Intrinsic protein disorder uncouples affinity from binding specificity

Engineering Protein-Polyelectrolyte Interactions for Cellular Applications

Protein-polyelectrolyte interactions are fundamental interactions in biology that occur at every length scale, from protein-DNA complexes to phase-separated organelles. They drive processes ranging from gene transcription and DNA synthesis to viral assembly. Protein engineering is a powerful way to modulate these interactions, both to probe endogenous function and to engineer novel interactions between species. In this review, we consider the various noncovalent interactions that govern the formation and behavior of these complexes, and we discuss how protein modifications such as changes to structure, charge, and charge patterning affect them. We highlight recent examples where engineering changes to protein-polyelectrolyte interactions have helped elucidate biological function, and we then focus on recent efforts toward de novo material design of synthetic biomolecular condensates and functional nanoassemblies.

Rethinking transcription factor dynamics and transcription regulation in eukaryotes

Transcription factors (TFs) control gene expression by binding to specific DNA motifs in cis-regulatory elements. Cooperativity has been thought to ensure TF binding specificity. Recent research suggests that, at least in yeast, the role of cooperativity has probably been overemphasized. Consequently, synergy - the collective recruitment of the transcriptional machinery by TFs bound at multiple DNA sites - emerges as a more significant mechanism for achieving the specificity of the transcriptional response. Furthermore, I argue that the concentration of TFs within phase-separated nuclear condensates and their covalent modifications play an underappreciated but crucial role in sharpening transcriptional responses through complementary mechanisms. A model integrating cooperativity, synergy, post-translational modifications, and phase separation provides a comprehensive framework to explain dynamic, context-specific transcriptional responses in eukaryotes.

No structure, no problem: Protein stabilization by Hero proteins and other chaperone-like IDPs

In order for a protein to function, it must fold into its proper three-dimensional structure. Otherwise, improperly folded proteins are typically prone to aggregate through a process that is detrimental to cellular health. It is widely known that a diverse group of proteins, called molecular chaperones, function to promote proper folding of other proteins and prevent aggregation. In contrast, intrinsically disordered proteins (IDPs) lack substantial tertiary structures, but nonetheless serve important functional roles. In some cases, IDPs have been observed to display remarkably chaperone-like activities, where they stabilize the activities of client proteins and prevent their aggregation. While it was previously thought that chaperone-like IDPs were mainly utilized by extremophilic organisms in their survival of extreme stress, we recently showed that a group of chaperone-like IDPs, we named heat-resistant obscure (Hero) proteins, are also widespread in non-extremophile animals, including humans and flies. Thus, we should consider the possibility that IDPs serve significant chaperone-like functions in protein stabilization relevant to physiological conditions. However, as most of our understanding of how chaperones function is based on insights from their structured domains, it is unclear how chaperone-like IDPs elicit chaperone-like effects without these structures. Here we summarize our understanding of Hero proteins to date and, based on experimental evidence, outline the features that are likely important for their protein stabilizing activities. We draw on concepts from the studies of chaperones and chaperone-like IDPs, in order to draft potential models of how chaperone-like IDPs achieve chaperone-like effects in the absence of well-defined structures.

Conformational Analyses of the AHD1-UBAN Region of TNIP1 Highlight Key Amino Acids for Interaction with Ubiquitin

Tumor necrosis factor ɑ (TNFɑ)-induced protein 3 (TNFAIP3)-interacting protein 1 (TNIP1) is genetically and functionally linked to limiting auto-immune and inflammatory responses. We have shown that TNIP1 (alias A20-binding inhibitor of NF-κB 1, ABIN1), functioning as a hub location to coordinate other proteins in repressing inflammatory signaling, aligns with biophysical traits indicative of its being an intrinsically disordered protein (IDP). IDPs move through a repertoire of three-dimensional structures rather than being in one set conformation. Here we employed bioinformatic analysis and biophysical interventions via amino acid mutations to assess and alter, respectively, conformational flexibility along a crucial region of TNIP1, encompassing the ABIN homology domain 1 and ubiquitin-binding domain in ABIN proteins and NEMO (AHD1-UBAN), by purposeful replacement of key residues. In vitro secondary structure measurements were mostly in line with, but not necessarily to the same degree as, expected results from in silico assessments. Notably, changes in single amino acids outside of the ubiquitin-binding region for gain-of-order effects had consequences along the length of the AHD1-UBAN propagating to that region. This is evidenced by differences in recognition of the partner protein polyubiquitin ≥ 28 residues away, depending on the mutation site, from the previously identified key binding site. These findings serve to demonstrate the role of conformational flexibility in protein partner recognition by TNIP1, thus identifying key amino acids likely to impact the molecular dynamics involved in TNIP1 repression of inflammatory signaling at large.

Separation of sticker-spacer energetics governs the coalescence of metastable condensates

Biological condensates often emerge as a multidroplet state and never coalesce into one large droplet within the experimental timespan. Previous work revealed that the sticker-spacer architecture of biopolymers may dynamically stabilize the multidroplet state. Here, we simulate the condensate coalescence using metadynamics approach and reveal two distinct physical mechanisms underlying the fusion of droplets. Condensates made of sticker-spacer polymers readily undergo a kinetic arrest when stickers exhibit slow exchange while fast exchanging stickers at similar levels of saturation allow merger to equilibrium states. On the other hand, condensates composed of homopolymers fuse readily until they reach a threshold density. Increase in entropy upon intercondensate mixing of chains drives the fusion of sticker-spacer chains. We map the range of mechanisms of kinetic arrest from slow sticker exchange dynamics to density mediated in terms of energetic separation of stickers and spacers. Our predictions appear to be in qualitative agreement with recent experiments probing dynamic nature of protein-RNA condensates.

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder

The nearly neutral theory of molecular evolution posits variation among species in the effectiveness of selection. In an idealized model, the census population size determines both this minimum magnitude of the selection coefficient required for deleterious variants to be reliably purged, and the amount of neutral diversity. Empirically, an 'effective population size' is often estimated from the amount of putatively neutral genetic diversity and is assumed to also capture a species' effectiveness of selection. A potentially more direct measure of the effectiveness of selection is the degree to which selection maintains preferred codons. However, past metrics that compare codon bias across species are confounded by among-species variation in %GC content and/or amino acid composition. Here, we propose a new Codon Adaptation Index of Species (CAIS), based on Kullback-Leibler divergence, that corrects for both confounders. We demonstrate the use of CAIS correlations, as well as the Effective Number of Codons, to show that the protein domains of more highly adapted vertebrate species evolve higher intrinsic structural disorder.

The molecular basis for cellular function of intrinsically disordered protein regions

Intrinsically disordered protein regions exist in a collection of dynamic interconverting conformations that lack a stable 3D structure. These regions are structurally heterogeneous, ubiquitous and found across all kingdoms of life. Despite the absence of a defined 3D structure, disordered regions are essential for cellular processes ranging from transcriptional control and cell signalling to subcellular organization. Through their conformational malleability and adaptability, disordered regions extend the repertoire of macromolecular interactions and are readily tunable by their structural and chemical context, making them ideal responders to regulatory cues. Recent work has led to major advances in understanding the link between protein sequence and conformational behaviour in disordered regions, yet the link between sequence and molecular function is less well defined. Here we consider the biochemical and biophysical foundations that underlie how and why disordered regions can engage in productive cellular functions, provide examples of emerging concepts and discuss how protein disorder contributes to intracellular information processing and regulation of cellular function.

Leucine Motifs Stabilize Residual Helical Structure in Disordered Proteins

Many examples are known of regions of intrinsically disordered proteins that fold into α-helices upon binding to their targets. These helical binding motifs (HBMs) can be partially helical also in the unbound state, and this so-called residual structure can affect binding affinity and kinetics. To investigate the underlying mechanisms governing the formation of residual helical structure, we assembled a dataset of experimental helix contents of 65 peptides containing HBM that fold-upon-binding. The average residual helicity is 17% and increases to 60% upon target binding. The helix contents of residual and target-bound structures do not correlate, however the relative location of helix elements in both states shows a strong overlap. Compared to the general disordered regions, HBMs are enriched in amino acids with high helix preference and these residues are typically involved in target binding, explaining the overlap in helix positions. In particular, we find that leucine residues and leucine motifs in HBMs are the major contributors to helix stabilization and target-binding. For the two model peptides, we show that substitution of leucine motifs to other hydrophobic residues (valine or isoleucine) leads to reduction of residual helicity, supporting the role of leucine as helix stabilizer. From the three hydrophobic residues only leucine can efficiently stabilize residual helical structure. We suggest that the high occurrence of leucine motifs and a general preference for leucine at binding interfaces in HBMs can be explained by its unique ability to stabilize helical elements.

De novo design of high-affinity binders of bioactive helical peptides

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Development of a novel prediction model based on protein structure for identifying RPE65-associated inherited retinal disease (IRDs) of missense variants

Purpose

This study aimed to develop a prediction model to classify RPE65-mediated inherited retinal disease (IRDs) based on protein secondary structure and to analyze phenotype-protein structure correlations of RPE65 missense variants in a Chinese cohort.

Methods

Pathogenic or likely pathogenic missense variants of RPE65 were obtained from UniProt, ClinVar, and HGMD databases. The three-dimensional structure of RPE65 was retrieved from the Protein Data Bank (PDB) and modified with Pymol software. A novel prediction model was developed using LASSO regression and multivariate logistic regression to identify RPE65-associated IRDs. A total of 21 Chinese probands with RPE65 variants were collected to analyze phenotype-protein structure correlations of RPE65 missense variants.

Results

The study found that both pathogenic and population missense variants were associated with structural features of RPE65. Pathogenic variants were linked to sheet, β-sheet, strands, β-hairpins, Fe2+ (iron center), and active site cavity, while population variants were related to helix, loop, helices, and helix-helix interactions. The novel prediction model showed accuracy and confidence in predicting the disease type of RPE65 variants (AUC = 0.7531). The study identified 25 missense variants in Chinese patients, accounting for 72.4% of total mutations. A significant correlation was observed between clinical characteristics of RPE65-associated IRDs and changes in amino acid type, specifically for missense variants of F8 (H68Y, P419S).

Conclusion

The study developed a novel prediction model based on the protein structure of RPE65 and investigated phenotype-protein structure correlations of RPE65 missense variants in a Chinese cohort. The findings provide insights into the precise diagnosis of RPE65-mutated IRDs.