A Molecular Grammar Governing the Driving Forces for Phase Separation of Prion-like RNA Binding Proteins.

Proteins such as FUS phase separate to form liquid-like condensates that can harden into less dynamic structures. However, how these properties emerge from the collective interactions of many amino acids remains largely unknown. Here, we use extensive mutagenesis to identify a sequence-encoded molecular grammar underlying the driving forces of phase separation of proteins in the FUS family and test aspects of this grammar in cells. Phase separation is primarily governed by multivalent interactions among tyrosine residues from prion-like domains and arginine residues from RNA-binding domains, which are modulated by negatively charged residues. Glycine residues enhance the fluidity, whereas glutamine and serine residues promote hardening. We develop a model to show that the measured saturation concentrations of phase separation are inversely proportional to the product of the numbers of arginine and tyrosine residues. These results suggest it is possible to predict phase-separation properties based on amino acid sequences.

Thermodynamic modulation of gephyrin condensation by inhibitory synapse components.

Phase separation drives compartmentalization of intracellular contents into various biomolecular condensates. Individual condensate components are thought to differentially contribute to the organization and function of condensates. However, how intermolecular interactions among constituent biomolecules modulate the phase behaviors of multicomponent condensates remains unclear. Here, we used core components of the inhibitory postsynaptic density (iPSD) as a model system to quantitatively probe how the network of intra- and intermolecular interactions defines the composition and cellular distribution of biomolecular condensates. We found that oligomerization-driven phase separation of gephyrin, an iPSD-specific scaffold, is critically modulated by an intrinsically disordered linker region exhibiting minimal homotypic attractions. Other iPSD components, such as neurotransmitter receptors, differentially promote gephyrin condensation through distinct binding modes and affinities. We further demonstrated that the local accumulation of scaffold-binding proteins at the cell membrane promotes the nucleation of gephyrin condensates in neurons. These results suggest that in multicomponent systems, the extent of scaffold condensation can be fine-tuned by scaffold-binding factors, a potential regulatory mechanism for self-organized compartmentalization in cells.

Probabilistic establishment of speckle-associated inter-chromosomal interactions.

Inter-chromosomal interactions play a crucial role in genome organization, yet the organizational principles remain elusive. Here, we introduce a novel computational method to systematically characterize inter-chromosomal interactions using in situ Hi-C results from various cell types. Our method successfully identifies two apparently hub-like inter-chromosomal contacts associated with nuclear speckles and nucleoli, respectively. Interestingly, we discover that nuclear speckle-associated inter-chromosomal interactions are highly cell-type invariant with a marked enrichment of cell-type common super-enhancers (CSEs). Validation using DNA Oligopaint fluorescence in situ hybridization (FISH) shows a strong but probabilistic interaction behavior between nuclear speckles and CSE-harboring genomic regions. Strikingly, we find that the likelihood of speckle-CSE associations can accurately predict two experimentally measured inter-chromosomal contacts from Hi-C and Oligopaint DNA FISH. Our probabilistic establishment model well describes the hub-like structure observed at the population level as a cumulative effect of summing individual stochastic chromatin-speckle interactions. Lastly, we observe that CSEs are highly co-occupied by MAZ binding and MAZ depletion leads to significant disorganization of speckle-associated inter-chromosomal contacts. Taken together, our results propose a simple organizational principle of inter-chromosomal interactions mediated by MAZ-occupied CSEs.

Intermolecular Interactions between Cysteine and Aromatic Amino Acids with a Phenyl Moiety in the DNA-Binding Domain of Heat Shock Factor 1 Regulate Thermal Stress-Induced Trimerization.

In this study, we investigated the trimerization mechanism and structure of heat shock factor 1 (HSF1) using western blotting, tryptophan (Trp) fluorescence spectroscopy, and molecular modeling. First, we examined the DNA-binding domains of human (Homo sapiens), goldfish (Carassius auratus), and walleye pollock (Gadus chalcogrammus) HSF1s by mutating key residues (36 and 103) that are thought to directly affect trimer formation. Human, goldfish, and walleye pollock HSF1s contain cysteine at residue 36 but cysteine (C), tyrosine (Y), and phenylalanine (F), respectively, at residue 103. The optimal trimerization temperatures for the wild-type HSF1s of each species were found to be 42, 37, and 20 °C, respectively. Interestingly, a mutation experiment revealed that trimerization occurred at 42 °C when residue 103 was cysteine, at 37 °C when it was tyrosine, and at 20 °C when it was phenylalanine, regardless of the species. In addition, it was confirmed that when residue 103 of the three species was mutated to alanine, trimerization did not occur. This suggests that in addition to trimerization via disulfide bond formation between the cysteine residues in human HSF1, trimerization can also occur via the formation of a different type of bond between cysteine and aromatic ring residues such as tyrosine and phenylalanine. We also confirmed that at least one cysteine is required for the trimerization of HSF1s, regardless of its position (residue 36 or 103). Additionally, it was shown that the trimer formation temperature is related to growth and survival in fish.

pH Dependence of HSF1 trimerization is shaped by intramolecular interactions.

Heat shock factor 1 (HSF1) primarily regulates various cellular stress responses. Previous studies have shown that low pH within the physiological range directly activates HSF1 function in vitro. However, the detailed molecular mechanisms remain unclear. This study proposes a molecular mechanism based on the trimerization behavior of HSF1 at different pH values. Extensive mutagenesis of human and goldfish HSF1 revealed that the optimal pH for trimerization depended on the identity of residue 103. In particular, when residue 103 was occupied by tyrosine, a significant increase in the optimal pH was observed, regardless of the rest of the sequence. This behavior can be explained by the protonation state of the neighboring histidine residues, His101 and His110. Residue 103 plays a key role in trimerization by forming disulfide or non-covalent bonds with Cys36. If tyrosine resides at residue 103 in an acidic environment, its electrostatic interactions with positively charged histidine residues prevent effective trimerization. His101 and His110 are neutralized at a higher pH, which releases Tyr103 to interact with Cys36 and drives the effective trimerization of HSF1. This study showed that the protonation state of a histidine residue can regulate the intramolecular interactions, which consequently leads to a drastic change in the oligomerization behavior of the entire protein.

Conformational landscapes of artificial peptides predicted by various force fields: are we ready to simulate ß-amino acids?

With the introduction of artificial peptides as antimicrobial agents and organic catalysts, numerous efforts have been made to design foldamers with desirable structures and functions. Computational tools are a helpful proxy for revealing the dynamic structures at atomic resolution and understanding foldamer's complex structure-function relationships. However, the performance of conventional force fields in predicting the structures of artificial peptides has not been systematically evaluated. In this study, we critically assessed three popular force fields, AMBER ff14SB, CHARMM36m, and OPLS-AA/L, in predicting conformational propensities of a ß-peptide foldamer at monomer and hexamer levels. Simulation results were compared to those obtained from quantum chemistry calculations and experimental data. We also utilised replica exchange molecular dynamics simulations to investigate the energy landscape of each force field and assess the similarities and differences between force fields. We compared different solvent systems in the AMBER ff14SB and CHARMM36m frameworks and confirmed the unanimous role of hydrogen bonds in shaping energy landscapes. We anticipate that our data will pave the way for further improvements to force fields and for understanding the role of solvents in peptide folding, crystallisation, and engineering.

Kinetic Modulation of Amyloid-ß (1-42) Aggregation and Toxicity by Structure-Based Rational Design.

Several point mutations can modulate protein structure and dynamics, leading to different natures. Especially in the case of amyloidogenic proteins closely related to neurodegenerative diseases, structural changes originating from point mutations can affect fibrillation kinetics. Herein, we rationally designed mutant candidates to inhibit the fibrillation process of amyloid-ß with its point mutants through multistep in silico analyses. Our results showed that the designed mutants induced kinetic self-assembly suppression and reduced the toxicity of the aggregate. A multidisciplinary biophysical approach with small-angle X-ray scattering, ion mobility-mass spectrometry, mass spectrometry, and additional in silico experiments was performed to reveal the structural basis associated with the inhibition of fibril formation. The structure-based design of the mutants with suppressed self-assembly performed in this study could provide a different perspective for modulating amyloid aggregation based on the structural understanding of the intrinsically disordered proteins.

Thermodynamics of π-π Interactions of Benzene and Phenol in Water.

The π-π interaction is a major driving force that stabilizes protein assemblies during protein folding. Recent studies have additionally demonstrated its involvement in the liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs). As the participating residues in IDPs are exposed to water, π-π interactions for LLPS must be modeled in water, as opposed to the interactions that are often established at the hydrophobic domains of folded proteins. Thus, we investigated the association of free energies of benzene and phenol dimers in water by integrating van der Waals (vdW)-corrected density functional theory (DFT) and DFT in classical explicit solvents (DFT-CES). By comparing the vdW-corrected DFT and DFT-CES results with high-level wavefunction calculations and experimental solvation free energies, respectively, we established the quantitative credibility of these approaches, enabling a reliable prediction of the benzene and phenol dimer association free energies in water. We discovered that solvation influences dimer association free energies, but not significantly when no direct hydrogen-bond-type interaction exists between two monomeric units, which can be explained by the enthalpy-entropy compensation. Our comprehensive computational study of the solvation effect on π-π interactions in water could help us understand the molecular-level driving mechanism underlying the IDP phase behaviors.

Current Understanding of Molecular Phase Separation in Chromosomes.

Biomolecular phase separation denotes the demixing of a specific set of intracellular components without membrane encapsulation. Recent studies have found that biomolecular phase separation is involved in a wide range of cellular processes. In particular, phase separation is involved in the formation and regulation of chromosome structures at various levels. Here, we review the current understanding of biomolecular phase separation related to chromosomes. First, we discuss the fundamental principles of phase separation and introduce several examples of nuclear/chromosomal biomolecular assemblies formed by phase separation. We also briefly explain the experimental and computational methods used to study phase separation in chromosomes. Finally, we discuss a recent phase separation model, termed bridging-induced phase separation (BIPS), which can explain the formation of local chromosome structures.

LASSI: A lattice model for simulating phase transitions of multivalent proteins.

Many biomolecular condensates form via spontaneous phase transitions that are driven by multivalent proteins. These molecules are biological instantiations of associative polymers that conform to a so-called stickers-and-spacers architecture. The stickers are protein-protein or protein-RNA interaction motifs and / or domains that can form reversible, non-covalent crosslinks with one another. Spacers are interspersed between stickers and their preferential interactions with solvent molecules determine the cooperativity of phase transitions. Here, we report the development of an open source computational engine known as LASSI (LAttice simulation engine for Sticker and Spacer Interactions) that enables the calculation of full phase diagrams for multicomponent systems comprising of coarse-grained representations of multivalent proteins. LASSI is designed to enable computationally efficient phenomenological modeling of spontaneous phase transitions of multicomponent mixtures comprising of multivalent proteins and RNA molecules. We demonstrate the application of LASSI using simulations of linear and branched multivalent proteins. We show that dense phases are best described as droplet-spanning networks that are characterized by reversible physical crosslinks among multivalent proteins. We connect recent observations regarding correlations between apparent stoichiometry and dwell times of condensates to being proxies for the internal structural organization, specifically the convolution of internal density and extent of networking, within condensates. Finally, we demonstrate that the concept of saturation concentration thresholds does not apply to multicomponent systems where obligate heterotypic interactions drive phase transitions. This emerges from the ellipsoidal structures of phase diagrams for multicomponent systems and it has direct implications for the regulation of biomolecular condensates in vivo.

Multivalent-Interaction-Driven Assembly of Discrete, Flexible, and Asymmetric Supramolecular Protein Nano-Prisms.

Current approaches to design monodisperse protein assemblies require rigid, tight, and symmetric interactions between oligomeric protein units. Herein, we introduce a new multivalent-interaction-driven assembly strategy that allows flexible, spaced, and asymmetric assembly between protein oligomers. We discovered that two polygonal protein oligomers (ranging from triangle to hexagon) dominantly form a discrete and stable two-layered protein prism nanostructure via multivalent interactions between fused binding pairs. We demonstrated that protein nano-prisms with long flexible peptide linkers (over 80 amino acids) between protein oligomer layers could be discretely formed. Oligomers with different structures could also be monodispersely assembled into two-layered but asymmetric protein nano-prisms. Furthermore, producing higher-order architectures with multiple oligomer layers, for example, 3-layered nano-prisms or nanotubes, was also feasible.

Evolution on the Biophysical Fitness Landscape of an RNA Virus.

Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics that propagates millions of independent small viral subpopulations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction, an atypical case in evolution. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs between molecular traits of viral proteins shape viral evolution.

ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms' proteomes.

Motivation: Protein evolution spans time scales and its effects span the length of an organism. A web app named ProteomeVis is developed to provide a comprehensive view of protein evolution in the Saccharomyces cerevisiae and Escherichia coli proteomes. ProteomeVis interactively creates protein chain graphs, where edges between nodes represent structure and sequence similarities within user-defined ranges, to study the long time scale effects of protein structure evolution. The short time scale effects of protein sequence evolution are studied by sequence evolutionary rate (ER) correlation analyses with protein properties that span from the molecular to the organismal level. Results: We demonstrate the utility and versatility of ProteomeVis by investigating the distribution of edges per node in organismal protein chain universe graphs (oPCUGs) and putative ER determinants. S.cerevisiae and E.coli oPCUGs are scale-free with scaling constants of 1.79 and 1.56, respectively. Both scaling constants can be explained by a previously reported theoretical model describing protein structure evolution. Protein abundance most strongly correlates with ER among properties in ProteomeVis, with Spearman correlations of -0.49 (P-value < 10-10) and -0.46 (P-value < 10-10) for S.cerevisiae and E.coli, respectively. This result is consistent with previous reports that found protein expression to be the most important ER determinant. Availability and implementation: ProteomeVis is freely accessible at http://proteomevis.chem.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Protein Homeostasis Imposes a Barrier on Functional Integration of Horizontally Transferred Genes in Bacteria.

Horizontal gene transfer (HGT) plays a central role in bacterial evolution, yet the molecular and cellular constraints on functional integration of the foreign genes are poorly understood. Here we performed inter-species replacement of the chromosomal folA gene, encoding an essential metabolic enzyme dihydrofolate reductase (DHFR), with orthologs from 35 other mesophilic bacteria. The orthologous inter-species replacements caused a marked drop (in the range 10-90%) in bacterial growth rate despite the fact that most orthologous DHFRs are as stable as E.coli DHFR at 37°C and are more catalytically active than E. coli DHFR. Although phylogenetic distance between E. coli and orthologous DHFRs as well as their individual molecular properties correlate poorly with growth rates, the product of the intracellular DHFR abundance and catalytic activity (kcat/KM), correlates strongly with growth rates, indicating that the drop in DHFR abundance constitutes the major fitness barrier to HGT. Serial propagation of the orthologous strains for ~600 generations dramatically improved growth rates by largely alleviating the fitness barriers. Whole genome sequencing and global proteome quantification revealed that the evolved strains with the largest fitness improvements have accumulated mutations that inactivated the ATP-dependent Lon protease, causing an increase in the intracellular DHFR abundance. In one case DHFR abundance increased further due to mutations accumulated in folA promoter, but only after the lon inactivating mutations were fixed in the population. Thus, by apparently distinguishing between self and non-self proteins, protein homeostasis imposes an immediate and global barrier to the functional integration of foreign genes by decreasing the intracellular abundance of their products. Once this barrier is alleviated, more fine-tuned evolution occurs to adjust the function/expression of the transferred proteins to the constraints imposed by the intracellular environment of the host organism.

The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution.

Homology modeling is a powerful tool for predicting a protein's structure. This approach is successful because proteins whose sequences are only 30% identical still adopt the same structure, while structure similarity rapidly deteriorates beyond the 30% threshold. By studying the divergence of protein structure as sequence evolves in real proteins and in evolutionary simulations, we show that this nonlinear sequence-structure relationship emerges as a result of selection for protein folding stability in divergent evolution. Fitness constraints prevent the emergence of unstable protein evolutionary intermediates, thereby enforcing evolutionary paths that preserve protein structure despite broad sequence divergence. However, on longer timescales, evolution is punctuated by rare events where the fitness barriers obstructing structure evolution are overcome and discovery of new structures occurs. We outline biophysical and evolutionary rationale for broad variation in protein family sizes, prevalence of compact structures among ancient proteins, and more rapid structure evolution of proteins with lower packing density.

Graph's Topology and Free Energy of a Spin Model on the Graph.

In this Letter we investigate a direct relationship between a graph's topology and the free energy of a spin system on the graph. We develop a method of separating topological and energetic contributions to the free energy, and find that considering the topology is sufficient to qualitatively compare the free energies of different graph systems at high temperature, even when the energetics are not fully known. This method was applied to the metal lattice system with defects, and we found that it partially explains why point defects are more stable than high-dimensional defects. Given the energetics, we can even quantitatively compare free energies of different graph structures via a closed form of linear graph contributions. The closed form is applied to predict the sequence-space free energy of lattice proteins, which is a key factor determining the designability of a protein structure.

Minimalistic predictor of protein binding energy: contribution of solvation factor to protein binding.

It has long been known that solvation plays an important role in protein-protein interactions. Here, we use a minimalistic solvation-based model for predicting protein binding energy to estimate quantitatively the contribution of the solvation factor in protein binding. The factor is described by a simple linear combination of buried surface areas according to amino-acid types. Even without structural optimization, our minimalistic model demonstrates a predictive power comparable to more complex methods, making the proposed approach the basis for high throughput applications. Application of the model to a proteomic database shows that receptor-substrate complexes involved in signaling have lower affinities than enzyme-inhibitor and antibody-antigen complexes, and they differ by chemical compositions on interfaces. Also, we found that protein complexes with components that come from the same genes generally have lower affinities than complexes formed by proteins from different genes, but in this case the difference originates from different interface areas. The model was implemented in the software PYTHON, and the source code can be found on the Shakhnovich group webpage: http://faculty.chemistry.harvard.edu/shakhnovich/software.

Evolution of specificity in protein-protein interactions.

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.

Multi-functionality of macroporous TiO2 spheres in dye-sensitized and hybrid heterojunction solar cells.

Micron-sized macroporous TiO2 spheres (MAC-TiO2) were synthesized using a colloidal templating process inside emulsions, which were then coated on a nanocrystalline TiO2 light absorption film to prepare a bilayered photoanode for liquid-based dye-sensitized solar cells (DSSC) and hybrid heterojunction solid-state solar cells. MAC-TiO2 layers can enhance light scattering as well as absorption, because their pore size and periodicity are comparable to light wavelength for unique multiple scattering and a porous surface can load dye more. Moreover, due to the bicontinuous nature of macropores and TiO2 walls, electrolyte could be transported much faster in between the TiO2 spheres rather than within the small TiO2 nonporous architectures. Electron transport was also facilitated along the interconnected TiO2 walls. In DSSCs with these MAC-TiO2 scattering layers, efficiency was higher than conventional DSSCs incorporating a commercial scattering layer. The unique geometry of MAC-TiO2 results in strong improvements in light scattering and infiltration of hole-transporting materials, thereby the MAC-TiO2-based solid-state device showed comparatively higher efficiency than the device with conventional nanocrystalline TiO2.