, Liam J.; Edmunds, Nicholas S.; Genc, Ahmet G; Alharbi, Shuaa M A; Salehe, Bajuna R; Adiyaman, Recep

doi:10.1093/nar/gkad297

Cited by 37 publications

(18 citation statements)

References 25 publications

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“…Structure of ClC3 and the functional ClC3 knockout were calculated from sequences as pdb (Protein Data Bank) files using IntFOLD7 ( McGuffin et al, 2023 ). From these structures, mages were produced using the pdb imaging program https://www.rcsb.org/3d-view with the region knocked out indicated (see Supplemental Fig.…”

Section: Methodsmentioning

confidence: 99%

Chloride/proton antiporters ClC3 and ClC5 support bone formation in mice

Tourkova,

Larrouture,

Liu

et al. 2024

Bone Reports

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Section: Methodsmentioning

confidence: 99%

Chloride/proton antiporters ClC3 and ClC5 support bone formation in mice

Tourkova,

Larrouture,

Liu

et al. 2024

Bone Reports

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“…Three of them used AI-based predictions: AF2 for subunit modeling, AF2 for subunit and subcomplex modeling, and AF2 and RF for subunit and subcomplex modeling . By the end of 2022, the CASP15 competition highlighted the efficiency of ML approaches for the structural determination of protein assemblies. , While the need for MSAs on both partners appears to be a limitation of AF2 when attempting to predict protein interactions, strategies have been proposed to overcome it. On the one hand, it has been shown that MSA can be denoised .…”

Section: Predicting Oligomeric Modelsmentioning

confidence: 99%

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Versini,

Sritharan,

Aykac Fas

et al. 2023

J. Chem. Inf. Model.

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AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

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“…Protein tertiary structures were modelled using AlphaFold2 (59) via the ColabFold v1.5.2-patch (60) or the AlphaFold patch of ChimeraX. Mutimeric structures were modelled by MultiFOLD (61) or AlphaFold2 and validated by ModFOLDdock (62). Structure similarity searches was done using Phyre2 (63) or FoldSeek (64).…”

Section: Protein Structure Prediction Protein Similarity Searches And...mentioning

confidence: 99%

Diverse Genetic Contexts of HicA Toxin Domains Propose a Role in Anti-Phage Defense

Gerdes

2023

Preprint

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Toxin – antitoxin (TA) modules are prevalent in prokaryotic genomes, often in substantial numbers. For instance, theMycobacterium tuberculosisgenome alone harbors close to 100 TA modules, half of which belong to a singular type. Traditionally ascribed multiple biological roles, recent insights challenge these notions and instead indicate a predominant function in phage defense. TAs are often located within Defense Islands, genomic regions that encode various defense systems. The analysis of genes within Defense Islands have unveiled a wide array of systems, including TAs that serve in anti-phage defence. Prokaryotic cells are equipped with anti-phage Viperins that, analogous to their mammalian counterparts, inhibit viral RNA transcription. Additionally, bacterial Structural Maintenance of Chromosome (SMC) proteins combat plasmid intrusion by recognizing foreign DNA signatures. This study undertakes a comprehensive bioinformatics analysis of genetic elements encoding the HicA double-stranded RNA-binding domain, complemented by protein structure modeling. The HicA toxin domains are found in at least 14 distinct contexts and thus exhibit a remarkable genetic diversity. Traditional bicistronic TA operons represent eight of these contexts, while four are characterized by monocistronic operons encoding fused HicA domains. Two contexts involvehicAadjacent to genes that encode bacterial Viperins. Notably, genes encoding RelE toxins are also adjacent to Viperin genes in some instances. This configuration hints at a synergistic enhancement of Viperin-mediated anti-phage action by HicA and RelE toxins. The discovery of a HicA domain merged with an SMC domain is compelling, prompting further investigation into its potential roles.ImportanceProkaryotic organisms harbor a multitude of Toxin – Antitoxin (TA) systems, which have long puzzled scientists as “genes in search for a function”. Recent scientific advancement have shed light on a primary role of TAs as anti-phage defense mechanisms. To gain an overview of TAs it is important to analyze their genetic contexts that can give hints on function and guide future experimental inquiries. This manuscript describes a thorough bioinformatics examination of genes encoding the HicA toxin domain, revealing its presence in no fewer than 14 unique genetic arrangements. Some configurations notably align with anti-phage activities, underscoring potential roles in microbial immunity. These insights robustly reinforce the hypothesis that HicA toxins are integral components of the prokaryotic anti-phage defense repertoire. The elucidation of these genetic contexts not only advances our understanding of TAs but also contributes to a paradigm shift in how we perceive their functionality within the microbial world.

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Prediction of protein structures, functions and interactions using the IntFOLD7, MultiFOLD and ModFOLDdock servers

Cited by 37 publications

References 25 publications

Chloride/proton antiporters ClC3 and ClC5 support bone formation in mice

Chloride/proton antiporters ClC3 and ClC5 support bone formation in mice

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Diverse Genetic Contexts of HicA Toxin Domains Propose a Role in Anti-Phage Defense

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