A sequence-based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum

Random, de novo, and conserved proteins: How structure and disorder predictors perform differently

Understanding the emergence and structural characteristics of de novo and random proteins is crucial for unraveling protein evolution and designing novel enzymes. However, experimental determination of their structures remains challenging. Recent advancements in protein structure prediction, particularly with AlphaFold2 (AF2), have expanded our knowledge of protein structures, but their applicability to de novo and random proteins is unclear. In this study, we investigate the structural predictions and confidence scores of AF2 and protein language model-based predictor ESMFold for de novo and conserved proteins from Drosophila and a dataset of comparable random proteins. We find that the structural predictions for de novo and random proteins differ significantly from conserved proteins. Interestingly, a positive correlation between disorder and confidence scores (pLDDT) is observed for de novo and random proteins, in contrast to the negative correlation observed for conserved proteins. Furthermore, the performance of structure predictors for de novo and random proteins is hampered by the lack of sequence identity. We also observe fluctuating median predicted disorder among different sequence length quartiles for random proteins, suggesting an influence of sequence length on disorder predictions. In conclusion, while structure predictors provide initial insights into the structural composition of de novo and random proteins, their accuracy and applicability to such proteins remain limited. Experimental determination of their structures is necessary for a comprehensive understanding. The positive correlation between disorder and pLDDT could imply a potential for conditional folding and transient binding interactions of de novo and random proteins.

AlphaFold2-guided description of CoBaHMA, a novel family of bacterial domains within the heavy-metal-associated superfamily

Three-dimensional (3D) structure information, now available at the proteome scale, may facilitate the detection of remote evolutionary relationships in protein superfamilies. Here, we illustrate this with the identification of a novel family of protein domains related to the ferredoxin-like superfold, by combining (i) transitive sequence similarity searches, (ii) clustering approaches, and (iii) the use of AlphaFold2 3D structure models. Domains of this family were initially identified in relation with the intracellular biomineralization of calcium carbonates by Cyanobacteria. They are part of the large heavy-metal-associated (HMA) superfamily, departing from the latter by specific sequence and structural features. In particular, most of them share conserved basic amino acids  (hence their name CoBaHMA for Conserved Basic residues HMA), forming a positively charged surface, which is likely to interact with anionic partners. CoBaHMA domains are found in diverse modular organizations in bacteria, existing in the form of monodomain proteins or as part of larger proteins, some of which are membrane proteins involved in transport or lipid metabolism. This suggests that the CoBaHMA domains may exert a regulatory function, involving interactions with anionic lipids. This hypothesis might have a particular resonance in the context of the compartmentalization observed for cyanobacterial intracellular calcium carbonates.

AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility

Predicting whether two proteins physically interact is one of the holy grails of computational biology, galvanized by rapid advancements in deep learning. AlphaFold2, although not developed with this goal, is promising in this respect. Here, I test the prediction capability of AlphaFold2 on a very challenging data set, where proteins are structurally compatible, even when they do not interact. AlphaFold2 achieves high discrimination between interacting and non-interacting proteins, and the cases of misclassifications can either be rescued by revisiting the input sequences or can suggest false positives and negatives in the data set. AlphaFold2 is thus not impaired by the compatibility between protein structures and has the potential to be applied on a large scale.

Toxoplasma membrane inositol phospholipid binding protein TgREMIND is essential for secretory organelle function and host infection

Apicomplexan parasites possess specialized secretory organelles called rhoptries, micronemes, and dense granules that play a vital role in host infection. In this study, we demonstrate that TgREMIND, a protein found in Toxoplasma gondii, is necessary for the biogenesis of rhoptries and dense granules. TgREMIND contains a Fes-CIP4 homology-Bin/Amphiphysin/Rvs (F-BAR) domain, which binds to membrane phospholipids, as well as a novel uncharacterized domain that we have named REMIND (regulator of membrane-interacting domain). Both the F-BAR domain and the REMIND are crucial for TgREMIND functions. When TgREMIND is depleted, there is a significant decrease in the abundance of dense granules and abnormal transparency of rhoptries, leading to a reduction in protein secretion from these organelles. The absence of TgREMIND inhibits host invasion and parasite dissemination, demonstrating that TgREMIND is essential for the proper function of critical secretory organelles required for successful infection by Toxoplasma.

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

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.

Insights into the solution structure and transcriptional regulation of the MazE9 antitoxin in Mycobacterium tuberculosis

The present study endeavors to decode the details of the transcriptional autoregulation effected by the MazE9 antitoxin of the Mycobacterium tuberculosis MazEF9 toxin-antitoxin system. Regulation of this bicistronic operon at the level of transcription is a critical biochemical process that is key for the organism's stress adaptation and virulence. Here, we have reported the solution structure of the DNA binding domain of MazE9 and scrutinized the thermodynamic and kinetic parameters operational in its interaction with the promoter/operator region, specific to the mazEF9 operon. A HADDOCK model of MazE9 bound to its operator DNA has been calculated based on the information on interacting residues obtained from these studies. The thermodynamics and kinetics of the interaction of MazE9 with the functionally related mazEF6 operon indicate that the potential for intracellular cross-regulation is unlikely. An interesting feature of MazE9 is the cis ⇌ trans conformational isomerization of proline residues in the intrinsically disordered C-terminal domain of this antitoxin.