Why are proteins marginally stable?

Structured protein domains enter the spotlight: modulators of biomolecular condensate form and function

Biomolecular condensates are membraneless organelles that concentrate proteins and nucleic acids. One of the primary components of condensates is multidomain proteins, whose domains can be broadly classified as structured and disordered. While structured protein domains are ubiquitous within biomolecular condensates, the physical ramifications of their unique properties have been relatively underexplored. Therefore, this review synthesizes current literature pertaining to structured protein domains within the context of condensates. We examine how the propensity of structured domains for high interaction specificity and low conformational heterogeneity contributes to the formation, material properties, and functions of biomolecular condensates. Finally, we propose unanswered questions on the behavior of structured protein domains within condensates, the answers of which will contribute to a more complete understanding of condensate biophysics.

Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics

Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights of homologous sequences. However, deciphering the evolutionary variability and complex dependencies among residues presents substantial hurdles. Here, we present a new machine-learning method based on variational autoencoders and evolutionary sampling strategy to address those limitations. We customized our method to generate novel sequences of model enzymes, haloalkane dehalogenases. Three design-build-test cycles improved the solubility of variants from 11% to 75%. Thorough experimental validation including the microfluidic device MicroPEX resulted in 20 multiple-point variants. Nine of them, sharing as little as 67% sequence similarity with the template, showed a melting temperature increase of up to 9 °C and an average improvement of 3 °C. The most stable variant demonstrated a 3.5-fold increase in activity compared to the template. High-quality experimental data collected with 20 variants represent a valuable data set for the critical validation of novel protein design approaches. Python scripts, jupyter notebooks, and data sets are available on GitHub (https://github.com/loschmidt/vae-dehalogenases), and interactive calculations will be possible via https://loschmidt.chemi.muni.cz/fireprotasr/.

The effect of extremolytes ectoine and hydroxyectoine on the heat-induced protein aggregation: The case of growth hormone

The extremolytes ectoine and hydroxyectoine are osmolytes found in extremophilic microorganisms. They are stabilisers of proteins and other macromolecules, including DNA and lipids. The aim of the study was to investigate the effect of the additives on the heat-induced aggregation of mink growth hormone as a model protein. The first-order rate constants of protein aggregation were determined at 60 °C depending on the additive concentration and pH of the solution. The onset temperature of aggregation was also recorded using a circular dichroism spectropolarimeter. The study showed that the effect of the additives depended on the pH of the solution. The first-order rate constants of aggregation were lower when the protein molecule had a negative charge. The effect also depended on the structure of the extremolyte itself. When the protein molecule was positively charged, hydroxyectoine destabilised the mink growth hormone molecule and promoted the aggregation. The different effects of the additives were determined by the different interactions with the protein molecules, as shown by circular dichroism measurements and previously by fluorescence spectroscopy. Therefore, when using ectoine or hydroxyectoine for protein formulation, the effect of the additive should be carefully analysed for each protein individually.

Protein binding and folding through an evolutionary lens

Protein-protein associations are often mediated by an intrinsically disordered protein region interacting with a folded domain in a coupled binding and folding reaction. Classic physical organic chemistry approaches together with structural biology have shed light on mechanistic aspects of such reactions. Further insight into general principles may be obtained by interpreting the results through an evolutionary lens. This review attempts to provide an overview on how the analysis of binding and folding reactions can benefit from an evolutionary approach, and is aimed at protein scientists without a background in evolution. Evolution constantly reshapes existing proteins by sampling more or less fit variants. Most new variants are weeded out as generations and new species come and go over hundreds to hundreds of millions of years. The huge ongoing genome sequencing efforts have provided us with a snapshot of existing adapted fit-for-purpose protein homologs in thousands of different organisms. Comparison of present-day orthologs and paralogs highlights general principles of the evolution of coupled binding and folding reactions and demonstrate a great potential for evolution to operate on disordered regions and modulate affinity and specificity of the interactions.

Protein CREATE enables closed-loop design of de novo synthetic protein binders

Proteins have proven to be useful agents in a variety of fields, from serving as potent therapeutics to enabling complex catalysis for chemical manufacture. However, they remain difficult to design and are instead typically selected for using extensive screens or directed evolution. Recent developments in protein large language models have enabled fast generation of diverse protein sequences in unexplored regions of protein space predicted to fold into varied structures, bind relevant targets, and catalyze novel reactions. Nevertheless, we lack methods to characterize these proteins experimentally at scale and update generative models based on those results. We describe Protein CREATE (Computational Redesign via an Experiment-Augmented Training Engine), an integrated computational and experimental pipeline that incorporates an experimental workflow leveraging next generation sequencing and phage display with single-molecule readouts to collect vast amounts of quantitative binding data for updating protein large language models. We use Protein CREATE to generate and assay thousands of designed binders to IL-7 receptor α and insulin receptor with parallel positive and negative selections to identify on-target binders. We discover not only individual novel binders but also features of ligand-receptor binding, including preservation of the IL7Rα - ligand hydrophobic interface specifically and existence of multiple approaches to contact the insulin receptor. We also demonstrate the importance of structural features, such as the lack of unpaired cysteine residues, toward design fidelity and find computational pre-screening metrics, such as interchain predicted TM scoring (iPTM), while useful, are imperfect predictors as they neither guarantee experimental binding nor rule it out. We use the data collected from Protein CREATE to score designs from the initial generative models. Globally, Protein CREATE will power future closed-loop design-build-test cycles to enable fine-grained design of protein binders.

Layered entrenchment maintains essentiality in the evolution of Form I Rubisco complexes

Protein complexes composed of strictly essential subunits are abundant in nature and often arise through the gradual complexification of ancestral precursor proteins. Essentiality can arise through the accumulation of changes that are tolerated in the complex state but would be deleterious for the standalone complex components. While this theoretical framework to explain how essentiality arises has been proposed long ago, it is unclear which factors cause essentiality to persist over evolutionary timescales. In this work we show that the central enzyme of photosynthesis, ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), can easily start to depend on a newly recruited interaction partner through multiple, genetically distinct mechanisms that affect stability, solubility, and catalysis. We demonstrate that layering multiple mechanisms of essentiality can lead to its persistence, even if any given mechanism reverts. More broadly, our work highlights that new interaction partners can drastically re-shape which substitutions are tolerated in the proteins they are recruited into. This can lead to the evolution of multilayered essentiality through the exploration of areas of sequence space that are only accessible in the complex state.

From propensities to patterns to principles in protein folding

As proposed here, β-turns play an essential role in protein self-assembly. This compact, four-residue motif affects protein conformation dramatically by reversing the overall chain direction. Turns are the "hinges" in globular proteins. This new proposal broadens a previous hypothesis that globular proteins solve the folding problem in part by filtering conformers with unsatisfied backbone hydrogen bonds, thereby preorganizing the folding population. Recapitulating that hypothesis: unsatisfied conformers would be dramatically destabilizing, shifting the U(nfolded) ⇌ N(ative) equilibrium far to the left. If even a single backbone polar group is satisfied by solvent when unfolded but buried and unsatisfied when folded, that energy penalty alone, approximately +5 kcal/mol, would rival almost the entire free energy of protein stabilization at room temperature. Consequently, globular proteins are built on scaffolds of hydrogen-bonded α-helices and/or strands of β-sheet, motifs that can be extended indefinitely, with intra-segment hydrogen bond partners for their backbone polar groups and without steric clash. Scaffolds foster a protein-wide hydrogen-bonded network, and, of thermodynamic necessity, they self-assemble cooperatively. Unlike elements of repetitive secondary structure, α-helices and β-sheet, a four-residue β-turn has only a single hydrogen bond (from i + 3 → i), not a cooperatively formed assembly of hydrogen bonds. As such, turns can form autonomously and are poised to initiate assembly of scaffold elements by bringing them together in an orientation and registration that promotes cooperative "zipping". The overall effect of this self-assembly mechanism is to induce substantial preorganization in the thermodynamically accessible folding population and, concomitantly, to reduce the folding entropy.

HSP90 buffers deleterious genetic variations in BRCA1

Protein-folding chaperone HSP90 buffers genetic variation in diverse organisms, but the clinical significance of HSP90 buffering in disease remains unclear. Here, we show that HSP90 buffers mutations in the BRCT domain of BRCA1. HSP90-buffered BRCA1 mutations encode protein variants that retain interactions with partner proteins and rely on HSP90 for protein stability and function in cell survival. Moreover, HSP90-buffered BRCA1 variants confer PARP inhibitor resistance in cancer cell lines. Low-level HSP90 inhibition alleviates this resistance, revealing a cryptic and mutant-specific HSP90-contingent synthetic lethality. Hence, by stabilizing metastable variants across the entirety of the BRCT domain, HSP90 reduces the clinical severity of BRCA1 mutations allowing them to accumulate in populations. We estimate that HSP90 buffers 11% to 28% of known human BRCA1- BRCT missense mutations. Our work extends the clinical significance of HSP90 buffering to a prevalent class of variations in BRCA1 , pioneering its importance in cancer predisposition and therapy resistance.

Identification of potential pharmacological chaperones that selectively stabilize mutated Aspartoacylases in Canavan disease

Canavan disease is caused by mutations in the ASPA gene, leading to diminished catalytic activity of aspartoacylase in the brain. Clinical missense mutations are found throughout the enzyme structure, with many of these mutated enzymes having not only decreased activity but also compromised stability. High-throughput screening of a small molecule library has identified several compounds that significantly increase the thermal stability of the E285A mutant enzyme, the most predominant clinical mutation in Canavan disease, while having a negligible effect on the native enzyme. Based on the initial successes, some structural analogs of these initial hits were selected for further examination. Glutathione, NAAG and patulin were each confirmed to be competitive inhibitors, indicating the binding of these compounds at the dimer interface or near the active site of the E285A enzyme. The experimental results were theoretically examined with the help of the docking analysis method. The structure activity-guided optimization of these compounds can potentially lead to potential pharmacological chaperones that could alleviate the detrimental effect of ASPA mutations in Canavan patients.

qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Thermostability, which is essential for the functional performance of enzymes, is largely determined by intramolecular physical interactions. Although many tools have been developed, existing computational methods have struggled to find the universal principles of protein thermostability. Recent advancements in structural proteomics have been driven by the introduction of deep neural networks such as AlphaFold2 and ESMFold. These innovations have enabled the characterization of protein structures with unprecedented speed and accuracy. Here, we introduce qProtein, a Python-implemented workflow designed for the quantitative analysis of physical interactions on the scale of structural proteomics. This platform accepts protein sequences as input and produces four structural features, including hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds. To demonstrate the use of qProtein, we investigate the structural features related to protein thermostability in six glycoside hydrolase (GH) families, comprising a total of 3,811 protein structures. Our results indicate that in five enzyme families (GH11, GH12, GH5_2, GH10, and GH48), the thermophilic enzymes have a larger average area of hydrophobic clusters compared to the nonthermophilic enzymes within each family. Furthermore, our analysis of the local-structure regions reveals that the hydrophobic clusters are predominantly distributed in the distal regions of the GH11 enzymes. In addition, the average hydrophobic cluster area of the thermophilic enzymes is significantly higher than that of the nonthermophilic enzymes in the distal regions of the GH11 enzymes. Therefore, qProtein is a well-suited platform for analyzing the structural features of thermal stability at the level of structural proteomics. We provide the source code for qProtein at https://github.com/bj600800/qProtein, and the web server is available at http://qProtein.sdu.edu.cn:8888.

Optimal strategy for stabilizing protein folding intermediates

To manipulate the protein concentration at a certain functional state through chemical stabilizers is crucial for protein-related studies. It not only plays a key role in protein structure analysis and protein folding kinetics, but also affects protein functionality to a large extent and thus has wide applications in medicine, food industry, etc. However, due to concerns about side effects or financial costs of stabilizers, identifying optimal strategies for enhancing protein stability with a minimal amount of stabilizers is of great importance. Here, we prove that either for the fixed terminal time (including both finite and infinite cases) or for the free one, the optimal control strategy for stabilizing the folding intermediates with a linear strategy for stabilizer addition belongs to the class of bang-bang controls. The corresponding optimal switching time is derived analytically, whose phase diagram with respect to several key parameters is explored in detail. The bang-bang control will be broken when nonlinear strategies for stabilizer addition are adopted. Moreover, the above theory is applied to the stabilization of erythropoietin by ten different kinds of chemicals, providing theoretical guidance for the selection and rational usage of stabilizers. Our current study on optimal strategies for protein stabilizers not only offers deep insights into the general picture of protein folding kinetics but also provides valuable theoretical guidance on treatments for protein-related diseases in medicine.

Heat shock proteins as hallmarks of cancer: insights from molecular mechanisms to therapeutic strategies

Heat shock proteins are essential molecular chaperones that play crucial roles in stabilizing protein structures, facilitating the repair or degradation of damaged proteins, and maintaining proteostasis and cellular functions. Extensive research has demonstrated that heat shock proteins are highly expressed in cancers and closely associated with tumorigenesis and progression. The "Hallmarks of Cancer" are the core features of cancer biology that collectively define a series of functional characteristics acquired by cells as they transition from a normal state to a state of tumor growth, including sustained proliferative signaling, evasion of growth suppressors, resistance to cell death, enabled replicative immortality, the induction of angiogenesis, and the activation of invasion and metastasis. The pivotal roles of heat shock proteins in modulating the hallmarks of cancer through the activation or inhibition of various signaling pathways has been well documented. Therefore, this review provides an overview of the roles of heat shock proteins in vital biological processes from the perspective of the hallmarks of cancer and summarizes the small-molecule inhibitors that target heat shock proteins to regulate various cancer hallmarks. Moreover, we further discuss combination therapy strategies involving heat shock proteins and promising dual-target inhibitors to highlight the potential of targeting heat shock proteins for cancer treatment. In summary, this review highlights how targeting heat shock proteins could regulate the hallmarks of cancer, which will provide valuable information to better elucidate and understand the roles of heat shock proteins in oncology and the mechanisms of cancer occurrence and development and aid in the development of more efficacious and less toxic novel anticancer agents.

Pharmaceutical proteins at the interfaces and the role of albumin

A critical measure of the quality of pharmaceutical proteins is the preservation of native conformations of the active pharmaceutical ingredients. Denaturation of the active proteins in any step before administration into patients could lead to loss of potency and/or aggregation, which is associated with an increased risk of immunogenicity of the products. Interfacial stress enhances protein instability as their adsorption to the air-liquid and liquid-solid interfaces are implicated in the formation of denatured proteins and aggregates. While excipients in protein formulations have been employed to reduce the risk of aggregation, the roles of albumin as a stabilizer have not been reviewed from practical and theoretical standpoints. The amphiphilic nature of albumin makes it accumulate at the interfaces. In this review, we aim to bridge the knowledge gap between interfacial instability and the influence of albumin as a surface-active excipient in the context of reducing the immunogenicity risk of protein formulations.

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

Human apo-metallothionein 1a is not a random coil: Evidence from guanidinium chloride, high temperature, and acidic pH unfolding studies

The structures of apo-metallothioneins (apo-MTs) have been relatively elusive due to their fluxional, disordered state which has been difficult to characterize. However, intrinsically disordered protein (IDP) structures are rather diverse, which raises questions about where the structure of apo-MTs fit into the protein structural spectrum. In this paper, the unfolding transitions of apo-MT1a are discussed with respect to the effect of the chemical denaturant GdmCl, temperature conditions, and pH environment. Cysteine modification in combination with electrospray ionization mass spectrometry was used to probe the unfolding transition of apo-MT1a in terms of cysteine exposure. Circular dichroism spectroscopy was also used to monitor the change in secondary structure as a function of GdmCl concentration. For both of these techniques, cooperative unfolding was observed, suggesting that apo-MT1a is not a random coil. More GdmCl was required to unfold the protein backbone than to expose the cysteines, indicating that cysteine exposure is likely an early step in the unfolding of apo-MT1a. MD simulations complement the experimental results, suggesting that apo-MT1a adopts a more compact structure than expected for a random coil. Overall, these results provide further insight into the intrinsically disordered structure of apo-MT.

Cellular and epigenetic perspective of protein stability and its implications in the biological system

Protein stability is a fundamental prerequisite in both experimental and therapeutic applications. Current advancements in high throughput experimental techniques and functional ontology approaches have elucidated that impairment in the structure and stability of proteins is intricately associated with the cause and cure of several diseases. Therefore, it is paramount to deeply understand the physical and molecular confounding factors governing the stability of proteins. In this review article, we comprehensively investigated the evolution of protein stability, examining its emergence over time, its relationship with organizational aspects and the experimental methods used to understand it. Furthermore, we have also emphasized the role of Epigenetics and its interplay with post-translational modifications (PTMs) in regulating the stability of proteins.

Towards the understanding of molecular motors and its relationship with local unfolding

Molecular motors are machines essential for life since they convert chemical energy into mechanical work. However, the precise mechanism by which nucleotide binding, catalysis, or release of products is coupled to the work performed by the molecular motor is still not entirely clear. This is due, in part, to a lack of understanding of the role of force in the mechanical-structural processes involved in enzyme catalysis. From a mechanical perspective, one promising hypothesis is the Haldane-Pauling hypothesis which considers the idea that part of the enzymatic catalysis is strain-induced. It suggests that enzymes cannot be efficient catalysts if they are fully complementary to the substrates. Instead, they must exert strain on the substrate upon binding, using enzyme-substrate energy interaction (binding energy) to accelerate the reaction rate. A novel idea suggests that during catalysis, significant strain energy is built up, which is then released by a local unfolding/refolding event known as 'cracking'. Recent evidence has also shown that in catalytic reactions involving conformational changes, part of the heat released results in a center-of-mass acceleration of the enzyme, raising the possibility that the heat released by the reaction itself could affect the enzyme's integrity. Thus, it has been suggested that this released heat could promote or be linked to the cracking seen in proteins such as adenylate kinase (AK). We propose that the energy released as a consequence of ligand binding/catalysis is associated with the local unfolding/refolding events (cracking), and that this energy is capable of driving the mechanical work.

Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants

Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r =  - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.

In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration

Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.

Unsupervisedly Prompting AlphaFold2 for Accurate Few-Shot Protein Structure Prediction

Data-driven predictive methods that can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining an accurate folding landscape using coevolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit coevolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologues. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in the low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method that could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.

PC_ali: a tool for improved multiple alignments and evolutionary inference based on a hybrid protein sequence and structure similarity score

MOTIVATION

Evolutionary inference depends crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships.

RESULTS

Here we examined four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence identity, fraction of superimposed residues, and contact overlap), finding that they are intimately correlated but none of them provides a complete and unbiased picture of conservation in proteins. Therefore, we propose the new hybrid protein sequence and structure similarity score PC_sim based on their main principal component. The corresponding divergence measure PC_div shows the strongest correlation with divergences obtained from individual similarities, suggesting that it infers accurate evolutionary divergences. We developed the program PC_ali that constructs protein MSAs either de novo or modifying an input MSA, using a similarity matrix based on PC_sim. The program constructs a starting MSA based on the maximal cliques of the graph of these PAs and it refines it through progressive alignments along the tree reconstructed with PC_div. Compared with eight state-of-the-art multiple structure or sequence alignment tools, PC_ali achieves higher or equal aligned fraction and structural scores, sequence identity higher than structure aligners although lower than sequence aligners, highest score PC_sim, and highest similarity with the MSAs produced by other tools and with the reference MSA Balibase.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ugobas/PC_ali.

Systematic thermal analysis of the Arabidopsis proteome: Thermal tolerance, organization, and evolution

Understanding the thermal stability of the plant proteome in the context of the native cellular environment would aid the design of crops with high thermal tolerance, but only limited such data are available. Here, we applied quantitative mass spectrometry to profile the thermal stability of the Arabidopsis proteome and identify thermo-sensitive and thermo-resilient protein networks in Arabidopsis, providing a basis for understanding heat-induced damage. We also show that the similarities of the protein-melting curves can be used as a proxy to evaluate system-wide protein-protein interactions in non-engineered plants and enable the identification of transient interactions exhibited by metabolons in the context of the cellular environment. Finally, we report a systematic comparison of the thermal stability of paralogs in Arabidopsis to aid the investigation and understanding of gene duplication and protein evolution. Taken together, our results could have broad implications for the fields of plant thermal tolerance, plant protein assemblies, and evolution.

A porphyrin-based molecular cage guided by designed local-electric field is highly selective and efficient

The present work outlines a general methodology for designing efficient catalytic machineries that can easily be tweaked to meet the demands of the target reactions. This work utilizes a principle of the designed local electric field (LEF) as the driver for an efficient catalyst. It is demonstrated that by tweaking the LEF, we can catalyze the desired hydroxylation products with enantioselectivity that can be changed at will. Using computation tools, we caged a synthetic analog of heme porphyrin (HM1) and investigated the pharmaceutically relevant conversion of tetralin to tetralol, inside the modified supramolecular cage. The QM/MM calculations demonstrate a resulting catalytic efficiency with virtually absolute R-selectivity for the tetralin hydroxylation. Our calculations show that the LEF of the supramolecular cage and HM1 exert a strong electric field along the Fe-O reaction axis, which is the main driving force for enhanced reactivity. At the same time, the supramolecular cage applies a lateral LEF that regulates the enantioselectivity. We further demonstrate that swapping the charged/polar substitution in the supramolecular cage switches the lateral LEF which changes the enantioselectivity of hydroxylation from R to S.

Design of cell-type-specific hyperstable IL-4 mimetics via modular de novo scaffolds

The interleukin-4 (IL-4) cytokine plays a critical role in modulating immune homeostasis. Although there is great interest in harnessing this cytokine as a therapeutic in natural or engineered formats, the clinical potential of native IL-4 is limited by its instability and pleiotropic actions. Here, we design IL-4 cytokine mimetics (denoted Neo-4) based on a de novo engineered IL-2 mimetic scaffold and demonstrate that these cytokines can recapitulate physiological functions of IL-4 in cellular and animal models. In contrast with natural IL-4, Neo-4 is hyperstable and signals exclusively through the type I IL-4 receptor complex, providing previously inaccessible insights into differential IL-4 signaling through type I versus type II receptors. Because of their hyperstability, our computationally designed mimetics can directly incorporate into sophisticated biomaterials that require heat processing, such as three-dimensional-printed scaffolds. Neo-4 should be broadly useful for interrogating IL-4 biology, and the design workflow will inform targeted cytokine therapeutic development.

Protein Stability─Analysis of Heat and Cold Denaturation without and with Unfolding Models

Protein stability is important in many areas of life sciences. Thermal protein unfolding is investigated extensively with various spectroscopic techniques. The extraction of thermodynamic properties from these measurements requires the application of models. Differential scanning calorimetry (DSC) is less common, but is unique as it measures directly a thermodynamic property, that is, the heat capacity C_p(T). The analysis of C_p(T) is usually performed with the chemical equilibrium two-state model. This is not necessary and leads to incorrect thermodynamic consequences. Here we demonstrate a straightforward model-independent evaluation of heat capacity experiments in terms of protein unfolding enthalpy ΔH(T), entropy ΔS(T), and free energy ΔG(T)). This now allows the comparison of the experimental thermodynamic data with the predictions of different models. We critically examined the standard chemical equilibrium two-state model, which predicts a positive free energy for the native protein, and diverges distinctly from the experimental temperature profiles. We propose two new models which are equally applicable to spectroscopy and calorimetry. The Θ_U(T)-weighted chemical equilibrium model and the statistical-mechanical two-state model provide excellent fits of the experimental data. They predict sigmoidal temperature profiles for enthalpy and entropy, and a trapezoidal temperature profile for the free energy. This is illustrated with experimental examples for heat and cold denaturation of lysozyme and β-lactoglobulin. We then show that the free energy is not a good criterion to judge protein stability. More useful parameters are discussed, including protein cooperativity. The new parameters are embedded in a well-defined thermodynamic context and are amenable to molecular dynamics calculations.

A split, conditionally active mimetic of IL-2 reduces the toxicity of systemic cytokine therapy

The therapeutic potential of recombinant cytokines has been limited by the severe side effects of systemic administration. We describe a strategy to reduce the dose-limiting toxicities of monomeric cytokines by designing two components that require colocalization for activity and that can be independently targeted to restrict activity to cells expressing two surface markers. We demonstrate the approach with a previously designed mimetic of cytokines interleukin-2 and interleukin-15-Neoleukin-2/15 (Neo-2/15)-both for trans-activating immune cells surrounding targeted tumor cells and for cis-activating directly targeted immune cells. In trans-activation mode, tumor antigen targeting of the two components enhanced antitumor activity and attenuated toxicity compared with systemic treatment in syngeneic mouse melanoma models. In cis-activation mode, immune cell targeting of the two components selectively expanded CD8+ T cells in a syngeneic mouse melanoma model and promoted chimeric antigen receptor T cell activation in a lymphoma xenograft model, enhancing antitumor efficacy in both cases.

A guide to studying protein aggregation

Disrupted protein folding or decreased protein stability can lead to the accumulation of (partially) un- or misfolded proteins, which ultimately cause the formation of protein aggregates. Much of the interest in protein aggregation is associated with its involvement in a wide range of human diseases and the challenges it poses for large-scale biopharmaceutical manufacturing and formulation of therapeutic proteins and peptides. On the other hand, protein aggregates can also be functional, as observed in nature, which triggered its use in the development of biomaterials or therapeutics as well as for the improvement of food characteristics. Thus, unmasking the various steps involved in protein aggregation is critical to obtain a better understanding of the underlying mechanism of amyloid formation. This knowledge will allow a more tailored development of diagnostic methods and treatments for amyloid-associated diseases, as well as applications in the fields of new (bio)materials, food technology and therapeutics. However, the complex and dynamic nature of the aggregation process makes the study of protein aggregation challenging. To provide guidance on how to analyse protein aggregation, in this review we summarize the most commonly investigated aspects of protein aggregation with some popular corresponding methods.

A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Design of an artificial phage-display library based on a new scaffold improved for average stability of the randomized proteins

Scaffold-based protein libraries are designed to be both diverse and rich in functional/folded proteins. However, introducing an extended diversity while preserving stability of the initial scaffold remains a challenge. Here we developed an original approach to select the ensemble of folded proteins from an initial library. The thermostable CheY protein from Thermotoga maritima was chosen as scaffold. Four loops of CheY were diversified to create a new binding surface. The subset of the library giving rise to folded proteins was first selected using a natural protein partner of the template scaffold. Then, a gene shuffling approach based on a single restriction enzyme was used to recombine DNA sequences encoding these filtrated variants. Taken together, the filtration strategy and the shuffling of the filtrated sequences were shown to enrich the library in folded and stable sequences while maintaining a large diversity in the final library (Lib-Cheytins 2.1). Binders of the Oplophorus luciferase Kaz domain were then selected by phage display from the final library, showing affinities in the μM range. One of the best variants induced a loss of 92% of luminescent activity, suggesting that this Cheytin preferentially binds to the Kaz active site.

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.

Evolutionary scaling of maximum growth rate with organism size

Data from nearly 1000 species reveal the upper bound to rates of biomass production achievable by natural selection across the Tree of Life. For heterotrophs, maximum growth rates scale positively with organism size in bacteria but negatively in eukaryotes, whereas for phototrophs, the scaling is negligible for cyanobacteria and weakly negative for eukaryotes. These results have significant implications for understanding the bioenergetic consequences of the transition from prokaryotes to eukaryotes, and of the expansion of some groups of the latter into multicellularity. The magnitudes of the scaling coefficients for eukaryotes are significantly lower than expected under any proposed physical-constraint model. Supported by genomic, bioenergetic, and population-genetic data and theory, an alternative hypothesis for the observed negative scaling in eukaryotes postulates that growth-diminishing mutations with small effects passively accumulate with increasing organism size as a consequence of associated increases in the power of random genetic drift. In contrast, conditional on the structural and functional features of ribosomes, natural selection has been able to promote bacteria with the fastest possible growth rates, implying minimal conflicts with both bioenergetic constraints and random genetic drift. If this extension of the drift-barrier hypothesis is correct, the interpretations of comparative studies of biological traits that have traditionally ignored differences in population-genetic environments will require revisiting.

An efficient method to predict protein thermostability in alanine mutation

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔG_exp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

The evolution of the HIV-1 protease folding stability

The evolution of structural proteins is generally constrained by the folding stability. However, little is known about the particular capacity of viral proteins to accommodate mutations that can potentially affect the protein stability and, in general, the evolution of the protein stability over time. As an illustrative model case, here, we investigated the evolution of the stability of the human immunodeficiency virus (HIV-1) protease (PR), which is a common HIV-1 drug target, under diverse evolutionary scenarios that include (1) intra-host virus evolution in a cohort of seventy-five patients sampled over time, (2) intra-host virus evolution sampled before and after specific PR-based treatments, and (3) inter-host evolution considering extant and ancestral (reconstructed) PR sequences from diverse HIV-1 subtypes. We also investigated the specific influence of currently known HIV-1 PR resistance mutations on the PR folding stability. We found that the HIV-1 PR stability fluctuated over time within a constant and wide range in any studied evolutionary scenario, accommodating multiple mutations that partially affected the stability while maintaining activity. We did not identify relationships between change of PR stability and diverse clinical parameters such as viral load, CD4⁺ T-cell counts, and a surrogate of time from infection. Counterintuitively, we predicted that nearly half of the studied HIV-1 PR resistance mutations do not significantly decrease stability, which, together with compensatory mutations, would allow the protein to adapt without requiring dramatic stability changes. We conclude that the HIV-1 PR presents a wide structural plasticity to acquire molecular adaptations without affecting the overall evolution of stability.

Increasing protein stability by inferring substitution effects from high-throughput experiments

We apply a computational model, global multi-mutant analysis (GMMA), to inform on effects of most amino acid substitutions from a randomly mutated gene library. Using a high mutation frequency, the method can determine mutations that increase the stability of even very stable proteins for which conventional selection systems have reached their limit. As a demonstration of this, we screened a mutant library of a highly stable and computationally redesigned model protein using an in vivo genetic sensor for folding and assigned a stability effect to 374 of 912 possible single amino acid substitutions. Combining the top 9 substitutions increased the unfolding energy 47 to 69 kJ/mol in a single engineering step. Crystal structures of stabilized variants showed small perturbations in helices 1 and 2, which rendered them closer in structure to the redesign template. This case study illustrates the capability of the method, which is applicable to any screen for protein function.

The mechanical cell - the role of force dependencies in synchronising protein interaction networks

The role of mechanical signals in the proper functioning of organisms is increasingly recognised, and every cell senses physical forces and responds to them. These forces are generated both from outside the cell or via the sophisticated force-generation machinery of the cell, the cytoskeleton. All regions of the cell are connected via mechanical linkages, enabling the whole cell to function as a mechanical system. In this Review, we define some of the key concepts of how this machinery functions, highlighting the critical requirement for mechanosensory proteins, and conceptualise the coupling of mechanical linkages to mechanochemical switches that enables forces to be converted into biological signals. These mechanical couplings provide a mechanism for how mechanical crosstalk might coordinate the entire cell, its neighbours, extending into whole collections of cells, in tissues and in organs, and ultimately in the coordination and operation of entire organisms. Consequently, many diseases manifest through defects in this machinery, which we map onto schematics of the mechanical linkages within a cell. This mapping approach paves the way for the identification of additional linkages between mechanosignalling pathways and so might identify treatments for diseases, where mechanical connections are affected by mutations or where individual force-regulated components are defective.

Design and optimization of enzymatic activity in a de novo β-barrel scaffold

While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded β-barrel protein to create catalysts for a retro-aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds.

Evolution avoids a pathological stabilizing interaction in the immune protein S100A9

Stability constrains evolution. While much is known about constraints on destabilizing mutations, less is known about the constraints on stabilizing mutations. We recently identified a mutation in the innate immune protein S100A9 that provides insight into such constraints. When introduced into human S100A9, M63F simultaneously increases the stability of the protein and disrupts its natural ability to activate Toll-like receptor 4. Using chemical denaturation, we found that M63F stabilizes a calcium-bound conformation of hS100A9. We then used NMR to solve the structure of the mutant protein, revealing that the mutation distorts the hydrophobic binding surface of hS100A9, explaining its deleterious effect on function. Hydrogen-deuterium exchange (HDX) experiments revealed stabilization of the region around M63F in the structure, notably Phe37. In the structure of the M63F mutant, the Phe37 and Phe63 sidechains are in contact, plausibly forming an edge-face π-stack. Mutating Phe37 to Leu abolished the stabilizing effect of M63F as probed by both chemical denaturation and HDX. It also restored the biological activity of S100A9 disrupted by M63F. These findings reveal that Phe63 creates a molecular staple with Phe37 that stabilizes a nonfunctional conformation of the protein, thus disrupting function. Using a bioinformatic analysis, we found that S100A9 proteins from different organisms rarely have Phe at both positions 37 and 63, suggesting that avoiding a pathological stabilizing interaction indeed constrains S100A9 evolution. This work highlights an important evolutionary constraint on stabilizing mutations, namely, that they must avoid inappropriately stabilizing nonfunctional protein conformations.

In Silico Analysis of PORD Mutations on the 3D Structure of P450 Oxidoreductase

Cytochrome P450 oxidoreductase (POR) is a membrane-bound flavoprotein that helps in transferring electrons from its NADPH domain to all cytochrome P450 (CYP450) enzymes. Mutations in the POR gene could severely affect the metabolism of steroid hormones and the development of skeletal muscles, a condition known as Cytochrome P450 oxidoreductase deficiency (PORD). PORD is associated with clinical presentations of disorders of sex development, Antley and Bixler’s syndrome (ABS), as well as an abnormal steroid hormone profile. We have performed an in silico analysis of POR 3D X-ray protein crystal structure to study the effects of reported mutations on the POR enzyme structure. A total of 32 missense mutations were identified, from 170 PORD patients, and mapped on the 3D crystal structure of the POR enzyme. In addition, five of the missense mutations (R457H, A287P, D210G, Y181D and Y607C) were further selected for an in-depth in silico analysis to correlate the observed changes in POR protein structure with the clinical phenotypes observed in PORD patients. Overall, missense mutations found in the binding sites of POR cofactors could lead to a severe form of PORD, emphasizing the importance of POR cofactor binding domains in transferring electrons to the CYP450 enzyme family.

Non-Equilibrium Protein Folding and Activation by ATP-Driven Chaperones

Recent experimental studies suggest that ATP-driven molecular chaperones can stabilize protein substrates in their native structures out of thermal equilibrium. The mechanism of such non-equilibrium protein folding is an open question. Based on available structural and biochemical evidence, I propose here a unifying principle that underlies the conversion of chemical energy from ATP hydrolysis to the conformational free energy associated with protein folding and activation. I demonstrate that non-equilibrium folding requires the chaperones to break at least one of four symmetry conditions. The Hsp70 and Hsp90 chaperones each break a different subset of these symmetries and thus they use different mechanisms for non-equilibrium protein folding. I derive an upper bound on the non-equilibrium elevation of the native concentration, which implies that non-equilibrium folding only occurs in slow-folding proteins that adopt an unstable intermediate conformation in binding to ATP-driven chaperones. Contrary to the long-held view of Anfinsen's hypothesis that proteins fold to their conformational free energy minima, my results predict that some proteins may fold into thermodynamically unstable native structures with the assistance of ATP-driven chaperones, and that the native structures of some chaperone-dependent proteins may be shaped by their chaperone-mediated folding pathways.

Conformationally engineering flexible peptides on silver nanoparticles

Molecular conformational engineering is to engineer flexible non-functional molecules into unique conformations to create novel functions just like natural proteins fold. Obviously, it is a grand challenge with tremendous opportunities. Based on the facts that natural proteins are only marginally stable with a net stabilizing energy roughly equivalent to the energy of two hydrogen bonds, and the energy barriers for the adatom diffusion of some metals are within a similar range, we propose that metal nanoparticles can serve as a general replacement of protein scaffolds to conformationally engineer protein fragments on the surface of nanoparticles. To prove this hypothesis, herein, we successfully restore the antigen-recognizing function of the flexible peptide fragment of a natural anti-lysozyme antibody on the surface of silver nanoparticles, creating a silver nanoparticle-base artificial antibody (Silverbody). A plausible mechanism is proposed, and some general principles for conformational engineering are summarized to guide future studies in this area.

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A silver NP-based artificial antibody is created by conformational engineering

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Function emerges on NPs from non-functional peptide by mimicking the protein folding

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A general mechanism is proposed for the conformational engineering on metal NPs

Proteins' Evolution upon Point Mutations

As the reader must be already aware, state-of-the-art protein folding prediction methods have reached a smashing success in their goal of accurately determining the three-dimensional structures of proteins. Yet, a solution to simple problems such as the effects of protein point mutations on their (i) native conformation; (ii) marginal stability; (iii) ensemble of high-energy nativelike conformations; and (iv) metamorphism propensity and, hence, their evolvability, remains as an unsolved problem. As a plausible solution to the latter, some properties of the amide hydrogen-deuterium exchange, a highly sensitive probe of the structure, stability, and folding of proteins, are assessed from a new perspective. The preliminary results indicate that the protein marginal stability change upon point mutations provides the necessary and sufficient information to estimate, through a Boltzmann factor, the evolution of the amide hydrogen exchange protection factors and, consequently, that of the ensemble of folded conformations coexisting with the native state. This work contributes to our general understanding of the effects of point mutations on proteins and may spur significant progress in our efforts to develop methods to determine the appearance of new folds and functions accurately.

Unraveling the molecular determinants of the anti-phagocytic protein cloak of plague bacteria

The pathogenic bacterium Yersina pestis is protected from macrophage engulfment by a capsule like antigen, F1, formed of long polymers of the monomer protein, Caf1. However, despite the importance of this pathogen, the mechanism of protection was not understood. Here we demonstrate how F1 protects the bacteria from phagocytosis. First, we show that Escherichia coli expressing F1 showed greatly reduced adherence to macrophages. Furthermore, the few cells that did adhere remained on the macrophage surface and were not engulfed. We then inserted, by mutation, an “RGDS” integrin binding motif into Caf1. This did not change the number of cells adhering to macrophages but increased the fraction of adherent cells that were engulfed. Therefore, F1 protects in two separate ways, reducing cell adhesion, possibly by acting as a polymer brush, and hiding innate receptor binding sites needed for engulfment. F1 is very robust and we show that E. coli expressing weakened mutant polymers are engulfed like the RGDS mutant. This suggests that innate attachment sites on the native cell surface are exposed if F1 is weakened. Single-molecule force spectroscopy (SMFS) experiments revealed that wild-type F1 displays a very high mechanical stability of 400 pN. However, the mechanical resistance of the destabilised mutants, that were fully engulfed, was only 20% weaker. By only marginally exceeding the mechanical force applied to the Caf1 polymer during phagocytosis it may be that the exceptional tensile strength evolved to resist the forces applied at this stage of engulfment.

Macrophages, a type of white blood cell, form an important element of our immune defence. They interrogate other cells’ surfaces for molecular clues and ingest those presenting a threat in a process known as phagocytosis. Not surprisingly, pathogenic bacteria have developed ways to evade this fate. The plague bacterium, Yersinia pestis, produces the long polymeric F1 coat protein which enables it to avoid ingestion, but the mechanism was unclear. We show that equipping Escherichia coli cells with an F1 coat protected them from phagocytosis by two separate mechanisms, reducing contact with the macrophage surface and hiding the signals that tell the macrophages they are targets. F1 is also a very stable protein polymer and using single molecule force spectroscopy we showed it also has a very high resistance to pulling forces. Surprisingly, mutations which reduced this by only 20% caused adherent bacteria to be fully ingested, indicating that cells are subject to significant forces prior to recognition and ingestion. Thus, F1 has evolved three notable properties (i) physical; creation of a hydrated polymer brush to inhibit surface interactions, (ii) chemical; absence of molecular recognition clues needed for engulfment and (iii) mechanical; strength that maintains the camouflage layer during surface stretching.

A Protein Data Bank survey of multimodal binding of thiocyanate to proteins: Evidence for thiocyanate promiscuity

Over the last one and half century, a myriad of studies has demonstrated that Hofmeister ions have a major impact on protein stability and solubility. Nevertheless, the definition of the physico-chemical basis of their activity has proved to be highly challenging and controversial. Here, by exploiting the enormous information content of the Protein Data Bank, we explored the binding to proteins of thiocyanate, the anion of the series exerting the highest solubilization/destabilization effects. The survey, which led to the identification and characterization of 712 thiocyanate binding sites, provides a comprehensive and atomic-level view of the varied interactions that the ion forms with proteins. The inspection of these sites highlights a limited tendency of thiocyanate to interact with structured water molecules, in line with the reported poor hydration of the ion. On the other hand, the thiocyanate makes interactions with protein nonpolar moieties, especially with the backbone C^α atom. In as many as 104 cases, the ion exclusively makes nonpolar contacts. In conclusion, these findings suggest that the ability of thiocyanate to bind all types of protein exposed patches may lead to the formation of a negatively charged electrostatic barrier that could prevent protein-protein aggregation and promote protein solubility. Moreover, the denaturing action of thiocyanate may be ascribed to its ability to establish multiple attractive interactions with protein surfaces.

Non-adaptive complexity and biochemical function

Intricate biochemical structures are usually thought to be useful, because natural selection preserves them from degradation by a constant hail of destructive mutations. Biochemists therefore often deliberately disrupt them to understand how complexity improves protein function or fitness. However, evolutionary theory suggests that even useless complexity that never improved fitness can become completely essential if a simple set of evolutionary conditions is fulfilled. We review evidence that stable protein complexes, protein-chaperone interactions, and complexes consisting of several paralogs all fulfill these conditions. This makes reverse genetics or destructive mutagenesis unsuitable for assigning functions to these kinds of complexity. Instead, we advocate that incorporating evolutionary approaches into biochemistry overcomes this difficulty and allows us to distinguish useless from useful biochemical complexity.

Accelerating therapeutic protein design

Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.

Evolution of amino acid propensities under stability-mediated epistasis

Site-specific amino acid preferences are influenced by the genetic background of the protein. The preferences for resident amino acids are expected to, on average, increase over time because of replacements at other sites - a nonadaptive phenomenon referred to as the 'evolutionary Stokes shift'. Alternatively, decreases in resident amino acid propensity have recently been viewed as evidence of adaptations to external environmental changes. Using population genetics theory and thermodynamic stability-constraints, we show that nonadaptive evolution can lead to both positive and negative shifts in propensities following the fixation of an amino acid, emphasizing that the detection of negative shifts is not conclusive evidence of adaptation. Considering shifts in propensities over windows between substitutions at a focal site, we find that following ≈ 50% of substitutions the propensity for the new resident amino acid decreases over time, and both positive and negative shifts were comparable in magnitude. Preferences were often conserved via a significant negative autocorrelation in propensity changes-increases in propensities often followed by decreases, and vice versa. Lastly, we explore the underlying mechanisms that lead propensities to fluctuate. We observe that stabilizing replacements increase the mutational tolerance at a site and in doing so decrease the propensity for the resident amino acid. In contrast, destabilizing substitutions result in more rugged fitness landscapes that tend to favor the resident amino acid. In summary, our results characterize propensity trajectories under nonadaptive stability-constrained evolution against which evidence of adaptations should be calibrated.

A thermodynamic atlas of proteomes reveals energetic innovation across the tree of life

Protein stability is a fundamental molecular property enabling organisms to adapt to their biological niches. How this is facilitated and whether there are kingdom specific or more general universal strategies is not known. A principal obstacle to addressing this issue is that the vast majority of proteins lack annotation, specifically thermodynamic annotation, beyond the amino acid and chromosome information derived from genome sequencing. To address this gap and facilitate future investigation into large-scale patterns of protein stability and dynamics within and between organisms, we applied a unique ensemble-based thermodynamic characterization of protein folds to a substantial portion of extant sequenced genomes. Using this approach, we compiled a database resource focused on the position-specific variation in protein stability. Interrogation of the database reveals; 1) domains of life exhibit distinguishing thermodynamic features, with eukaryotes particularly different from both archaea and bacteria, 2) the optimal growth temperature of an organism is proportional to the average apolar enthalpy of its proteome, 3) intrinsic disorder content is also proportional to the apolar enthalpy (but unexpectedly not the predicted stability at 25 °C), and 4) secondary structure and global stability information of individual proteins is extractable. We hypothesize that wider access to residue-specific thermodynamic information of proteomes will result in deeper understanding of mechanisms driving functional adaptation and protein evolution. Our database is free for download at https://afc-science.github.io/thermo-env-atlas/.