Protein folding: then and now

Driving Osteocytogenesis from Mesenchymal Stem Cells in Osteon-like Biomimetic Nanofibrous Scaffolds

The treatment of critical-sized bone defects caused by tumor removal, skeletal injuries, or infections continues to pose a major clinical challenge. A popular potential alternative solution to autologous bone grafts is a tissue-engineered approach that utilizes the combination of mesenchymal stromal/stem cells (MSCs) with synthetic biomaterial scaffolds. This approach aims to support new bone formation by mimicking many of the biochemical and biophysical cues present within native bone. Regrettably, osteocyte cells, crucial for bone maturation and homeostasis, are rarely produced within MSC-seeded scaffolds, thereby restricting the development of fully mature cortical bone from these synthetic implants. In this work, we have constructed a multimodal scaffold by combining electrospun poly(lactic-co-glycolic acid) (PLGA) fibrous scaffolds with poly(ethylene glycol) (PEG)-based hydrogels that mimic the functional unit of cortical bone, osteon (osteon-mimetic) scaffolds. These scaffolds were decorated with a novel bone morphogenic protein-6 (BMP6) peptide (BMP6p) after our findings revealed that the BMP6p drives higher levels of Smad signaling than the full-length protein counterpart, soluble or when bound to the PEG hydrogel backbone. We show that our osteon-mimetic scaffolds, in presenting concentric layers of BMP6p-PEG hydrogel overlaid on MSC-seeded PLGA nanofibers, promoted the rapid formation of osteocyte-like cells with a phenotypic dendritic morphology, producing early osteocyte markers, including E11/gp38 (E11). Maturation of these osteocyte-like cells was further confirmed by the observation of significant dentin matrix protein 1 (DMP1) throughout our bilayered scaffolds after 3 weeks, even when cultured in a medium without dexamethasone (DEX) or any other osteogenic supplements. These results demonstrate that these osteon-mimetic scaffolds, in presenting biochemical and topographical cues reminiscent of the forming osteon, can drive the formation of osteocyte-like cells in vitro from hBMSCs without the need for any osteogenic factor media supplementation.

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.

Highly Abundant Proteins Are Highly Thermostable

Highly abundant proteins tend to evolve slowly (a trend called E-R anticorrelation), and a number of hypotheses have been proposed to explain this phenomenon. The misfolding avoidance hypothesis attributes the E-R anticorrelation to the abundance-dependent toxic effects of protein misfolding. To avoid these toxic effects, protein sequences (particularly those of highly expressed proteins) would be under selection to fold properly. One prediction of the misfolding avoidance hypothesis is that highly abundant proteins should exhibit high thermostability (i.e., a highly negative free energy of folding, ΔG). Thus far, only a handful of analyses have tested for a relationship between protein abundance and thermostability, producing contradictory results. These analyses have been limited by 1) the scarcity of ΔG data, 2) the fact that these data have been obtained by different laboratories and under different experimental conditions, 3) the problems associated with using proteins' melting energy (Tm) as a proxy for ΔG, and 4) the difficulty of controlling for potentially confounding variables. Here, we use computational methods to compare the free energy of folding of pairs of human-mouse orthologous proteins with different expression levels. Even though the effect size is limited, the most highly expressed ortholog is often the one with a more negative ΔG of folding, indicating that highly expressed proteins are often more thermostable.

Size-Dependent Interplay of Volume Exclusion Versus Soft Interactions: Cytochrome c in Macromolecular Crowded Environment

Even though there are a great number of possible conformational states, how a protein generated as a linear unfolded polypeptide efficiently folds into its physiologically active form remained a fascinating and unanswered enigma inside crowded conditions of cells. In this study, various spectroscopic techniques have been exploited to know and understand the effect and mechanism of action of two different sizes of polyethylene glycols, or PEGs (molecular mass ∼10 and ∼20 kilo Daltons, kDa), on cytochrome c (cyt c). The outcomes showed that small size of the PEG leads to perturbation of the protein structure, and conversely, large size of the PEG has stabilizing effect on cyt c. Moreover, binding measurements showed that small size of PEG interacts strongly via soft interactions compared to the larger size of PEG, the latter being governed more by excluded volume effect or preferential exclusion from the protein. Overall, this finding suggests that conformations of protein may be influenced in cellular crowded conditions via interactions which depend upon the size of molecule in the environment. This study proposes that both volume exclusion and soft (chemical) interactions governs the protein’s conformation and functional activities. The cellular environment’s internal architecture as evident from crowder size and shape in this study has a significant role.

Insights into Fluctuations of Structure of Proteins: Significance of Intermediary States in Regulating Biological Functions

Proteins are indispensable to cellular communication and metabolism. The structure on which cells and tissues are developed is deciphered from proteins. To perform functions, proteins fold into a three-dimensional structural design, which is specific and fundamentally determined by their characteristic sequence of amino acids. Few of them have structural versatility, allowing them to adapt their shape to the task at hand. The intermediate states appear momentarily, while protein folds from denatured (D) ⇔ native (N), which plays significant roles in cellular functions. Prolific effort needs to be taken in characterizing these intermediate species if detected during the folding process. Protein folds into its native structure through definite pathways, which involve a limited number of transitory intermediates. Intermediates may be essential in protein folding pathways and assembly in some cases, as well as misfolding and aggregation folding pathways. These intermediate states help to understand the machinery of proper folding in proteins. In this review article, we highlight the various intermediate states observed and characterized so far under in vitro conditions. Moreover, the role and significance of intermediates in regulating the biological function of cells are discussed clearly.

LISTERIN E3 Ubiquitin Ligase and Ribosome-Associated Quality Control (RQC) Mechanism

According to cellular demands, ribosomes synthesize and maintain the desired pool of proteins inside the cell. However, sometimes due to defects in ribosomal machinery and faulty mRNAs, these nascent polypeptides are constantly under threat to become non-functional. In such conditions, cells acquire the help of ribosome-associated quality control mechanisms (RQC) to eliminate such aberrant nascent proteins. The primary regulator of RQC is RING domain containing LISTERIN E3 ubiquitin ligase, which is associated with ribosomes and alleviates non-stop proteins-associated stress in cells. Mouse RING finger protein E3 ubiquitin ligase LISTERIN is crucial for embryonic development, and a loss in its function causes neurodegeneration. LISTERIN is overexpressed in the mouse brain and spinal cord regions, and its perturbed functions generate neurological and motor deficits, but the mechanism of the same is unclear. Overall, LISTERIN is crucial for brain health and brain development. The present article systematically describes the detailed nature, molecular functions, and cellular physiological characterization of LISTERIN E3 ubiquitin ligase. Improve comprehension of LISTERIN's neurological roles may uncover pathways linked with neurodegeneration, which in turn might elucidate a promising novel therapeutic intervention against human neurodegenerative diseases.

Protein Unfolding: Denaturant vs. Force

While protein refolding has been studied for over 50 years since the pioneering work of Christian Anfinsen, there have been a limited number of studies correlating results between chemical, thermal, and mechanical unfolding. The limited knowledge of the relationship between these processes makes it challenging to compare results between studies if different refolding methods were applied. Our current work compares the energetic barriers and folding rates derived from chemical, thermal, and mechanical experiments using an immunoglobulin-like domain from the muscle protein titin as a model system. This domain, I83, has high solubility and low stability relative to other Ig domains in titin, though its stability can be modulated by calcium. Our experiments demonstrated that the free energy of refolding was equivalent with all three techniques, but the refolding rates exhibited differences, with mechanical refolding having slightly faster rates. This suggests that results from equilibrium-based measurements can be compared directly but care should be given comparing refolding kinetics derived from refolding experiments that used different unfolding methods.

Protons Are Fast and Smart; Proteins Are Slow and Dumb: On the Relationship of Electrospray Ionization Charge States and Conformations

We present simple considerations of how differences in time scales of motions of protons, the lightest and fastest chemical moiety, and the much longer time scales associated with the dynamics of proteins, among the heaviest and slowest analytes, may allow many protein conformations from solution to be kinetically trapped during the process of electrospraying protein solutions into the gas phase. In solution, the quantum nature of protons leads them to change locations by tunneling, an instantaneous process; moreover, the Grotthuss mechanism suggests that these small particles can respond nearly instantaneously to the dynamic motions of proteins that occur on much longer time scales. A conformational change is accompanied by favorable or unfavorable variations in the free energy of the system, providing the impetus for solvent ↔ protein proton exchange. Thus, as thermal distributions of protein conformations interconvert, protonation states rapidly respond, as specific acidic and basic sites are exposed or protected. In the vacuum of the mass spectrometer, protons become immobilized in locations that are specific to the protein conformations from which they were incorporated. In this way, conformational states from solution are preserved upon electrospraying them into the gas phase. These ideas are consistent with the exquisite sensitivity of electrospray mass spectra to small changes of the local environment that alter protein structure in solution. We might remember this approximation for the protonation of proteins in solution with the colloquial expression-protons are fast and smart; proteins are slow and dumb.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Mechanistic insights into the urea-induced denaturation of human sphingosine kinase 1

Sphingosine kinase 1 (SphK1) plays a significant role in various cellular processes, including cell proliferation, apoptosis, and angiogenesis. SphK1 is considered as an attractive target for drug development owing to its connection with several diseases, including cancer. In the current work, the urea-induced unfolding of SphK1 was performed at pH 8.0 and 25 °C using CD and fluorescence spectroscopy. SphK1 follows a biphasic unfolding transition (N ⇌ I ⇌ D) with an intermediate (I) state populated around 4.0 M urea concentration. The circular dichroism ([θ]₂₂₂) and fluorescence emission spectra (λ_max) of SphK1 with increasing concentrations of urea were analyzed to calculate Gibbs free energy (ΔG⁰) for both the transitions (N ⇌ I and I ⇌ D). A significant overlap of both the transitions obtained by two spectroscopic properties ([θ]₂₂₂ and λ_max) was observed, indicating that both N ⇌ I and I ⇌ D transition follow two-step equilibrium unfolding pattern. Also, we performed 100 ns molecular dynamics (MD) simulations to get atomistic insights into the structural changes in SphK1 with increasing urea concentrations. Our results showed a consistent pattern of the SphK1 unfolding with increasing urea concentrations. Together, spectroscopic and MD simulation findings provide deep insights into the unfolding mechanism and conformational features of SphK1.

Folding and Unfolding of the Short Light-Triggered β-Hairpin Peptide AzoChignolin Occurs within 100 ns

To map the underlying molecular mechanisms of folding dynamics in proteins, light-operated peptides have emerged as promising tools. In this study, we reveal the complete sequence of light-induced structural changes of AzoChignolin, a short β-hairpin peptide containing an azobenzene photoswitch in its loop region. Light-triggered structural changes were monitored by time-resolved IR spectroscopy. Formation and destruction of the hairpin structure is very fast and occurs within 100 ns for AzoChignolin in methanol. Atomistic molecular dynamics simulations using two explicit solvents, methanol and water, revealed the underlying molecular processes and allowed us to gain further insight into the reaction mechanism. Despite its rapid reaction time, hairpin formation in these solvents is not force-driven by the molecular switch but proceeded via formation of interstrand hydrogen bonds and contacts between aromatic residues. Moreover, the combined experimental and theoretical study demonstrates that the solvent (methanol vs water) does not dictate the velocity of β-hairpin formation in the AzoChignolin peptide comprising only a few hydrophobic residues in the strands.

Experimentally-driven protein structure modeling

Revolutions in natural and exact sciences started at the dawn of last century have led to the explosion of theoretical, experimental, and computational approaches to determine structures of molecules, complexes, as well as their rich conformational dynamics. Since different experimental methods produce information that is attributed to specific time and length scales, corresponding computational methods have to be tailored to these scales and experiments. These methods can be then combined and integrated in scales, hence producing a fuller picture of molecular structure and motion from the "puzzle pieces" offered by various experiments. Here, we describe a number of computational approaches to utilize experimental data to glance into structure of proteins and understand their dynamics. We will also discuss the limitations and the resolution of the constraints-based modeling approaches. SIGNIFICANCE: Experimentally-driven computational structure modeling and determination is a rapidly evolving alternative to traditional approaches for molecular structure determination. These new hybrid experimental-computational approaches are proving to be a powerful microscope to glance into the structural features of intrinsically or partially disordered proteins, dynamics of molecules and complexes. In this review, we describe various approaches in the field of experimentally-driven computational structure modeling.

Differences in stability and calcium sensitivity of the Ig domains in titin's N2A region

Titin is a large filamentous protein that spans half a sarcomere, from Z-disk to M-line. The N2A region within the titin molecule exists between the proximal immunoglobulin (Ig) region and the PEVK region and protein-protein interactions involving this region are required for normal muscle function. The N2A region consists of four Ig domains (I80-I83) with a 105 amino acid linker region between I80 and I81 that has a helical nature. Using chemical stability measurements, we show that predicted differences between the adjacent Ig domains (I81-I83) correlate with experimentally determined differences in chemical stability and refolding kinetics. Our work further shows that I83 has the lowest ΔG_unfolding , which is increased in the presence of calcium (pCa 4.3), indicating that Ca²⁺ plays a role in stabilizing this immunoglobulin domain. The characteristics of N2A's three Ig domains provide insight into the stability of the binding sites for proteins that interact with the N2A region. This work also provides insights into how Ca²⁺ might influence binding events involving N2A.

Cotranslational Folding of Proteins on the Ribosome

Many proteins in the cell fold cotranslationally within the restricted space of the polypeptide exit tunnel or at the surface of the ribosome. A growing body of evidence suggests that the ribosome can alter the folding trajectory in many different ways. In this review, we summarize the recent examples of how translation affects folding of single-domain, multiple-domain and oligomeric proteins. The vectorial nature of translation, the spatial constraints of the exit tunnel, and the electrostatic properties of the ribosome-nascent peptide complex define the onset of early folding events. The ribosome can facilitate protein compaction, induce the formation of intermediates that are not observed in solution, or delay the onset of folding. Examples of single-domain proteins suggest that early compaction events can define the folding pathway for some types of domain structures. Folding of multi-domain proteins proceeds in a domain-wise fashion, with each domain having its role in stabilizing or destabilizing neighboring domains. Finally, the assembly of protein complexes can also begin cotranslationally. In all these cases, the ribosome helps the nascent protein to attain a native fold and avoid the kinetic traps of misfolding.

A comparative study for the intermediate states of myelin oligodendrocyte glycoprotein in the absence and presence of glycan - A computational approach

Myelin Oligodendrocyte glycoprotein (MOG) is found to play an important role in providing structural integrity to myelin sheath at the same time it acts as an auto-antigen which might lead to Multiple Sclerosis (MS). What causes this specific property of being an auto-antigen is still not known. Here we present molecular dynamics simulation studies of unfolding and folding of the protein MOG in both the absence and presence of N-glycan in order to understand the role of glycosylation in the stability and flexibility of the protein. The main results from these studies show that the glycosylation increases the stability of the protein MOG and inhibits the complete unfolding of MOG in the SMD. From the folding studies using TMD, it was observed that the glycan helps the protein to attain the near-native folded conformation. However, it was also observed from the direct TMD studies that the pathway of protein folding was enhanced by the trace-back of intermediate states in the presence of glycan.

Distinct Binding Modes of Vinculin Isoforms Underlie Their Functional Differences

Vinculin and its splice isoform metavinculin play key roles in regulating cellular morphology, motility, and force transduction. Vinculin is distinct from metavinculin in its ability to bundle filamentous actin (F-actin). To elucidate the molecular basis for these differences, we employed computational and experimental approaches. Results from these analyses suggest that the C terminus of both vinculin and metavinculin form stable interactions with the F-actin surface. However, the metavinculin tail (MVt) domain contains a 68 amino acid insert, with helix 1 (H1) sequestered into a globular subdomain, which protrudes from the F-actin surface and prevents actin bundling by sterically occluding actin filaments. Consistent with our model, deletion and selective point mutations within the MVt H1 disrupt this protruding structure, and facilitate actin bundling similar to vinculin tail (Vt) domain.

A Multiscale Model for the Self-Assembly of Coat Proteins in Bacteriophage MS2

The self-assembly of viral capsids is an essential step to the formation of infectious viruses. Elucidating the kinetic mechanisms of how a capsid or virus-like particle assembles could advance our knowledge about the viral lifecycle, as well as the general principles in self-assembly of biomaterials. However, current understanding of capsid assembly remains incomplete for many viruses due to the fact that the transient intermediates along the assembling pathways are experimentally difficult to be detected. In this paper, we constructed a new multiscale computational framework to simulate the self-assembly of virus-like particles. We applied our method to the coat proteins of bacteriophage MS2 as a specific model system. This virus-like particle of bacteriophage MS2 has a unique feature that its 90 sequence-identical dimers can be classified into two structurally various groups: one is the symmetric CC dimer, and the other is the asymmetric AB dimer. The homotypic interactions between AB dimers result in a 5-fold symmetric contact, while the heterotypic interactions between AB and CC dimers result in 6-fold symmetric contact. We found that the assembly can be described as a physical process of phase transition that is regulated by various factors such as concentration and specific stoichiometry between AB and CC dimers. Our simulations also demonstrate that heterotypic and homotypic interfaces play distinctive roles in modulating the assembling kinetics. The interaction between AB and CC dimers is much more dynamic than that between two AB dimers. We therefore suggest that the alternate growth of viral capsid through the heterotypic dimer interactions dominates the assembling pathways. This is, to the best of our knowledge, the first multiscale model to simulate the assembling process of coat proteins in bacteriophage MS2. The generality of this approach opens the door to its further applications in assembly of other viral capsids, virus-like particles, and novel drug delivery systems.

Limits in accuracy and a strategy of RNA structure prediction using experimental information

RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40-60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.

Stabilization of μ-opioid receptor facilitates its cellular translocation and signaling

The G protein-coupled μ-opioid receptor (μ-OR) mediates the majority of analgesia effects for morphine and other pain relievers. Despite extensive studies of its structure and activation mechanisms, the inherently low maturation efficiency of μ-OR represents a major hurdle to understanding its function. Here we computationally designed μ-OR mutants with altered stability to probe the relationship between cell-surface targeting, signal transduction, and agonist efficacy. The stabilizing mutation T315Y enhanced μ-OR trafficking to the plasma membrane and significantly promoted the morphine-mediated inhibition of downstream signaling. In contrast, the destabilizing mutation R165Y led to intracellular retention of μ-OR and reduced the response to morphine stimulation. These findings suggest that μ-OR stability is an important factor in regulating receptor signaling and provide a viable avenue to improve the efficacy of analgesics.

Multilayer nanoscale functionalization to treat disorders and enhance regeneration of bone tissue

The coatings application onto medical devices has experienced a continuous growth in the last few years. Medical device coating market is expected to grow at a CAGR of 5.16% to reach USD 10 million by 2023 due to the increasing geriatric population and the growing demand for continuous innovation. Layer-by-Layer (LbL) assembly represents a versatile method to modify the surface properties, in order to control cell interaction and thus enhance biological functions. Furthermore, LbL is environmentally friendly, able to coat all types of surfaces with the creation of homogenous film and to include and control the release of biomolecules/drugs. This feature review provides a critical overview on recent progresses in functionalizing materials by LbL assembly for bone regeneration and disorder treatment. An overview of emerging and visionary opportunities on LbL technologies and further combination with other existing methods used in biomedical field, is also discussed to evidence the new challenges and potential developments in bone regenerative medicine.

Graphene quantum dots against human IAPP aggregation and toxicity in vivo

The development of biocompatible nanomaterials has become a new frontier in the detection, treatment and prevention of human amyloid diseases. Here we demonstrated the use of graphene quantum dots (GQDs) as a potent inhibitor against the in vivo aggregation and toxicity of human islet amyloid polypeptide (IAPP), a hallmark of type 2 diabetes. GQDs initiated contact with IAPP through electrostatic and hydrophobic interactions as well as hydrogen bonding, which subsequently drove the peptide fibrillization off-pathway to eliminate the toxic intermediates. Such interactions, probed in vitro by a thioflavin T kinetic assay, fluorescence quenching, circular dichroism spectroscopy, a cell viability assay and in silico by discrete molecular dynamics simulations, translated to a significant recovery of embryonic zebrafish from the damage elicited by IAPP in vivo, as indicated by improved hatching as well as alleviated reactive oxygen species production, abnormality and mortality of the organism. This study points to the potential of using zero-dimensional nanomaterials for in vivo mitigation of a range of amyloidosis.

Identification and Characterization of an Inside-Out Folding Intermediate of T4 Phage Sliding Clamp

Protein folding process involves formation of transiently occurring intermediates that are difficult to isolate and characterize. It is both necessary and interesting to characterize the structural conformations adopted by these intermediates, also called molten globules (MG), to understand protein folding. Here, we investigated the equilibrium (un)folding intermediate state of T4 phage gene product 45 (gp45, also known as DNA polymerase processivity factor or sliding clamp) obtained during chemical denaturation. We show that gp45 undergoes substantial conformational rearrangement during unfolding and forms an expanded dry-MG. By monitoring the fluorescence of tryptophans that were strategically introduced at various sites, we demonstrate that the urea-treated molecule has its surface residues flip inside the core, and closely placed residues move farther. We were also able to isolate and purify the MG form of gp45 in native condition (i.e., nondenaturing buffer, at physiological pH and temperature); characteristics of this purified molecule substantially match with urea-treated wild-type gp45. To the best of our knowledge, this is one of the few reports that demonstrate the isolation and purification of a protein folding intermediate in native condition. We believe that our work not only allows us to dissect the process of protein folding, but will also help in the designing of folding inhibitors against sliding clamps to treat a wide variety of diseases from bacterial infection to cancer, due to the vast presence of clamps in all the domains of life.

Multilayer Nanoscale Encapsulation of Biofunctional Peptides to Enhance Bone Tissue Regeneration In Vivo

Bone tissue healing is a dynamic process that is initiated by the recruitment of osteoprogenitor cells followed by their migration, proliferation, differentiation, and development of a mineralizing extracellular matrix. The work aims to manufacture a functionalized porous membrane that stimulates early events in bone healing for initiating a regenerative cascade. Layer-by-layer (LbL) assembly is proposed to modify the surface of osteoconductive electrospun meshes, based on poly(lactic-co-glycolic acid) and nanohydroxyapatite, by using poly(allylamine hydrochloride) and poly(sodium 4-styrenesulfonate) as polyelectrolytes. Molecular cues are incorporated by grafting peptide fragments into the discrete nanolayers. KRSR (lysine-arginine-serine-arginine) sequence is grafted to enhance cell adhesion and proliferation, NSPVNSKIPKACCVPTELSAI to guide bone marrow mesenchymal stem cells differentiation in osteoblasts, and FHRRIKA (phenylalanine-histidine-arginine-arginine-isoleucine-lysine-alanine) to improve mineralization matrix formation. Scanning electron microscopy, infrared spectroscopy, and X-ray photoelectron spectroscopy demonstrate the successful surface functionalization. Furthermore, the peptide incorporation enhances cellular processes, with good viability and significant increase of alkaline phosphatase activity, osteopontin, and osteocalcin. The functionalized membrane induces a favorable in vivo response after implantation for four weeks in nonhealing rat calvarial defect model. It is concluded that the multilayer nanoencapsulation of biofunctional peptides using LbL approach has significant potential as innovative manufacturing technique to improve bone regeneration in orthopedic and craniofacial medical devices.

On the polymer physics origins of protein folding thermodynamics

A remarkable feature of the spontaneous folding of many small proteins is the striking similarity in the thermodynamics of the folding process. This process is characterized by simple two-state thermodynamics with large and compensating changes in entropy and enthalpy and a funnel-like free energy landscape with a free-energy barrier that varies linearly with temperature. One might attribute the commonality of this two-state folding behavior to features particular to these proteins (e.g., chain length, hydrophobic/hydrophilic balance, attributes of the native state) or one might suspect that this similarity in behavior has a more general polymer-physics origin. Here we show that this behavior is also typical for flexible homopolymer chains with sufficiently short range interactions. Two-state behavior arises from the presence of a low entropy ground (folded) state separated from a set of high entropy disordered (unfolded) states by a free energy barrier. This homopolymer model exhibits a funneled free energy landscape that reveals a complex underlying dynamics involving competition between folding and non-folding pathways. Despite the presence of multiple pathways, this simple physics model gives the robust result of two-state thermodynamics for both the cases of folding from a basin of expanded coil states and from a basin of compact globule states.

Nanoscale Structure and Spectroscopic Probing of Aβ1-40 Fibril Bundle Formation

Amyloid plaques composed of fibrillar Amyloid-β (Aβ) are hallmarks of Alzheimer's disease. However, Aβ fibrils are morphologically heterogeneous. Conformation sensitive luminescent conjugated oligothiophenes (LCOs) are versatile tools for monitoring such fibril polymorphism in vivo and in vitro. Biophysical methods applied on in vitro generated Aβ fibrils, stained with LCOs with different binding and fluorescence properties, can be used to characterize the Aβ fibrillation in depth, far beyond that possible for in vivo generated amyloid plaques. In this study, in vitro fibrillation of the Aβ1-40 peptide was monitored by time-lapse transmission electron microscopy, LCO fluorescence, and atomic force microscopy. Differences in the LCO binding in combination with nanoscale imaging revealed that spectral variation correlated with fibrils transforming from solitary filaments (Ø~2.5 nm) into higher order bundled structures (Ø~5 nm). These detailed in vitro experiments can be used to derive data that reflects the heterogeneity of in vivo generated Aβ plaques observed by LCO fluorescence. Our work provides new structural basis for targeted drug design and molecular probe development for amyloid imaging.

Controlling Allosteric Networks in Proteins

Allosteric transition, defined as conformational changes induced by ligand binding, is one of the fundamental properties of proteins. Allostery has been observed and characterized in many proteins, and has been recently utilized to control protein function via regulation of protein activity. Here, we review the physical and evolutionary origin of protein allostery, as well as its importance to protein regulation, drug discovery, and biological processes in living systems. We describe recently developed approaches to identify allosteric pathways, connected sets of pairwise interactions that are responsible for propagation of conformational change from the ligand-binding site to a distal functional site. We then present experimental and computational protein engineering approaches for control of protein function by modulation of allosteric sites. As an example of application of these approaches, we describe a synergistic computational and experimental approach to rescue the cystic-fibrosis-associated protein cystic fibrosis transmembrane conductance regulator, which upon deletion of a single residue misfolds and causes disease. This example demonstrates the power of allosteric manipulation in proteins to both elucidate mechanisms of molecular function and to develop therapeutic strategies that rescue those functions. Allosteric control of proteins provides a tool to shine a light on the complex cascades of cellular processes and facilitate unprecedented interrogation of biological systems.

Isolated neutral peptides

This chapter examines the structural characterisation of isolated neutral amino-acids and peptides. After a presentation of the experimental and theoretical state-of-the-art in the field, a review of the major structures and shaping interactions is presented. Special focus is made on conformationally-resolved studies which enable one to go beyond simple structural characterisation; probing flexibility and excited-state photophysics are given as examples of promising future directions.

Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools

Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find “hot spots” in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html) is a public database that consists of thousands of protein mutants’ experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG) and melting temperature change (dTm) were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor) and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models.

Improvement of the treatment of loop structures in the UNRES force field by inclusion of coupling between backbone- and side-chain-local conformational states

The UNited RESidue (UNRES) coarse-grained model of polypeptide chains, developed in our laboratory, enables us to carry out millisecond-scale molecular-dynamics simulations of large proteins effectively. It performs well in ab initio predictions of protein structure, as demonstrated in the last Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP10). However, the resolution of the simulated structure is too coarse, especially in loop regions, which results from insufficient specificity of the model of local interactions. To improve the representation of local interactions, in this work we introduced new side-chain-backbone correlation potentials, derived from a statistical analysis of loop regions of 4585 proteins. To obtain sufficient statistics, we reduced the set of amino-acid-residue types to five groups, derived in our earlier work on structurally optimized reduced alphabets, based on a statistical analysis of the properties of amino-acid structures. The new correlation potentials are expressed as one-dimensional Fourier series in the virtual-bond-dihedral angles involving side-chain centroids. The weight of these new terms was determined by a trial-and-error method, in which Multiplexed Replica Exchange Molecular Dynamics (MREMD) simulations were run on selected test proteins. The best average root-mean-square deviations (RMSDs) of the calculated structures from the experimental structures below the folding-transition temperatures were obtained with the weight of the new side-chain-backbone correlation potentials equal to 0.57. The resulting conformational ensembles were analyzed in detail by using the Weighted Histogram Analysis Method (WHAM) and Ward's minimum-variance clustering. This analysis showed that the RMSDs from the experimental structures dropped by 0.5 Å on average, compared to simulations without the new terms, and the deviation of individual residues in the loop region of the computed structures from their counterparts in the experimental structures (after optimum superposition of the calculated and experimental structure) decreased by up to 8 Å. Consequently, the new terms improve the representation of local structure.

Molecular structure of RADA16-I designer self-assembling peptide nanofibers

The designer self-assembling peptide RADA16-I forms nanofiber matrices which have shown great promise for regenerative medicine and three-dimensional cell culture. The RADA16-I amino acid sequence has a β-strand-promoting alternating hydrophobic/charged motif, but arrangement of β-strands into the nanofiber structure has not been previously determined. Here we present a structural model of RADA16-I nanofibers, based on solid-state NMR measurements on samples with different schemes for (13)C isotopic labeling. NMR peak positions and line widths indicate an ordered structure composed of β-strands. The NMR data show that the nanofibers are composed of two stacked β-sheets stabilized by a hydrophobic core formed by alanine side chains, consistent with previous proposals. However, the previously proposed antiparallel β-sheet structure is ruled out by measured (13)C-(13)C dipolar couplings. Instead, neighboring β-strands within β-sheets are parallel, with a registry shift that allows cross-strand staggering of oppositely charged arginine and aspartate side chains. The resulting structural model is compared to nanofiber dimensions observed via images taken by transmission electron microscopy and atomic force microscopy. Multiple NMR peaks for each alanine side chain were observed and could be attributed to multiple configurations of side chain packing within a single scheme for intermolecular packing.

Changes of protein stiffness during folding detect protein folding intermediates

Single-molecule force-quench atomic force microscopy (FQ-AFM) is used to detect folding intermediates of a simple protein by detecting changes of molecular stiffness of the protein during its folding process. Those stiffness changes are obtained from shape and peaks of an autocorrelation of fluctuations in end-to-end length of the folding molecule. The results are supported by predictions of the equipartition theorem and agree with existing Langevin dynamics simulations of a simplified model of a protein folding. In the light of the Langevin simulations the experimental data probe an ensemble of random-coiled collapsed states of the protein, which are present both in the force-quench and thermal-quench folding pathways.

Can the energy gap in the protein-ligand binding energy landscape be used as a descriptor in virtual ligand screening?

The ranking of scores of individual chemicals within a large screening library is a crucial step in virtual screening (VS) for drug discovery. Previous studies showed that the quality of protein-ligand recognition can be improved using spectrum properties and the shape of the binding energy landscape. Here, we investigate whether the energy gap, defined as the difference between the lowest energy pose generated by a docking experiment and the average energy of all other generated poses and inferred to be a measure of the binding energy landscape sharpness, can improve the separation power between true binders and decoys with respect to the use of the best docking score. We performed retrospective single- and multiple-receptor conformation VS experiments in a diverse benchmark of 40 domains from 38 therapeutically relevant protein targets. Also, we tested the performance of the energy gap on 36 protein targets from the Directory of Useful Decoys (DUD). The results indicate that the energy gap outperforms the best docking score in its ability to discriminate between true binders and decoys, and true binders tend to have larger energy gaps than decoys. Furthermore, we used the energy gap as a descriptor to measure the height of the native binding phase and obtained a significant increase in the success rate of near native binding pose identification when the ligand binding conformations within the boundaries of the native binding phase were considered. The performance of the energy gap was also evaluated on an independent test case of VS-identified PKR-like ER-localized eIF2α kinase (PERK) inhibitors. We found that the energy gap was superior to the best docking score in its ability to more highly rank active compounds from inactive ones. These results suggest that the energy gap of the protein-ligand binding energy landscape is a valuable descriptor for use in VS.

Solvent effect on the folding dynamics and structure of E6-associated protein characterized from ab initio protein folding simulations

Solvent effect on protein conformation and folding mechanism of E6-associated protein (E6ap) peptide are investigated using a recently developed charge update scheme termed as adaptive hydrogen bond-specific charge (AHBC). On the basis of the close agreement between the calculated helix contents from AHBC simulations and experimental results, we observed based on the presented simulations that the two ends of the peptide may simultaneously take part in the formation of the helical structure at the early stage of folding and finally merge to form a helix with lowest backbone RMSD of about 0.9 Å in 40% 2,2,2-trifluoroethanol solution. However, in pure water, the folding may start at the center of the peptide sequence instead of at the two opposite ends. The analysis of the free energy landscape indicates that the solvent may determine the folding clusters of E6ap, which subsequently leads to the different final folded structure. The current study demonstrates new insight to the role of solvent in the determination of protein structure and folding dynamics.

Physical microscopic model of proteins under force

Nature has evolved proteins to counteract forces applied on living cells, and has designed proteins that can sense forces. One can appreciate Nature's ingenuity in evolving these proteins to be highly sensitive to force and to have a high dynamic force range at which they operate. To achieve this level of sensitivity, many of these proteins are composed of multiple domains and linking peptides connecting these domains, each of them having their own force response regimes. Here, using a simple model of a protein, we address the question of how each individual domain responds to force. We also ask how multidomain proteins respond to forces. We find that the end-to-end distance of individual domains under force scales linearly with force. In multidomain proteins, we find that the force response has a rich range: at low force, extension is predominantly governed by "weaker" linking peptides or domain intermediates, while at higher force, the extension is governed by unfolding of individual domains. Overall, the force extension curve comprises multiple sigmoidal transitions governed by unfolding of linking peptides and domains. Our study provides a basic framework for the understanding of protein response to force, and allows for interpretation experiments in which force is used to study the mechanical properties of multidomain proteins.

Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction

Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.

Computational model for protein unfolding simulation

The protein folding problem is one of the fundamental and important questions in molecular biology. However, the all-atom molecular dynamics studies of protein folding and unfolding are still computationally expensive and severely limited by the time scale of simulation. In this paper, a simple and fast protein unfolding method is proposed based on the conformational stability analyses and structure modeling. In this method, two structure-based conditions are considered to identify the unstable regions of proteins during the unfolding processes. The protein unfolding trajectories are mimicked through iterative structure modeling according to conformational stability analyses. Two proteins, chymotrypsin inhibitor 2 (CI2) and α -spectrin SH3 domain (SH3) were simulated by this method. Their unfolding pathways are consistent with the previous molecular dynamics simulations. Furthermore, the transition states of the two proteins were identified in unfolding processes and the theoretical Φ values of these transition states showed significant correlations with the experimental data (the correlation coefficients are >0.8). The results indicate that this method is effective in studying protein unfolding. Moreover, we analyzed and discussed the influence of parameters on the unfolding simulation. This simple coarse-grained model may provide a general and fast approach for the mechanism studies of protein folding.

Molecular modeling tools and approaches for CFTR and cystic fibrosis

Cystic fibrosis is a multi-faceted disease resulting from the dysfunction of the CFTR channel. Understanding the structural basis of channel function and the structural origin of the defect is imperative in the development of therapeutic strategies. Here, we describe molecular modeling tools that, in conjunction with complementary experimental tools, lead to significant findings on CFTR channel function and on the effect of the pathogenic mutant F508del.

Stability and folding behavior analysis of zinc-finger using simple models

Zinc-fingers play crucial roles in regulating gene expression and mediating protein-protein interactions. In this article, two different proteins (Sp1f2 and FSD-1) are investigated using the Gaussian network model and anisotropy elastic network model. By using these simple coarse-grained methods, we analyze the structural stabilization and establish the unfolding pathway of the two different proteins, in good agreement with related experimental and molecular dynamics simulation data. From the analysis, it is also found that the folding process of the zinc-finger motif is predominated by several factors. Both the zinc ion and C-terminal loop affect the folding pathway of the zinc-finger motif. Knowledge about the stability and folding behavior of zinc-fingers may help in understanding the folding mechanisms of the zinc-finger motif and in designing new zinc-fingers. Meanwhile, these simple coarse-grained analyses can be used as a general and quick method for mechanistic studies of metalloproteins.

Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework

Energy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode analysis can provide such variables in cases where functional motions are dictated by the molecule's architecture. Principal component analysis is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long molecular dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, ‘preexisting equilibrium’ and ‘induced fit’, are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some experimental evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.