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.

The accumulation of protein deposits in neurodegenerative diseases has been hypothesized to depend on a metastable subproteome vulnerable to aggregation. To investigate this phenomenon and the mechanisms that regulate it, we measured the solubility of the proteome in the mouse Neuro2a cell line under six different protein homeostasis stresses: 1) Huntington’s disease proteotoxicity, 2) Hsp70, 3) Hsp90, 4) proteasome, 5) endoplasmic reticulum (ER)-mediated folding inhibition, and 6) oxidative stress. Overall, we found that about one-fifth of the proteome changed solubility with almost all of the increases in insolubility were counteracted by increases in solubility of other proteins. Each stress directed a highly specific pattern of change, which reflected the remodeling of protein complexes involved in adaptation to perturbation, most notably, stress granule (SG) proteins, which responded differently to different stresses. These results indicate that the protein homeostasis system is organized in a modular manner and aggregation patterns were not correlated with protein folding stability (ΔG). Instead, distinct cellular mechanisms regulate assembly patterns of multiple classes of protein complexes under different stress conditions.

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Parasitic protists belonging to the genus Leishmania synthesize the non-canonical carbohydrate reserve, mannogen, which is composed of b-1,2-mannan oligosaccharides. Here, we identify a class of dualactivity mannosyltransferase/phosphorylases (MTPs) that catalyze both the sugar nucleotide-dependent biosynthesis and phosphorolytic turnover of mannogen. Structural and phylogenic analysis shows that while the MTPs are structurally related to bacterial mannan phosphorylases, they constitute a distinct family of glycosyltransferases (GT108) that have likely been acquired by horizontal gene transfer from gram-positive bacteria. The seven MTPs catalyze the constitutive synthesis and turnover of mannogen. This metabolic rheostat protects obligate intracellular parasite stages from nutrient excess, and is essential for thermotolerance and parasite infectivity in the mammalian host. Our results suggest that the acquisition and expansion of the MTP family in Leishmania increased the metabolic flexibility of these protists and contributed to their capacity to colonize new host niches.

A crucial factor for the approval and success of any drug is how it behaves in the body. Many drugs, however, do not reach the market due to poor efficacy or unacceptable side effects. It is therefore important to take these into consideration early in the drug development process, both in the prioritization of potential hits, and optimization of lead compounds. In silico approaches offer a cost and time-effective approach to rapidly screen and optimize pharmacokinetic and toxicity properties. Here we demonstrate the use of the comprehensive analysis system pkCSM, to allow early identification of potential problems, prioritization of hits, and optimization of leads.

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Graphical abstract

This Viewpoint proposes using large longitudinal databases of patient information that describe clinical trajectories and outcomes to preemptively quantify the risk of overdiagnosis posed by new digital screening technologies.