Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure ...predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The link between cofactor binding and protein activity is well-established. However, how cofactor interactions modulate folding of large proteins remains unknown. We use optical tweezers, clustering ...and global fitting to dissect the folding mechanism of Drosophila cryptochrome (dCRY), a 542-residue protein that binds FAD, one of the most chemically and structurally complex cofactors in nature. We show that the first dCRY parts to fold are independent of FAD, but later steps are FAD-driven as the remaining polypeptide folds around the cofactor. FAD binds to largely unfolded intermediates, yet with association kinetics above the diffusion-limit. Interestingly, not all FAD moieties are required for folding: whereas the isoalloxazine ring linked to ribitol and one phosphate is sufficient to drive complete folding, the adenosine ring with phosphates only leads to partial folding. Lastly, we propose a dCRY folding model where regions that undergo conformational transitions during signal transduction are the last to fold.
Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we ...present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.
Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation ...of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.
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IJS, KILJ, NUK, PNG, UL, UM
Cyclic-nucleotide binding (CNB) domains are structurally and evolutionarily conserved signaling modules that regulate proteins with diverse folds and functions. Despite a wealth of structural ...information, the mechanisms by which CNB domains couple cyclic-nucleotide binding to conformational changes involved in signal transduction remain unknown. Here we combined single-molecule and computational approaches to investigate the conformation and folding energetics of the two CNB domains of the regulatory subunit of protein kinase A (PKA). We found that the CNB domains exhibit different conformational and folding signatures in the apo state, when bound to cAMP, or when bound to the PKA catalytic subunit, underscoring their ability to adapt to different binding partners. Moreover, we show while the two CNB domains have near-identical structures, their thermodynamic coupling signatures are divergent, leading to distinct cAMP responses and differential mutational effects. Specifically, we demonstrate mutation W260A exerts local and allosteric effects that impact multiple steps of the PKA activation cycle. Taken together, these results highlight the complex interplay between folding energetics, conformational dynamics, and thermodynamic signatures that underlies structurally conserved signaling modules in response to ligand binding and mutational effects.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we ...develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.Biomolecular condensates help cells organise their content in space and time. Here the authors report a machine learning driven methodology to predict the composition of biomolecular condensates and they then validate their predictions against the composition of known biomolecular condensates.
Objective: Prospective studies have suggested that substituting whole grain for refined grain products may lower the risk of overweight and obesity. Breakfast cereal intake is a major source of whole ...and refined grains and has also been associated with having a lower BMI. The aim of this study was to prospectively assess the association between whole and refined grain breakfast cereal intakes and risk of overweight (BMI ≥ 25 kg/m2) and weight gain.
Research Methods and Procedures: We examined 17, 881 U.S. male physicians 40 to 84 years of age in 1982 who were free of cardiovascular disease, diabetes mellitus, and cancer at baseline and reported measures of breakfast cereal intake, weight, and height.
Results: Over 8 and 13 years of follow‐up, respectively, men who consumed breakfast cereal, regardless of type, consistently weighed less than those who consumed breakfast cereals less often (p value for trend = 0.01). Whole and refined grain breakfast cereal intake was inversely associated with body weight gain over 8 years, after adjustment for age, smoking, baseline BMI, alcohol intake, physical activity, hypertension, high cholesterol, and use of multivitamins. Compared with men who rarely or never consumed breakfast cereals, those who consumed ≥1 serving/d of breakfast cereals were 22% and 12% less likely to become overweight during follow‐up periods of 8 and 13 years (relative risk, 0.78 and 0.88; 95% confidence interval, 0.67 to 0.91 and 0.76 to 1.00, respectively).
Discussion: BMI and weight gain were inversely associated with intake of breakfast cereals, independently of other risk factors.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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