Proteins-Structure Function and Bioinformatics最新文献

Human islet amyloid polypeptide (amylin or hIAPP) is a 37 residue hormone co-secreted with insulin from β cells of the pancreas. In patients suffering from type-2 diabetes, amylin self-assembles into amyloid fibrils, ultimately leading to the death of the pancreatic cells. However, a research gap exists in preventing and treating such amyloidosis. Plumbagin, a natural compound, has previously been demonstrated to have inhibitory potential against insulin amyloidosis. Our investigation unveils collapsible regions within hIAPP that, upon collapse, facilitates hydrophobic and pi-pi interactions, ultimately leading to aggregation. Intriguingly plumbagin exhibits the ability to bind these specific collapsible regions, thereby impeding the aforementioned interactions that would otherwise drive hIAPP aggregation. We have used atomistic molecular dynamics approach to determine secondary structural changes. MSM shows metastable states forming native like hIAPP structure in presence of PGN. Our in silico results concur with in vitro results. The ThT assay revealed a striking 50% decrease in fluorescence intensity at a 1:1 ratio of hIAPP to Plumbagin. This finding suggests a significant inhibition of amyloid fibril formation by plumbagin, as ThT fluorescence directly correlates with the presence of these fibrils. Further TEM images revealed disappearance of hIAPP fibrils in plumbagin pre-treated hIAPP samples. Also, we have shown that plumbagin disrupts the intermolecular hydrogen bonding in hIAPP fibrils leading to an increase in the average beta strand spacing, thereby causing disaggregation of pre-formed fibrils demonstrating overall disruption of the aggregation machinery of hIAPP. Our work is the first to report a detailed atomistic simulation of 22 μs for hIAPP. Overall, our studies put plumbagin as a potential candidate for both preventive and therapeutic candidate for hIAPP amyloidosis.

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.

Valacyclovir, enzymatically hydrolyzed in the body to acyclovir, is a guanine-based nucleoside analog commonly prescribed as an antiviral therapy. Previous reports suggest that guanosine analogs bind to guanine deaminase; however, it is unclear whether they act as inhibitors or substrates. Data from our laboratory suggest that inhibition of guanine deaminase by small molecules attenuates spinal cord injury-induced neuropathic pain. Here, we examine whether the guanosine analogs valacyclovir and acyclovir are deaminated by cypin (cytosolic PSD-95 interactor), the major guanine deaminase in the body, or if they act as cypin inhibitors. Using purified Rattus norvegicus cypin, we use NADH-coupled assay to confirm deamination of valacyclovir and determined Michaelis-Menten constants. Subsequently, we use tryptophan fluorescence quenching assay to calculate dissociation constants for valacyclovir and acyclovir and find that inclusion of the valine motif in valacyclovir increases affinity for cypin compared to acyclovir. To our knowledge, neither K_m nor K_D values for cypin has been previously reported for either compound. We use Amplex Red assay and demonstrate that both valacyclovir and acyclovir are cypin substrates and that their metabolites are further processed by xanthine oxidase and uricase. Using molecular dynamics simulations, we demonstrate that an alpha helix near the active site is displaced when valacyclovir binds to cypin. Furthermore, we used LC-MS-based assay to directly confirm deamination of valacyclovir by cypin. Taken together, our results demonstrate a novel role for cypin in deamination of valacyclovir and acyclovir and suggest that therapeutics based on purine structures may be inactivated by cypin, decreasing inhibitory efficacy.

While many computational methods accurately predict destabilizing mutations, identifying stabilizing mutations has remained a challenge, because of their relative rarity. We tested ΔΔG⁰ predictions from computational predictors such as Rosetta, ThermoMPNN, RaSP, and DeepDDG, using 82 mutants of the bacterial toxin CcdB as a test case. On this dataset, the best computational predictor is ThermoMPNN, which identifies stabilizing mutations with a precision of 68%. However, the average increase in T_m for these predicted mutations was only 1°C for CcdB, and predictions were poorer for a more challenging target, influenza neuraminidase. Using data from multiple previously described yeast surface display libraries and in vitro thermal stability measurements, we trained logistic regression models to identify stabilizing mutations with a precision of 90% and an average increase in T_m of 3°C for CcdB. When such libraries contain a population of mutants with significantly enhanced binding relative to the corresponding wild type, there is no benefit in using computational predictors. It is then possible to predict stabilizing mutations without any training, simply by examining the distribution of mutational binding scores. This avoids laborious steps of in vitro expression, purification, and stability characterization. When this is not the case, combining data from computational predictors with high-throughput experimental binding data enhances the prediction of stabilizing mutations. However, this requires training on stability data measured in vitro with known stabilized mutants. It is thus feasible to predict stabilizing mutations rapidly and accurately for any system of interest that can be subjected to a binding selection or screen.

Understanding kinase-inhibitor selectivity continues to be a major objective in kinase drug discovery. We probe the molecular basis of selectivity of an allosteric inhibitor (MSC1609119A-1) of the insulin-like growth factor-I receptor kinase (IGF1RK), which has been shown to be ineffective for the homologous insulin receptor kinase (IRK). Specifically, we investigated the structural and energetic basis of the allosteric binding of this inhibitor to each kinase by combining molecular modeling, molecular dynamics (MD) simulations, and thermodynamic calculations. We predict the inhibitor conformation in the binding pocket of IRK and highlight that the charged residues in the histidine-arginine-aspartic acid (HRD) and aspartic acid-phenylalanine-glycine (DFG) motifs and the nonpolar residues in the binding pocket govern inhibitor interactions in the allosteric pocket of each kinase. We suggest that the conformational changes in the IGF1RK residues M1054 and M1079, movement of the ⍺C-helix, and the conformational stabilization of the DFG motif favor the selectivity of the inhibitor toward IGF1RK. Our thermodynamic calculations reveal that the observed selectivity can be rationalized through differences observed in the electrostatic interaction energy of the inhibitor in each inhibitor/kinase complex and the hydrogen bonding interactions of the inhibitor with the residue V1063 in IGF1RK that are not attained with the corresponding residue V1060 in IRK. Overall, our study provides a rationale for the molecular basis of recognition of this allosteric inhibitor by IGF1RK and IRK, which is potentially useful in developing novel inhibitors with improved affinity and selectivity.

Aquaporin (AQP) is a water channel protein from the family of transmembrane proteins which facilitates the movement of water across the cell membrane. It is ubiquitous in nature, however the understanding of the water transport mechanism, especially for AQPs in microbes adapted to low temperatures, remains limited. AQP also has been recognized for its ability to be used for water filtration, but knowledge of the biochemical features necessary for its potential applications in industrial processes has been lacking. Therefore, this research was conducted to express, extract, solubilize, purify, and study the functional adaptations of the aquaporin Z family from Pseudomonas sp. AMS3 via molecular approaches. In this study, AqpZ1 AMS3 was successfully subcloned and expressed in E. coli BL21 (DE3) as a recombinant protein. The AqpZ1 AMS3 gene was expressed under optimized conditions and the best optimized condition for the AQP was in 0.5 mM IPTG incubated at 25°C for 20 h induction time. A zwitterionic mild detergent [(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate was the suitable surfactant for the protein solubilization. The protein was then purified via affinity chromatography. Liposome and proteoliposome was reconstituted to determine the particle size using dynamic light scattering. This information obtained from this psychrophilic AQP identified provides new insights into the structural adaptation of this protein at low temperatures and could be useful for low temperature application and molecular engineering purposes in the future.

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by the formation of extracellular amyloid-β (Aβ) plaques. The underlying cause of AD is unknown, however, post-translational modifications (PTMs) of Aβ have been found in AD patients and are thought to play a role in protein aggregation. One such PTM is pyroglutamylation, which can occur at two sites in Aβ, Glu3 and Glu11. This modification of Aβ involves the truncation and charge-neutralization of N-terminal glutamate, causing Aβ to become more hydrophobic and prone to aggregation. The molecular mechanism by which the introduction of pyroglutamate (pE) promotes aggregation has not been determined. To gain a greater understanding of the role that charge neutralization and truncation of the N-terminus plays on Aβ conformational sampling, we used the Drude polarizable force field (FF) to perform molecular dynamics simulations on Aβ_pE3-42 and Aβ_pE11-42 and comparing their properties to previous simulations of Aβ_1-42. The Drude polarizable FF allows for a more accurate representation of electrostatic interactions, therefore providing novel insights into the role that charge plays in protein dynamics. Here, we report the parametrization of pE in the Drude polarizable FF and the effect of pyroglutamylation on Aβ. We found that Aβ_pE3-42 and Aβ_pE11-42 alter the permanent and induced dipoles of the peptide. Specifically, we found that Aβ_pE3-42 and Aβ_pE11-42 have modification-specific backbone and sidechain polarization response and perturbed solvation properties that shift the Aβ conformational ensemble.