Folding Rate Research Articles

A Novel 6,8,9-Trisubstituted Purine Analogue Drives Breast Cancer Luminal A Subtype MCF-7 to Apoptosis and Senescence through Hsp70 Inhibition.

Cancer cells restrain apoptotic and senescence pathways through intracellular heat shock protein 70 (Hsp 70). These cells aid stimulus-independent growth, and their higher metabolism rate requires Hsps. Hsps compensate abnormally increased substrate protein folding rate of cancer cells. Misfolding of substrate proteins especially signaling substrate proteins, may not function properly. Therefore, Hsp70 folds these substrate proteins into their native-fully functional states, and this mode of action helps cancer cell survival. Targeting Hsps is promising cancer therapy, and in this study, 6,8,9-trisubstituted purine derivatives were designed and synthesized to inhibit Hsp70 and drive cancer cells to apoptosis. Further, oncogenic stimuli through inhibitors can induce an irreversible senescent state and senescence is a barrier to transformation. Hsp70 helps cancer cells to bypass the cellular senescence program, however, binding of N6-(4- isopropylaniline) analogue (7) depletes Hsp70 function as evidenced by aggregation assay and Hsp70 depletion induces senescence pathway. The purine-based inhibitor-compound 7 effectively inhibits MCF-7 cell line. Moreover, the therapeutic potential with regard to the senescence-associated secretory phenotype has complementary action. Dual action of the inhibitor not only drives the cells to apoptosis but also force the cells to be in the senescence state and provides promising results specially for luminal A type breast cancer therapy.

Computational prediction of protein folding rate using structural parameters and network centrality measures

Protein folding is a complex physicochemical process whereby a polymer of amino acids samples numerous conformations in its unfolded state before settling on an essentially unique native three-dimensional (3D) structure. To understand this process, several theoretical studies have used a set of 3D structures, identified different structural parameters, and analyzed their relationships using the natural logarithmic protein folding rate (ln(kf)). Unfortunately, these structural parameters are specific to a small set of proteins that are not capable of accurately predicting ln(kf) for both two-state (TS) and non-two-state (NTS) proteins. To overcome the limitations of the statistical approach, a few machine learning (ML)-based models have been proposed using limited training data. However, none of these methods can explain plausible folding mechanisms. In this study, we evaluated the predictive capabilities of ten different ML algorithms using eight different structural parameters and five different network centrality measures based on newly constructed datasets. In comparison to the other nine regressors, support vector machine was found to be the most appropriate for predicting ln(kf) with mean absolute differences of 1.856, 1.55, and 1.745 for the TS, NTS, and combined datasets, respectively. Furthermore, combining structural parameters and network centrality measures improves the prediction performance compared to individual parameters, indicating that multiple factors are involved in the folding process.

Examining the importance of termini location in successful protein folding with HaloTag circular permutants in vitro, in E. coli, and in mammalian cells.

Though in vitro studies have produced insight into mechanisms underlying protein folding, determining how cellular factors modulate folding remains challenging. Proteins have the opportunity to fold when synthesized from N- to C-terminus by the ribosome at a rate of around five to ten residues per second in cells. This is much slower than the average folding rate of most small proteins, giving the nascent chain time to sample many conformations during translation. When refolding in vitro, HaloTag populates an aggregation-prone intermediate that contains structure from both the N- and C-termini, but this intermediate does not form co-translationally. Could altering the chain connectivity of HaloTag decrease its folding efficiency? We have shown how changing the vectorial synthesis of HaloTag through circular permutation affects folding efficiency in vitro and in vivo. We assessed all 298 circular permutants of HaloTag in E. coli cell lysate and showed that circular permutation positions differentially affect protein solubility and function. FACS experiments in live mammalian HEK293T cells demonstrate that many circular permutants retain function in an alternative cellular environment. Combining these data with both standard in vitro protein folding assays and hydrogen-exchange MS studies to map the folding process yields insight into how circular permutation alters the folding landscape of HaloTag and the role of co-translational folding in this process.

What does alphafold know about protein folding and peptide binding.

Machine learning approaches have recently revolutionized structural biology by predicting 3D structures of proteins at atomistic level from their sequence with high confidence and accuracy. The community is pushing the limits of interpretability and application of these algorithms beyond their original objective. Here, we present our studies on what deep neural network models for protein structure prediction such as AlphaFold can shed light on two fundamental biological processes: (1) protein folding and (2) protein-peptide binding. We first demonstrate our studies on deciphering the folding mechanism of protein G, L and their mutants, two pair of proteins with low sequence similarity that fold into the same topology. By using large-scale molecular dynamics simulations with advanced enhanced sampling methods and Markov state modeling analysis, we observed how the sequence difference impacts folding pathways and rate in unprecedented detail. Then, we show a novel fragment decomposition approach using AlphaFold to identify preferences for secondary structure element combinations that follow the order of events observed in the folding pathways. Standing upon the highly sensitive mapping of AlphaFold between the sequence and structure, we discovered how to directly use the model to identify high affinity peptide binders using a competitive binding assay. For systems in which the individual structures of the peptides are well predicted, the assay captures the higher affinity binder in the bound state, and the other peptide in the unbound form. The speed and robustness of the method will make it readily applicable in screening libraries of peptide sequences to prioritize for detailed experimental characterization.

FkpA enhances outer membrane protein folding using an extensive interaction surface.

Outer membrane protein (OMP) biogenesis in gram-negative bacteria is managed by a network of periplasmic chaperones that includes SurA, Skp, and FkpA. These chaperones bind unfolded OMPs (uOMPs) in dynamic conformational ensembles to suppress uOMP aggregation, facilitate diffusion across the periplasm, and enhance OMP folding. FkpA primarily responds to heat-shock stress, but its mechanism is comparatively understudied. To determine FkpA chaperone function, we monitored the folding of a cognate client uOmpA171 and found that FkpA increases the folded uOmpA171population but also slows the folding rate, dual functions distinct from the other periplasmic chaperones. The results indicate that FkpA behaves as a chaperone and not as a folding catalyst to directly influence the uOmpA171 folding trajectory. We determine the binding affinity between FkpA and uOmpA171 by globally fitting sedimentation velocity titrations and found it to be intermediate between the known affinities of Skp and SurA for uOMP clients. Notably, complex formation steeply depends on the urea concentration, suggestive of an extensive binding interface. Initial characterizations of the complex using photo-crosslinking indicates that the binding interface spans the inner surfaces of the entire FkpA molecule. In contrast to prior findings, folding and binding experiments performed using subdomain constructs of FkpA demonstrate that the full-length chaperone is required for full activity. Together these results support that FkpA has a distinct and direct effect on uOMP folding and that it achieves this by utilizing an extensive chaperone-client interface.

Probing the Energy Landscape of Spectrin R15 and R16 and the Effects of Non-native Interactions.

Understanding the details of a protein folding mechanism can be a challenging and complex task. One system with an interesting folding behavior is the α-spectrin domain, where the R15 folds three-orders of magnitude faster than its homologues R16 and R17, despite having similar structures. The molecular origins that explain these folding rate differences remain unclear, but our previous work revealed that a combined effect produced by non-native interactions could be a reasonable cause for these differences. In this study, we explore further the folding process by identifying the molecular paths, metastable states, and the collective motions that lead these unfolded proteins to their native state conformation. Our results uncovered the differences between the folding pathways for the wild-type R15 and R16 and an R16 mutant. The metastable ensembles that speed down the folding were identified using an energy landscape visualization method (ELViM). These ensembles correspond to similar experimentally reported configurations. Our observations indicate that the non-native interactions are also associated with secondary structure misdocking. This computational methodology can be used as a fast, straightforward protocol for shedding light on systems with unclear folding or conformational traps.

Antioxidants affect endoplasmic reticulum stress-related diseases.

The endoplasmic reticulum (ER) is a complex multifunctional organelle that maintains cell homeostasis. Intrinsic and extrinsic factors alter ER functions, including the rate of protein folding that triggers the accumulation of misfolded proteins and alters homeostasis, thus generating stress in the ER, which activates the unfolded protein response (UPR) pathway to promote cell survival and restore their homeostasis; however, if the damage is not corrected, it could also trigger cell death. In addition, ER stress and oxidative stress are closely related because excessive production of reactive oxygen species (ROS), a well-known inducer of ER stress, promotes the accumulation of misfolded proteins; at the same time, the ER stress enhances ROS production, generating a pathological cycle. Furthermore, it has been described that the dysregulation of the UPR contributes to the progression of various diseases, so the use of compounds capable of regulating ER stress, such as antioxidants, has been used in several experimental models of diseases to alleviate the damage induced by the maladaptive signaling of the UPR, the mechanism of action of antioxidants generally is dose-dependent, and it is specific in each tissue and pathology, could decrease or enhance specific proteins of the UPR to have beneficial or detrimental effects.

Metal-binding and circular permutation-dependent thermodynamic and kinetic stability of azurin.

Native topology is known to determine the folding kinetics and the energy landscape of proteins. Furthermore, the circular permutation (CP) of proteins alters the order of the secondary structure connectivity while retaining the three-dimensional structure, making it an elegant and powerful approach to altering native topology. Previous studies elucidated the influence of CP in proteins with different folds such as Greek key β-barrel, β-sandwich, β-α-β, and all α-Greek key. CP mainly affects the protein stability and unfolding kinetics, while folding kinetics remains mostly unaltered. However, the effect of CP on metalloproteins is yet to be elaborately studied. The active site of metalloproteins poses an additional complexity in studying protein folding. Here, we investigate a CP variant (cpN42) of azurin-in both metal-free and metal-bound (holo) forms. As observed earlier in other proteins, apo-forms of wild-type (WT) and cpN42 fold with similar rates. In contrast, zinc-binding accelerates the folding of WT but decelerates the folding of cpN42. On zinc-binding, the spontaneous folding rate of WT increases by >250 times that of cpN42, which is unprecedented and the highest for any CP to date. On the other hand, zinc-binding reduces the spontaneous unfolding rate of cpN42 by ~100 times, making the WT and CP azurins unfold at similar rates. Our study demonstrates metal binding as a novel way to modulate the unfolding and folding rates of CPs compared to their WT counterparts. We hope our study increases the understanding of the effect of CP on the folding mechanism and energy landscape of metalloproteins.

Structural and functional analysis of actin point mutations leading to nemaline myopathy to elucidate their role in actin function.

In this work, we analyzed 78 mutations in the actin protein that cause the disease nemaline myopathy. We analyzed how these mutations are distributed in important regions of the actin molecule (folding nucleus, core of the filament, amyloidogenic regions, disordered regions, regions involved in interaction with other proteins). It was found that 54 mutations (43 residues) fall into the folding nucleus (Ф ≥ 0.5), 11 mutations (10 residues) into the filament core, 14 mutations into the amyloidogenic regions (11 residues), 14 mutations (9 residues) in the unstructured regions, and 24 mutations (22 residues) in regions involved in interaction with other proteins. It was also found that the occurrence of single mutations G44V, V45F, T68I, P72R, K338Iand S350L leads to the appearance of new amyloidogenic regions that are not present in native actin. The largest number of mutations (54 out of 78) occurs in the folding nucleus; these mutations are important for folding and therefore can affect the protein folding rate. We have shown that almost all of the considered mutations are associated with the structural characteristics of the actin molecule, and some of the residues we have considered have several important characteristics.

Moderate activity of RNA chaperone maximizes the yield of self-spliced pre-RNA in vivo

CYT-19 is a DEAD-box protein whose adenosine-triphosphate (ATP)-dependent helicase activity facilitates the folding of group I introns in precursor RNA (pre-RNA) of Neurospora crassa (N. crassa). In the process, they consume a substantial amount of ATP. While much of the mechanistic insight into CYT-19 activity has been gained through the studies on the folding of Tetrahymena group I intron ribozyme, the more biologically relevant issue, namely the effect of CYT-19 on the self-splicing of pre-RNA, remains largely unexplored. Here, we employ a kinetic network model, based on the generalized iterative annealing mechanism (IAM), to investigate the relation between CYT-19 activity, rate of ribozyme folding, and the kinetics of the self-splicing reaction. The network rate parameters are extracted by analyzing the recent biochemical data for CYT-19-facilitated folding of Tetrahymena ribozyme. We then build extended models to explore the metabolism of pre-RNA. We show that the timescales of chaperone-mediated folding of group I ribozyme and self-splicing reaction compete with each other. As a consequence, in order to maximize the self-splicing yield of group I introns in pre-RNA, the chaperone activity must be sufficiently large to unfold the misfolded structures, but not too large to unfold the native structures prior to the self-splicing event. We discover that despite the promiscuous action on structured RNAs, the helicase activity of CYT-19 on group I ribozyme gives rise to self-splicing yields that are close to the maximum.

Fuzzy spherical truncation-based multi-linear protein descriptors: From their definition to application in structural-related predictions.

This study introduces a set of fuzzy spherically truncated three-dimensional (3D) multi-linear descriptors for proteins. These indices codify geometric structural information from kth spherically truncated spatial-(dis)similarity two-tuple and three-tuple tensors. The coefficients of these truncated tensors are calculated by applying a smoothing value to the 3D structural encoding based on the relationships between two and three amino acids of a protein embedded into a sphere. At considering, the geometrical center of the protein matches with center of the sphere, the distance between each amino acid involved in any specific interaction and the geometrical center of the protein can be computed. Then, the fuzzy membership degree of each amino acid from an spherical region of interest is computed by fuzzy membership functions (FMFs). The truncation value is finally a combination of the membership degrees from interacting amino acids, by applying the arithmetic mean as fusion rule. Several fuzzy membership functions with diverse biases on the calculation of amino acids memberships (e.g., Z-shaped (close to the center), PI-shaped (middle region), and A-Gaussian (far from the center)) were considered as well as traditional truncation functions (e.g., Switching). Such truncation functions were comparatively evaluated by exploring: 1) the frequency of membership degrees, 2) the variability and orthogonality analyses among them based on the Shannon Entropy’s and Principal Component’s methods, respectively, and 3) the prediction performance of alignment-free prediction of protein folding rates and structural classes. These analyses unraveled the singularity of the proposed fuzzy spherically truncated MDs with respect to the classical (non-truncated) ones and respect to the MDs truncated with traditional functions. They also showed an improved prediction power by attaining an external correlation coefficient of 95.82% in the folding rate modelling and an accuracy of 100% in distinguishing structural protein classes. These outcomes are better than the ones attained by existing approaches, justifying the theoretical contribution of this report. Thus, the fuzzy spherically truncated-based protein descriptors from MuLiMs-MCoMPAs (http://tomocomd.com/mulims-mcompas) are promising alignment-free predictors for modeling protein functions and properties.

Threading single proteins through pores to compare their energy landscapes

Translocation of proteins is correlated with structural fluctuations that access conformational states higher in free energy than the folded state. We use electric fields at the solid-state nanopore to control the relative free energy and occupancy of different protein conformational states at the single-molecule level. The change in occupancy of different protein conformations as a function of electric field gives rise to shifts in the measured distributions of ionic current blockades and residence times. We probe the statistics of the ionic current blockades and residence times for three mutants of the [Formula: see text]-repressor family in order to determine the number of accessible conformational states of each mutant and evaluate the ruggedness of their free energy landscapes. Translocation becomes faster at higher electric fields when additional flexible conformations are available for threading through the pore. At the same time, folding rates are not correlated with ease of translocation; a slow-folding mutant with a low-lying intermediate state translocates faster than a faster-folding two-state mutant. Such behavior allows us to distinguish among protein mutants by selecting for the degree of current blockade and residence time at the pore. Based on these findings, we present a simple free energy model that explains the complementary relationship between folding equilibrium constants and translocation rates.

DAU‐Net: An unsupervised 3D brain MRI registration model with dual‐attention mechanism

AbstractMedical image registration plays an important role in clinical treatment. However, the convolution‐based registration frameworks fail to address the local excessive deformation between images. Furthermore, the folding point in the displacement vector field (DVF) reduces the reliability of registration results. In this study, we propose a dual‐attention mechanism‐based U‐shaped registration framework (dubbed DAU‐Net). Firstly, the multi‐scale attention mechanism is introduced to extract the long‐range dependence to deal with the local excessive deformation. Then, the channel attention mechanism is proposed to enhance the information fusion between channels, which not only fuses the features between different layers in the dual‐attention network but also improves the non‐linear mapping ability of the registration network. In the end, the objective function with the folding penalty regularization term is designed to improve the smoothness of the DVF. The model is evaluated on LPBA40 and Mindboggle101 open datasets. The registration accuracy in LPBA40 and Mindboggle101 datasets has been increased by 2.9% and 3.1%, respectively, while the folding rate is reduced by nearly 40 times compared with VoxelMorph. Combined multi‐scale attention mechanism with channel attention mechanism, the registration accuracy of DAU‐Net is improved. By utilizing the folding penalty regularization term, the folding rate is decreased significantly.

FRTpred: A novel approach for accurate prediction of protein folding rate and type

Protein folding rate is an important property that is essential for understanding the protein folding process and is helpful for designing proteins. Predicting such properties from either sequence or structural information is a challenging task in bioinformatics. Although several computational methods have been developed in the past, only one sequence-based method is publicly available that shows limited accuracy when evaluated using a standardized independent dataset. This study proposes a novel approach, called FRTpred, that simultaneously predicts the logarithmic protein folding rate constant, ln(kf), and folding type from the provided sequence. First, 30 baseline models (regression models for ln(kf) and classification models for folding type) were constructed by integrating 10 representative feature extraction methods and three commonly used machine-learning algorithms. Subsequently, the predicted values of the 30 baseline models were combined and inputted into the random forest algorithm to construct the final prediction model. Cross-validation analysis showed that FRTpred achieved mean absolute deviations of 1.491, 2.016, and 1.954 for non-two-state, two-state, and combined models, respectively, when predicting ln(kf). Moreover, FRTpred predicts the folding type with an accuracy of 0.843. Performance comparisons based on independent tests against existing methods showed that FRTpred is more precise for both ln(kf) and folding type prediction. Thus, FRTpred is a powerful tool that may accelerate the characterization of the foldomics protein data and further inspire the development of next-generation predictors. The proposed model is available in the form of a web server that is freely accessible at http://thegleelab.org/FRTpred.

Some useful ideas for multistate protein design: Effect of amino acid substitutions on the multistate proteins stability and the rate of protein structure formation.

The design of new protein variants is usually confined to slightly “fixing” an already existing protein, adapting it to certain conditions or to a new substrate. This is relatively easy to do if the fragment of the protein to be affected, such as the active site of the protein, is known. But what if you need to “fix” the stability of a protein or the rate of its native or intermediate state formation? Having studied a large number of protein mutant forms, we have established the effect of various amino acid substitutions on the energy landscape of the protein. As a result, we have revealed a number of patterns to help researchers identify amino acid residues that determine the folding rate and the stability of globular proteins states and design a mutant form of a protein with desired properties.

Free-energy landscape of two-state protein acylphosphatase with large contact order revealed by force-dependent folding and unfolding dynamics.

Acylphosphatase (AcP) is a small protein with 98 amino acid residues that catalyzes the hydrolysis of carboxyl-phosphate bonds. AcP is a typical two-state protein with slow folding rate due to its relatively large contact order in the native structure. The mechanical properties and unfolding behavior of AcP has been studied by atomic force microscope. Here using stable magnetic tweezers, we measured the force-dependent folding rates within a force range 1-3 pN, and unfolding rates 15-40 pN. The obtained unfolding rates show different force sensitivities at forces below and above ∼27 pN, which determines a free-energy landscape with two energy barriers. Our results indicate that the free-energy landscape of small globule proteins have general Bactrian camel shape, and large contact order of the native state produces a high barrier dominate at low forces.

Effects of Surface Tethering on the Thermodynamics and Kinetics of Frustrated Protein Folding.

The interaction between the protein and surface plays an important role in biology and biotechnology. To understand how surface tethering influences the folding behavior of frustrated proteins, in this work, we systematically study the thermodynamics and folding kinetics of the bacterial immunity protein Im7 and Fyn SH3 domain tethered to a surface using Langevin dynamics simulations. Upon surface tethering, the stabilization often results from the entropic effect, whereas the destabilization is usually caused by either an energetic or entropic effect. For the Fyn SH3 domain with a two-state folding manner, the influence of nonnative interactions on thermodynamic stability is not significant, while nonnative interactions can weaken the effect of surface tethering on the change in the folding rate. By contrast, for the frustrated protein Im7, depending on where the protein is tethered, the surface tethering can promote or suppress misfolding by modulating specific nonnative contacts, thereby altering the folding rate and folding mechanism. Because surface tethering can change the intrachain diffusivity of unfolding, the kinetic stability cannot be well captured by the thermodynamic stability at some tether points. This study should be helpful in general to understand how surface tethering affects the folding energy landscape of frustrated proteins.

Comparative and quantitative morphology of the pig and wild boar cerebellum for identifying possible effects of domestication

The domestic pig (Sus scrofa domesticus) stems from the Eurasian wild boar (Sus scrofa): this offers an appealing window to study microanatomical changes related to the process of domestication, the symbiotic relationship between human and animal. In this light, we quantitatively demonstrated significant microanatomical differences between pig and wild boar cerebella. Calbindin D-28, a calcium binding protein, was employed as immunohistochemical marker of the Purkinje cells. Our results showed that: (i) the foliation index, expressing the rate of cerebellar cortical folding, and the number of granular cells were not significantly different between pigs and wild boars; (ii) area of the granular layer and the molecular layer, and area of white matter were lower in pigs; (iii) the fraction area, grey matter/white matter, was higher in pigs; (iv) the Purkinje cell linear density and their soma area were higher in wild boars. Despite the morphological data alone are not sufficient to draw any final conclusions, our findings on Purkinje cells may represent good indicators of a reduction of the pig cerebellum motor and cognitive functions during the process wild boar-to-pig domestication.

Pyrazolyl‐Benzoxazinone Derivatives as Dual Hsp Inhibitors in Human Breast Cancer

AbstractHeat Shock Proteins (Hsps) play major role on the onset of several cancers. Metabolic rates of cancer cells are higher compared to that of untransformed cells. This accelerated rate force functional substrate proteins to fold faster than normal folding rate. Although, the process leads cell cycle halting and eventually induces apoptosis, Hsps help cell survival and inhibit apoptosis and fold substrate proteins especially signaling proteins. When cancer cells accelerate the metabolism for invasion and metastasis, substrate proteins must fold to their native state rapidly. Since, functional forms of the proteins must be folded properly, cancer cells overexpress Hsps to fold substrate proteins and avoid apoptosis. Hsp90 and Hsp70 play key role in these processes. Inhibition of either Hsp90 or Hsp70 display complementary function. Therefore, dual inhibition of Hsp70 and Hsp90 potentially provides anticancer affect. In silico studies showed that pyrazolyl‐benzoxazine derivatives display binding activity for both Hsps. For this purpose, pyrazole‐3‐carbonyl chloride were converted to pyrazolyl‐benzoxazine derivatives via reactions of anthranilic acids in good yields (68–83 %). The structures of the newly synthesized compounds were elucidated by IR‐NMR spectroscopy, elemental analysis, and single‐crystal X‐ray diffraction. Binding of the compounds inhibit function of Hsps and cause cytotoxic effect over MCF‐7 cells. The compounds display potential anticancer effects.

Circuit topology predicts pathogenicity of missense mutations.

The contact topology of a protein determines important aspects of the folding process. The topological measure of contact order has been shown to be predictive of the rate of folding. Circuit topology is emerging as another fundamental descriptor of biomolecular structure, with predicted effects on the folding rate. We analyze the residue‐based circuit topological environments of 21 K mutations labeled as pathogenic or benign. Multiple statistical lines of reasoning support the conclusion that the number of contacts in two specific circuit topological arrangements, namely inverse parallel and cross relations, with contacts involving the mutated residue have discriminatory value in determining the pathogenicity of human variants. We investigate how results vary with residue type and according to whether the gene is essential. We further explore the relationship to a number of structural features and find that circuit topology provides nonredundant information on protein structures and pathogenicity of mutations. Results may have implications for the polymer physics of protein folding and suggest that “local” topological information, including residue‐based circuit topology and residue contact order, could be useful in improving state‐of‐the‐art machine learning algorithms for pathogenicity prediction.