The Protein Folding Problem: The Role of Theory

Stimuli-Responsive Control over Self-Assembled Nanostructures in Sequence-Specific Functional Block Copolymers

The precise sequence of a protein's primary structure is essential in determining its folding pathways. To emulate the complexity of these biomolecules, functional block copolymers consisting of segmented triblocks with distinct functionalities positioned in a sequence-specific manner are designed to control the polymer chain compaction. Triblock polymers P- b -C- b -F and P- b -F- b -C and random diblock copolymer P- b -C- r -F consist of a hydrophilic poly(ethylene oxide) (PEO) block and a hydrophobic block with coumarin (C) and ferrocene (F) moieties that are grafted in a sequence-specific or random manner onto the hydrophilic block. External stimuli such as UVB light, redox, and chemical cues influence the functional hydrophobic block to alter the packing parameters that are monitored with spectroscopic and scattering techniques. Interestingly, the positioning of the stimuli-responsive moiety within the hydrophobic block of P- b -C- b -F, P- b -F- b -C, and P- b -C- r -F affects the extent of the hydrophobic-hydrophilic balance in block copolymers that renders orthogonal control in stimuli-responsive transformation of self-assembled vesicles to micelles.

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.

Analytical Aspects of ANSA-BSA Association: A Thermodynamic and Conformational Approach

Many studies have demonstrated the manner in which ANS interacts with bovine serum albumin (BSA), although they are limited by the extremely low solubility of dye. The present study demonstrates the binding of ANSA dye with BSA, and since this dye can easily replace ANS, it not only simplifies research but also improves sensor accuracy for serum albumin. A combination of calorimetry and spectroscopy has been employed to establish the thermodynamic signatures associated with the interaction of ANSA with the protein and the consequent conformational changes in the latter. The results of differential scanning calorimetry reveal that when the concentration of ANSA in solution is increased, the thermal stability of the protein increases substantially. The fluorescence data demonstrated a decrease in the binding affinity of ANSA with the protein when pH increased but was unable to identify a change in the mode of interaction of the ligand. ITC has demonstrated that the mode of interaction between ANSA and the protein varies from a single set of binding sites at pH 5 and 7.4 to a sequential binding site at pH 10, emphasizing the potential relevance of protein conformational changes. TCSPC experiments suggested a dynamic type in the presence of ANSA. Molecular docking studies suggest that ANSA molecules are able to find ionic centers in the hydrophobic pockets of BSA. The findings further imply that given its ease of use in experiments, ANSA may be a useful probe for tracking the presence of serum albumin and partially folded protein states.

Hydrophobic core evolution of major histocompatibility complex class I chain-related protein A for dramatic enhancing binding affinity

Interface residues at sites of protein-protein interaction (PPI) are the focus for affinity optimisation. However, protein hydrophobic cores (HCs) play critical roles and shape the protein surface. We hypothesise that manipulating protein HCs can enhance PPI interaction affinities. A cell stress molecule, major histocompatibility complex class I chain-related protein A (MICA), binds to the natural killer group 2D (NKG2D) homodimer to form three molecule interactions. MICA was used as a study subject to support our hypothesis. We redesigned MICA HCs by directed mutagenesis and isolated high-affinity variants through a newly designed partial-denature panning (PDP) method. A few mutations in MICA HCs increased the NKG2D-MICA interaction affinity by 325-5613-fold. Crystal structures of the NKG2D-MICA variant complexes indicated that mutagenesis of MICA HCs stabilised helical elements for decreasing intermolecular interactive free energy (ΔG) of the NKG2D-MICA heterotrimer. The repacking of MICA HC mutants maintained overall surface residues and the authentic binding specificity of MICA. In conclusion, this study provides a new method for MICA redesign and affinity optimisation through HC manipulation without mutating PPI interface residues. Our study introduces a novel approach to protein manipulation, potentially expanding the toolkit for protein affinity optimisation.

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.

Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure

Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the evolutionary kinetics of DRMs and how they evolve together remains limited. Epistasis, the interaction between a DRM and other residues in HIV-1 protein sequences, is key to the temporal evolution of drug resistance. We use a Potts sequence-covariation statistical-energy model of HIV-1 protein fitness under drug selection pressure, which captures epistatic interactions between all positions, combined with kinetic Monte-Carlo simulations of sequence evolutionary trajectories, to explore the acquisition of DRMs as they arise in an ensemble of drug-naive patient protein sequences. We follow the time course of 52 DRMs in the enzymes protease, RT, and integrase, the primary targets of antiretroviral therapy. The rates at which DRMs emerge are highly correlated with their observed acquisition rates reported in the literature when drug pressure is applied. This result highlights the central role of epistasis in determining the kinetics governing DRM emergence. Whereas rapidly acquired DRMs begin to accumulate as soon as drug pressure is applied, slowly acquired DRMs are contingent on accessory mutations that appear only after prolonged drug pressure. We provide a foundation for using computational methods to determine the temporal evolution of drug resistance using Potts statistical potentials, which can be used to gain mechanistic insights into drug resistance pathways in HIV-1 and other infectious agents.

Elucidating the influence of electrostatic force on the re-arrangement of H-bonds of protein polymers in the presence of salts

Polyampholytes/proteins have an intriguing network of hydrogen bonds (H-bonds), especially their secondary structure, which plays a crucial role in determining the conformational stability of the polymer. The changes in protein secondary structure in the protein-salt system have been extensively deciphered by researchers, yet their pathways for breakage and recreation are unknown. Understanding the mechanism of protein conformational changes towards their biological activities, like protein folding, remains one of the main challenges and requires multiscale analysis of this strongly correlated system. Herein, salts have been used to reveal the re-arrangement behavior in the H-bond network of proteins under the influence of electrostatic interactions, as the strength of electrostatic forces is much stronger than that of H-bonds. At lower salt concentrations, there are negligible changes in the secondary structures as the electrostatic forces induced by the salt ions are less. Later, the existing H-bonds break and reconstruct new H-bonds at higher salt concentrations due to the influence of the stronger electrostatic interaction induced by the large number of salt ions. Molecular dynamics (MD) simulations and FTIR studies have been used rigorously to decipher the reason behind the re-arrangement of the H-bonds within gelatin (protein). The re-arrangement in the H-bond has also been observed with time from simulations and experiments. Thus, this study could provide a fresh perspective on the conformational changes of polyampholytes/proteins and will also influence the studies of protein folding-unfolding interaction in the presence of salt ions.

Expanding the molecular grammar of polar residues and arginine in FUS prion-like domain phase separation and aggregation

A molecular grammar governing low-complexity prion-like domains phase separation (PS) has been proposed based on mutagenesis experiments that identified tyrosine and arginine as primary drivers of phase separation via aromatic-aromatic and aromatic-arginine interactions. Here we show that additional residues make direct favorable contacts that contribute to phase separation, highlighting the need to account for these contributions in PS theories and models. We find that tyrosine and arginine make important contacts beyond only tyrosine-tyrosine and tyrosine-arginine, including arginine-arginine contacts. Among polar residues, glutamine in particular contributes to phase separation with sequence/position-specificity, making contacts with both tyrosine and arginine as well as other residues, both before phase separation and in condensed phases. For glycine, its flexibility, not its small solvation volume, favors phase separation by allowing favorable contacts between other residues and inhibits the liquid-to-solid (LST) transition. Polar residue types also make sequence-specific contributions to aggregation that go beyond simple rules, which for serine positions is linked to formation of an amyloid-core structure by the FUS low-complexity domain. Hence, here we propose a revised molecular grammar expanding the role of arginine and polar residues in prion-like domain protein phase separation and aggregation.

Mendelian Randomization Validates the Immune Landscape Mediated by Aggrephagy in Esophageal Squamous Cell Carcinoma Patients from the Perspectives of Multi-omics

Objective: To delineate the immune landscape of ESCC patients mediated by aggrephagy through bioinformatics and identify prognostic cell cluster genes with causal attributes to esophageal cancer through Mendelian randomization. Methods: Quality control, dimension reduction, and annotation were performed on the ESCC single-cell dataset. NMF clustering of various cell subgroups was carried out based on the expression of AGG-related genes, and AGG-related genes in each cluster were identified. Pseudo-temporal analysis was used to observe changes in the expression of AGG-related genes in each cluster. Cell communication analysis was employed to observe interactions between cell subgroups. Changes in classification, metabolism, or KEGG pathways in related subgroups were observed based on different cell characteristics. The AGG cluster attributes of TCGA and GEO samples were assessed based on GSVA, and the prognosis of each cluster was observed. The immune treatment situation and the relationship between mutation level and prognosis of AGG cluster-related samples were observed through the TIDE database and microsatellite instability. Finally, the eQTL of genes in each prognostic AGG cluster was used as an instrumental variable, with esophageal cancer as the outcome factor. Through Mendelian randomization analysis, AGG cluster-related genes with a causal relationship to esophageal cancer were established. Results: Dimension reduction clustering of single-cell transcriptome data identified 19 different cell subgroups. After re-annotation of the 19 cell subgroups, it was found that the CAF cells, B cells, T cells, NK cells, etc., of ESCA patients were all elevated compared to the control group. CAF cells had a high degree of communication with most cells. There were significant differences in macrophage metabolism and B-cell-mediated signal transduction pathways in different AGG clusters. The TUBA1B+Mac-C0 cluster, along with other clusters, exhibits predictive prognostic and immunotherapeutic potential at the transcriptional level. Mendelian randomization analysis revealed a causal relationship between genes such as CTSZ, CTSC, DAD, COLEC12, ATOX1, within the AGG cluster, and the onset of esophageal cancer. Conclusion: Aggrephagy mediates and influences the alterations and interactions of various immune cells in patients with ESCC. We elucidate the roles of AGG-related clusters, such as TUBA1B+Mac-C0, VIM+CD8+T_cells-C0, UBB+Mac-C2, in mediating prognosis and immune therapy in ESCC patients. Genes causally associated with the occurrence of esophageal cancer are identified within the AGG cluster, including CTSZ, CTSC, DAD, COLEC12, ATOX1, etc., offering new evidence for clinical immune therapy. These findings underscore the significance of these gene clusters in influencing both prognosis and immune responses in the context of esophageal cancer, shedding light on potential therapeutic targets and prognostic markers.

Protein thermal sensing regulates physiological amyloid aggregation

To survive, cells must respond to changing environmental conditions. One way that eukaryotic cells react to harsh stimuli is by forming physiological, RNA-seeded subnuclear condensates, termed amyloid bodies (A-bodies). The molecular constituents of A-bodies induced by different stressors vary significantly, suggesting this pathway can tailor the cellular response by selectively aggregating a subset of proteins under a given condition. Here, we identify critical structural elements that regulate heat shock-specific amyloid aggregation. Our data demonstrates that manipulating structural pockets in constituent proteins can either induce or restrict their A-body targeting at elevated temperatures. We propose a model where selective aggregation within A-bodies is mediated by the thermal stability of a protein, with temperature-sensitive structural regions acting as an intrinsic form of post-translational regulation. This system would provide cells with a rapid and stress-specific response mechanism, to tightly control physiological amyloid aggregation or other cellular stress response pathways.

Understanding the heterogeneity intrinsic to protein folding

Relating the native fold of a protein to its amino acid sequence remains a fundamental problem in biology. While computer algorithms have demonstrated recently their prowess in predicting what structure a particular amino acid sequence will fold to, an understanding of how and why a specific protein fold is achieved remains elusive. A major challenge is to define the role of conformational heterogeneity during protein folding. Recent experimental studies, utilizing time-resolved FRET, hydrogen-exchange coupled to mass spectrometry, and single-molecule force spectroscopy, often in conjunction with simulation, have begun to reveal how conformational heterogeneity evolves during folding, and whether an intermediate ensemble of defined free energy consists of different sub-populations of molecules that may differ significantly in conformation, energy and entropy.

Heat and Wheat: Adaptation strategies with respect to heat shock proteins and antioxidant potential; an era of climate change

Extreme changes in weather including heat-wave and high-temperature fluctuations are predicted to increase in intensity and duration due to climate change. Wheat being a major staple crop is under severe threat of heat stress especially during the grain-filling stage. Widespread food insecurity underscores the critical need to comprehend crop responses to forthcoming climatic shifts, pivotal for devising adaptive strategies ensuring sustainable crop productivity. This review addresses insights concerning antioxidant, physiological, molecular impacts, tolerance mechanisms, and nanotechnology-based strategies and how wheat copes with heat stress at the reproductive stage. In this study stress resilience strategies were documented for sustainable grain production under heat stress at reproductive stage. Additionally, the mechanisms of heat resilience including gene expression, nanomaterials that trigger transcription factors, (HSPs) during stress, and physiological and antioxidant traits were explored. The most reliable method to improve plant resilience to heat stress must include nano-biotechnology-based strategies, such as the adoption of nano-fertilizers in climate-smart practices and the use of advanced molecular approaches. Notably, the novel resistance genes through advanced molecular approach and nanomaterials exhibit promise for incorporation into wheat cultivars, conferring resilience against imminent adverse environmental conditions. This review will help scientific communities in thermo-tolerance wheat cultivars and new emerging strategies to mitigate the deleterious impact of heat stress.

Ion correlation-driven like-charge attraction in multivalent salt solutions

The electrostatic double layer force is key to determining the stability and self-assembly of charged colloids and many other soft matter systems. Fully understanding the attractive force between two like-charged surfaces remains a great challenge. Here, we apply the modified Gaussian renormalized fluctuation theory to study ion correlation-driven like-charge attraction in multivalent salt solutions. The effects of spatially varying ion correlations on the structure of overlapping double layers and their free energy are self-consistently accounted for. In the presence of multivalent salts, increasing surface charge or counterion valency leads to a short-range attraction. We demonstrate that although both overcharging and like-charge attraction are outcomes of ion correlation, there is no causal relationship between them. Our theory also captures the non-monotonic dependence of like-charge attraction on multivalent salt concentration. The reduction of attraction at high salt concentrations could be a contributing factor toward the reentrant stability of charged colloidal suspensions. Our theoretical predictions are consistent with the observations reported in experiments and simulations.

Development of self-cooperative nanochaperones with enhanced activity to facilitate protein refolding

Regulating protein folding including assisting de novo folding, preventing misfolding and aggregation, and facilitating refolding of proteins are of significant importance for retaining protein's biological activities. Here, we report a mixed shell polymeric micelle (MSPM)-based self-cooperative nanochaperone (self-CO-nChap) with enhanced activity to facilitate protein refolding. This self-CO-nChap was fabricated by introducing Hsp40-mimetic artificial carriers into the traditional nanochaperone to cooperate with the Hsp70-mimetic confined hydrophobic microdomains. The artificial carrier facilitates transfer and immobilization of client proteins into confined hydrophobic microdomains, by which significantly improving self-CO-nChap's capability to inhibit unfolding and aggregation of client proteins, and finally facilitating refolding. Compared to traditional nanochaperones, the self-CO-nChap significantly enhances the thermal stability of horseradish peroxidase (HRP) epicyclically under harsher conditions. Moreover, the self-CO-nChap efficiently protects misfolding-prone proteins, such as immunoglobulin G (IgG) antibody from thermal denaturation, which is hardly achieved using traditional nanochaperones. In addition, a kinetic partitioning mechanism was devised to explain how self-CO-nChap facilitates refolding by regulating the cooperative effect of kinetics between the nanochaperone and client proteins. This work provides a novel strategy for the design of protein folding regulatory materials, including nanochaperones.

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.

Structural, kinetic, and thermodynamic aspects of insulin aggregation

Given the significance of protein aggregation in proteinopathies and the development of therapeutic protein pharmaceuticals, revamped interest in assessing and modelling the aggregation kinetics has been observed. Quantitative analysis of aggregation includes data of gradual monomeric depletion followed by the formation of subvisible particles. Kinetic and thermodynamic studies are essential to gain key insights into the aggregation process. Despite being the medical marvel in the world of diabetes, insulin suffers from the challenge of aggregation. Physicochemical stresses are experienced by insulin during industrial formulation, storage, delivery, and transport, considerably impacting product quality, efficacy, and effectiveness. The present review briefly describes the pathways, mathematical kinetic models, and thermodynamics of protein misfolding and aggregation. With a specific focus on insulin, further discussions include the structural heterogeneity and modifications of the intermediates incurred during insulin fibrillation. Finally, different model equations to fit the kinetic data of insulin fibrillation are discussed. We believe that this review will shed light on the conditions that induce structural changes in insulin during the lag phase of fibrillation and will motivate scientists to devise strategies to block the initialization of the aggregation cascade. Subsequent abrogation of insulin fibrillation during bioprocessing will ensure stable and globally accessible insulin for efficient management of diabetes.

Protein structure prediction from the complementary science perspective

A comparative analysis between two problems-apparently unrelated-which are solved in a period of ~400 years, viz., the accurate prediction of both the planetary orbits and the protein structures, leads to inferred conjectures that go far beyond the existence of a common path in their resolution, i.e., observation → pattern recognition → modeling. The preliminary results from this analysis indicate that complementary science, together with a new perspective on protein folding, may help us discover common features that could contribute to a more in-depth understanding of still-unsolved problems such as protein folding.

Protein nanocondensates: the next frontier

Over the past decade, myriads of studies have highlighted the central role of protein condensation in subcellular compartmentalization and spatiotemporal organization of biological processes. Conceptually, protein condensation stands at the highest level in protein structure hierarchy, accounting for the assembly of bodies ranging from thousands to billions of molecules and for densities ranging from dense liquids to solid materials. In size, protein condensates range from nanocondensates of hundreds of nanometers (mesoscopic clusters) to phase-separated micron-sized condensates. In this review, we focus on protein nanocondensation, a process that can occur in subsaturated solutions and can nucleate dense liquid phases, crystals, amorphous aggregates, and fibers. We discuss the nanocondensation of proteins in the light of general physical principles and examine the biophysical properties of several outstanding examples of nanocondensation. We conclude that protein nanocondensation cannot be fully explained by the conceptual framework of micron-scale biomolecular condensation. The evolution of nanocondensates through changes in density and order is currently under intense investigation, and this should lead to the development of a general theoretical framework, capable of encompassing the full range of sizes and densities found in protein condensates.

Integrative modeling of diverse protein-peptide systems using CABS-dock

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.

Assessing Fairness of AlphaFold2 Prediction of Protein 3D Structures

AlphaFold2 is reshaping biomedical research by enabling the prediction of a protein's 3D structure solely based on its amino acid sequence. This breakthrough reduces reliance on labor-intensive experimental methods traditionally used to obtain protein structures, thereby accelerating the pace of scientific discovery. Despite the bright future, it remains unclear whether AlphaFold2 can uniformly predict the wide spectrum of proteins equally well. Systematic investigation into the fairness and unbiased nature of its predictions is still an area yet to be thoroughly explored. In this paper, we conducted an in-depth analysis of AlphaFold2's fairness using data comprised of five million reported protein structures from its open-access repository. Specifically, we assessed the variability in the distribution of PLDDT scores, considering factors such as amino acid type, secondary structure, and sequence length. Our findings reveal a systematic discrepancy in AlphaFold2's predictive reliability, varying across different types of amino acids and secondary structures. Furthermore, we observed that the size of the protein exerts a notable impact on the credibility of the 3D structural prediction. AlphaFold2 demonstrates enhanced prediction power for proteins of medium size compared to those that are either smaller or larger. These systematic biases could potentially stem from inherent biases present in its training data and model architecture. These factors need to be taken into account when expanding the applicability of AlphaFold2.

Polymer folding through active processes recreates features of genome organization

From proteins to chromosomes, polymers fold into specific conformations that control their biological function. Polymer folding has long been studied with equilibrium thermodynamics, yet intracellular organization and regulation involve energy-consuming, active processes. Signatures of activity have been measured in the context of chromatin motion, which shows spatial correlations and enhanced subdiffusion only in the presence of adenosine triphosphate. Moreover, chromatin motion varies with genomic coordinate, pointing toward a heterogeneous pattern of active processes along the sequence. How do such patterns of activity affect the conformation of a polymer such as chromatin? We address this question by combining analytical theory and simulations to study a polymer subjected to sequence-dependent correlated active forces. Our analysis shows that a local increase in activity (larger active forces) can cause the polymer backbone to bend and expand, while less active segments straighten out and condense. Our simulations further predict that modest activity differences can drive compartmentalization of the polymer consistent with the patterns observed in chromosome conformation capture experiments. Moreover, segments of the polymer that show correlated active (sub)diffusion attract each other through effective long-ranged harmonic interactions, whereas anticorrelations lead to effective repulsions. Thus, our theory offers nonequilibrium mechanisms for forming genomic compartments, which cannot be distinguished from affinity-based folding using structural data alone. As a first step toward exploring whether active mechanisms contribute to shaping genome conformations, we discuss a data-driven approach.

Rethinking the protein folding problem from a new perspective

One of the main concerns of Anfinsen was to reveal the connection between the amino-acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, the thermodynamic hypothesis. As to the how, this remains an unsolved challenge. Consequently, the protein folding problem is examined here from a new perspective, namely, as an 'analytic whole'. Conceiving the protein folding in this way enabled us to (i) examine in detail why the force-field-based approaches have failed, among other purposes, in their ability to predict the three-dimensional structure of a protein accurately; (ii) propose how to redefine them to prevent these shortcomings, and (iii) conjecture on the origin of the state-of-the-art numerical-methods success to predict the tridimensional structure of proteins accurately.

Coding From Binding? Molecular Interactions at the Heart of Translation

The mechanism and the evolution of DNA replication and transcription, the key elements of the central dogma of biology, are fundamentally well explained by the physicochemical complementarity between strands of nucleic acids. However, the determinants that have shaped the third part of the dogma-the process of biological translation and the universal genetic code-remain unclear. We review and seek parallels between different proposals that view the evolution of translation through the prism of weak, noncovalent interactions between biological macromolecules. In particular, we focus on a recent proposal that there exists a hitherto unrecognized complementarity at the heart of biology, that between messenger RNA coding regions and the proteins that they encode, especially if the two are unstructured. Reflecting the idea that the genetic code evolved from intrinsic binding propensities between nucleotides and amino acids, this proposal promises to forge a link between the distant past and the present of biological systems.

Origins of life: first came evolutionary dynamics

When life arose from prebiotic molecules 3.5 billion years ago, what came first? Informational molecules (RNA, DNA), functional ones (proteins), or something else? We argue here for a different logic: rather than seeking a molecule type, we seek a dynamical process. Biology required an ability to evolve before it could choose and optimise materials. We hypothesise that the evolution process was rooted in the peptide folding process. Modelling shows how short random peptides can collapse in water and catalyse the elongation of others, powering both increased folding stability and emergent autocatalysis through a disorder-to-order process.

Darwinian evolution as a dynamical principle

Darwinian evolution (DE)-biology's powerful process of adaptation-is remarkably different from other known dynamical processes. It is antithermodynamic, driving away from equilibrium; it has persisted for 3.5 billion years; and its target, fitness, can seem like "Just So" stories. For insights, we make a computational model. In the Darwinian Evolution Machine (DEM) model, resource-driven duplication and competition operate inside a cycle of search/compete/choose. We find the following: 1) DE requires multiorganism coexistence for its long-term persistence and ability to cross fitness valleys. 2) DE is driven by resource dynamics, like booms and busts, not just by mutational change. And, 3) fitness ratcheting requires a mechanistic separation between variation and selection steps, perhaps explaining biology's use of separate polymers, DNA and proteins.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

Metal-Enhanced Helical Chirality of Coil Macromolecules: Bioinspired by Metal Coordination-Induced Protein Folding

Although the supramolecular helical structures of biomacromolecules have been studied, the examples of supramolecular systems that are assembled using coils to form helical polymer chains are still limited. Inspired by enhanced helical chirality at the supramolecular level in metal coordination-induced protein folding, a series of alanine-based coil copolymers (poly-(l-co-d)-ala-NH₂) carrying (l)- and (d)-alanine pendants were synthesized as a fresh research model to study the cooperative processes between homochirality property and metal coordination. The complexes of poly-(l-co-d)-ala-NH₂ and metal ions underwent a coil-to-helix transition and exhibited remarkable nonlinear effects based on the enantiomeric excess of the monomer unit in the copolymers, affording enhanced helical chirality compared to poly-(l-co-d)-ala-NH₂. More importantly, the synergistic effect of amplification of asymmetry and metal coordination triggered the formation of a helical molecular orbital on the polymer backbone via the coordination with the d orbital of copper ions. Thus, the helical chirality enhancement degree of poly-(l-co-d)-ala-NH₂/Cu²⁺ complexes (31.4) is approximately 3 times higher than that of poly-(l-co-d)-ala-NH₂/Ag⁺ complexes (9.8). This study not only provides important mechanistic insights into the enhancement of helical chirality for self-assembly but also establishes a new strategy for studying the homochiral amplification of asymmetry in biological supramolecular systems.

Before and after AlphaFold2: An overview of protein structure prediction

Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

Protein conformation and biomolecular condensates

Protein conformation and cell compartmentalization are fundamental concepts and subjects of vast scientific endeavors. In the last two decades, we have witnessed exciting advances that unveiled the conjunction of these concepts. An avalanche of studies highlighted the central role of biomolecular condensates in membraneless subcellular compartmentalization that permits the spatiotemporal organization and regulation of myriads of simultaneous biochemical reactions and macromolecular interactions. These studies have also shown that biomolecular condensation, driven by multivalent intermolecular interactions, is mediated by order-disorder transitions of protein conformation and by protein domain architecture. Conceptually, protein condensation is a distinct level in protein conformational landscape in which collective folding of large collections of molecules takes place. Biomolecular condensates arise by the physical process of phase separation and comprise a variety of bodies ranging from membraneless organelles to liquid condensates to solid-like conglomerates, spanning lengths from mesoscopic clusters (nanometers) to micrometer-sized objects. In this review, we summarize and discuss recent work on the assembly, composition, conformation, material properties, thermodynamics, regulation, and functions of these bodies. We also review the conceptual framework for future studies on the conformational dynamics of condensed proteins in the regulation of cellular processes.

Cooperativity and Folding Kinetics in a Multidomain Protein with Interwoven Chain Topology

Although a large percentage of eukaryotic proteomes consist of proteins with multiple domains, not much is known about their assembly mechanism, especially those with intricate native state architectures. Some have a complex topology in which the structural elements along the sequence are interwoven in such a manner that the domains cannot be separated by cutting at any location along the sequence. Such proteins are multiply connected multidomain proteins (MMPs) with the three-domain (NMP, LID, and CORE) phosphotransferase enzyme adenylate kinase (ADK) being an example. We devised a coarse-grained model to simulate ADK folding initiated by changing either the temperature or guanidinium chloride (GdmCl) concentration. The simulations reproduce the experimentally measured melting temperatures (associated with two equilibrium transitions), FRET efficiency as a function of GdmCl concentration, and the folding times quantitatively. Although the NMP domain orders independently, cooperative interactions between the LID and the CORE domains are required for complete assembly of the enzyme. Kinetic simulations show that, on the collapse time scale, multiple interconnected metastable states are populated, attesting to the folding heterogeneity. The network of kinetically connected states reveals that the CORE domain folds only after the NMP and LID domains, reflecting the interwoven nature of the chain topology.

AlphaFold2: A Role for Disordered Protein/Region Prediction?

The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).

Folding of two-dimensional nanoparticle superlattices enabled by emulsion-confined supramolecular co-assembly

Folding of two-dimensional nanoparticle superlattices is achieved through templated assembly on as-formed supramolecular nanosheets, which undergo a folding process within the emulsion droplets during the evaporation of the inner phase liquid. Building the folded nanoparticle superlattices opens a new gateway to reshape the properties of inorganic solids.

Heterogeneity in Protein Folding and Unfolding Reactions

Proteins have dynamic structures that undergo chain motions on time scales spanning from picoseconds to seconds. Resolving the resultant conformational heterogeneity is essential for gaining accurate insight into fundamental mechanistic aspects of the protein folding reaction. The use of high-resolution structural probes, sensitive to population distributions, has begun to enable the resolution of site-specific conformational heterogeneity at different stages of the folding reaction. Different states populated during protein folding, including the unfolded state, collapsed intermediate states, and even the native state, are found to possess significant conformational heterogeneity. Heterogeneity in protein folding and unfolding reactions originates from the reduced cooperativity of various kinds of physicochemical interactions between various structural elements of a protein, and between a protein and solvent. Heterogeneity may arise because of functional or evolutionary constraints. Conformational substates within the unfolded state and the collapsed intermediates that exchange at rates slower than the subsequent folding steps give rise to heterogeneity on the protein folding pathways. Multiple folding pathways are likely to represent distinct sequences of structure formation. Insight into the nature of the energy barriers separating different conformational states populated during (un)folding can also be obtained by resolving heterogeneity.

From Protein Design to the Energy Landscape of a Cold Unfolding Protein

Understanding protein folding is crucial for protein sciences. The conformational spaces and energy landscapes of cold (unfolded) protein states, as well as the associated transitions, are hardly explored. Furthermore, it is not known how structure relates to the cooperativity of cold transitions, if cold and heat unfolded states are thermodynamically similar, and if cold states play important roles for protein function. We created the cold unfolding 4-helix bundle DCUB1 with a de novo designed bipartite hydrophilic/hydrophobic core featuring a hydrogen bond network which extends across the bundle in order to study the relative importance of hydrophobic versus hydrophilic protein-water interactions for cold unfolding. Structural and thermodynamic characterization resulted in the discovery of a complex energy landscape for cold transitions, while the heat unfolded state is a random coil. Below ∼0 °C, the core of DCUB1 disintegrates in a largely cooperative manner, while a near-native helical content is retained. The resulting cold core-unfolded state is compact and features extensive internal dynamics. Below -5 °C, two additional cold transitions are seen, that is, (i) the formation of a water-mediated, compact, and highly dynamic dimer, and (ii) the onset of cold helix unfolding decoupled from cold core unfolding. Our results suggest that cold unfolding is initiated by the intrusion of water into the hydrophilic core network and that cooperativity can be tuned by varying the number of core hydrogen bond networks. Protein design has proven to be invaluable to explore the energy landscapes of cold states and to robustly test related theories.

Is Protein Folding a Thermodynamically Unfavorable, Active, Energy-Dependent Process?

The prevailing current view of protein folding is the thermodynamic hypothesis, under which the native folded conformation of a protein corresponds to the global minimum of Gibbs free energy G. We question this concept and show that the empirical evidence behind the thermodynamic hypothesis of folding is far from strong. Furthermore, physical theory-based approaches to the prediction of protein folds and their folding pathways so far have invariably failed except for some very small proteins, despite decades of intensive theory development and the enormous increase of computer power. The recent spectacular successes in protein structure prediction owe to evolutionary modeling of amino acid sequence substitutions enhanced by deep learning methods, but even these breakthroughs provide no information on the protein folding mechanisms and pathways. We discuss an alternative view of protein folding, under which the native state of most proteins does not occupy the global free energy minimum, but rather, a local minimum on a fluctuating free energy landscape. We further argue that ΔG of folding is likely to be positive for the majority of proteins, which therefore fold into their native conformations only through interactions with the energy-dependent molecular machinery of living cells, in particular, the translation system and chaperones. Accordingly, protein folding should be modeled as it occurs in vivo, that is, as a non-equilibrium, active, energy-dependent process.

Application of magnetic field (MF) as an effective method to improve the activity of immobilized Candida antarctica lipase B (CALB)

The process of immobilized enzyme and the change mechanism of enzyme in magnetic field.

Temperature and Guanidine Hydrochloride Effects on the Folding Thermodynamics of WW Domain and Variants

We used simulations based on an all-atom Go model to calculate the folding temperatures (T_fs) and free energies (ΔGs) of two variants of the WW domain, which is a small all-β-sheet protein. The results, without adjusting any parameter, are in good agreement with experiments, thus validating the simulations. We then used the molecular transfer model to predict the changes in the ΔGs and T_fs as the guanidine hydrochloride concentration is varied. The predictions can be readily tested in experiments.