Design of intrinsically disordered protein variants with diverse structural properties

Pathogenic mechanism of the K141E mutation in HSPB8: Insights from smFRET and simulations

Pathogenic mutations can have a large impact on the conformational ensemble of intrinsically disordered proteins, but revealing those effects and their physiological relevance can be challenging. We used large-scale all-atom explicit-solvent molecular dynamics simulations and single-molecule Förster resonance energy transfer (smFRET) experiments to investigate the conformational dynamics of the chaperone protein HSPB8 and its K141E mutant that is linked to motor neuropathies. Our findings revealed that the HSPB8-K141E mutant exhibits increased conformational flexibility compared to the wild-type protein, particularly at high physiological ionic strengths, leading to a more extended conformational ensemble. Bayesian maximum entropy reweighting was applied to improve agreement between simulated and experimental smFRET data, further emphasizing the mutation's influence on protein dynamics. While both WT and K141E showed similar primary smFRET peaks after reweighting, the mutant displayed a higher occurrence of a secondary peak at lower FRET, indicative of an unfolded state. Additionally, differences in salt bridge networks between the variants highlighted the role of ionic interactions in modulating protein structure and suggest a possible connection between rapid dynamics and conformational stability. These results suggest that the pathogenicity of the K141E mutation may be, at least in part, due to the enhanced conformational variability that negatively influences the protein function. The study underscores the significance of ionic strength in the structural dynamics of intrinsically disordered proteins like HSPB8, providing insights into the functional implications of these changes and how stability changes can manifest across different timescales.

Peptide nanoarchitectonics beyond long-range ordering

Long-range disordered structures are ubiquitous in biological organisms and hold crucial significance for their unique structure and function. Inspired by these natural architectures, much attention has been devoted to constructing long-range disordered materials based on biomolecules in vitro. Peptides, especially short peptides consisting of several to dozens of amino acids, have emerged as ideal building blocks due to their versatile structural and functional diversity, along with their notable biocompatibility and biodegradability. As a result, significant efforts have been made to develop short peptide nanoarchitectonics with long-range disorder (SPNLRD). Understanding the fundamental mechanisms underlying the formation of SPNLRD is crucial for the precise design and construction of these architectures with specific functionalities. This review summarizes the latest advancements in the construction and application of SPNLRD. We place particular emphasis on the design principles for SPNLRD construction and stabilization, based on a comprehensive discussion from the perspectives of thermodynamics, kinetics and intermolecular interactions. Finally, we assess the critical challenges currently facing SPNLRD and highlight the future directions in the field, proposing research strategies aimed at enhancing the stability and improving the precision of control over these materials.

Methods for Theoretical Treatment of Local Fields in Proteins and Enzymes

Electric fields generated by protein scaffolds are crucial in enzymatic catalysis. This review surveys theoretical approaches for detecting, analyzing, and comparing electric fields, electrostatic potentials, and their effects on the charge density within enzyme active sites. Pioneering methods like the empirical valence bond approach rely on evaluating ionic and covalent resonance forms influenced by the field. Strategies employing polarizable force fields also facilitate field detection. The vibrational Stark effect connects computational simulations to experimental Stark spectroscopy, enabling direct comparisons. We highlight how protein dynamics induce fluctuations in local fields, influencing enzyme activity. Recent techniques assess electric fields throughout the active site volume rather than only at specific bonds, and machine learning helps relate these global fields to reactivity. Quantum theory of atoms in molecules captures the entire electron density landscape, providing a chemically intuitive perspective on field-driven catalysis. Overall, these methodologies show protein-generated fields are highly dynamic and heterogeneous, and understanding both aspects is critical for elucidating enzyme mechanisms. This holistic view empowers rational enzyme engineering by tuning electric fields, promising new avenues in drug design, biocatalysis, and industrial applications. Future directions include incorporating electric fields as explicit design targets to enhance catalytic performance and biochemical functionalities.

Prediction of phase-separation propensities of disordered proteins from sequence

Phase separation is one possible mechanism governing the selective cellular enrichment of biomolecular constituents for processes such as transcriptional activation, mRNA regulation, and immune signaling. Phase separation is mediated by multivalent interactions of macromolecules including intrinsically disordered proteins and regions (IDRs). Despite considerable advances in experiments, theory, and simulations, the prediction of the thermodynamics of IDR phase behavior remains challenging. We combined coarse-grained molecular dynamics simulations and active learning to develop a fast and accurate machine learning model to predict the free energy and saturation concentration for phase separation directly from sequence. We validate the model using computational and previously measured experimental data, as well as new experimental data for six proteins. We apply our model to all 27,663 IDRs of chain length up to 800 residues in the human proteome and find that 1,420 of these (5%) are predicted to undergo homotypic phase separation with transfer free energies < -2 kBT. We use our model to understand the relationship between single-chain compaction and phase separation and find that changes from charge- to hydrophobicity-mediated interactions can break the symmetry between intra- and intermolecular interactions. We also provide proof of principle for how the model can be used in force field refinement. Our work refines and quantifies the established rules governing the connection between sequence features and phase-separation propensities, and our prediction models will be useful for interpreting and designing cellular experiments on the role of phase separation, and for the design of IDRs with specific phase-separation propensities.

Single-point mutations in disordered proteins: Linking sequence, ensemble, and function

Mutations in genomic DNA often result in single-point missense mutations in proteins. For folded proteins, the functional effect of these missense mutations can often be understood by their impact on structure. However, missense mutations in intrinsically disordered protein regions (IDRs) remain poorly understood. In IDRs, function can depend on the structural ensemble- the collection of accessible, interchanging conformations that is encoded in their amino acid sequence. We argue that, analogously to folded proteins, single-point mutations in IDRs can alter their structural ensemble, and consequently alter their biological function. To make this argument, we first provide experimental evidence from the literature showcasing how single-point missense mutations in IDRs affect their ensemble dimensions. Then, we use genomic data from patients to show that disease-linked missense mutations occurring in IDRs can, in many cases, significantly alter IDR structural ensembles. We hope this analysis prompts further study of disease-linked, single-point mutations in IDRs.

Machine learning methods to study sequence-ensemble-function relationships in disordered proteins

Recent years have seen tremendous developments in the use of machine learning models to link amino-acid sequence, structure, and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences that comprise intrinsically disordered regions. We here review developments in the study of sequence-ensemble-function relationships of disordered proteins that exploit or are used to train machine learning models. These include methods for generating conformational ensembles and designing new sequences, and for linking sequences to biophysical properties and biological functions. We highlight how these developments are built on a tight integration between experiment, theory and simulations, and account for evolutionary constraints, which operate on sequences of disordered regions differently than on those of folded domains.

Charge Arrangement Determines the Sensitivity of Aggregation Patterns between Peptide-Chains to the Surrounding Ionic Environment

The molecular basis for the liquid-liquid phase separation (LLPS) behavior of various biomolecular components in the cell is the formation of multivalent and low-affinity interactions. When the content of these components exceeds a certain critical concentration, the molecules will spontaneously coalesce to form a new liquid phase; i.e., LLPS occurs. Intrinsically disordered proteins (IDPs) are usually rich in amino acids with charged side-chains, and thus, LLPS-involving interactions between their side-chains are of great interest. However, the molecular details of the coalescence of such charged IDPs in a salt solution are still lacking. Here, we focus on two types of peptide-chains with oppositely charged amino acids in extreme arrangements and investigate their aggregation patterns in various ionic environments. The results show that the interaction patterns between peptide-chains with nonuniform charge arrangement sequences are more sensitive to the surrounding cationic environment, and Na+ ions are more likely to cause aggregation of ASP residues compared to Mg2+ ions. As the ionic concentration increases, the electrostatic interactions between oppositely charged residues are gradually converted into a negative-negative amino acid interaction network bridged by Na+ ions, while the positive charge-rich regions are more strongly inclined to be exposed to the solvent environment and gain greater freedom of movement. Simultaneously, this effect will reach saturation with a further increase of salt concentration. The present study enriches insights into the electrostatic dominant factors in phase separation phenomena at the atomic level, which will hopefully inspire the design and application of targeted LLPS in the future.

Sequence determinants of protein phase separation and recognition by protein phase-separated condensates through molecular dynamics and active learning

Elucidating how protein sequence determines the properties of disordered proteins and their phase-separated condensates is a great challenge in computational chemistry, biology, and biophysics. Quantitative molecular dynamics simulations and derived free energy values can in principle capture how a sequence encodes the chemical and biological properties of a protein. These calculations are, however, computationally demanding, even after reducing the representation by coarse-graining; exploring the large spaces of potentially relevant sequences remains a formidable task. We employ an "active learning" scheme introduced by Yang et al. (bioRxiv, 2022, https://doi.org/10.1101/2022.08.05.502972) to reduce the number of labelled examples needed from simulations, where a neural network-based model suggests the most useful examples for the next training cycle. Applying this Bayesian optimisation framework, we determine properties of protein sequences with coarse-grained molecular dynamics, which enables the network to establish sequence-property relationships for disordered proteins and their self-interactions and their interactions in phase-separated condensates. We show how iterative training with second virial coefficients derived from the simulations of disordered protein sequences leads to a rapid improvement in predicting peptide self-interactions. We employ this Bayesian approach to efficiently search for new sequences that bind to condensates of the disordered C-terminal domain (CTD) of RNA Polymerase II, by simulating molecular recognition of peptides to phase-separated condensates in coarse-grained molecular dynamics. By searching for protein sequences which prefer to self-interact rather than interact with another protein sequence we are able to shape the morphology of protein condensates and design multiphasic protein condensates.

Amino Acid Transfer Free Energies Reveal Thermodynamic Driving Forces in Biomolecular Condensate Formation

The self-assembly of intrinsically disordered proteins into biomolecular condensates shows a dependence on the primary sequence of the protein, leading to sequence-dependent phase separation. Methods to investigate this sequence-dependent phase separation rely on effective residue-level interaction potentials that quantify the propensity for the residues to remain in the dilute phase versus the dense phase. The most direct measure of these effective potentials are the distribution coefficients of the different amino acids between the two phases, but due to the lack of availability of these coefficients, proxies, most notably hydropathy, have been used. However, recent work has demonstrated the limitations of the assumption of hydropathy-driven phase separation. In this work, we address this fundamental gap by calculating the transfer free energies associated with transferring each amino acid side chain analog from the dilute phase to the dense phase of a model biomolecular condensate. We uncover an interplay between favorable protein-mediated and unfavorable water-mediated contributions to the overall free energies of transfer. We further uncover an asymmetry between the contributions of positive and negative charges in the driving forces for condensate formation. The results presented in this work provide an explanation for several non-trivial trends observed in the literature and will aid in the interpretation of experiments aimed at elucidating the sequence-dependent driving forces underlying the formation of biomolecular condensates.

Extending computational protein design to intrinsically disordered proteins

Advances in the accuracy and throughput of molecular simulations usher in a new era in the structural biology of disordered proteins.