Entropy-driven Denaturation for Sustainable Regeneration of Protein Waste.

Electrolytes are essential parts of the environment for all life forms, where proteins, water, and solutes interplay to support vital activities. However, a fundamental understanding of the effect of ionic solutes on proteins remains elusive for more than a century. Here we show how some ionic solutes can serve as potent denaturants despite the absence of direct protein-ion interactions. We demonstrate dramatic differences between denaturation potency of different ionic solutes with lithium bromide (LiBr) being the strongest denaturant and sodium bromide (NaBr) being the least potent. Experiments and simulations indicate the presence of certain ions disrupts the structure of water network, thereby induce protein denaturation indirectly via an entropy-driven mechanism. We further introduce a scalable strategy for protein waste revalorization, distinguished by the closed-loop recycling of denaturants, straightforward protein separation, and facile manufacturing, all enabled by the entropy-driven denaturation by LiBr. Through successful isolation and systematic study of indirect solute effects, our findings suggest a unified and generally applicable framework for decoding of the protein-water-solute nexus, where all current studies can be easily incorporated. Besides, our regeneration approach underscores the feasibility of repurposing protein waste into valuable biomaterials in a sustainable way with wide-reaching application potential.

Biophysical principles predict fitness of SARS-CoV-2 variants.

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

A Spatial Signal of Niche Differentiation in Tropical Forests.

AbstractExplaining diversity in tropical forests remains a challenge in community ecology. Theory tells us that species differences can stabilize communities by reducing competition, while species similarities can promote diversity by reducing fitness differences and thus prolonging the time to competitive exclusion. Combined, these processes may lead to clustering of species such that species are niche differentiated across clusters and share a niche within each cluster. Here, we characterize this partial niche differentiation in a tropical forest in Panama by measuring spatial clustering of woody plants and relating these clusters to local soil conditions. We find that species were spatially clustered and the clusters were associated with specific concentrations of soil nutrients, reflecting the existence of nutrient niches. Species were almost twice as likely to recruit in their own nutrient niche. A decision tree algorithm showed that local soil conditions correctly predicted the niche of the trees with up to 85% accuracy. Iron, zinc, phosphorus, manganese, and soil pH were among the best predictors of species clusters.

Biophysical principles predict fitness of SARS-CoV-2 variants.

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the discovery of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by binding constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto a epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

Highly mismatch-tolerant homology testing by RecA could explain how homology length affects recombination.

In E. coli, double strand breaks (DSBs) are resected and loaded with RecA protein. The genome is then rapidly searched for a sequence that is homologous to the DNA flanking the DSB. Mismatches in homologous partners are rare, suggesting that RecA should rapidly reject mismatched recombination products; however, this is not the case. Decades of work have shown that long lasting recombination products can include many mismatches. In this work, we show that in vitro RecA forms readily observable recombination products when 16% of the bases in the product are mismatched. We also consider various theoretical models of mismatch-tolerant homology testing. The models test homology by comparing the sequences of Ltest bases in two single-stranded DNAs (ssDNA) from the same genome. If the two sequences pass the homology test, the pairing between the two ssDNA becomes permanent. Stringency is the fraction of permanent pairings that join ssDNA from the same positions in the genome. We applied the models to both randomly generated genomes and bacterial genomes. For both randomly generated genomes and bacterial genomes, the models show that if no mismatches are accepted stringency is ∼ 99% when Ltest = 14 bp. For randomly generated genomes, stringency decreases with increasing mismatch tolerance, and stringency improves with increasing Ltest. In contrast, in bacterial genomes when Ltest ∼ 75 bp, stringency is ∼ 99% for both mismatch-intolerant and mismatch-tolerant homology testing. Furthermore, increasing Ltest does not improve stringency because most incorrect pairings join different copies of repeats. In sum, for bacterial genomes highly mismatch tolerant homology testing of 75 bp provides the same stringency as homology testing that rejects all mismatches and testing more than ∼75 base pairs is not useful. Interestingly, in vivo commitment to recombination typically requires homology testing of ∼ 75 bp, consistent with highly mismatch intolerant testing.

Effects of Vimentin Intermediate Filaments on the Structure and Dynamics of In Vitro Multicomponent Interpenetrating Cytoskeletal Networks.

We investigate the rheological properties of interpenetrating networks reconstituted from the main cytoskeletal components: filamentous actin, microtubules, and vimentin intermediate filaments. The elastic modulus is determined largely by actin, with little contribution from either microtubules or vimentin. However, vimentin dramatically impacts the relaxation, with even small amounts significantly increasing the relaxation time of the interpenetrating network. This highly unusual decoupling between dissipation and elasticity may reflect weak attractive interactions between vimentin and actin networks.

Effect of Divalent Cations on the Structure and Mechanics of Vimentin Intermediate Filaments.

Divalent cations behave as effective cross-linkers of intermediate filaments (IFs) such as vimentin IF (VIF). These interactions have been mostly attributed to their multivalency. However, ion-protein interactions often depend on the ion species, and these effects have not been widely studied in IFs. Here, we investigate the effects of two biologically important divalent cations, Zn2+ and Ca2+, on VIF network structure and mechanics in vitro. We find that the network structure is unperturbed at micromolar Zn2+ concentrations, but strong bundle formation is observed at a concentration of 100 µM. Microrheological measurements show that network stiffness increases with cation concentration. However, bundling of filaments softens the network. This trend also holds for VIF networks formed in the presence of Ca2+, but remarkably, a concentration of Ca2+ that is two orders higher is needed to achieve the same effect as with Zn2+, which suggests the importance of salt-protein interactions as described by the Hofmeister effect. Furthermore, we find evidence of competitive binding between the two divalent ion species. Hence, specific interactions between VIFs and divalent cations are likely to be an important mechanism by which cells can control their cytoplasmic mechanics.