Cell fate decisions are critical for life, yet little is known about how their reliability is achieved when signals are noisy and fluctuating with time. In this study, we show that in budding yeast, the decision of cell cycle commitment (Start) is determined by the time integration of its triggering signal Cln3. We further identify the Start repressor, Whi5, as the integrator. The instantaneous kinase activity of Cln3-Cdk1 is recorded over time on the phosphorylated Whi5, and the decision is made only when phosphorylated Whi5 reaches a threshold. Cells adjust the threshold by modulating Whi5 concentration in different nutrient conditions to coordinate growth and division. Our work shows that the strategy of signal integration, which was previously found in decision-making behaviors of animals, is adopted at the cellular level to reduce noise and minimize uncertainty.

Since December 2019, the newly identified coronavirus SARS-CoV-2 has caused a massive health crisis worldwide and resulted in over 70 000 COVID-19 infections so far. Clinical drugs targeting SARS-CoV-2 are urgently needed to decrease the high fatality rate of confirmed COVID-19 patients. Traditional de novo drug discovery needs more than 10 years, so drug repurposing seems the best option currently to find potential drugs for treating COVID-19. Compared with traditional non-covalent drugs, covalent drugs have attracted escalating attention recent years due to their advantages in potential specificity upon careful design, efficiency and patient burden. We recently developed a computational protocol named as SCAR (steric-clashes alleviating receptors) for discovering covalent drugs. In this work, we used the SCAR protocol to identify possible covalent drugs (approved or clinically tested) targeting the main protease (3CLpro) of SARS-CoV-2. We identified 11 potential hits, among which at least six hits were exclusively enriched by the SCAR protocol. Since the preclinical or clinical information of these identified drugs is already available, they might be ready for being clinically tested in the treatment of COVID-19.

Signaling pathways depend on regulatory protein-protein interactions; controlling these interactions in cells has important applications for reengineering biological functions. As many regulatory proteins are modular, considerable progress in engineering signaling circuits has been made by recombining commonly occurring domains. Our ability to predictably engineer cellular functions, however, is constrained by complex crosstalk observed in naturally occurring domains. Here we demonstrate a strategy for improving and simplifying protein network engineering: using computational design to create orthogonal (non-crossreacting) protein-protein interfaces. We validated the design of the interface between a key signaling protein, the GTPase Cdc42, and its activator, Intersectin, biochemically and by solving the crystal structure of the engineered complex. The designed GTPase ( ortho Cdc42) is activated exclusively by its engineered cognate partner ( ortho Intersectin), but maintains the ability to interface with other GTPase signaling circuit components in vitro. In mammalian cells, ortho Cdc42 activity can be regulated by ortho Intersectin, but not wild-type Intersectin, showing that the designed interaction can trigger complex processes. Computational design of protein interfaces thus promises to provide specific components that facilitate the predictable engineering of cellular functions.

Protein–protein interface design is one of the most exciting fields in protein science; however, designing nonnatural protein–protein interaction pairs remains difficult. In this article we report a de novo design of a nonnatural protein–protein interaction pair by scanning the Protein Data Bank for suitable scaffold proteins that can be used for grafting key interaction residues and can form stable complexes with the target protein after additional mutations. Using our design algorithm, an unrelated protein, rat PLCδ ₁ -PH (pleckstrin homology domain of phospholipase C-δ1), was successfully designed to bind the human erythropoietin receptor (EPOR) after grafting the key interaction residues of human erythropoietin binding to EPOR. The designed mutants of rat PLCδ ₁ -PH were expressed and purified to test their binding affinities with EPOR. A designed triple mutation of PLCδ ₁ -PH (ERPH1) was found to bind EPOR with high affinity ( K _D of 24 nM and an IC ₅₀ of 5.7 μM) both in vitro and in a cell-based assay, respectively, although the WT PLCδ ₁ -PH did not show any detectable binding under the assay conditions. The in vitro binding affinities of the PLCδ ₁ -PH mutants correlate qualitatively to the computational binding affinities, validating the design and the protein–protein interaction model. The successful practice of finding a proper protein scaffold and making it bind with EPOR demonstrates a prospective application in protein engineering targeting protein–protein interfaces.

The recent breakthrough from AlphaFold2 and RoseTTAFold set a profound milestone for solving the protein folding problem, but they were not explicitly trained to predict protein foldability, i.e., if a protein can really fold into the predicted 3D structure. We wondered if the computational models from AlphaFold2 and RoseTTAFold might carry protein foldability information. Therefore, we predicted the structural models of 159 circular permutants and 158 alanine insertion mutants of the 159-residue dihydrofolate reductase. Our data showed that although AlphaFold2 and RoseTTAFold cannot directly identify unfoldable proteins, the RMSD values of computational models are correlated with protein foldability, with higher RMSD values indicating lower protein foldability. Furthermore, this correlation is independent of secondary structures, and the RMSD values of computational models are quantitatively correlated with protein foldability but not protein functions. Additionally, using a dataset of 129 de novo designed proteins, we showed that inter-model RMSD values between AlphaFold2 models and RoseTTAFold models are a good indicator of protein foldability. At last, we showed that inter-model RMSD values are also useful for evaluating protein solubility by modeling 1664 natural proteins. Our work could be of great value to the design of novel proteins and the prediction of protein foldability.