Protein folding and unfolding at atomic resolution

TPPU_DSF: A Web Application to Calculate Thermodynamic Parameters Using DSF Data

Here we present TPPU_DSF (https://maciasnmr.net/tppu_dsf/). This is a free and open-source web application that opens, converts, fits, and calculates the thermodynamic parameters of protein unfolding from standard differential scanning fluorimetry (DSF) data in an automated manner. The software has several applications. In the context of screening compound libraries for protein binders, obtaining thermodynamic parameters provides a more robust approach to detecting hits than the changes in the melting temperature (Tm) alone, thereby helping to increase the number of positive hits in screening campaigns. Moreover, changes in ΔGuo indicate protein response to binding at lower compound concentrations than those in the Tm, thereby reducing the costs associated with the amounts of protein and compounds required for the assays. Also, by adding thermodynamic information to the Tm comparison, the software can contribute to the optimization of protein constructs and buffer conditions, a common practice before structural and functional projects.

Protein Fold Usages in Ribosomes: Another Glance to the Past

The analysis of protein fold usage, similar to codon usage, offers profound insights into the evolution of biological systems and the origins of modern proteomes. While previous studies have examined fold distribution in modern genomes, our study focuses on the comparative distribution and usage of protein folds in ribosomes across bacteria, archaea, and eukaryotes. We identify the prevalence of certain 'super-ribosome folds,' such as the OB fold in bacteria and the SH3 domain in archaea and eukaryotes. The observed protein fold distribution in the ribosomes announces the future power-law distribution where only a few folds are highly prevalent, and most are rare. Additionally, we highlight the presence of three copies of proto-Rossmann folds in ribosomes across all kingdoms, showing its ancient and fundamental role in ribosomal structure and function. Our study also explores early mechanisms of molecular convergence, where different protein folds bind equivalent ribosomal RNA structures in ribosomes across different kingdoms. This comparative analysis enhances our understanding of ribosomal evolution, particularly the distinct evolutionary paths of the large and small subunits, and underscores the complex interplay between RNA and protein components in the transition from the RNA world to modern cellular life. Transcending the concept of folds also makes it possible to group a large number of ribosomal proteins into five categories of urfolds or metafolds, which could attest to their ancestral character and common origins. This work also demonstrates that the gradual acquisition of extensions by simple but ordered folds constitutes an inexorable evolutionary mechanism. This observation supports the idea that simple but structured ribosomal proteins preceded the development of their disordered extensions.

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.

Molecular Chaperonin HSP60: Current Understanding and Future Prospects

Molecular chaperones are highly conserved across evolution and play a crucial role in preserving protein homeostasis. The 60 kDa heat shock protein (HSP60), also referred to as chaperonin 60 (Cpn60), resides within mitochondria and is involved in maintaining the organelle's proteome integrity and homeostasis. The HSP60 family, encompassing Cpn60, plays diverse roles in cellular processes, including protein folding, cell signaling, and managing high-temperature stress. In prokaryotes, HSP60 is well understood as a GroEL/GroES complex, which forms a double-ring cavity and aids in protein folding. In eukaryotes, HSP60 is implicated in numerous biological functions, like facilitating the folding of native proteins and influencing disease and development processes. Notably, research highlights its critical involvement in sustaining oxidative stress and preserving mitochondrial integrity. HSP60 perturbation results in the loss of the mitochondria integrity and activates apoptosis. Currently, numerous clinical investigations are in progress to explore targeting HSP60 both in vivo and in vitro across various disease models. These studies aim to enhance our comprehension of disease mechanisms and potentially harness HSP60 as a therapeutic target for various conditions, including cancer, inflammatory disorders, and neurodegenerative diseases. This review delves into the diverse functions of HSP60 in regulating proteo-homeostasis, oxidative stress, ROS, apoptosis, and its implications in diseases like cancer and neurodegeneration.

From covalent transition states in chemistry to noncovalent in biology: from β- to Φ-value analysis of protein folding

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the β-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of β- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Mechanisms and pathology of protein misfolding and aggregation

Despite advances in machine learning-based protein structure prediction, we are still far from fully understanding how proteins fold into their native conformation. The conventional notion that polypeptides fold spontaneously to their biologically active states has gradually been replaced by our understanding that cellular protein folding often requires context-dependent guidance from molecular chaperones in order to avoid misfolding. Misfolded proteins can aggregate into larger structures, such as amyloid fibrils, which perpetuate the misfolding process, creating a self-reinforcing cascade. A surge in amyloid fibril structures has deepened our comprehension of how a single polypeptide sequence can exhibit multiple amyloid conformations, known as polymorphism. The assembly of these polymorphs is not a random process but is influenced by the specific conditions and tissues in which they originate. This observation suggests that, similar to the folding of native proteins, the kinetics of pathological amyloid assembly are modulated by interactions specific to cells and tissues. Here, we review the current understanding of how intrinsic protein conformational propensities are modulated by physiological and pathological interactions in the cell to shape protein misfolding and aggregation pathology.

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.

Folding-unfolding asymmetry and a RetroFold computational algorithm

We treat protein folding as molecular self-assembly, while unfolding is viewed as disassembly. Fracture is typically a much faster process than self-assembly. Self-assembly is often an exponentially decaying process, since energy relaxes due to dissipation, while fracture is a constant-rate process as the driving force is opposed by damping. Protein folding takes two orders of magnitude longer than unfolding. We suggest a mathematical transformation of variables, which makes it possible to view self-assembly as time-reversed disassembly, thus folding can be studied as reversed unfolding. We investigate the molecular dynamics modelling of folding and unfolding of the short Trp-cage protein. Folding time constitutes about 800 ns, while unfolding (denaturation) takes only about 5.0 ns and, therefore, fewer computational resources are needed for its simulation. This RetroFold approach can be used for the design of a novel computation algorithm, which, while approximate, is less time-consuming than traditional folding algorithms.

Prion Propagation is Dependent on Key Amino Acids in Charge Cluster 2 within the Prion Protein

To dissect the N-terminal residues within the cellular prion protein (PrP^C) that are critical for efficient prion propagation, we generated a library of point, double, or triple alanine replacements within residues 23-111 of PrP, stably expressed them in cells silenced for endogenous mouse PrP^C and challenged the reconstituted cells with four common but biologically diverse mouse prion strains. Amino acids (aa) 105-111 of Charge Cluster 2 (CC2), which is disordered in PrP^C, were found to be required for propagation of all four prion strains; other residues had no effect or exhibited strain-specific effects. Replacements in CC2, including aa105-111, dominantly inhibited prion propagation in the presence of endogenous wild type PrP^C whilst other changes were not inhibitory. Single alanine replacements within aa105-111 identified leucine 108 and valine 111 or the cluster of lysine 105, threonine 106 and asparagine 107 as critical for prion propagation. These residues mediate specific ordering of unstructured CC2 into β-sheets in the infectious prion fibrils from Rocky Mountain Laboratory (RML) and ME7 mouse prion strains.

Elucidating activation and deactivation dynamics of VEGFR-2 transmembrane domain with coarse-grained molecular dynamics simulations

The vascular endothelial growth factor receptor 2 (VEGFR-2) is a member of receptor tyrosine kinases (RTKs) and is a dimeric membrane protein that functions as a primary regulator of angiogenesis. As is usual with RTKs, spatial alignment of its transmembrane domain (TMD) is essential toward VEGFR-2 activation. Experimentally, the helix rotations within TMD around their own helical axes are known to participate importantly toward the activation process in VEGFR-2, but the detailed dynamics of the interconversion between the active and inactive TMD forms have not been clearly elucidated at the molecular level. Here, we attempt to elucidate the process by using coarse grained (CG) molecular dynamics (MD) simulations. We observe that inactive dimeric TMD in separation is structurally stable over tens of microseconds, suggesting that TMD itself is passive and does not allow spontaneous signaling of VEGFR-2. By starting from the active conformation, we reveal the mechanism of TMD inactivation through analyzing the CG MD trajectories. We observe that interconversions between a left-handed overlay and a right-handed one are essential for the process of going from an active TMD structure to the inactive form. In addition, our simulations find that the helices can rotate properly when the overlaying structure of the helices interconverts and when the crossing angle of the two helices changes by larger than ~40 degrees. As the activation right after the ligand attachment on VEGFR-2 will take place in the reverse manner of this inactivation process, these structural aspects will also appear importantly for the activation process. The rather large change in helix configuration for activation also explains why VEGFR-2 rarely self-activate and how the activating ligand structurally drive the whole VEGFR-2. This mechanism of TMD activation / inactivation within VEGFR-2 may help in further understanding the overall activation processes of other RTKs.

A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.

High Energy Channeling and Malleable Transition States: Molecular Dynamics Simulations and Free Energy Landscapes for the Thermal Unfolding of Protein U1A and 13 Mutants

The spliceosome protein U1A is a prototype case of the RNA recognition motif (RRM) ubiquitous in biological systems. The in vitro kinetics of the chemical denaturation of U1A indicate that the unfolding of U1A is a two-state process but takes place via high energy channeling and a malleable transition state, an interesting variation of typical two-state behavior. Molecular dynamics (MD) simulations have been applied extensively to the study of two-state unfolding and folding of proteins and provide an opportunity to obtain a theoretical account of the experimental results and a molecular model for the transition state ensemble. We describe herein all-atom MD studies including explicit solvent of up to 100 ns on the thermal unfolding (UF) of U1A and 13 mutants. Multiple MD UF trajectories are carried out to ensure accuracy and reproducibility. A vector representation of the MD unfolding process in RMSD space is obtained and used to calculate a free energy landscape for U1A unfolding. A corresponding MD simulation and free energy landscape for the protein CI2, well known to follow a simple two state folding/unfolding model, is provided as a control. The results indicate that the unfolding pathway on the MD calculated free energy landscape of U1A shows a markedly extended transition state compared with that of CI2. The MD results support the interpretation of the observed chevron plots for U1A in terms of a high energy, channel-like transition state. Analysis of the MDUF structures shows that the transition state ensemble involves microstates with most of the RRM secondary structure intact but expanded by ~14% with respect to the radius of gyration. Comparison with results on a prototype system indicates that the transition state involves an ensemble of molten globule structures and extends over the region of ~1-35 ns in the trajectories. Additional MDUF simulations were carried out for 13 U1A mutants, and the calculated φ-values show close accord with observed results and serve to validate our methodology.

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.

New Insights for Biosensing: Lessons from Microbial Defense Systems

Microorganisms have gained defense systems during the lengthy process of evolution over millions of years. Such defense systems can protect them from being attacked by invading species (e.g., CRISPR-Cas for establishing adaptive immune systems and nanopore-forming toxins as virulence factors) or enable them to adapt to different conditions (e.g., gas vesicles for achieving buoyancy control). These microorganism defense systems (MDS) have inspired the development of biosensors that have received much attention in a wide range of fields including life science research, food safety, and medical diagnosis. This Review comprehensively analyzes biosensing platforms originating from MDS for sensing and imaging biological analytes. We first describe a basic overview of MDS and MDS-inspired biosensing platforms (e.g., CRISPR-Cas systems, nanopore-forming proteins, and gas vesicles), followed by a critical discussion of their functions and properties. We then discuss several transduction mechanisms (optical, acoustic, magnetic, and electrical) involved in MDS-inspired biosensing. We further detail the applications of the MDS-inspired biosensors to detect a variety of analytes (nucleic acids, peptides, proteins, pathogens, cells, small molecules, and metal ions). In the end, we propose the key challenges and future perspectives in seeking new and improved MDS tools that can potentially lead to breakthrough discoveries in developing a new generation of biosensors with a combination of low cost; high sensitivity, accuracy, and precision; and fast detection. Overall, this Review gives a historical review of MDS, elucidates the principles of emulating MDS to develop biosensors, and analyzes the recent advancements, current challenges, and future trends in this field. It provides a unique critical analysis of emulating MDS to develop robust biosensors and discusses the design of such biosensors using elements found in MDS, showing that emulating MDS is a promising approach to conceptually advancing the design of biosensors.

Determination of the Most Stable Packing of Peptides from Ribosomal S1 Protein, Protein Bgl2p, and Aβ peptide in β-layers During Molecular Dynamics Simulations

Our task was to determine the most stable packing of peptides in β-layers to construct an oligomer structure for fibril growth. The β-layers consisting of eight short peptides with the amino acid sequences IVRGVVVAID, VDSWNVLVAG (VESWNVLVAG), KLVFFAEDVG, and IIGLMVGGVV were built. These sequences correspond to the amyloidogenic regions of ribosomal S1 protein from E. coli, protein glucantransferase Bgl2p from the yeast cell wall, and Aβ peptide. First, the amyloidogenic regions were predicted theoretically, and then were confirmed experimentally. Four β-layers with different orientation of the peptides in the layers and the layers relative to each other were constructed. To determine the most stable packing of β-strands, the molecular dynamic (MD) simulations in explicit water were carried out. Two charge states (pH3 and pH5) for each β-layer were considered. The fraction of the secondary structure was a measure of stability for β-layers. β-Layers, in which β-strands are antiparallel relative to each other, were the most stable. Using this packing for β-strands, we constructed the oligomer structures and also checked their stability by using MD simulations.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

An anionic ligand snap-locks a long-range interaction in a magnesium-folded riboswitch

The archetypical transcriptional crcB fluoride riboswitch from Bacillus cereus is an intricately structured non-coding RNA element enhancing gene expression in response to toxic levels of fluoride. Here, we used single molecule FRET to uncover three dynamically interconverting conformations appearing along the transcription process: two distinct undocked states and one pseudoknotted docked state. We find that the fluoride anion specifically snap-locks the magnesium-induced, dynamically docked state. The long-range, nesting, single base pair A40-U48 acts as the main linchpin, rather than the multiple base pairs comprising the pseudoknot. We observe that the proximally paused RNA polymerase further fine-tunes the free energy to promote riboswitch docking. Finally, we show that fluoride binding at short transcript lengths is an early step toward partitioning folding into the docked conformation. These results reveal how the anionic fluoride ion cooperates with the magnesium-associated RNA to govern regulation of downstream genes needed for fluoride detoxification of the cell.

Towards the Idea of Molecular Brains

How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors.

Rationalizing the Role of Monosodium Glutamate in the Protein Aggregation Through Biophysical Approaches: Potential Impact on Neurodegeneration

Monosodium glutamate (MSG) is the world’s most extensively used food additive and is generally recognized as safe according to the FDA. However, it is well reported that MSG is associated with a number of neurological diseases, and in turn, neurological diseases are associated with protein aggregation. This study rationalized the role of MSG in protein aggregation using different biophysical techniques such as absorption, far-UV CD, DLS, and ITC. Kinetic measurements revealed that MSG causes significant enhancement of aggregation of BSA through a nucleation-dependent polymerization mechanism. Also, CTAB-BSA aggregation is enhanced by MSG significantly. MSG-induced BSA aggregation also exhibits the formation of irreversible aggregates, temperature dependence, non-Arrhenius behavior, and enhancement of hydrodynamic diameter. From the isothermal titration calorimetry measurement, the significant endothermic heat of the interaction of BSA-MSG indicates that protein aggregation may be due to the coupling of MSG with the protein. The determined enthalpy change (ΔH) is largely positive, also suggesting an endothermic nature, whereas entropy change (ΔS) is positive and Gibbs free energy change (ΔG) is largely negative, suggesting the spontaneous nature of the interaction. Furthermore, even a low concentration of MSG is involved in the unfolding of the secondary structure of protein with the disappearance of original peaks and the formation of a unique peak in the far-UV CD, which is an attention-grabbing observation. This is the first investigation which links the dietary MSG with protein aggregation and thus will be very instrumental in understanding the mechanism of various MSG-related human physiological as well as neurological diseases.

The concept of protein folding/unfolding and its impacts on human health

Proteins have evolved in specific 3D structures and play different functions in cells and determine various reactions and pathways. The newly synthesized amino acid chains once depart ribosome must crumple into three-dimensional structures so can be biologically active. This process of protein that makes a functional molecule is called protein folding. The protein folding is both a biological and a physicochemical process that depends on the sequence of it. In fact, this process occurs more complicated and in some cases and in exposure to some molecules like glucose (glycation), mistaken folding leads to amyloid structures and fatal disorders called conformational diseases. Such conditions are detected by the quality control system of the cell and these abnormal proteins undergo renovation or degradation. This scenario takes place by the chaperones, chaperonins, and Ubiquitin-proteasome complex. Understanding of protein folding mechanisms from different views including experimental and computational approaches has revealed some intermediate ensembles such as molten globule and has been subjected to biophysical and molecular biology attempts to know more about prevalent conformational diseases.

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.

While temperature impacts the function of all cellular components, it’s hard to rule out how the temperature dependence of cell phenotypes emerged from the dependence of individual components. Here, the authors develop a Bayesian genome scale modelling approach to identify thermal determinants of yeast metabolism.

Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data

Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.

Site-Specific Protein Labeling with Fluorophores as a Tool To Monitor Protein Turnover

Proteins that terminally fail to acquire their native structure are detected and degraded by cellular quality control systems. Insights into cellular protein quality control are key to a better understanding of how cells establish and maintain the integrity of their proteome and of how failures in these processes cause human disease. Here we have used genetic code expansion and fast bio‐orthogonal reactions to monitor protein turnover in mammalian cells through a fluorescence‐based assay. We have used immune signaling molecules (interleukins) as model substrates and shown that our approach preserves normal cellular quality control, assembly processes, and protein functionality and works for different proteins and fluorophores. We have further extended our approach to a pulse‐chase type of assay that can provide kinetic insights into cellular protein behavior. Taken together, this study establishes a minimally invasive method to investigate protein turnover in cells as a key determinant of cellular homeostasis.

Mosaic Cooperativity in Slow Polypeptide Topological Isomerization Revealed by Residue-Specific NMR Thermodynamic Analysis

Slow polypeptide conformational changes on time scales of >1 s are generally assumed to be highly cooperative two-state transitions, reflecting the high energy barrier. However, few experimental characterizations have tested the validity of this assumption. We performed residue-specific NMR thermodynamic analysis of the 27-residue lantibiotic peptide, nukacin ISK-1, to characterize the isomerization between two topological states on the second time scale. Unexpectedly, the thermal transition behaviors were distinct among peptide regions, indicating that the topological isomerization process is a mosaic of different degrees of cooperativity. The conformational change path between the two NMR structures was deduced by a targeted molecular dynamics simulation. The unique side-chain threading motions through the monosulfide rings are the structural basis of the high energy barrier, and the nonlocal interactions in the hydrophobic core are the structural basis of the cooperativity. Taken together, we provide an energetic description of the topological isomerization of nukacin ISK-1.

Cancer-associated mutations in the ribosomal protein L5 gene dysregulate the HDM2/p53-mediated ribosome biogenesis checkpoint

Perturbations in ribosome biogenesis have been associated with cancer. Such aberrations activate p53 through the RPL5/RPL11/5S rRNA complex-mediated inhibition of HDM2. Studies using animal models have suggested that this signaling pathway might constitute an important anticancer barrier. To gain a deeper insight into this issue in humans, here we analyze somatic mutations in RPL5 and RPL11 coding regions, reported in The Cancer Genome Atlas and International Cancer Genome Consortium databases. Using a combined computational and statistical approach, complemented by a range of biochemical and functional analyses in human cancer cell models, we demonstrate the existence of several mechanisms by which RPL5 mutations may impair wild-type p53 upregulation and ribosome biogenesis. Unexpectedly, the same approach provides only modest evidence for a similar role of RPL11, suggesting that RPL5 represents a preferred target during human tumorigenesis in cancers with wild-type p53. Furthermore, we find that several functional cancer-associated RPL5 somatic mutations occur as rare germline variants in general population. Our results shed light on the so-far enigmatic role of cancer-associated mutations in genes encoding ribosomal proteins, with implications for our understanding of the tumor suppressive role of the RPL5/RPL11/5S rRNA complex in human malignancies.

Learning the changes of barnase mutants thermostability from structural fluctuations obtained using anisotropic network modeling

In biotechnology applications, rational design of new proteins with improved physico-chemical properties includes a number of important tasks. One of the greatest practical and fundamental challenges is the design of highly thermostable protein enzymes that maintain catalytic activity at high temperatures. This problem may be solved by introducing mutations into the wild-type enzyme protein. In this work, to predict the impact of such mutations in barnase protein we applied the anisotropic network modeling approach, revealing atomic fluctuations in structural regions that are changed in mutants compared to the wild-type protein. A regression model was constructed based on these structural features that can allow one to predict the thermal stability of new barnase mutants. Moreover, the analysis of regression model provides a mechanistic explanation of how the structural features can contribute to the thermal stability of barnase mutants.

Multistep Protein Unfolding Scenarios from the Rupture of a Complex Metal Cluster Cd3S9

Protein (un)folding is a complex and essential process. With the rapid development of single-molecule techniques, we can detect multiple and transient proteins (un)folding pathways/intermediates. However, the observation of multiple multistep (>2) unfolding scenarios for a single protein domain remains limited. Here, we chose metalloprotein with relatively stable and multiple metal-ligand coordination bonds as a system for such a purpose. Using AFM-based single-molecule force spectroscopy (SMFS), we successfully demonstrated the complex and multistep protein unfolding scenarios of the β-domain of a human protein metallothionein-3 (MT). MT is a protein of ~60 amino acids (aa) in length with 20 cysteines for various metal binding, and the β-domain (βMT) is of ~30 aa with an M3S9 metal cluster. We detected four different types of three-step protein unfolding scenarios from the Cd-βMT, which can be possibly explained by the rupture of Cd-S bonds in the complex Cd3S9 metal cluster. In addition, complex unfolding scenarios with four rupture peaks were observed. The Cd-S bonds ruptured in both single bond and multiple bonds modes. Our results provide not only evidence for multistep protein unfolding phenomena but also reveal unique properties of metalloprotein system using single-molecule AFM.

Nonnative contact effects in protein folding

The folding simulations of three ββα-motifs and β-barrel structured proteins (NTL9, NuG2b, and CspA) were performed to determine the important roles of native and nonnative contacts in protein folding.

In situ monitoring of molecular aggregation using circular dichroism

The aggregation of molecules plays an important role in determining their function. Electron microscopy and other methods can only characterize the variation of microstructure, but are not capable of monitoring conformational changes. These techniques are also complicated, expensive and time-consuming. Here, we demonstrate a simple method to monitor in-situ and in real-time the conformational change of (R)-1,1'-binaphthyl-based polymers during the aggregation process using circular dichroism. Based on results from molecular dynamics simulations and experimental circular dichroism measurements, polymers with "open" binaphthyl rings are found to show stronger aggregation-annihilated circular dichroism effects, with more negative torsion angles between the two naphthalene rings. In contrast, the polymers with "locked" rings show a more restrained aggregation-annihilated circular dichroism effect, with only a slight change of torsion angle. This work provides an approach to monitor molecular aggregation in a simple, accurate, and efficient way.

Insulin hexamer dissociation dynamics revealed by photoinduced T-jumps and time-resolved X-ray solution scattering

The structural dynamics of insulin hexamer dissociation were studied by the photoinduced temperature jump technique and monitored by time-resolved X-ray scattering. The process of hexamer dissociation was found to involve several transient intermediates, including an expanded hexamer and an unstable tetramer. Our findings provide insights into the mechanisms of protien-protein association.

External release of entropy by synchronized movements of local secondary structures drives folding of a small, disulfide-bonded protein

A crucial mechanism to the formation of native, fully functional, 3D structures from local secondary structures is unraveled in this study. Through the introduction of various amino acid substitutions at four canonical β-turns in a three-fingered protein, Toxin α from Naja nigricollis, we found that the release of internal entropy to the external environment through the globally synchronized movements of local substructures plays a crucial role. Throughout the folding process, the folding species were saturated with internal entropy so that intermediates accumulated at the equilibrium state. Their relief from the equilibrium state was accomplished by the formation of a critical disulfide bridge, which could guide the synchronized movement of one of the peripheral secondary structure. This secondary structure collided with a core central structure, which flanked another peripheral secondary structure. This collision displaced the internal thermal fluctuations from the first peripheral structure to the second peripheral structure, where the displaced thermal fluctuations were ultimately released as entropy. Two protein folding processes that acted in succession were identified as the means to establish the flow of thermal fluctuations. The first process was the time-consuming assembly process, where stochastic combinations of colliding, native-like, secondary structures provided candidate structures for the folded protein. The second process was the activation process to establish the global mutual relationships of the native protein in the selected candidate. This activation process was initiated and propagated by a positive feedback process between efficient entropy release and well-packed local structures, which moved in synchronization. The molecular mechanism suggested by this experiment was assessed with a well-defined 3D structure of erabutoxin b because one of the turns that played a critical role in folding was shared with erabutoxin b.

Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning

Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide time-series data, such as donor-acceptor distances, whereas the latter give atomistic information, although this information is often biased by model parameters. Here, we devise a machine-learning method to combine the complementary information from the two approaches and construct a consistent model of conformational dynamics. It is applied to the folding dynamics of the formin-binding protein WW domain. MD simulations over 400 μs led to an initial Markov state model (MSM), which was then "refined" using single-molecule Förster resonance energy transfer (FRET) data through hidden Markov modeling. The refined or data-assimilated MSM reproduces the FRET data and features hairpin one in the transition-state ensemble, consistent with mutation experiments. The folding pathway in the data-assimilated MSM suggests interplay between hydrophobic contacts and turn formation. Our method provides a general framework for investigating conformational transitions in other proteins.

The intrinsic flexibility of the aptamer targeting the ribosomal protein S8 is a key factor for the molecular recognition

BACKGROUND

Aptamers are RNA/DNA biomolecules representing an emerging class of protein interactors and regulators. Despite the growing interest in these molecules, current understanding of chemical-physical basis of their target recognition is limited. Recently, the characterization of the aptamer targeting the protein-S8 has suggested that flexibility plays important functional roles. We investigated the structural versatility of the S8-aptamer by molecular dynamics simulations.

METHODS

Five different simulations have been conducted by varying starting structures and temperatures.

RESULTS

The simulation of S8-aptamer complex provides a dynamic view of the contacts occurring at the complex interface. The simulation of the aptamer in ligand-free state indicates that its central region is intrinsically endowed with a remarkable flexibility. Nevertheless, none of the trajectory structures adopts the structure observed in the S8-aptamer complex. The aptamer ligand-bound is very rigid in the simulation carried out at 300 K. A structural transition of this state, providing insights into the aptamer-protein recognition process, is observed in a simulation carried out at 400 K. These data indicate that a key event in the binding is linked to the widening of the central region of the aptamer. Particularly relevant is switch of the A26 base from its ligand-free state to a location that allows the G13-C28 base-pairing.

CONCLUSIONS

Intrinsic flexibility of the aptamer is essential for partner recognition. Present data indicate that S8 recognizes the aptamer through an induced-fit rather than a population-shift mechanism.

GENERAL SIGNIFICANCE

The present study provides deeper understanding of the structural basis of the structural versatility of aptamers.

Hsp90 Breaks the Deadlock of the Hsp70 Chaperone System

Protein folding in the cell requires ATP-driven chaperone machines such as the conserved Hsp70 and Hsp90. It is enigmatic how these machines fold proteins. Here, we show that Hsp90 takes a key role in protein folding by breaking an Hsp70-inflicted folding block, empowering protein clients to fold on their own. At physiological concentrations, Hsp70 stalls productive folding by binding hydrophobic, core-forming segments. Hsp90 breaks this deadlock and restarts folding. Remarkably, neither Hsp70 nor Hsp90 alters the folding rate despite ensuring high folding yields. In fact, ATP-dependent chaperoning is restricted to the early folding phase. Thus, the Hsp70-Hsp90 cascade does not fold proteins, but instead prepares them for spontaneous, productive folding. This stop-start mechanism is conserved from bacteria to man, assigning also a general function to bacterial Hsp90, HtpG. We speculate that the decreasing hydrophobicity along the Hsp70-Hsp90 cascade may be crucial for enabling spontaneous folding.

Roles of conformational disorder and downhill folding in modulating protein-DNA recognition

Transcription factors are thought to efficiently search for their target DNA site via a combination of conventional 3D diffusion and 1D diffusion along the DNA molecule mediated by non-specific electrostatic interactions. This process requires the DNA-binding protein to quickly exchange between a search competent and a target recognition mode, but little is known as to how these two binding modes are encoded in the conformational properties of the protein. Here, we investigate this issue on the engrailed homeodomain (EngHD), a DNA-binding domain that folds ultrafast and exhibits a complex conformational behavior consistent with the downhill folding scenario. We explore the interplay between folding and DNA recognition using a coarse-grained computational model that allows us to manipulate the folding properties of the protein and monitor its non-specific and specific binding to DNA. We find that conformational disorder increases the search efficiency of EngHD by promoting a fast gliding search mode in addition to sliding. When gliding, EngHD remains loosely bound to DNA moving linearly along its length. A partially disordered EngHD also binds more dynamically to the target site, reducing the half-life of the specific complex via a spring-loaded mechanism. These findings apply to all conditions leading to partial disorder. However, we also find that at physiologically relevant temperatures EngHD is well folded and can only obtain the conformational flexibility required to accelerate 1D diffusion when it folds/unfolds within the downhill scenario (crossing a marginal free energy barrier). In addition, the conformational flexibility of native downhill EngHD enables its fast reconfiguration to lock into the specific binding site upon arrival, thereby affording finer control of the on- and off-rates of the specific complex. Our results provide key mechanistic insights into how DNA-binding domains optimize specific DNA recognition through the control of their conformational dynamics and folding mechanism.

A component analysis of the free energies of folding of 35 proteins: A consensus view on the thermodynamics of folding at the molecular level

What factors favor protein folding? This is a textbook question. Parsing the experimental free energies of folding/unfolding into diverse enthalpic and entropic components of solute and solvent favoring or disfavoring folding is not an easy task. In this study, we present a computational protocol for estimating the free energy contributors to protein folding semi-quantitatively using ensembles of unfolded and native states generated via molecular dynamics simulations. We tested the methodology on 35 proteins with diverse structural motifs and sizes and found that the calculated free energies correlate well with experiment (correlation coefficient ∼ 0.85), enabling us to develop a consensus view of the energetics of folding. As a more sensitive test of the methodology, we also investigated the free energies of folding of an additional 33 single point mutants and obtained a correlation coefficient of 0.8. A notable observation is that the folding free energy components appear to carry signatures of the fold (SCOP classification) of the protein. © 2017 Wiley Periodicals, Inc.

Nanopore Sensing of Protein Folding

Single-molecule studies of protein folding hold keys to unveiling protein folding pathways and elusive intermediate folding states-attractive pharmaceutical targets. Although conventional single-molecule approaches can detect folding intermediates, they presently lack throughput and require elaborate labeling. Here, we theoretically show that measurements of ionic current through a nanopore containing a protein can report on the protein's folding state. Our all-atom molecular dynamics (MD) simulations show that the unfolding of a protein lowers the nanopore ionic current, an effect that originates from the reduction of ion mobility in proximity to a protein. Using a theoretical model, we show that the average change in ionic current produced by a folding-unfolding transition is detectable despite the orientational and conformational heterogeneity of the folded and unfolded states. By analyzing millisecond-long all-atom MD simulations of multiple protein transitions, we show that a nanopore ionic current recording can detect folding-unfolding transitions in real time and report on the structure of folding intermediates.