Protein Interaction Studies Research Articles

Proteomics and Its Current Application in Biomedical Area: Concise Review.

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

Structural assembly of the bacterial essential interactome.

The study of protein interactions in living organisms is fundamental for understanding biological processes and central metabolic pathways. Yet, our knowledge of the bacterial interactome remains limited. Here, we combined gene deletion mutant analysis with deep-learning protein folding using AlphaFold2 to predict the core bacterial essential interactome. We predicted and modeled 1402 interactions between essential proteins in bacteria and generated 146 high-accuracy models. Our analysis reveals previously unknown details about the assembly mechanisms of these complexes, highlighting the importance of specific structural features in their stability and function. Our work provides a framework for predicting the essential interactomes of bacteria and highlight the potential of deep-learning algorithms in advancing our understanding of the complex biology of living organisms. Also, the results presented here offer a promising approach to identify novel antibiotic targets.

Kinetic study of membrane protein interactions: from three to two dimensions

Molecular interactions are contingent upon the system’s dimensionality. Notably, comprehending the impact of dimensionality on protein–protein interactions holds paramount importance in foreseeing protein behaviour across diverse scenarios, encompassing both solution and membrane environments. Here, we unravel interactions among membrane proteins across various dimensionalities by quantifying their binding rates through fluorescence recovery experiments. Our findings are presented through the examination of two protein systems: streptavidin–biotin and a protein complex constituting a bacterial efflux pump. We present here an original approach for gauging a two-dimensional binding constant between membrane proteins embedded in two opposite membranes. The quotient of protein binding rates in solution and on the membrane represents a metric denoting the exploration distance of the interacting sites—a novel interpretation.

Whole exome sequencing coupled with in silico functional analysis identified NID1 as a novel candidate gene causing neuro-psychiatric disorder in a Pakistani family

Intellectual disability (ID) is a neuro-developmental condition that affects a person’s cognitive ability and results in a learning defect. It affects 1–3% of the general population; however, the ratio may be expected to be more in a consanguineous population. Herein in the present study, we identified a nuclear family from Dera Ismail Khan City in Pakistan. Whole exome sequencing was performed to map the pathogenic variant. Protein structural modeling and interaction studies were carried out to validate the variant with disease association. Molecular modeling of normal and mutated proteins was performed through I-TASSER and Chimera tools, while docking and interaction analysis was carried out using Cluspro. Clinical analysis of the patient determined mild intellectual disability and gait problem. Candidate gene analysis in this family found a homozygous missense mutation NM_002508:c.C2512T (p.Arg838Cys) in the 12th exon of NID1 gene. Molecular modeling of wild-type and mutant NID1 proteins determined a significant effect on the protein’s secondary and tertiary structure. Hence, based on the exome sequence analysis, NID1 is proposed to be a strong novel candidate ID gene in this family. The genetic mapping of the present family led us to determine a novel candidate gene to be associated with intellectual disability. Linkage of additional ID families with genes would confirm its validity and strengthen our notion. Furthermore, expression studies and pathway analysis will help in exploring the biological mechanism of learning and memory.

Orchestration of Genetic Alterations in PSEN1 and PSEN2 Genes in Development of Alzheimer's Disease through Computational Analysis.

Dementia is a syndrome that can cause a number of progressive illnesses that affect memory, thinking, and ability to perform everyday tasks. Alzheimer's disease (AD) is the most common cause of dementia and represents a major public health problem. AD is a progressive disease, where in early stages there is mild memory loss and in late-stage patient loses the ability to carry on a conversation. AD (for which there is no exact cause and cure known so far) is the sixth leading cause of deaths in the United States. Every 68 second someone develops AD. This study focuses on protein structure modeling of genes presenilin 1 and 2 ( PSEN1 and PSEN2 ) and their mutated forms (Asn141Tyr found in Chinese family, Gly34Ser identified in a Japanese patient, and Arg62Cys & Val214Leu identified in the Korean patients). It also involves wild and mutant type comparison, protein interaction studies, docking and phylogenetic history based on representative ortholog species and also sheds insight into the comparative evolutionary rates of coding sequence across various orthologs. This study gives a time and cost-effective analysis of genes ( PSEN1 and PSEN2 ) underlying AD and genetic alterations that drive development and causes of disease.

AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high-quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high-quality biological model suitable for applications such as drug design, protein interaction, or molecular dynamics studies.

Synthesis and characterization of novel uranyl clusters supported by bis(pyrazolyl) methane ligands: biomimetic catalytic oxidation, BSA protein interaction and cytotoxicity studies.

Two novel uranyl complexes were synthesized using bis-pyrazolyl methane ligands. The complexes were characterized by several spectroscopic techniques, including UV-Vis, IR, NMR, mass spectrometry, fluorescence, electrochemical, and thermogravimetric analysis. The solid-state structure of the complex C1 was determined with the help of single-crystal X-ray diffraction studies. The complexes C1 and C2 efficiently catalyse the oxidation of 3,5-di-tert-butyl catechol and 2-aminophenol in the atmospheric air, imitating the catalytic activity of the catechol oxidase and phenoxazinone synthase enzymes. The kinetic parameters and the catalytic efficiency (K cat/K M) of the reactions were calculated. Formation of organic free radicals in the catalytic reactions was confirmed by EPR spectroscopy. The interaction of these complexes with the protein, bovine serum albumin, was investigated by using UV-Vis and fluorescence spectral analysis. The cytotoxicity of the complexes against MDAMB-231 and A549 cell lines was investigated, and IC50 values were determined.

Visualization of Engineered M13 Phages Bound to Bacterial Targets by Transmission Electron Microscopy.

The filamentous phage M13 is one of the most well-studied and characterized phages, particularly since it was introduced as a scaffold for phage display, a technique to express and evolve fusion proteins on the M13 phage's coat to study protein or peptide binding interactions. Since phages can be engineered or evolved to specifically bind to a variety of targets, engineered M13 phages have been explored for applications such as drug delivery, biosensing, and cancer therapy, among others. Specifically, with the rising challenge of antimicrobial resistance among bacteria, chimeric M13 phages have been explored both as detection and therapeutic agents due to the flexibility in tuning target specificity. Transmission electron microscopy (TEM) is a powerful tool enabling researchers to directly visualize and characterize binding of phages to bacterial surfaces. However, the filamentous phage structure poses a challenge for this technique, as the phages have similar morphology to bacterial structures such as pili. In order to differentiate between bacterial structures and the filamentous phages, here we describe a protocol to prepare TEM samples of engineered M13 phages bound to bacterial cells, in which the phage virions have been specifically labeled by decoration of the major capsid proteins with gold nanoparticles. This protocol enables clear visualization and unambiguous identification of attached filamentous phages within the context of bacterial cells expressing numerous pili.

Protein-Protein Interactions Visualized by Bimolecular Fluorescence Complementation in Arabidopsis thaliana Protoplasts from Leaf.

Bimolecular fluorescence complementation (BiFC) is a powerful tool for studying protein-protein interactions in living cells. By fusing interacting proteins to fluorescent protein fragments, BiFC allows visualization of spatial localization patterns of protein complexes. This method has been adapted to a variety of expression systems in different organisms and is widely used to study protein interactions in plant cells. The Agrobacterium-mediated transient expression protocol for BiFC assays in Nicotiana benthamiana (N. benthamiana) leaf cells is widely used, but in this chapter, a method for BiFC assay using Arabidopsis thaliana protoplasts is presented.

Evolution of techniques and tools for replication fork proteome and protein interaction studies.

Understanding the complex network of protein-protein interactions (PPI) that govern cellular functions is essential for unraveling the molecular basis of biological processes and diseases. Mass spectrometry (MS) has emerged as a powerful tool for studying protein dynamics, enabling comprehensive analysis of protein function, structure, post-translational modifications, interactions, and localization. This article provides an overview of MS techniques and their applications in proteomics studies, with a focus on the replication fork proteome. The replication fork is a multi-protein assembly involved in DNA replication, and its proper functioning is crucial for maintaining genomic integrity. By combining quantitative MS labeling techniques with various data acquisition methods, researchers have made significant strides in elucidating the complex processes and molecular mechanisms at the replication fork. Overall, MS has revolutionized our understanding of protein dynamics, offering valuable insights into cellular processes and potential targets for therapeutic interventions.

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction.

Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative approach that combines ARTINA with AlphaFold and UCBShift, enabling chemical shift assignment with reduced experimental data, increased accuracy, and enhanced robustness for larger systems, as presented in a comprehensive study with more than 5000 automated assignment calculations on 89 proteins. We demonstrate that five 3D spectra yield more accurate assignments (92.59%) than pure ARTINA runs using all experimentally available NMR data (on average 10 3D spectra per protein, 91.37%), considerably reducing the required measurement time. We also showcase automated assignments of only 15N-labeled samples, and report improved assignment accuracy in larger synthetic systems of up to 500 residues.

Abstract 17705: BAG5 Plays a Critical Role in the Regulation of Cellular Oxidative Stress and Heart Function

Introduction: Molecular chaperone and co-chaperone play important roles in maintaining cardiac function by regulating cellular proteostasis and reducing oxidative stress during hypertrophy. Bcl2 binding proteinBAG5 belongs to Bcl-2 associated anthanogene (BAG) family and acts as a co-chaperone to HSP70. Genetic mutation of BAG5 induces dilated cardiomyopathy and arrhythmia. However, the molecular mechanism of BAG5 in heart function is unclear. In this study, we explore the regulatory role of BAG5 in heart function. Hypothesis: We hypothesized that BAG5 is an important Co-chaperone of cardiomyocytes and regulates heart function through the regulation of cellular proteostasis and oxidative stress. Method: We have generated adenovirus to overexpress and knockdown the BAG5 protein in cardiomyocytes. Additionally, we have generated BAG5 knockout mice to test the effect of BAG5 deficiency on cardiac function. Further, we have conducted a proteomics study to explore the role of BAG5 in heart function. Additionally, we tested the role of BAG5 in isoproterenol-induced cardiac hypertrophy. Results: Our results show that deficiency of BAG5 induces cellular hypertrophy and induction of cellular autophagy. Cardiac function analysis by echocardiography shows that the knockdown of BAG5 compromises heart function. Further, a protein interaction study shows that BAG5 participates in several critical heart signaling, including mitochondrial oxphos and glutathione-mediated regulation of oxidative stress. Additionally, we found that the knockdown of BAG5 worsens heart function during isoproterenol treatment. Conclusion: Our study shows a novel function of BAG5 and BAG5 can be exploited in treating cardiac hypertrophy and reducing oxidative stress.

Top-Down Machine Learning of Coarse-Grained Protein Force Fields.

Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov state models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies.

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Chemical shift transfer: an effective strategy for protein NMR assignment with ARTINA.

Chemical shift transfer (CST) is a well-established technique in NMR spectroscopy that utilizes the chemical shift assignment of one protein (source) to identify chemical shifts of another (target). Given similarity between source and target systems (e.g., using homologs), CST allows the chemical shifts of the target system to be assigned using a limited amount of experimental data. In this study, we propose a deep-learning based workflow, ARTINA-CST, that automates this procedure, allowing CST to be carried out within minutes or hours of computational time and strictly without any human supervision. We characterize the efficacy of our method using three distinct synthetic and experimental datasets, demonstrating its effectiveness and robustness even when substantial differences exist between the source and target proteins. With its potential applications spanning a wide range of NMR projects, including drug discovery and protein interaction studies, ARTINA-CST is anticipated to be a valuable method that facilitates research in the field.

Chromatin and non-chromatin immunoprecipitations to capture protein-protein and protein-nucleic acid interactions in living cells.

Proteins are expressed from genes via sequential biological processes of transcription, mRNA processing, export and translation, and play their roles in maintaining cellular functions via interactions with proteins, DNAs or RNAs. Thus, it is important to study the protein interactions during biological processes in living cells towards understanding their mechanisms-of-action in real time. Methodologies have been developed over the years to study protein interactions in vivo. One state-of-the-art approach is formaldehyde crosslinking-based immuno- or chemi-precipitation to analyze selective as well as genome/proteome-wide interactions in living cells. It is a popular and widely used methodology for cellular analysis of the protein-protein and protein-nucleic acid interactions. Here, we describe this approach to analyze protein-protein/nucleic acid interactions in vivo.

Measuring protein-membrane interaction through radial fluorescence correlation in 2 dimensions

The cell membrane has a fundamental role in the cell life cycle but there’s still much to be learned about its heterogeneous structure, regulation, and protein interaction. Additionally, the protein-membrane interaction is often overlooked when studying specific protein dynamics. In this work, we present a new tool for a better understanding of protein dynamics and membrane function using live cells and fast non-invasive techniques without the need for individual particle tracking. To this end, we used the 2D-pair correlation function (2D-pCF) to study protein interactions across cellular membranes. We performed numerical simulations and confocal experiments using a GAP-mEGFP fusion construct known to interact with the plasmatic membrane. Our results demonstrate that based on a quantitative correlation analysis as the 2D pair correlation of the signal intensities, is possible to characterize protein-membrane interactions in live systems and real-time. Combining experimental and numerical results this work presents a new powerful approach to the study of the dynamic protein-membrane interaction.

An automated interface for sedimentation velocity analysis in SEDFIT.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an indispensable tool for the study of particle size distributions in biopharmaceutical industry, for example, to characterize protein therapeutics and vaccine products. In particular, the diffusion-deconvoluted sedimentation coefficient distribution analysis, in the software SEDFIT, has found widespread applications due to its relatively high resolution and sensitivity. However, a lack of suitable software compatible with Good Manufacturing Practices (GMP) has hampered the use of SV-AUC in this regulatory environment. To address this, we have created an interface for SEDFIT so that it can serve as an automatically spawned module with controlled data input through command line parameters and output of key results in files. The interface can be integrated in custom GMP compatible software, and in scripts that provide documentation and meta-analyses for replicate or related samples, for example, to streamline analysis of large families of experimental data, such as binding isotherm analyses in the study of protein interactions. To test and demonstrate this approach we provide a MATLAB script mlSEDFIT.

Comprehensive Molecular Evaluation of HNF-1 Alpha, miR-27a, and miR-146 Gene Variants and Their Link with Predisposition and Progression in Type 2 Diabetes Patients.

Type 2 diabetes (T2D) is a metabolic condition induced by insulin resistance and pancreatic beta cell dysfunction. MicroRNAs (miRNAs) have biological significance because they regulate processes such as the molecular signaling pathways involved in the pathophysiology of diabetes mellitus. The hepatocyte nuclear factor-1 alpha (HNF-1 alpha) is a transcription factor found in hepatocytes and the pancreas. Mutations in the HNF-1 alpha gene were reportedly associated with maturity-onset diabetes of the young (MODY). The objective of the present study was to examine the associations between MiR-27a, MiR-146, and HNF-1 alpha single-nucleotide variations (SNVs) with T2D risk in the Saudi population. We evaluated the association of SNVs of miR-27a rs895819 A>G, 146a-rs2910164 C>G, and HNF-1 alpha rs1169288 G>T (I27L) with the risk of T2D in Saudi patients with the Amplification Refractory Mutation System PCR (ARMS-PCR). For the miR-27a SNVs, we used 115 cases (82 males, 33 females) and 117 matched healthy controls (HCs); for the Mir-146 SNVs, we used 103 cases (70 males, 33 females) and 108 matched HCs; and for the HNF-1 alpha, we employed 110 patients (80 males, 30 females) and 110 HCs. The blood biochemistry of the participants was essayed using commercial kits, and the methods of statistical analysis used were the Chi-square test, the Fisher exact test, and a multivariate analysis based on logistic regression, like the odds ratio (OD) and risk ratio (RR), with 95% confidence intervals (CIs). The MiR-27a rs895819 AG genotype was linked to increased T2D susceptibility, with OR = 2.01 and p-value = 0.011, and the miR-146 rs2910164 CG genotype and C allele were linked to an elevated risk of T2D, with OR = 2.75, p-value < 0.0016, OR = 1.77, and p-value = 0.004. The results also showed that the GT genotype and T allele of the HNF-1 alpha (rs1169288) G>T is linked to T2D, with OR = 2.18, p-value = 0.0061, and 1.77, p-value = 0.0059. The SNVs in miR-27a, miR-146, and HNF-1 alpha can be potential loci for T2D risk. The limitations of this study include the relatively small sample size and the fact that it was a cross-sectional study. To our knowledge, this is the first study to highlight the association between miR-27a, miR-146, and HNF-1 alpha SNVs and the risk of T2D in the Saudi population. Future large-scale case-control studies, as well as studies on the functions of the proteins and protein interaction studies for HNF-1 alpha, are required to verify our findings. Furthermore, these findings can be used for the identification and stratification of at-risk populations via genetic testing for T2D-prevention strategies.

Abstract P2053: Bag5 Plays A Critical Role In The Regulation Of Cellular Homeostasis Through Fine-tuning Of Cellular Protein Quality Control

Background: Cardiac hypertrophy is one of the heart's adaptive responses, which may play a compensatory role in enhancing cardiac function during stress conditions. However, prolonged hypertrophy condition can lead to the progression of cardiac dysfunction and heart failure. Cellular chaperones and co-chaperones play a critical role in maintaining cellular homeostasis by regulating cellular protein quality control. A Bcl2 binding protein BAG5 acts as a co-chaperone to heat shock protein 70 and is known to be upregulated during oxidative stress. A recent study suggests that mutation of the BAG5 gene induces cardiac dysfunction by promoting dilated cardiomyopathy and arrhythmia. However, its role in cardiac hypertrophy is not clear. Method and Results: This study explores the BAG5 function through a protein-protein interaction study by immunoprecipitation and mass spectroscopy. Further, the role of BAG5 in cellular protein quality control and cardiac hypertrophy was tested using cardiomyocytes. Our protein interaction study suggests that BAG5 interacts with several cellular proteins involved in the biological process like Golgi-associated vesicular transport, fatty acid degradation, endoplasmic reticulum-assisted protein folding, chaperone-mediated protein degradation, and deubiquitinating process. Further, we found that knockdown of BAG5 causes induction of cellular hypertrophy, and overexpression of BAG5 reduces PE and ISO-induced cellular hypertrophy. Additionally, we found that BAG5 maintains a balance of cellular protein quality control, and knockdown of BAG5 causes upregulation of cellular autophagy Conclusion: BAG5 plays a critical role in maintaining cellular homeostasis and is protective during cardiac hypertrophy. Additionally, the reduction of BAG5 protein can promote maladaptive autophagy.