The nature of protein folding pathways

Unraveling the unfolding mechanism of pseudoazurin: Insights into stabilizing cupredoxin fold as a common domain of Cu-containing proteins

Understanding protein unfolding mechanisms is crucial for comprehending protein-folding related diseases, developing diagnostic methods, and designing proteins with desired stability for medicinal or industrial applications. However, investigating structures at atomic resolution is often difficult due to the flexibility and transiency of unfolding intermediate states. Pseudoazurin (PAz) is a well-characterized simple cupredoxin composed of a small polypeptide (124 amino acids) and a single metal cofactor (Cu2+), making it suitable to study the unfolding mechanism. In this study, combining the merits of structure determination by small-angle neutron scattering (SANS) and molecular dynamics (MD) simulations enabled us to access the details of the unfolding mechanism. The unfolding of PAz proceeds through a two-step mechanism involving the "native", "open-domain", and "random-coil" states. Several amino acid residues at the vicinity of Cu2+ ion are involved in the structural transitions, where the interactions among these residues are important in controlling the stability of PAz. These findings may be applicable to stabilizing metalloproteins with cupredoxin domain structures.

MultiPassMerger: Automated data processing for multipass cyclic ion mobility HDX-MS

Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) is a powerful tool for studying protein structure and dynamics. As a bottom-up LC-MS technique, its success largely depends on peptide identifications made by peptide mapping prior to HDX measurements. We previously demonstrated that combining peptide mapping results from complementary single- and multipass cyclic ion mobility-mass spectrometry (cIM-MS) experiments, an approach we term "multi-sequence" cIM-MS, can enhance HDX-MS by increasing peptide identifications. However, this approach required labor-intensive, manual handling of the acquired data, including lengthy optimization of drift time (DT) versus DT full width half maximum (FWHM) trendlines during peak detection processing to combat cyclic wrap-around effects. Here, we present MultiPassMerger, an open-source software tool that automates the processing, merging, and filtering of single- and multipass cIM-MS peptide mapping data. MultiPassMerger was validated through re-analysis of several model proteins. Its automated capabilities enabled better optimization of DT versus DT FWHM trendlines on a protein-specific basis, enhancing peptide identification relative to manually optimized trendlines. Beyond automating our previous approach, MultiPassMerger also introduces a novel "multi-trendline" processing method, involving iterative processing using multiple trendlines and merging of results to better sample ions across the DT versus DT FWHM distribution. Using MultiPassMerger with both multi-sequence and multi-trendline strategies increased peptide identifications up to 392% relative to SYNAPT G2-Si with linear ion mobility and 102% relative to use of single-pass cIM-MS alone. Consequently, MultiPassMerger can enhance peptide mapping and makes this approach practical and more accessible to the wider HDX-MS community. MultiPassMerger is available as a downloadable Windows executable at https://politislab.uk/multipassmerger.

Nonequilibrium Self-Assembly Control by the Stochastic Landscape Method

Self-assembly of building blocks is a fundamental process in nanotechnology, materials science, and biological systems, offering pathways to the formation of complex and functional structures through local interactions. However, the lack of effective error correction mechanisms often limits the efficiency and precision of assembly, particularly in systems with strong binding energies. Inspired by cellular processes and stochastic resetting, we present a closed-loop feedback control method that employs transient modulations in interaction energies, mimicking, for instance, the global effect of pH changes as nonequilibrium drives to optimize assembly outcomes in real time. By leveraging the stochastic landscape method, a framework using energy trend-based segmentation to predict self-assembly behavior, our approach dynamically analyzes the system's state and energy trends to guide control actions. We show that the transient energy modulation during kinetic trapping conditions substantially enhances assembly yields and reduces assembly times across diverse scenarios. This strategy provides a broadly applicable, data-driven framework for optimizing nonequilibrium assembly processes, with potential implications for precision manufacturing and responsive materials design, while also advancing our understanding of controlled molecular assembly in biological and synthetic contexts.

Single-Crystal-to-Single-Crystal Synthesis of a Polymer in Two Distinct Topologies

A squaramide-based monomer, designed for topochemical azide-alkyne cycloaddition (TAAC) polymerization, crystallizes as two polymorphs, M1 and M2, both having crystal packing suitable for topochemical polymerization. The hydrogen-bonding between squaramide units bias the molecular organization in both the polymorphs. 3D packing of H-bonded stacks of monomer lead to juxtaposition of azide and alkyne units of adjacent molecules in a transition-state-like arrangement for their regiospecific cycloaddition reaction. The monomers are arranged as supramolecular sheets and supramolecular helices in polymorph M1 and M2 respectively. Both the polymorphs undergo slow and spontaneous regiospecific TAAC polymerization at room temperature, but react quickly at higher temperatures, resulting in 1,4-traizolyl-linked polymer, with distinct mechanical responses. Upon heating, single crystals of polymorph M1 show expansion followed by contraction without any permanent dimensional change, whereas crystals of polymorph M2 undergo splitting. At moderate temperatures, both the polymorphs undergo single-crystal-to-single-crystal (SCSC) polymerization, resulting in two polymer-polymorphs with distinct topologies that can be studied at atomic resolution by single-crystal X-ray crystallography. The polymorph M1 reacts to polymer P1 with β-sheet-like topology, and polymorph M2 reacts to polymer P2 having polymer chains of helical conformation. Nanoindentation experiments with crystals of these polymers revealed their distinct mechanical properties.

Hierarchical Self-Assembly of J-Aggregated 1,2-Bis(2-(benzyloxy)benzylidene) Hydrazine@2β-Cyclodextrin into Left-Handed Superhelix and Its External Stimuli-Responsive Unwinding

Induction of chirality to nanosized superstructures from hierarchical self-assembly of achiral monomeric units is an important area to understand the natural chiral amplification and evolution of life processes. We report herein that the complexation of salicylaldehyde azine, 1,2-bis(2-(benzyloxy)benzylidene)hydrazine (BSAZ), with β-cyclodextrin (β-CD) in aqueous solution results in the formation of a slipped J-aggregate (θ < 54.7°) that aggregates further into a left-handed superhelix through sterical constraints triggered by the hydrophobic effect. The structure of the monomeric BSAZ@2β-CD was elucidated by ultraviolet-visible (UV-vis), Fourier transform infrared spectroscopy (FT-IR), mass, powder X-ray diffraction (PXRD), and 1H, 13C, and 13C CP/MAS nuclear magnetic resonance (NMR) spectroscopy. The size, shape, and morphology of the self-aggregated hierarchy were evidenced by dynamic light scattering (DLS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) studies. The system showed excellent aggregation induced circular dichroism (AICD) with a negative Cotton effect and a high fluorescence quantum yield of 0.33 at 620 nm in a poor solvent, water, because of the formation of a higher order excimer (N ≈ 46). The helical superstructure showed responsiveness under 254 nm UV light irradiation. Light irradiation slowly unwinds the supercoiled structure into a single strand, as was visualized by a SEM image taken after 15 min of continuous light irradiation. The excellent solvatochromic effect and the control over the formed hierarchical morphology show how a supramolecular approach tailored by noncovalent interactions can develop chiral superstructures from completely achiral molecular building blocks that would have a considerable practical value in chiroptics, templates, and chiral sensing.

Small Heat Shock Proteins: Protein Aggregation Amelioration and Neuro- and Age-Protective Roles

Protein misfolding, aggregation, and aberrant aggregate accumulation play a central role in neurodegenerative disease progression. The proteotoxic factors also govern the aging process to a large extent. Molecular chaperones modulate proteostasis and thereby impact aberrant-protein-induced proteotoxicity. These chaperones have a diverse functional spectrum, including nascent protein folding, misfolded protein sequestration, refolding, or degradation. Small heat shock proteins (sHsps) possess an ATP-independent chaperone-like activity that prevents protein aggregation by keeping target proteins in a folding-competent state to be refolded by ATP-dependent chaperones. Due to their near-universal upregulation and presence in sites of proteotoxic stress like diseased brains, sHsps were considered pathological. However, gene knockdown and overexpression studies have established their protective functions. This review provides an updated overview of the sHsp role in protein aggregation amelioration and highlights evidence for sHsp modulation of neurodegenerative disease-related protein aggregation and aging.

Enhancing Biocatalysis: Metal-Organic Frameworks as Multifunctional Enzyme Hosts

Enzymes are highly efficient and selective catalysts that operate under mild conditions, making them invaluable for various chemical transformations. However, their limitations, such as instability and high cost, call for advancements in enzyme immobilization and the development of suitable host materials. Metal-organic frameworks (MOFs), characterized by high porosity, crystallinity, and tunability, are promising candidates for enzyme encapsulation. Among these, zirconium-based MOFs (Zr-MOFs) stand out due to their exceptional structural diversity and chemical stability. The physical and chemical properties of Zr-MOFs can be tuned and characterized with atomic precision, and their interactions with enzymes can be analyzed through a range of techniques spanning from chemistry and materials science to biochemistry. This tunable platform provides opportunities to systematically investigate the impact of encapsulation on the stability and activity of enzymes in order to develop design rules for enzyme hosts. In this Account, we discuss experimentally validated concepts for designing MOF hosts based on their structural properties and enzyme encapsulation mechanisms. We present methods to enhance enzyme catalytic performance through encapsulation and strategies for creating multifunctional enzyme@MOF systems via host modifications. We start by highlighting the importance of host structural design that maximizes substrate diffusion and enzyme availability, with particular focus on MOFs containing hierarchical mesoporous structures such as those in the csq topology. We then delve into the encapsulation process and host-guest interactions examined through techniques such as microscopy, calorimetry, and computational methods, which provide guidelines to fine-tune the local pore chemical environment to enhance enzyme stability and catalytic activity. Techniques found in biochemistry, such as isothermal titration calorimetry (ITC) and confocal laser scanning microscopy (CLSM), were developed to investigate enzyme encapsulation mechanisms, revealing high-entropy-driven host-guest affinity. Additionally, we discuss cases in which enzyme@MOF systems demonstrated enhanced catalytic activities and multifunctional capabilities. Encapsulated enzymes have demonstrated improved thermal and chemical stabilities compared to their free counterparts, maintaining activity under conditions that typically lead to denaturation. Additionally, the highly tunable nature of the MOF platforms allows them to support more complex systems such as tandem reactions, enabling applications in biophotocatalysis, bioelectrocatalysis, and targeted therapeutic protein delivery. The versatility of enzyme@MOFs promises extensive applications in both research and industrial processes across fields including biotechnology, pharmaceutical development, and environmental science. We provide an outlook for promising directions for enzyme@MOF research, with the aim of continuing innovation and exploration. We hope that this Account can benefit chemists, biologists, and material scientists toward designing efficient and adaptable next-generation biocatalytic composite materials.

Probing Aromatic Side Chains Reveals the Site-Specific Melting in the SUMO1 Molten Globule

The conventional idea that a well-defined protein structure governs its functions is being challenged by the evolving significance of conformational flexibility and disorder in influencing protein activity. Here, we focus on the Small Ubiquitin-like MOdifier 1 (SUMO1) protein, a post-translational modifier, which binds various target proteins during the process of SUMOylation. We present evidence supporting the presence of both folded and "ordered" molten globule (MG) states in SUMO1 under physiological conditions. We investigate the MG state using a combination of near-UV and far-UV circular dichroism (CD) experiments. Moreover, we dissect the information from the near-UV CD data to gain specific insights about the MG intermediate. This is achieved by mutating specific aromatic amino acids, particularly creating a single-tyrosine mutant S1Y51 (by introducing Y21F and Y91F mutations) and a tryptophan mutant S1F66W. Spectroscopic studies of the mutants as a function of temperature revealed multiple insights. The transition from the folded to the MG state involves a site-specific loss of tertiary packing near Y51 but the region surrounding F66 retained most of its tertiary contacts, suggesting an ordered MG structure. We further demonstrate the increased solvent exposure of Y51 in the MG state by using time-resolved fluorescence and steady-state quenching experiments. The observed conformational flexibility and solvent accessibility, particularly around Y51 that is known to be involved in binding the cognate ligands such as PIASX and its peptide analogues, have biological and functional implications in mediating protein-protein interactions during the SUMOylation process.

Tracing the birth and intrinsic disorder of loops and domains in protein evolution

Protein loops and structural domains are building blocks of molecular structure. They hold evolutionary memory and are largely responsible for the many functions and processes that drive the living world. Here, we briefly review two decades of phylogenomic data-driven research focusing on the emergence and evolution of these elemental architects of protein structure. Phylogenetic trees of domains reconstructed from the proteomes of organisms belonging to all three superkingdoms and viruses were used to build chronological timelines describing the origin of each domain and its embedded loops at different levels of structural abstraction. These timelines consistently recovered six distinct evolutionary phases and a most parsimonious evolutionary progression of cellular life. The timelines also traced the birth of domain structures from loops, which allowed to model their growth ab initio with AlphaFold2. Accretion decreased the disorder of the growing molecules, suggesting disorder is molecular size-dependent. A phylogenomic survey of disorder revealed that loops and domains evolved differently. Loops were highly disordered, disorder increased early in evolution, and ordered and moderate disordered structures were derived. Gradual replacement of loops with α-helix and β-strand bracing structures over time paved the way for the dominance of more disordered loop types. In contrast, ancient domains were ordered, with disorder evolving as a benefit acquired later in evolution. These evolutionary patterns explain inverse correlations between disorder and sequence length of loops and domains. Our findings provide a deep evolutionary view of the link between structure, disorder, flexibility, and function.

Heterogeneous folding landscapes and predetermined breaking points within a protein family

The accurate prediction of protein structures with artificial intelligence has been a spectacular success. Yet, how proteins fold into their native structures inside the cell remains incompletely understood. Of particular interest is to rationalize how proteins interact with the protein homeostasis network, an organism specific set of protein folding and quality control enzymes. Failure of protein homeostasis leads to widespread misfolding and aggregation, and thus neurodegeneration. Here, I present a comparative analysis of the folding of 16 single-domain proteins from the same organism across a protein family, the Saccharomyces cerevisiae small GTPases. Using computational modeling to directly probe protein folding dynamics, this work shows how near identical structures from the same folding environment can exhibit heterogeneous folding landscapes. Remarkably, yeast small GTPases are found to unfold along different pathways either via the N- or C-terminus initiated by structure-encoded predetermined breaking points. Degrons as recognition signals for ubiquitin-dependent degradation were systematically absent from the initial unfolding sites, as if to protect from too rapid degradation upon spontaneous unfolding or before completion of the folding. The presented results highlight a direct coordination of folding pathway and protein homeostasis interaction signals across a protein family. A deeper understanding of the interdependence of proteins with their folding environment will help to rationalize and combat diseases linked to protein misfolding and dysregulation. More generally, this work underlines the importance of understanding protein folding in the cellular context, and highlights valuable constraints towards a systems-level understanding of protein homeostasis.

Modulating Coarse-Grained Dynamics by Perturbing Free Energy Landscapes

We introduce an approach to describe the long-time dynamics of multiatomic molecules by modulating the free energy landscape (FEL) to capture dominant features of the energy-barrier crossing dynamics of the all-atom (AA) system. Notably, we establish that the self-diffusion coefficient of coarse-grained (CG) systems can be accurately delineated by enhancing conservative force fields with high-frequency perturbations. Using theoretical arguments, we show that these perturbations do not alter the lower-order distribution functions, thereby preserving the structure of the AA system after coarse-graining. We demonstrate the utility of this approach using molecular dynamics simulations of simple molecules in bulk with distinct dynamical characteristics with and without time scale separations as well as for inhomogeneous systems where a fluid is confined in a slit-like nanochannel. Additionally, we also apply our approach to more powerful many-body potentials optimized by using machine learning (ML).

Positions of cysteine residues reveal local clusters and hidden relationships to Sequons and Transmembrane domains in Human proteins

Membrane proteins often possess critical structural features, such as transmembrane domains (TMs), N-glycosylation, and disulfide bonds (SS bonds), which are essential to their structure and function. Here, we extend the study of the motifs carrying N-glycosylation, i.e. the sequons, and the Cys residues supporting the SS bonds, to the whole human proteome with a particular focus on the Cys positions in human proteins with respect to those of sequons and TMs. As the least abundant amino acid residue in protein sequences, the positions of Cys residues in proteins are not random but rather selected through evolution. We discovered that the frequency of Cys residues in proteins is length dependent, and the frequency of CC gaps formed between adjacent Cys residues can be used as a classifier to distinguish proteins with special structures and functions, such as keratin-associated proteins (KAPs), extracellular proteins with EGF-like domains, and nuclear proteins with zinc finger C2H2 domains. Most importantly, by comparing the positions of Cys residues to those of sequons and TMs, we discovered that these structural features can form dense clusters in highly repeated and mutually exclusive modalities in protein sequences. The evolutionary advantages of such complementarity among the three structural features are discussed, particularly in light of structural dynamics in proteins that are lacking from computational predictions. The discoveries made here highlight the sequence-structure-function axis in biological organisms that can be utilized in future protein engineering toward synthetic biology.

Slow Conformational Exchange between Partially Folded and Near-Native States of Ubiquitin: Evidence for a Multistate Folding Model

The mechanism by which small proteins fold, i.e., via intermediates or via a two-state mechanism, is a subject of intense investigation. Intermediate states in the folding pathways of these proteins are sparsely populated due to transient lifetimes under normal conditions rendering them transparent to a majority of the biophysical methods employed for structural, thermodynamic, and kinetic characterization, which attributes are essential for understanding the cooperative folding/unfolding of such proteins. Dynamic NMR spectroscopy has enabled the characterization of folding intermediates of ubiquitin that exist in equilibrium under conditions of low pH and denaturants. At low pH, an unlocked state defined as N' is in fast exchange with an invisible state, U″, as observed by CEST NMR. Addition of urea to ubiquitin at pH 2 creates two new states F' and U', which are in slow exchange (kF'→U' = 0.14 and kU'→F' = 0.28 s-1) as indicated by longitudinal ZZ-magnetization exchange spectroscopy. High-resolution solution NMR structures of F' show it to be in an "unlocked" conformation with measurable changes in rotational diffusion, translational diffusion, and rotational correlational times. U' is characterized by the presence of just the highly conserved N-terminal β1-β2 hairpin. The folding of ubiquitin is cooperative and is nucleated by the formation of an N-terminal β-hairpin followed by significant hydrophobic collapse of the protein core resulting in the formation of bulk of the secondary structural elements stabilized by extensive tertiary contacts. U' and F' may thus be described as early and late folding intermediates in the ubiquitin folding pathway.

Graph-based analysis of H-bond networks and unsupervised learning reveal conformational coupling in prion peptide segments

In this study, we employed a comprehensive computational approach to investigate the physical chemistry of the water networks surrounding hydrated peptide segments, as derived from molecular dynamics simulations. Our analysis uncovers a complex interplay of direct and water-mediated hydrogen bonds that intricately weave through the peptides. We demonstrate that these hydrogen bond networks encode critical information about the peptides' conformational behavior, with the dimensionality of these networks showing sensitivity to the peptides' conformations. Additionally, we estimated the free-energy landscape of the peptides across various conformations, revealing that their structures are predominantly characterized by unfolded, partially folded, and folded configurations, resulting in broad and rugged free-energy surfaces due to the numerous degrees of freedom contributed by the surrounding solvent. Importantly, the structured nature of this free-energy landscape becomes obscured when conventional collective variables, such as the number of hydrogen bonds, are used. Our findings provide new insights into the molecular mechanisms that couple protein and solvent degrees of freedom, highlighting their significance in the functioning of biological systems.

Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered an ongoing global pandemic, necessitating rapid and accurate diagnostic tools to monitor emerging variants and preparedness for the next outbreak. This study introduces a multidisciplinary approach combining Fourier Transform Infrared (FTIR) microspectroscopy and Machine learning to comprehensively characterize and strain-type SARS-CoV-2 variants. FTIR analysis of pharyngeal swabs from different pandemic waves revealed distinct vibrational profiles, particularly in nucleic acid and protein vibrations. The spectral wavenumber range between 1150 and 1240 cm-1 was identified as the classification marker, distinguishing Healthy (noninfected) and infected samples. Machine learning algorithms, with neural networks exhibiting superior performance, successfully classified SARS-CoV-2 variants with a remarkable accuracy of 98.6%. Neural networks were also able to identify and differentiate a small cohort infected with influenza A variants, H1N1 and H3N2, from SARS-CoV-2-infected and Healthy samples. FTIR measurements further show distinct red shifts in vibrational energy and secondary structural alterations in the spike proteins of more transmissible forms of SARS-CoV-2 variants, providing experimental validation of the computational data. This integrated approach presents a promising avenue for rapid and reliable SARS-CoV-2 variant identification, enhancing our understanding of viral evolution and aiding in diagnostic advancements, particularly for an infectious disease with unknown etiology.

Protein Microarrays for High Throughput Hydrogen/Deuterium Exchange Monitored by FTIR Imaging

Proteins form the fastest-growing therapeutic class. Due to their intrinsic instability, loss of native structure is common. Structure alteration must be carefully evaluated as structural changes may jeopardize the efficiency and safety of the protein-based drugs. Hydrogen deuterium exchange (HDX) has long been used to evaluate protein structure and dynamics. The rate of exchange constitutes a sensitive marker of the conformational state of the protein and of its stability. It is often monitored by mass spectrometry. Fourier transform infrared (FTIR) spectroscopy is another method with very promising capabilities. Combining protein microarrays with FTIR imaging resulted in high throughput HDX FTIR measurements. BaF2 slides bearing the protein microarrays were covered by another slide separated by a spacer, allowing us to flush the cell continuously with a flow of N2 gas saturated with 2H2O. Exchange occurred simultaneously for all proteins and single images covering ca. 96 spots of proteins that could be recorded on-line at selected time points. Each protein spot contained ca. 5 ng protein, and the entire array covered 2.5 × 2.5 mm2. Furthermore, HDX could be monitored in real time, and the experiment was therefore not subject to back-exchange problems. Analysis of HDX curves by inverse Laplace transform and by fitting exponential curves indicated that quantitative comparison of the samples is feasible. The paper also demonstrates how the whole process of analysis can be automatized to yield fast analyses.

Photothermally Heated Asymmetric Thin Nanopores Suggest the Influence of Temperature on the Intermediate Conformational State of Cytochrome c in an Electric Field

Nanopore sensing is a label-free single-molecule technique that enables the study of the dynamical structural properties of proteins. Here, we detect the translocation of cytochrome c (Cyt c) through an asymmetric thin nanopore with photothermal heating to evaluate the influence of temperature on Cyt c conformation during its translocation in an electric field. Before Cyt c translocates through an asymmetric thin SiNx nanopore, ∼1 ms trapping events occur due to electric field-induced denaturation. These trapping events were corroborated by a control analysis with a transmission electron microscopy-drilled pore and denaturant buffer. Cyt c translocation events exhibited markedly greater broad current blockade when the pores were photothermally heated. Collectively, our molecular dynamics simulation predicted that an increased temperature facilitates denaturation of the α-helical structure of Cyt c, resulting in greater blockade current during Cyt c trapping. Our photothermal heating method can be used to study the influence of temperature on protein conformation at the single-molecule level in a label-free manner.

A Stochastic Landscape Approach for Protein Folding State Classification

Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer's and Parkinson's. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.

The detection methods currently available for protein aggregation in neurological diseases

Protein aggregation is a pathological feature in various neurodegenerative diseases and is thought to play a crucial role in the onset and progression of neurological disorders. This pathological phenomenon has attracted increasing attention from researchers, but the underlying mechanism has not been fully elucidated yet. Researchers are increasingly interested in identifying chemicals or methods that can effectively detect protein aggregation or maintain protein stability to prevent aggregation formation. To date, several methods are available for detecting protein aggregates, including fluorescence correlation spectroscopy, electron microscopy, and molecular detection methods. Unfortunately, there is still a lack of methods to observe protein aggregation in situ under a microscope. This article reviews the two main aspects of protein aggregation: the mechanisms and detection methods of protein aggregation. The aim is to provide clues for the development of new methods to study this pathological phenomenon.

FoldPAthreader: predicting protein folding pathway using a novel folding force field model derived from known protein universe

Protein folding has become a tractable problem with the significant advances in deep learning-driven protein structure prediction. Here we propose FoldPAthreader, a protein folding pathway prediction method that uses a novel folding force field model by exploring the intrinsic relationship between protein evolution and folding from the known protein universe. Further, the folding force field is used to guide Monte Carlo conformational sampling, driving the protein chain fold into its native state by exploring potential intermediates. On 30 example targets, FoldPAthreader successfully predicts 70% of the proteins whose folding pathway is consistent with biological experimental data.

Differentiating stable and unstable protein using convolution neural network and molecular dynamics simulations

Protein stability is a critical aspect of molecular biology and biochemistry, hinges on an intricate balance of thermodynamic and structural factors. Determining protein stability is crucial for understanding and manipulating biological machineries, as it directly correlated with the protein function. Thus, this study delves into the intricacies of protein stability, highlighting its dependence on various factors, including thermodynamics, thermal conditions, and structural properties. Moreover, a notable focus is placed on the free energy change of unfolding (ΔGunfolding), change in heat capacity (ΔCp) with protein structural transition, melting temperature (Tm) and number of disulfide bonds, which are critical parameters in understanding protein stability. In this study, a machine learning (ML) predictive model was developed to estimate these four parameters using the primary sequence of the protein. The shortfall of available tools for protein stability prediction based on multiple parameters propelled the completion of this study. Convolutional Neural Network (CNN) with multiple layers was adopted to develop a more reliable ML model. Individual predictive models were prepared for each property, and all the prepared models showed results with high accuracy. The R2 (coefficient of determination) of these models were 0.79, 0.78, 0.92 and 0.92, respectively, for ΔG, ΔCp, Tm and disulfide bonds. A case study on stability analysis of two homologous proteins was presented to validate the results predicted through the developed model. The case study included in silico analysis of protein stability using molecular docking and molecular dynamic simulations. This validation study assured the accuracy of each model in predicting the stability associated properties. The alignment of physics-based principles with ML models has provided an opportunity to develop a fast machine learning solution to replace the computationally demanding physics-based calculations used to determine protein stability. Furthermore, this work provided valuable insights into the impact of mutation on protein stability, which has implications for the field of protein engineering. The source codes are available at https://github.com/Growdeatechnology.

Amyloids, amorphous aggregates and assemblies of peptides - Assessing aggregation

Amyloid and amorphous aggregates represent the two major categories of aggregates associated with diseases, and although exhibiting distinct features, researchers often treat them as equivalent, which demonstrates the need for more thorough characterization. Here, we compare amyloid and amorphous aggregates based on their biochemical properties, kinetics, and morphological features. To further decipher this issue, we propose the use of peptide self-assemblies as minimalistic models for understanding the aggregation process. Peptide building blocks are significantly smaller than proteins that participate in aggregation, however, they make a plausible means to bridge the gap in discerning the aggregation process at the more complex, protein level. Additionally, we explore the potential use of peptide-inspired models to research the liquid-liquid phase separation as a feasible mechanism preceding amyloid formation. Connecting these concepts can help clarify our understanding of aggregation-related disorders and potentially provide novel drug targets to impede and reverse these serious illnesses.

Cyclic Ion Mobility for Hydrogen/Deuterium Exchange-Mass Spectrometry Applications

Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) has emerged as a powerful tool to probe protein dynamics. As a bottom-up technique, HDX-MS provides information at peptide-level resolution, allowing structural localization of dynamic changes. Consequently, the HDX-MS data quality is largely determined by the number of peptides that are identified and monitored after deuteration. Integration of ion mobility (IM) into HDX-MS workflows has been shown to increase the data quality by providing an orthogonal mode of peptide ion separation in the gas phase. This is of critical importance for challenging targets such as integral membrane proteins (IMPs), which often suffer from low sequence coverage or redundancy in HDX-MS analyses. The increasing complexity of samples being investigated by HDX-MS, such as membrane mimetic reconstituted and in vivo IMPs, has generated need for instrumentation with greater resolving power. Recently, Giles et al. developed cyclic ion mobility (cIM), an IM device with racetrack geometry that enables scalable, multipass IM separations. Using one-pass and multipass cIM routines, we use the recently commercialized SELECT SERIES Cyclic IM spectrometer for HDX-MS analyses of four detergent solubilized IMP samples and report its enhanced performance. Furthermore, we develop a novel processing strategy capable of better handling multipass cIM data. Interestingly, use of one-pass and multipass cIM routines produced unique peptide populations, with their combined peptide output being 31 to 222% higher than previous generation SYNAPT G2-Si instrumentation. Thus, we propose a novel HDX-MS workflow with integrated cIM that has the potential to enable the analysis of more complex systems with greater accuracy and speed.

Pathways of amyloid fibril formation and protein aggregation

The main cause of many neurodegenerative diseases and systemic amyloidoses is protein and peptide aggregation and the formation of amyloid fibrils. The study of aggregation mechanisms, the discovery and description of aggregate structures, and a comprehensive understanding of the molecular mechanisms of amyloid formation are of great importance for the diagnostic processes at the molecular level and for the development of therapeutic strategies to counter aggregation-associated disorders. Given that understanding protein misfolding phenomena is directly related to the protein folding process, we will briefly explain the protein folding mechanism and then discuss the important factors involved in protein aggregation. In the following, we review different mechanisms of amyloid formation and finally represent the current knowledge on how amyloid fibrils are formed based on kinetic and thermodynamic factors.

Impact of Hydrodynamic Interactions on the Kinetic Pathway of Protein Folding

Protein folding is a fundamental process critical to cellular function and human health, but it remains a grand challenge in biophysics. Hydrodynamic interaction (HI) plays a vital role in the self-organization of soft and biological materials, yet its role in protein folding is not fully understood despite folding occurring in a fluid environment. Here, we use the fluid particle dynamics method to investigate many-body hydrodynamic couplings between amino acid residues and fluid motion in the folding kinetics of a coarse-grained four-α-helices bundle protein. Our results reveal that HI helps select fast folding pathways to the native state without being kinetically trapped, significantly speeding up the folding kinetics compared to its absence. First, the directional flow along the protein backbone expedites protein collapse. Then, the incompressibility-induced squeezing flow effects retard the accumulation of non-native hydrophobic contacts, thus preventing the protein from being trapped in local energy minima during the conformational search of the native structure. We also find that the significance of HI in folding kinetics depends on temperature, with a pronounced effect under biologically relevant conditions. Our findings suggest that HI, particularly the short-range squeezing effect, may be crucial in avoiding protein misfolding.

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS2) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS1-based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

Stepwise introduction of stabilizing mutations reveals nonlinear additive effects in de novo TIM barrels

Over the past decades, the TIM-barrel fold has served as a model system for the exploration of how changes in protein sequences affect their structural, stability, and functional characteristics, and moreover, how this information can be leveraged to design proteins from the ground up. After numerous attempts to design de novo proteins with this specific fold, sTIM11 was the first validated de novo design of an idealized four-fold symmetric TIM barrel. Subsequent efforts to enhance the stability of this initial design resulted in the development of DeNovoTIMs, a family of de novo TIM barrels with various stabilizing mutations. In this study, we present an investigation into the biophysical and thermodynamic effects upon introducing a varying number of stabilizing mutations per quarter along the sequence of a four-fold symmetric TIM barrel. We compared the base design DeNovoTIM0 without any stabilizing mutations with variants containing mutations in one, two, three, and all four quarters-designated TIM1q, TIM2q, TIM3q, and DeNovoTIM6, respectively. This analysis revealed a stepwise and nonlinear change in the thermodynamic properties that correlated with the number of mutated quarters, suggesting positive nonadditive effects. To shed light on the significance of the location of stabilized quarters, we engineered two variants of TIM2q which contain the same number of mutations but positioned in different quarter locations. Characterization of these TIM2q variants revealed that the mutations exhibit varying effects on the overall protein stability, contingent upon the specific region in which they are introduced. These findings emphasize that the amount and location of stabilized interfaces among the four quarters play a crucial role in shaping the conformational stability of these four-fold symmetric TIM barrels. Analysis of de novo proteins, as described in this study, enhances our understanding of how sequence variations can finely modulate stability in both naturally occurring and computationally designed proteins.

HetMM: A Michaelis-Menten model for non-homogeneous enzyme mixtures

The Michaelis-Menten model requires its reaction velocities to come from a preparation of homogeneous enzymes, with identical or near-identical catalytic activities. However, this condition is not always met. We introduce a kinetic model that relaxes this requirement, by assuming there are an unknown number of enzyme species drawn from a probability distribution whose standard deviation is estimated. Through simulation studies, we demonstrate the method accurately discriminates between homogeneous and heterogeneous data, even with moderate levels of experimental error. We applied this model to three homogeneous and three heterogeneous biological systems, showing that the standard and heterogeneous models outperform respectively. Lastly, we show that heterogeneity is not readily distinguished from negatively cooperative binding under the Hill model. These two distinct attributes-inequality in catalytic ability and interference between binding sites-yield similar Michaelis-Menten curves that are not readily resolved without further experimentation. Our user-friendly software package allows homogeneity testing and parameter estimation.

Recent Advances and Challenges in Protein Structure Prediction

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

What are the minimal folding seeds in proteins? Experimental and theoretical assessment of secondary structure propensities of small peptide fragments

Certain peptide sequences, some of them as short as amino acid triplets, are significantly overpopulated in specific secondary structure motifs in folded protein structures. For example, 74% of the EAM triplet is found in α-helices, and only 3% occurs in the extended parts of proteins (typically β-sheets). In contrast, other triplets (such as VIV and IYI) appear almost exclusively in extended parts (79% and 69%, respectively). In order to determine whether such preferences are structurally encoded in a particular peptide fragment or appear only at the level of a complex protein structure, NMR, VCD, and ECD experiments were carried out on selected tripeptides: EAM (denoted as pro-'α-helical' in proteins), KAM(α), ALA(α), DIC(α), EKF(α), IYI(pro-β-sheet or more generally, pro-extended), and VIV(β), and the reference α-helical CATWEAMEKCK undecapeptide. The experimental data were in very good agreement with extensive quantum mechanical conformational sampling. Altogether, we clearly showed that the pro-helical vs. pro-extended propensities start to emerge already at the level of tripeptides and can be fully developed at longer sequences. We postulate that certain short peptide sequences can be considered minimal "folding seeds". Admittedly, the inherent secondary structure propensity can be overruled by the large intramolecular interaction energies within the folded and compact protein structures. Still, the correlation of experimental and computational data presented herein suggests that the secondary structure propensity should be considered as one of the key factors that may lead to understanding the underlying physico-chemical principles of protein structure and folding from the first principles.

Appraisal of blob-Based Approaches in the Prediction of Protein Folding Times

A series of reports published in the last 3 years has illustrated that a blob-based model (BBM) can predict the folding time of proteins from their primary amino acid (aa) sequence based on three simple rules established to characterize the long-range backbone dynamics (LRBD) of racemic polypeptides. The sole use of LRBD to predict protein folding times with the BBM represents a radical departure from all other prediction methods currently applied to determine protein folding times, which rely instead on parameters such as the structure content, folding kinetics, chain length, amino acid properties, or contact topography of proteins. Furthermore, the built-in modularity of the BBM enables the parametrization and inclusion of new phenomena affecting the LRBD of polypeptides, while its conceptual simplicity makes it an interesting new mathematical tool for studying protein folding. However, its novelty implies that its relationship with many other methods used to predict protein folding times has not been well researched. Consequently, the purpose of this report is to uncover the physical phenomena encountered during protein folding that are best described by the BBM through the identification of parameters that have been recognized over the years as being strong predictors for protein folding, such as protein size, topology, structural class, and folding kinetics. This was accomplished by determining the parameters most strongly correlated with the folding times predicted by the BBM. While the BBM in its present form appears to be a good indicator of the folding times of the vast majority of the 195 proteins considered so far, this report finds that it excels for moderately large proteins that are primarily composed of locally formed structural motifs such as α-helices or for proteins that fold in multiple steps. Altogether, these observations based on the use of the BBM support the notion that proteins fold the way they do because the LRBD of polypeptides is mostly driven by the local interactions experienced between aa's within reach of one another.

Light induced spiking of proteinoids

Proteinoids, or thermal proteins, are produced by heating amino acids to their melting point and initiating polymerisation to produce polymeric chains. In aqueous solutions proteinoids swell into hollow microspheres. These microspheres produce endogenous burst of electrical potential spikes and change patterns of their electrical activity in response to illumination. We report results on a detailed investigation on the effects of white cold light on the spiking of proteinoids. We study how different types and intensities of light determine proteinoids' spiking amplitude, period, and pattern. The results of this study will be utilised to evaluate proteinoids for their potential as optical sensors and their application in unconventional computing.

Evaluating the Cysteine-Rich and Catalytic Subdomains of Human Tyrosinase and OCA1-Related Mutants Using 1 μs Molecular Dynamics Simulation

The inherited disorder oculocutaneous albinism type 1 (OCA1) is caused by mutations in the TYR gene encoding tyrosinase (Tyr), an enzyme essential to producing pigments throughout the human body. The intramelanosomal domain of Tyr consists of the cysteine-rich and tyrosinase catalytic subdomains, which are essential for enzymatic activity. In protein unfolding, the roles of these subdomains are not well established. Here, we performed six molecular dynamics simulations at room temperature for Tyr and OCA1-related mutant variants P406L and R402Q intramelanosomal domains. The proteins were simulated for 1 μs in water and urea to induce unfolding. In urea, we observed increases in surface area, decreases in intramolecular hydrogen bonding, and decreases in hydrophobic interactions, suggesting a 'molten globule' state for each protein. Between all conditions, the cysteine-rich subdomain remains stable, whereas the catalytic subdomain shows increased flexibility. This flexibility is intensified by the P406L mutation, while R402Q increases the catalytic domain's rigidity. The cysteine-rich subdomain is rigid, preventing the protein from unfolding, whereas the flexibility of the catalytic subdomain accommodates mutational changes that could inhibit activity. These findings match the conclusions from our experimental work suggesting the function alteration by the P406L mutation, and the potential role of R402Q as a polymorphism.

Structural basis for binding of Drosophila Smaug to the GPCR Smoothened and to the germline inducer Oskar

Drosophila Smaug and its orthologs comprise a family of mRNA repressor proteins that exhibit various functions during animal development. Smaug proteins contain a characteristic RNA-binding sterile-α motif (SAM) domain and a conserved but uncharacterized N-terminal domain (NTD). Here, we resolved the crystal structure of the NTD of the human SAM domain-containing protein 4A (SAMD4A, a.k.a. Smaug1) to 1.6 Å resolution, which revealed its composition of a homodimerization D subdomain and a subdomain with similarity to a pseudo-HEAT-repeat analogous topology (PHAT) domain. Furthermore, we show that Drosophila Smaug directly interacts with the Drosophila germline inducer Oskar and with the Hedgehog signaling transducer Smoothened through its NTD. We determined the crystal structure of the NTD of Smaug in complex with a Smoothened α-helical peptide to 2.0 Å resolution. The peptide binds within a groove that is formed by both the D and PHAT subdomains. Structural modeling supported by experimental data suggested that an α-helix within the disordered region of Oskar binds to the NTD of Smaug in a mode similar to Smoothened. Together, our data uncover the NTD of Smaug as a peptide-binding domain.

Protein folding rate evolution upon mutations

Despite the spectacular success of cutting-edge protein fold prediction methods, many critical questions remain unanswered, including why proteins can reach their native state in a biologically reasonable time. A satisfactory answer to this simple question could shed light on the slowest folding rate of proteins as well as how mutations-amino-acid substitutions and/or post-translational modifications-might affect it. Preliminary results indicate that (i) Anfinsen's dogma validity ensures that proteins reach their native state on a reasonable timescale regardless of their sequence or length, and (ii) it is feasible to determine the evolution of protein folding rates without accounting for epistasis effects or the mutational trajectories between the starting and target sequences. These results have direct implications for evolutionary biology because they lay the groundwork for a better understanding of why, and to what extent, mutations-a crucial element of evolution and a factor influencing it-affect protein evolvability. Furthermore, they may spur significant progress in our efforts to solve crucial structural biology problems, such as how a sequence encodes its folding.

Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies

Solubility and folding stability are key concerns for difficult-to-express proteins (DEPs) restricted by amino acid sequences and superarchitecture, resolved by the precise distribution of amino acids and molecular interactions as well as the assistance of the expression system. Therefore, an increasing number of tools are available to achieve efficient expression of DEPs, including directed evolution, solubilization partners, chaperones, and affluent expression hosts, among others. Furthermore, genome editing tools, such as transposons and CRISPR Cas9/dCas9, have been developed and expanded to construct engineered expression hosts capable of efficient expression ability of soluble proteins. Accounting for the accumulated knowledge of the pivotal factors in the solubility and folding stability of proteins, this review focuses on advanced technologies and tools of protein engineering, protein quality control systems, and the redesign of expression platforms in prokaryotic expression systems, as well as advances of the cell-free expression technologies for membrane proteins production.

Picophotonic localization metrology beyond thermal fluctuations

Despite recent tremendous progress in optical imaging and metrology1-6, there remains a substantial resolution gap between atomic-scale transmission electron microscopy and optical techniques. Is optical imaging and metrology of nanostructures exhibiting Brownian motion possible with such resolution, beyond thermal fluctuations? Here we report on an experiment in which the average position of a nanowire with a thermal oscillation amplitude of ∼150 pm is resolved in single-shot measurements with subatomic precision of 92 pm, using light at a wavelength of λ = 488 nm, providing an example of such sub-Brownian metrology with ∼λ/5,300 precision. To localize the nanowire, we employ a deep-learning analysis of the scattering of topologically structured light, which is highly sensitive to the nanowire's position. This non-invasive metrology with absolute errors down to a fraction of the typical size of an atom, opens a range of opportunities to study picometre-scale phenomena with light.

Hydrogen/deuterium exchange for the analysis of carbohydrates

Carbohydrates and glycans are integral to many biological processes, including cell-cell recognition and energy storage. However, carbohydrates are often difficult to analyze due to the high degree of isomerism present. One method being developed to distinguish these isomeric species is hydrogen/deuterium exchange-mass spectrometry (HDX-MS). In HDX-MS, carbohydrates are exposed to a deuterated reagent and the functional groups with labile hydrogen atoms, including hydroxyls and amides, exchange with the 1 amu heavier isotope, deuterium. These labels can then be detected by MS, which monitors the mass increase with the addition of D-labels. The observed rate of exchange is dependent on the exchanging functional group, the accessibility of the exchanging functional group, and the presence of hydrogen bonds. Herein, we discuss how HDX has been applied in the solution-phase, gas-phase, and during MS ionization to label carbohydrates and glycans. Additionally, we compare differences in the conformations that are labeled, the labeling timeframes, and applications of each of these methods. Finally, we comment on future opportunities for development and use of HDX-MS to analyze glycans and glycoconjugates.

Osmolytes: Wonder molecules to combat protein misfolding against stress conditions

The proper functioning of any protein depends on its three dimensional conformation which is achieved by the accurate folding mechanism. Keeping away from the exposed stress conditions leads to cooperative unfolding and sometimes partial folding, forming the structures like protofibrils, fibrils, aggregates, oligomers, etc. leading to several neurodegenerative diseases like Parkinson's disease, Alzheimer's, Cystic fibrosis, Huntington, Marfan syndrome, and also cancers in some cases, too. Hydration of proteins is necessary, which may be achieved by the presence of organic solutes called osmolytes within the cell. Osmolytes belong to different classes in different organisms and play their role by preferential exclusion of osmolytes and preferential hydration of water molecules and achieves the osmotic balance in the cell otherwise it may cause problems like cellular infection, cell shrinkage leading to apoptosis and cell swelling which is also the major injury to the cell. Osmolyte interacts with protein, nucleic acids, intrinsically disordered proteins by non-covalent forces. Stabilizing osmolytes increases the Gibbs free energy of the unfolded protein and decreases that of folded protein and vice versa with denaturants (urea and guanidinium hydrochloride). The efficacy of each osmolyte with the protein is determined by the calculation of m value which reflects its efficiency with protein. Hence osmolytes can be therapeutically considered and used in drugs.

Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader

Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we propose a method, PAthreader, to recognize remote templates and explore folding pathways. Firstly, we design a three-track alignment between predicted distance profiles and structure profiles extracted from PDB and AlphaFold DB, to improve the recognition accuracy of remote templates. Secondly, we improve the performance of AlphaFold2 using the templates identified by PAthreader. Thirdly, we explore protein folding pathways based on our conjecture that dynamic folding information of protein is implicitly contained in its remote homologs. The results show that the average accuracy of PAthreader templates is 11.6% higher than that of HHsearch. In terms of structure modelling, PAthreader outperform AlphaFold2 and ranks first on the CAMEO blind test for the latest three months. Furthermore, we predict protein folding pathways for 37 proteins, in which the results of 7 proteins are almost consistent with those of biological experiments, and the other 30 human proteins have yet to be verified by biological experiments, revealing that folding information can be exploited from remote homologous structures.

Urea induced unfolding of rai seed cystatin: Influence of glycerol as a chemical chaperone

Cystatin superfamily members, by virtue of their thiol protease regulatory properties, show involvement in myriad physiological processes important for survival and well-being. The current study involves urea-induced denaturation of a novel variant of the cystatin superfamily, rai seed cystatin (RSC), employing a variety of biophysical assays in order to characterize different folding intermediates generated on unfolding. Urea as a denaturant presented the passage of RSC through a series of events resulting in the loss of RSC functional capability, accompanied by changes in the archetype at secondary and tertiary structural levels, as evident from protease inhibitory, UV absorption, and intrinsic fluorescence assays, respectively. ANS fluorescence also revealed routing of RSC through discrete multiple sub-states thus presenting the generation of intermediate states somewhat close to the pre-molten globule and/or molten globule forms of RSC. Furthermore, far-UV circular dichroism analysis revealed a concentration-dependent gradual loss in typical -helical RSC peaks, indicating a nearly 50 % loss in secondary structural elements around 5 M urea treatment. The study also reports the possible role of glycerol in the refolding and/or reactivation of the urea unfolded RSC form. Glycerol presented itself as a potent structural stabilizer as it assisted in the refolding and reactivation of the unfolded RSC in a dosage-dependent manner, concomitantly paving the way for unravelling the mechanistic approach involved in the phenomenon, which can facilitate future studies.

Stability of the Aβ42 Peptide in Mixed Solutions of Denaturants and Proline

Amyloid β-peptide (Aβ) is responsible for the neuronal damage and death of a patient with Alzheimer's disease (AD). Aβ42 oligomeric forms are dominant neurotoxins and are related to neurodegeneration. Their different forms are related to various pathological conditions in the brain. We investigated Aβ42 peptides in different environments of proline, urea, and GdmCl solutions (in pure and mixed binary forms) through atomistic molecular dynamics simulations. Preferential exclusion from the protein surface and facile formation of a large number of weak molecular interactions are the driving forces for the osmolyte's action. We have focused on these interactions between peptide monomers and pure/mixed osmolytes and denaturants. Urea, as usual, denatures the peptide strongly compared to the GdmCl by accumulation around the peptide. GdmCl shows lesser build-up around protein in contrast to urea but is involved in destabilizing the salt bridge formation of Asp23 and Lys28. Proline as an osmolyte protects the peptide from aggregation when mixed with urea and GdmCl solutions. In mixed solutions of two denaturants and osmolyte plus denaturant, the peptide shows enhanced stability as compared to pure denaturant urea solution. The enhanced stability of peptides in proline may be attributed to its exclusion from the peptide surface and favoring salt bridge formation.

Landing Proteins on Graphene Trampoline Preserves Their Gas-Phase Folding on the Surface

Molecule-surface collisions are known to initiate dynamics that lead to products inaccessible by thermal chemistry. These collision dynamics, however, have mostly been examined on bulk surfaces, leaving vast opportunities unexplored for molecular collisions on nanostructures, especially on those that exhibit mechanical properties radically different from those of their bulk counterparts. Probing energy-dependent dynamics on nanostructures, particularly for large molecules, has been challenging due to their fast time scales and high structural complexity. Here, by examining the dynamics of a protein impinging on a freestanding, single-atom-thick membrane, we discover molecule-on-trampoline dynamics that disperse the collision impact away from the incident protein within a few picoseconds. As a result, our experiments and ab initio calculations show that cytochrome c retains its gas-phase folded structure when it collides onto freestanding single-layer graphene at low energies (∼20 meV/atom). The molecule-on-trampoline dynamics, expected to be operative on many freestanding atomic membranes, enable reliable means to transfer gas-phase macromolecular structures onto freestanding surfaces for their single-molecule imaging, complementing many bioanalytical techniques.

Synergetic Effects of Alanine and Glycine in Blob-Based Methods for Predicting Protein Folding Times

The polypeptide PGlyAlaGlu was prepared with 20 mol % glycine (Gly), 36 mol % d,l-alanine (Ala), and 44 mol % d,l-glutamic acid (Glu) and labeled with the dye 1-pyrenemethylamine to yield a series of Py-PGlyAlaGlu samples. The fluorescence decays of the Py-PGlyAlaGlu samples were analyzed according to the fluorescence blob model (FBM) to obtain the number N_blob^exp of amino acids (aa's) encompassed inside the subvolume V_blob of the polypeptide probed by an excited pyrene. An N_blob^exp value of 29 (±2) was retrieved for Py-PGlyAlaGlu, which was much larger than for any of the copolypeptide PGlyGlu or PAlaGlu prepared with either Gly and Glu or Ala and Glu, respectively. The continuous increase in N_blob^exp with decreasing side chain size (SCS) from 10 aa's for PGlu to 16 aa's for PAlaGlu and 22 aa's for PGlyGlu was used earlier to define the reach of an aa and determine the groups of aa's that could interact with each other along a polypeptide backbone according to their SCS. These groups of aa's, referred to as blobs, led to the implementation of blob-based models (BBM) to predict the folding time τ_F^theo,BBM of 145 proteins, which was found to match their experimental folding time τ_F^exp with a relatively high 0.71 correlation coefficient. Nevertheless, the much higher N_blob^exp value found for Py-PGlyAlaGlu compared to all other pyrene-labeled polypeptides studied to date indicates that the reach of aa's along a polypeptide sequence is affected not only by SCS but also by synergetic effects between different aa's. Following this new insight, a revised BBM was implemented to predict τ_F^theo,BBM for 195 proteins assuming the existence or absence of synergies to control the interactions between aa's along a polypeptide sequence. Similarly good correlation coefficients of 0.71 and 0.74 were obtained for a direct 1:1 comparison of τ_F^exp and τ_F^theo,BBM for the 195 proteins without and with synergies, respectively. This result suggests that synergetic effects between different aa's have little effect on τ_F^theo,BBM predicted from BBM underlying the robustness of this methodology.

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.

Hysteretic Pressure Dependence of Ca2+ Binding in LH1 Bacterial Membrane Chromoproteins

Much of the thermodynamic parameter values that support life are set by the properties of proteins. While the denaturing effects of pressure and temperature on proteins are well documented, their precise structural nature is rarely revealed. This work investigates the destabilization of multiple Ca²⁺ binding sites in the cyclic LH1 light-harvesting membrane chromoprotein complexes from two Ca-containing sulfur purple bacteria by hydrostatic high-pressure perturbation spectroscopy. The native (Ca-saturated) and denatured (Ca-depleted) phases of these complexes are well distinguishable by much-shifted bacteriochlorophyll a exciton absorption bands serving as innate optical probes in this study. The pressure-induced denaturation of the complexes related to the failure of the protein Ca-binding pockets and the concomitant breakage of hydrogen bonds between the pigment chromophores and protein environment were found cooperative, involving all or most of the Ca²⁺ binding sites, but irreversible. The strong hysteresis observed in the spectral and kinetic characteristics of phase transitions along the compression and decompression pathways implies asymmetry in the relevant free energy landscapes and activation free energy distributions. A phase transition pressure equal to about 1.9 kbar was evaluated for the complexes from Thiorhodovibrio strain 970 from the pressure dependence of biphasic kinetics observed in the minutes to 100 h time range.

Enhanced statistical sampling reveals microscopic complexity in the talin mechanosensor folding energy landscape

Statistical mechanics can describe the major conformational ensembles determining the equilibrium free-energy landscape of a folding protein. The challenge is to capture the full repertoire of low-occurrence conformations separated by high kinetic barriers that define complex landscapes. Computationally, enhanced sampling methods accelerate the exploration of molecular rare events. However, accessing the entire protein's conformational space in equilibrium experiments requires technological developments to enable extended observation times. We developed single-molecule magnetic tweezers to capture over a million individual transitions as a single talin protein unfolds and refolds under force in equilibrium. When observed at classically-probed timescales, talin folds in an apparently uncomplicated two-state manner. As the sampling time extends from minutes to days, the underlying energy landscape exhibits gradually larger signatures of complexity, involving a finite number of well-defined rare conformations. A fluctuation analysis allows us to propose plausible structures of each low-probability conformational state. The physiological relevance of each distinct conformation can be connected to the binding of the cytoskeletal protein vinculin, suggesting an extra layer of complexity in talin-mediated mechanotransduction. More generally, our experiments directly test the fundamental notion that equilibrium dynamics depend on the observation timescale.

The Non-native Disulfide-Bond-Containing Landscape Orthogonal to the Oxidative Protein-Folding Trajectory: A Necessary Evil?

Oxidative protein folding describes the process by which disulfide-bond-containing proteins mature from their ribosomal, fully reduced and unfolded, origins. Over the past 40 years, a number of exemplar proteins including bovine pancreatic ribonuclease A (RNaseA), bovine pancreatic trypsin inhibitor (BPTI), and hen egg-white lysozyme (HEWL), among others, have provided rich insight into the nature of the intermolecular interactions that drive the formation of the native, biologically active fold. In this Review Article, we revisit the oxidative folding process of RNase A with a focus on reconciling the role of non-native disulfide-bond-containing species that populate the oxidative folding landscape. Toward gaining such an understanding, we project the regeneration pathway onto a Cartesian coordinate system. This helps not only to recognize the magnitude of the seemingly "fruitless", non-native disulfide-bond-containing species that lie orthogonal to the "native-protein-forming" reaction progress but also to reconcile a role for their existence in the regenerative trajectory. Finally, we superimpose the folding funnel onto the regeneration trajectory to draw parallels between oxidative folders and conformational folders (proteins that lack disulfide bonds). The overall objective is to provide the reader with a semi-quantitative description of oxidative protein folding and the barriers to successful regeneration while underscoring a role of seemingly fruitless intermediates.

Evolutionary adaptation of the protein folding pathway for secretability

Secretory preproteins of the Sec pathway are targeted post-translationally and cross cellular membranes through translocases. During cytoplasmic transit, mature domains remain non-folded for translocase recognition/translocation. After translocation and signal peptide cleavage, mature domains fold to native states in the bacterial periplasm or traffic further. We sought the structural basis for delayed mature domain folding and how signal peptides regulate it. We compared how evolution diversified a periplasmic peptidyl-prolyl isomerase PpiA mature domain from its structural cytoplasmic PpiB twin. Global and local hydrogen-deuterium exchange mass spectrometry showed that PpiA is a slower folder. We defined at near-residue resolution hierarchical folding initiated by similar foldons in the twins, at different order and rates. PpiA folding is delayed by less hydrophobic native contacts, frustrated residues and a β-turn in the earliest foldon and by signal peptide-mediated disruption of foldon hierarchy. When selected PpiA residues and/or its signal peptide were grafted onto PpiB, they converted it into a slow folder with enhanced in vivo secretion. These structural adaptations in a secretory protein facilitate trafficking.

Protein structure prediction in the deep learning era

Significant advances have been achieved in protein structure prediction, especially with the recent development of the AlphaFold2 and the RoseTTAFold systems. This article reviews the progress in deep learning-based protein structure prediction methods in the past two years. First, we divide the representative methods into two categories: the two-step approach and the end-to-end approach. Then, we show that the two-step approach is possible to achieve similar accuracy to the state-of-the-art end-to-end approach AlphaFold2. Compared to the end-to-end approach, the two-step approach requires fewer computing resources. We conclude that it is valuable to keep developing both approaches. Finally, a few outstanding challenges in function-orientated protein structure prediction are pointed out for future development.