Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

The association between genetic variations and morphology-based brain networks changes in Alzheimer's disease

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short- and long-range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology-based brain networks, leading to functional deficits; the second hypothesis is that protein-protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative-1 database. A co-sparse reduced-rank regression model was fit to study the relationship between non-synonymous mutations and morphology-based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co-immunoprecipitation (Co-IP) experiments. Eight genes affecting morphology-based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co-IP. Hence, gene variations influence morphology-based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co-IP, suggests that the effect is robust.

Proteomic Characterization of Human Placenta: Insights into Potential Therapeutic Applications for Osteoarthritis

Biologics have become increasingly prominent as therapeutics in recent years due to their innate immune-privileged nature, biocompatibility, and high levels of protein biofactors. The aim of the study is to characterise the biologic, lyophilized human placenta (LHP) and explore its therapeutic potential for osteoarthritis (OA). The presence of six bioactive constituents that regulate cell-extracellular matrix interaction was identified by liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF/MS). Metalloproteinase inhibitor 3 (TIMP3), alpha-1 anti-trypsin (a1AT), basic fibroblast growth factor (bFGF), and transforming growth factor β1 (TGFβ1) were detected and quantified using ELISA. The total protein content present in LHP by Bradford assay was found to be 409.35 ± 0.005 μg/ml. The analytical techniques such as Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR), solid state carbon-13 Nuclear Magnetic Resonance (ssC13 NMR) spectroscopy, and Differential Scanning Calorimetry (DSC) revealed the secondary structure and conformational stability of LHP. X-Ray diffraction (XRD) studies showed its amorphous nature. Bioactivity assessment of LHP was performed in human keratinocytes (HaCaT) and human dermal fibroblasts (HDF) by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The LHP was highly proliferative against skin cells and non-toxic, based on the findings of the bioactivity assay. LHP has the potential to be used as a therapeutic agent for OA, as its characterisation unveiled its physical stability, significant concentration of bioactive components that are pertinent to cartilage repair and its conformational stability.

In-silico screening and identification of glycomimetic as potential human sodium-glucose co-transporter 2 inhibitor

Sodium-glucose co-transporter 2 (SGLT2) is one of the important targets against type II diabetes mellitus. A typical SGLT2 inhibitor acts by inhibiting glucose reabsorption, thus lowering the blood glucose level. Unlike SGLT1, SGLT2 is responsible for almost 90% glucose reabsorption from glomerular filtrate. The current SGLT2 inhibitors include gliflozins, often prescribed as second or third-line agents in diabetes mellitus. The SGLT2 inhibitors also benefit patients with heart and kidney disease. Due to instability issues with the natural O-aryl glycoside analogues C-glycoside analogues were developed and showed improved stability. Despite enhanced bioavailability and selectivity of newer derivatives, some serious side effects are associated with gliflozin analogues. At the present study, we applied in-silico approaches to find new glycomimetic compounds as potent SGLT2 inhibitors that could show improvement in side effects associated with current analogues. This work applied both ligand-based and structure-based drug approaches to find potential compounds. We developed a 3D-QSAR method to screen potential inhibitors from a library of ten thousand compounds and performed docking studies. The compounds were ranked based on predicted pIC50 and docking score. An initial screening of five thousand compounds was conducted, and the subsequently selected top 12 compounds were based on binding free energy calculations. These selected compounds were subjected to molecular dynamics (MD) simulations. Remarkably, our simulations identified nine compounds that exhibited significant and sustained binding affinity compared to the co-crystallized Empagliflozin. Collectively, considering the anticipated pharmacokinetic profiles and toxicity assessments, several of these compounds emerged as promising candidates for further in-depth evaluation.

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.

Genetic architecture of hippocampus subfields volumes in Alzheimer's disease

BACKGROUND

The hippocampus is a heterogeneous structure, comprising histologically and functionally distinguishable hippocampal subfields. The volume reductions in hippocampal subfields have been demonstrated to be linked with Alzheimer's disease (AD). The aim of our study is to investigate the hippocampal subfields' genetic architecture based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set.

METHODS

After preprocessing the downloaded genetic variants and imaging data from the ADNI database, a co-sparse reduced rank regression model was applied to analyze the genetic architecture of hippocampal subfields volumes. Homology modeling, docking, molecular dynamics simulations, and Co-IP experiments for protein-protein interactions were used to verify the function of target protein on hippocampal subfields successively. After that, the association analysis between the candidated genes on the hippocampal subfields volume and clinical scales were performed.

RESULTS

The results of the association analysis revealed five unique genetic variants (e.g., ubiquitin-specific protease 10 [USP10]) changed in nine hippocampal subfields (e.g., the granule cell and molecular layer of the dentate gyrus [GC-ML-DG]). Among five genetic variants, USP10 had the strongest interaction effect with BACE1, which affected hippocampal subfields verified by MD and Co-IP experiments. The results of association analysis between the candidated genes on the hippocampal subfields volume and clinical scales showed that candidated genes influenced the volume and function of hippocampal subfields.

CONCLUSIONS

Current evidence suggests that hippocampal subfields have partly distinct genetic architecture and may improve the sensitivity of the detection of AD.

How does ultrasound-assisted ionic liquid treatment affect protein? A comprehensive review of their potential mechanisms, safety evaluation, and physicochemical and functional properties

Proteins are essential to human health with enormous food applications. Despite their advantages, plant and animal proteins often exhibit limited molecular flexibility and poor solubility due to hydrogen bonds, hydrophobic interactions, and ionic interactions within their molecular structures. Thus, there is an urgent need to modify the rigid structure of proteins to enhance their stability and functional properties. Ultrasound-assisted ionic liquid (UA-IL) treatment for developing compound modification and producing proteins with excellent functional properties has received interest. However, no review specifically addresses the interactions between UA-ILs and proteins. Hence, this review focused on recent research advancements concerning the effects and potential reaction mechanisms of UA-ILs on the physicochemical properties (including particle size; primary, secondary, and tertiary structure; and surface morphology) as well as the functionality (such as solubility, emulsifying properties, and foaming ability) of proteins. Moreover, the safety evaluation of modified proteins was also discussed from various perspectives, such as acute and chronic toxicity, genotoxicity, cytotoxicity, and environmental and microbial toxicity. This review demonstrated that UA-IL treatment-induced protein structural changes significantly impact the functional characteristics of proteins. This treatment approach efficiently promotes protein structure stretching and spatial rearrangement through cavitation, thermal effects, and ionic interactions. As a result, the functional properties of modified proteins exhibited an obvious enhancement, thereby bringing more opportunities to utilize modified protein products in the food industry. Potential future directions for protein modification using UA-ILs were also proposed.

The effect of mutation on neurotoxicity reduction of new chimeric reteplase, a computational study

Background and purpose

Excitotoxicity in nerve cells is a type of neurotoxicity in which excessive stimulation of receptors (such as N-methyl-d-aspartate glutamate receptors (NMDAR)) leads to the influx of high-level calcium ions into cells and finally cell damage or death. This complication can occur after taking some of the plasminogen activators like tissue plasminogen activator and reteplase. The interaction of the kringle2 domain in such plasminogen activator with the amino-terminal domain (ATD) of the NR1 subunit of NMDAR finally leads to excitotoxicity. In this study, we assessed the interaction of two new chimeric reteplase, mutated in the kringle2 domain, with ATD and compared the interaction of wild-type reteplase with ATD, computationally.

Experimental approach

Homology modeling, protein docking, molecular dynamic simulation, and molecular dynamics trajectory analysis were used for the assessment of this interaction.

Findings/Results

The results of the free energy analysis between reteplase and ATD (wild reteplase: -2127.516 ± 0.0, M1-chr: -1761.510 ± 0.0, M2-chr: -521.908 ± 0.0) showed lower interaction of this chimeric reteplase with ATD compared to the wild type.

Conclusion and implications

The decreased interaction between two chimeric reteplase and ATD of NR1 subunit in NMDAR which leads to lower neurotoxicity related to these drugs, can be the start of a way to conduct more tests and if the results confirm this feature, they can be considered potential drugs in acute ischemic stroke treatment.

Specific Interaction of DARPin with HIV-1 CANTD Disturbs the Distribution of Gag, RNA Packaging, and Tetraspanin Remodelling in the Membrane

A designed repeat scaffold protein (Ank^GAG1D4) recognizing the human immunodeficiency virus-1 (HIV-1) capsid (CA) was formerly established with antiviral assembly. Here, we investigated the molecular mechanism of Ank^GAG1D4 function during the late stages of the HIV-1 replication cycle. By applying stimulated emission-depletion (STED) microscopy, Gag polymerisation was interrupted at the plasma membrane. Disturbance of Gag polymerisation triggered Gag accumulation inside producer cells and trapping of the CD81 tetraspanin on the plasma membrane. Moreover, reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) experiments were performed to validate the packaging efficiency of RNAs. Our results advocated that Ank^GAG1D4 interfered with the Gag precursor protein from selecting HIV-1 and cellular RNAs for encapsidation into viral particles. These findings convey additional information on the antiviral activity of Ank^GAG1D4 at late stages of the HIV-1 life cycle, which is potential for an alternative anti-HIV molecule.