Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Identifying a Ubiquitinated Adaptor Protein by a Viral E3 Ligase Through Co-immunoprecipitation

Co-immunoprecipitation is a technique widely utilized to isolate protein complexes and study protein-protein interactions. Ubiquitinated proteins could be identified by combining co-immunoprecipitation with SDS-PAGE followed by immunoblotting. In this chapter, we use Herpes Simplex Virus 1 immediate-early protein ICP0-mediated polyubiquitination of p50 as an example to describe the method to identify a ubiquitinated adaptor protein by a viral E3 ligase by co-immunoprecipitation.

Identifying an Abnormal Phosphorylated Adaptor by Viral Kinase Using Mass Spectrometry

Mass spectrometers are widely used to identify protein phosphorylation sites. The process usually involves selective isolation of phosphoproteins and subsequent fragmentation to identify both the peptide sequence and phosphorylation site. Immunoprecipitation could capture and purify the protein of interest, greatly reducing sample complexity before submitting it for mass spectrometry analysis. This chapter describes a method to identify an abnormal phosphorylated site of the adaptor protein by a viral kinase through immunoprecipitation followed by LC-MS/MS.

Recent advances and challenges in protein complex model accuracy estimation

Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model

Introduction

The oral microbial group typically represents the human body's highly complex microbial group ecosystem. Oral microorganisms take part in human diseases, including Oral cavity inflammation, mucosal disease, periodontal disease, tooth decay, and oral cancer. On the other hand, oral microbes can also cause endocrine disorders, digestive function, and nerve function disorders, such as diabetes, digestive system diseases, and Alzheimer's disease. It was noted that the proteins of oral microbes play significant roles in these serious diseases. Having a good knowledge of oral microbes can be helpful in analyzing the procession of related diseases. Moreover, the high-dimensional features and imbalanced data lead to the complexity of oral microbial issues, which can hardly be solved with traditional experimental methods.

Methods

To deal with these challenges, we proposed a novel method, which is oral_voting_transfer, to deal with such classification issues in the field of oral microorganisms. Such a method employed three features to classify the five oral microorganisms, including Streptococcus mutans, Staphylococcus aureus, abiotrophy adjacent, bifidobacterial, and Capnocytophaga. Firstly, we utilized the highly effective model, which successfully classifies the organelle's proteins and transfers to deal with the oral microorganisms. And then, some classification methods can be treated as the local classifiers in this work. Finally, the results are voting from the transfer classifiers and the voting ones.

Results and discussion

The proposed method achieved the well performances in the five oral microorganisms. The oral_voting_transfer is a standalone tool, and all its source codes are publicly available at https://github.com/baowz12345/voting_transfer.

Molecular modeling, docking and dynamics studies of fenugreek (Trigonella foenum-graecum) α-amylase

α-Amylase catalyses the hydrolysis of glucosidic bonds in polysaccharides such as starch, glycogen and their degradation products. In the present study, the three-dimensional structure of fenugreek (Trigonella foenum-graecum) α-amylase was determined using a homology modeling-based technique. The best predicted model was deposited in PMDB server with PMDB ID PM0084364. The phylogenetic tree was created using the UPGMA method with 8 homologous protein sequences, Trigonella foenum-graecum was utilized as the target protein. Alignment of the phylogenetic tree identified two primary functional groupings (A and B). α-Amylase from the target genome Trigonella foenum-graecum (Acc. No: GHNA01022531.1) was clustered with Medicago truncatula (Acc. No: XP003589186.1), Cicer arietinum (Acc. No: XP004499059.1), Cajanus cajan (Acc. No: XP020231823.1), Vigna angularis (Acc. No: NP001316768.1) and Vigna mungo (Acc. No: P17859.1), in group A cluster, while Hordeum vulgare (Acc. No: Q40015) and Oryza sativa (PDB ID: 3WN6) were in cluster B. The molecular dynamics simulations were performed to understand the molecular basis and mode of action of Trigonella foenum-graecum α-amylase. Additionally, a geometry-based molecular docking technique was used to evaluate potential binding interactions between the modeled structure of α-amylase and maltose. The results show that Trp228, Glu226, Arg199, His308, Tyr165, Asp309, Phe202 and Asp201 from Trigonella foenum-graecum α-amylase enzyme is involved in the binding to the substrate maltose. Our study provides a 3D model of Trigonella foenum-graecum α-amylase and aids in understanding the atomic level molecular underpinnings of the mechanism of α-amylase interaction with substrate maltose. Ca2+ are essential for the stability of domain B since they are connected to it. Ca2+ site ligands are Asp139, Glu130, Thr133, Asp135 and Gly131 residues. HIGHLIGHTSIn silico analysis, gene prediction of α-amylase was carried from Trigonella foenum-graecum.Analysis of the structure of α-amylase was carried out using homology modelling.Calcium binding sites and their interactions with α-amylase were visualised using BIOVIA DISCOVERY STUDIO 2019.The molecular interaction between Trigonella foenum-graecum α-amylase and maltose was studied in silico using a molecular docking-based method.To give the required simulation parameters, RMSD, RMSF, and Total Energy were calculated using BIOVIA DISCOVERY STUDIO 2019.[Figure: see text]Communicated by Ramaswamy H. Sarma.

Combination of in vivo proximity labeling and co-immunoprecipitation identifies the host target network of a tumor-inducing effector in the fungal maize pathogen Ustilago maydis

Plant pathogens secrete effectors, which target host proteins to facilitate infection. The Ustilago maydis effector UmSee1 is required for tumor formation in the leaf during infection of maize. UmSee1 interacts with maize SGT1 (suppressor of G2 allele of skp1) and blocks its phosphorylation in vivo. In the absence of UmSee1, U. maydis cannot trigger tumor formation in the bundle sheath. However, it remains unclear which host processes are manipulated by UmSee1 and the UmSee1-SGT1 interaction to cause the observed phenotype. Proximity-dependent protein labeling involving the turbo biotin ligase tag (TurboID) for proximal labeling of proteins is a powerful tool for identifying the protein interactome. We have generated transgenic U. maydis that secretes biotin ligase-fused See1 effector (UmSee1-TurboID-3HA) directly into maize cells. This approach, in combination with conventional co-immunoprecipitation, allowed the identification of additional UmSee1 interactors in maize cells. Collectively, our data identified three ubiquitin-proteasome pathway-related proteins (ZmSIP1, ZmSIP2, and ZmSIP3) that either interact with or are close to UmSee1 during host infection of maize with U. maydis. ZmSIP3 represents a cell cycle regulator whose degradation appears to be promoted in the presence of UmSee1. Our data provide a possible explanation of the requirement for UmSee1 in tumor formation during U. maydis-Zea mays interaction.

Proteomics Analysis of Lymphoblastoid Cell Lines from Patients with Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) consists of the progressive degeneration of motor neurons, caused by poorly understood mechanisms for which there is no cure. Some of the cellular perturbations associated with ALS can be detected in peripheral cells, including lymphocytes from blood. A related cell system that is very suitable for research consists of human lymphoblastoid cell lines (LCLs), which are immortalized lymphocytes. LCLs that can be easily expanded in culture and can be maintained for long periods as stable cultures. We investigated, on a small set of LCLs, if a proteomics analysis using liquid chromatography followed by tandem mass spectrometry reveals proteins that are differentially present in ALS versus healthy controls. We found that individual proteins, the cellular and molecular pathways in which these proteins participate, are detected as differentially present in the ALS samples. Some of these proteins and pathways are already known to be perturbed in ALS, while others are new and present interest for further investigations. These observations suggest that a more detailed proteomics analysis of LCLs, using a larger number of samples, represents a promising approach for investigating ALS mechanisms and to search for therapeutic agents. Proteomics data are available via ProteomeXchange with identifier PXD040240.

RF_phage virion: Classification of phage virion proteins with a random forest model

Introduction: Phages play essential roles in biological procession, and the virion proteins encoded by the phage genome constitute critical elements of the assembled phage particle. Methods: This study uses machine learning methods to classify phage virion proteins. We proposed a novel approach, RF_phage virion, for the effective classification of the virion and non-virion proteins. The model uses four protein sequence coding methods as features, and the random forest algorithm was employed to solve the classification problem. Results: The performance of the RF_phage virion model was analyzed by comparing the performance of this algorithm with that of classical machine learning methods. The proposed method achieved a specificity (Sp) of 93.37%%, sensitivity (Sn) of 90.30%, accuracy (Acc) of 91.84%, Matthews correlation coefficient (MCC) of .8371, and an F1 score of .9196.

Intranasal Peptide Therapeutics: A Promising Avenue for Overcoming the Challenges of Traditional CNS Drug Development

The central nervous system (CNS) has, among all organ systems in the human body, the highest failure rate of traditional small-molecule drug development, ranging from 80-100% depending on the area of disease research. This has led to widespread abandonment by the pharmaceutical industry of research and development for CNS disorders, despite increased diagnoses of neurodegenerative disorders and the continued lack of adequate treatment options for brain injuries, stroke, neurodevelopmental disorders, and neuropsychiatric illness. However, new approaches, concurrent with the development of sophisticated bioinformatic and genomic tools, are being used to explore peptide-based therapeutics to manipulate endogenous pathways and targets, including "undruggable" intracellular protein-protein interactions (PPIs). The development of peptide-based therapeutics was previously rejected due to systemic off-target effects and poor bioavailability arising from traditional oral and systemic delivery methods. However, targeted nose-to-brain, or intranasal (IN), approaches have begun to emerge that allow CNS-specific delivery of therapeutics via the trigeminal and olfactory nerve pathways, laying the foundation for improved alternatives to systemic drug delivery. Here we review a dozen promising IN peptide therapeutics in preclinical and clinical development for neurodegenerative (Alzheimer's, Parkinson's), neuropsychiatric (depression, PTSD, schizophrenia), and neurodevelopmental disorders (autism), with insulin, NAP (davunetide), IGF-1, PACAP, NPY, oxytocin, and GLP-1 agonists prominent among them.

In silico functional annotation of hypothetical proteins from the Bacillus paralicheniformis strain Bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments

Members of the Bacillus genus are industrial cell factories due to their capacity to secrete significant quantities of biomolecules with industrial applications. The Bacillus paralicheniformis strain Bac84 was isolated from the Red Sea and it shares a close evolutionary relationship with Bacillus licheniformis. However, a significant number of proteins in its genome are annotated as functionally uncharacterized hypothetical proteins. Investigating these proteins' functions may help us better understand how bacteria survive extreme environmental conditions and to find novel targets for biotechnological applications. Therefore, the purpose of our research was to functionally annotate the hypothetical proteins from the genome of B. paralicheniformis strain Bac84. We employed a structured in-silico approach incorporating numerous bioinformatics tools and databases for functional annotation, physicochemical characterization, subcellular localization, protein-protein interactions, and three-dimensional structure determination. Sequences of 414 hypothetical proteins were evaluated and we were able to successfully attribute a function to 37 hypothetical proteins. Moreover, we performed receiver operating characteristic analysis to assess the performance of various tools used in this present study. We identified 12 proteins having significant adaptational roles to unfavorable environments such as sporulation, formation of biofilm, motility, regulation of transcription, etc. Additionally, 8 proteins were predicted with biotechnological potentials such as coenzyme A biosynthesis, phenylalanine biosynthesis, rare-sugars biosynthesis, antibiotic biosynthesis, bioremediation, and others. Evaluation of the performance of the tools showed an accuracy of 98% which represented the rationality of the tools used. This work shows that this annotation strategy will make the functional characterization of unknown proteins easier and can find the target for further investigation. The knowledge of these hypothetical proteins' potential functions aids B. paralicheniformis strain Bac84 in effectively creating a new biotechnological target. In addition, the results may also facilitate a better understanding of the survival mechanisms in harsh environmental conditions.

Functional Annotation of Hypothetical Proteins From the Enterobacter cloacae B13 Strain and Its Association With Pathogenicity

Enterobacter cloacae B13 strain is a rod-shaped gram-negative bacterium that belongs to the Enterobacteriaceae family. It can cause respiratory and urinary tract infections, and is responsible for several outbreaks in hospitals. E. cloacae has become an important pathogen and an emerging global threat because of its opportunistic and multidrug resistant ability. However, little knowledge is present about a large portion of its proteins and functions. Therefore, functional annotation of the hypothetical proteins (HPs) can provide an improved understanding of this organism and its virulence activity. The workflow in the study included several bioinformatic tools which were utilized to characterize functions, family and domains, subcellular localization, physiochemical properties, and protein-protein interactions. The E. cloacae B13 strain has overall 604 HPs, among which 78 were functionally annotated with high confidence. Several proteins were identified as enzymes, regulatory, binding, and transmembrane proteins with essential functions. Furthermore, 23 HPs were predicted to be virulent factors. These virulent proteins are linked to pathogenesis with their contribution to biofilm formation, quorum sensing, 2-component signal transduction or secretion. Better knowledge about the HPs’ characteristics and functions will provide a greater overview of the proteome. Moreover, it will help against E. cloacae in neonatal intensive care unit (NICU) outbreaks and nosocomial infections.

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.

Functional Analysis of Hypothetical Proteins of Vibrio parahaemolyticus Reveals the Presence of Virulence Factors and Growth-Related Enzymes With Therapeutic Potential

Vibrio parahaemolyticus, an aquatic pathogen, is a major concern in the shrimp aquaculture industry. Several strains of this pathogen are responsible for causing acute hepatopancreatic necrosis disease as well as other serious illness, both of which result in severe economic losses. The genome sequence of two pathogenic strains of V. parahaemolyticus, MSR16 and MSR17, isolated from Bangladesh, have been reported to gain a better understanding of their diversity and virulence. However, the prevalence of hypothetical proteins (HPs) makes it challenging to obtain a comprehensive understanding of the pathogenesis of V. parahaemolyticus. The aim of the present study is to provide a functional annotation of the HPs to elucidate their role in pathogenesis employing several in silico tools. The exploration of protein domains and families, similarity searches against proteins with known function, gene ontology enrichment, along with protein-protein interaction analysis of the HPs led to the functional assignment with a high level of confidence for 656 proteins out of a pool of 2631 proteins. The in silico approach used in this study was important for accurately assigning function to HPs and inferring interactions with proteins with previously described functions. The HPs with function predicted were categorized into various groups such as enzymes involved in small-compound biosynthesis pathway, iron binding proteins, antibiotics resistance proteins, and other proteins. Several proteins with potential druggability were identified among them. In addition, the HPs were investigated in search of virulent factors, which led to the identification of proteins that have the potential to be exploited as vaccine candidate. The findings of the study will be effective in gaining a better understanding of the molecular mechanisms of bacterial pathogenesis. They may also provide an insight into the process of evaluating promising targets for the development of drugs and vaccines against V. parahaemolyticus.

iTRAQ-based proteomic analysis of differentially expressed proteins in sera of seronegative and seropositive rheumatoid arthritis patients

Objective

The diagnosis of seronegative rheumatoid arthritis (SNRA) is often difficult due to the unavailability of reliable laboratory markers. The aim of this study was to identify differentially expressed proteins in sera of SNRA, seropositive RA (SPRA), and healthy donors (HD).

Methods

A total of 32 seropositive RA patients, 32 SNRA patients, and 35 HD were enrolled in our study. Differentially expressed proteins between 3 groups were identified via isobaric tags for relative and absolute quantitation (iTRAQ)‐based proteomic analysis, and an ELISA test was used for the validation test. Correlation analysis was conducted by GraphPad Prism.

Results

Using iTRAQ quantitative proteomics, we identified 14 proteins were significantly different between SPRA and SNRA, including 4 upregulated proteins and 10 downregulated proteins. Four differentially expressed proteins were validated by ELISA test, and the results showed that SAA1 protein was significantly higher in SPRA and SNRA patients compared with HD, and PSME1 was elevated in SPRA patients. What's more, SAA1 was increased in the anti‐CCP or RF high‐level group in RA patients, and PSME1 was increased in the RF high‐level group. Alternatively, SAA1 was positively correlated with inflammation indicators in RA patients, while PSME1 showed no correlation with inflammation indicators.

Conclusions

iTRAQ proteomic approaches revealed variations in serum protein composition among SPRA patients, SNRA patients, and HD and provided new idea for advanced diagnostic methods and precision treatment of RA.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

African Swine Fever Virus Protein E199L Promotes Cell Autophagy through the Interaction of PYCR2

African swine fever virus (ASFV), as a member of the large DNA viruses, may regulate autophagy and apoptosis by inhibiting programmed cell death. However, the function of ASFV proteins has not been fully elucidated, especially the role of autophagy in ASFV infection. One of three Pyrroline-5-carboxylate reductases (PYCR), is primarily involved in conversion of glutamate to proline. Previous studies have shown that depletion of PYCR2 was related to the induction of autophagy. In the present study, we found for the first time that ASFV E199L protein induced a complete autophagy process in Vero and HEK-293T cells. Through co-immunoprecipitation coupled with mass spectrometry (CoIP-MS) analysis, we firstly identified that E199L interact with PYCR2 in vitro. Importantly, our work provides evidence that E199L down-regulated the expression of PYCR2, resulting in autophagy activation. Overall, our results demonstrate that ASFV E199L protein induces complete autophagy through interaction with PYCR2 and down-regulate the expression level of PYCR2, which provide a valuable reference for the role of autophagy during ASFV infection and contribute to the functional clues of PYCR2.

An Overview of Genomics, Phylogenomics and Proteomics Approaches in Ascomycota

Fungi are among the most successful eukaryotes on Earth: they have evolved strategies to survive in the most diverse environments and stressful conditions and have been selected and exploited for multiple aims by humans. The characteristic features intrinsic of Fungi have required evolutionary changes and adaptations at deep molecular levels. Omics approaches, nowadays including genomics, metagenomics, phylogenomics, transcriptomics, metabolomics, and proteomics have enormously advanced the way to understand fungal diversity at diverse taxonomic levels, under changeable conditions and in still under-investigated environments. These approaches can be applied both on environmental communities and on individual organisms, either in nature or in axenic culture and have led the traditional morphology-based fungal systematic to increasingly implement molecular-based approaches. The advent of next-generation sequencing technologies was key to boost advances in fungal genomics and proteomics research. Much effort has also been directed towards the development of methodologies for optimal genomic DNA and protein extraction and separation. To date, the amount of proteomics investigations in Ascomycetes exceeds those carried out in any other fungal group. This is primarily due to the preponderance of their involvement in plant and animal diseases and multiple industrial applications, and therefore the need to understand the biological basis of the infectious process to develop mechanisms for biologic control, as well as to detect key proteins with roles in stress survival. Here we chose to present an overview as much comprehensive as possible of the major advances, mainly of the past decade, in the fields of genomics (including phylogenomics) and proteomics of Ascomycota, focusing particularly on those reporting on opportunistic pathogenic, extremophilic, polyextremotolerant and lichenized fungi. We also present a review of the mostly used genome sequencing technologies and methods for DNA sequence and protein analyses applied so far for fungi.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.

Tissue- and isoform-specific protein complex analysis with natively processed bait proteins

Protein-protein interaction analysis is an important tool to elucidate the function of proteins and protein complexes as well as their dynamic behavior. To date, the analysis of tissue- or even cell- or compartment-specific protein interactions is still relying on the availability of specific antibodies suited for immunoprecipitation. Here, we aimed at establishing a method that allows identification of protein interactions and complexes from intact tissues independent of specific, high affinity antibodies used for protein pull-down and isolation. Tagged bait proteins were expressed in human HEK293T cells and residual interactors removed by SDS. The resulting tag-fusion protein was then used as bait to pull proteins from tissue samples. Tissue-specific interactions were reproducibly identified from porcine retina as well as from retinal pigment epithelium using the ciliary protein lebercilin as bait. Further, murine heart-specific interactors of two gene products of the 3',5'-cyclic guanosine monophosphate (cGMP)-dependent protein kinase type 1 (cGK1) were investigated. Here, specific interactions were associated with the cGK1α and β gene products, that differ only in their unique amino-terminal region comprising about 100 aa. As such, the new protocol provides a fast and reliable method for tissue-specific protein complex analysis which is independent of the availability or suitability of antibodies for immunoprecipitation. SIGNIFICANCE: Protein-protein interaction in the functional relevant tissue is still difficult due to the dependence on specific antibodies or bait production in bacteria or insect cells. Here, the tagged protein of interest is produced in a human cell line and bound proteins are gently removed using SDS. Because applying the suitable SDS concentration is a critical step, different SDS solutions were tested to demonstrate their influence on interactions and the clean-up process. The established protocol enabled a tissue-specific analysis of the ciliary proteins lebercilin and TMEM107 using pig eyes. In addition, two gene products of the 3',5'-cyclic guanosine monophosphate (cGMP)-dependent protein kinase type 1 showed distinct protein interactions in mouse heart tissue. With the easy, fast and cheap protocol presented here, deep insights in tissue-specific and functional relevant protein complex formation is possible.

Advances of proteomics technologies for multidrug-resistant mechanisms

Multidrug resistance (MDR) is a vital issue in cancer treatment. Drug resistance can be developed through a variety of mechanisms, including increased drug efflux, activation of detoxifying systems and DNA repair mechanisms, and escape of drug-induced apoptosis. Identifying the exact mechanism related in a particular case is a difficult task. Proteomics is the large-scale study of proteins, particularly their expression, structures and functions. In recent years, comparative proteomic methods have been performed to analyze MDR mechanisms in drug-selected model cancer cell lines. In this paper, we review the recent developments and progresses by comparative proteomic approaches to identify potential MDR mechanisms in drug-selected model cancer cell lines, which may help understand and design chemical sensitizers.

Microfluidic approaches for the analysis of protein-protein interactions in solution

Exploration and characterisation of the human proteome is a key objective enabling a heightened understanding of biological function, malfunction and pharmaceutical design. Since proteins typically exhibit their behaviour by binding to other proteins, the challenge of probing protein-protein interactions has been the focus of new and improved experimental approaches. Here, we review recently developed microfluidic techniques for the study and quantification of protein-protein interactions. We focus on methodologies that utilise the inherent strength of microfluidics for the control of mass transport on the micron scale, to facilitate surface and membrane-free interrogation and quantification of interacting proteins. Thus, the microfluidic tools described here provide the capability to yield insights on protein-protein interactions under physiological conditions. We first discuss the defining principles of microfluidics, and methods for the analysis of protein-protein interactions that utilise the diffusion-controlled mixing characteristic of fluids at the microscale. We then describe techniques that employ electrophoretic forces to manipulate and fractionate interacting protein systems for their biophysical characterisation, before discussing strategies that use microdroplet compartmentalisation for the analysis of protein interactions. We conclude by highlighting future directions for the field, such as the integration of microfluidic experiments into high-throughput workflows for the investigation of protein interaction networks.

Analysis of HrpG regulons and HrpG-interacting proteins by ChIP-seq and affinity proteomics in Xanthomonas campestris

Gamma-proteobacteria Xanthomonas spp. cause at least 350 different plant diseases among important agricultural crops, which result in serious yield losses. Xanthomonas spp. rely mainly on the type III secretion system (T3SS) to infect their hosts and induce a hypersensitive response in nonhosts. HrpG, the master regulator of the T3SS, plays the dominant role in bacterial virulence. In this study, we used chromatin immunoprecipitation followed by sequencing (ChIP-seq) and tandem affinity purification (TAP) to systematically characterize the HrpG regulon and HrpG interacting proteins in vivo. We obtained 186 candidate HrpG downstream genes from the ChIP-seq analysis, which represented the genomic-wide regulon spectrum. A consensus HrpG-binding motif was obtained and three T3SS genes, hpa2, hrcU, and hrpE, were confirmed to be directly transcriptionally activated by HrpG in the inducing medium. A total of 273 putative HrpG interacting proteins were identified from the TAP data and the DNA-binding histone-like HU protein of Xanthomonas campestris pv. campestris (HU_xcc ) was proved to be involved in bacterial virulence by increasing the complexity and intelligence of the bacterial signalling pathways in the T3SS.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

Bottlenecks in Proteomics: An Update

Mass spectrometry (MS) is the core for advanced methods in proteomic experiments. When effectively used, proteomics may provide extensive information about proteins and their post-translational modifications, as well as their interaction partners. However, there are also many problems that one can encounter during a proteomic experiment, including, but not limited to sample preparation, sample fractionation, sample analysis, data analysis & interpretation and biological significance. Here we discuss some of the problems that researchers should be aware of when performing a proteomic experiment.

Comparative acetylomic analysis reveals differentially acetylated proteins regulating anther and pollen development in kenaf cytoplasmic male sterility line

Cytoplasmic male sterility (CMS) is widely used in plant breeding and represents a perfect model to understand cyto-nuclear interactions and pollen development research. Lysine acetylation in proteins is a dynamic and reversible posttranslational modification (PTM) that plays an important roles in diverse cell processes and signaling. However, studies addressing acetylation PTM regarding to anther and pollen development in CMS background are largely lacking. To reveal the possible mechanism of kenaf (Hibiscus cannabinus L.) CMS and pollen development, we performed a label-free-based comparative acetylome analysis in kenaf anther of a CMS line and wild-type (Wt). Using whole transcriptome unigenes of kenaf as the reference genome, we identified a total of 1204 Kac (lysin acetylation) sites on 1110 peptides corresponding to 672 unique proteins. Futher analysis showed 56 out of 672 proteins were differentially acetylated between CMS and Wt line, with 13 and 43 of those characterized up- and downregulated, respectively. Thirty-eight and 82 proteins were detected distinctively acetylated in CMS and Wt lines, respectively. And evaluation of the acetylomic and proteomic results indicated that the most significantly acetylated proteins were not associated with abundant changes at the protein level. Bioinformatics analysis demonstrated that many of these proteins were involved in various biological processes which may play key roles in pollen development, inculding tricarboxylic acid (TCA) cycle and energy metabolism, protein folding, protein metabolism, cell signaling, gene expression regulation. Taken together, our results provide insight into the CMS molecular mechanism and pollen development in kenaf from a protein acetylation perspective.

Protein-protein interactions of the nicotinamide/nicotinate mononucleotide adenylyltransferase of Leishmania braziliensis

BACKGROUND

Nicotinamide adenine dinucleotide (NAD) plays a central role in energy metabolism and integrates cellular metabolism with signalling and gene expression. NAD biosynthesis depends on the enzyme nicotinamide/nicotinate mononucleotide adenylyltransferase (NMNAT; EC: 2.7.7.1/18), in which converge the de novo and salvage pathways.

OBJECTIVE

The purpose of this study was to analyse the protein-protein interactions (PPI) of NMNAT of Leishmania braziliensis (LbNMNAT) in promastigotes.

METHODS

Transgenic lines of L. braziliensis promastigotes were established by transfection with the pSP72αneoαLbNMNAT-GFP vector. Soluble protein extracts were prepared, co-immunoprecipitation assays were performed, and the co-immunoprecipitates were analysed by mass spectrometry. Furthermore, bioinformatics tools such as network analysis were applied to generate a PPI network.

FINDINGS

Proteins involved in protein folding, redox homeostasis, and translation were found to interact with the LbNMNAT protein. The PPI network indicated enzymes of the nicotinate and nicotinamide metabolic routes, as well as RNA-binding proteins, the latter being the point of convergence between our experimental and computational results.

MAIN CONCLUSION

We constructed a model of PPI of LbNMNAT and showed its association with proteins involved in various functions such as protein folding, redox homeostasis, translation, and NAD synthesis.

The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs)

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

Mass Spectrometry- and Computational Structural Biology-Based Investigation of Proteins and Peptides

Recent developments of mass spectrometry (MS) allow us to identify, estimate, and characterize proteins and protein complexes. At the same time, structural biology helps to determine the protein structure and its structure-function relationship. Together, they aid to understand the protein structure, property, function, protein-complex assembly, protein-protein interaction, and dynamics. The present chapter is organized with illustrative results to demonstrate how experimental mass spectrometry can be combined with computational structural biology for detailed studies of protein's structures. We have used tumor differentiation factor protein/peptide as ligand and Hsp70/Hsp90 as receptor protein as examples to study ligand-protein interaction. To investigate possible protein conformation, we will describe two proteins-lysozyme and myoglobin. As an application of MS-based assignment of disulfide bridges, the case of the spider venom polypeptide Phα1β will also be discussed.

Chromatographic separation strategies for precision mass spectrometry to study protein-protein interactions and protein phosphorylation

Investigating protein-protein interactions and protein phosphorylation can be of great significance when studying biological processes and human diseases at the molecular level. However, sample complexity, presence of low abundance proteins, and dynamic nature of the proteins often impede in achieving sufficient analytical depth in proteomics research. In this regard, chromatographic separation methodologies have played a vital role in the identification and quantification of proteins in complex sample mixtures. The combination of peptide and protein fractionation techniques with advanced high-performance mass spectrometry has allowed the researchers to successfully study the protein-protein interactions and protein phosphorylation. Several new fractionation strategies for large scale analysis of proteins and peptides have been developed to study protein-protein interactions and protein phosphorylation. These emerging chromatography methodologies have enabled the identification of several hundred protein complexes and even thousands of phosphorylation sites in a single study. In this review, we focus on current workflow strategies and chromatographic tools, highlighting their advantages and disadvantages, and examining their associated challenges and future potential.

Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance

Exiguobacterium antarcticum strain B7 is a psychrophilic Gram-positive bacterium that possesses enzymes that can be used for several biotechnological applications. However, many proteins from its genome are considered hypothetical proteins (HPs). These functionally unknown proteins may indicate important functions regarding the biological role of this bacterium, and the use of bioinformatics tools can assist in the biological understanding of this organism through functional annotation analysis. Thus, our study aimed to assign functions to proteins previously described as HPs, present in the genome of E. antarcticum B7. We used an extensive in silico workflow combining several bioinformatics tools for function annotation, sub-cellular localization and physicochemical characterization, three-dimensional structure determination, and protein-protein interactions. This genome contains 2772 genes, of which 765 CDS were annotated as HPs. The amino acid sequences of all HPs were submitted to our workflow and we successfully attributed function to 132 HPs. We identified 11 proteins that play important roles in the mechanisms of adaptation to adverse environments, such as flagellar biosynthesis, biofilm formation, carotenoids biosynthesis, and others. In addition, three predicted HPs are possibly related to arsenic tolerance. Through an in vitro assay, we verified that E. antarcticum B7 can grow at high concentrations of this metal. The approach used was important to precisely assign function to proteins from diverse classes and to infer relationships with proteins with functions already described in the literature. This approach aims to produce a better understanding of the mechanism by which this bacterium adapts to extreme environments and to the finding of targets with biotechnological interest.

SPR Biosensors in Direct Molecular Fishing: Implications for Protein Interactomics

We have developed an original experimental approach based on the use of surface plasmon resonance (SPR) biosensors, applicable for investigation of potential partners involved in protein⁻protein interactions (PPI) as well as protein⁻peptide or protein⁻small molecule interactions. It is based on combining a SPR biosensor, size exclusion chromatography (SEC), mass spectrometric identification of proteins (LC-MS/MS) and direct molecular fishing employing principles of affinity chromatography for isolation of potential partner proteins from the total lysate of biological samples using immobilized target proteins (or small non-peptide compounds) as ligands. Applicability of this approach has been demonstrated within the frame of the Human Proteome Project (HPP) and PPI regulation by a small non-peptide biologically active compound, isatin.

Isolation, identification and in silico analysis of alpha-amylase gene of Aspergillus niger strain CSA35 obtained from cassava undergoing spoilage

In this investigation, a gene (CDF_Amyl) encoding extracellular α-amylase in Aspergillus niger strain CSA35 associated with cassava spoilage was amplified using specific primers and characterized in silico. The gene had a partial nucleotide sequence of 968 bp and encoded a protein of 222 aa residues with a molecular weight and isoelectric point of 25.13 kDa and 4.17, respectively. Its catalytic site was located in the active site domain. BLASTp analysis showed that the protein primary sequence of the α-amylase gene had 98% and 99% homologies with the α-amylase of A. niger and A. oryzae RIB40, respectively. The gene is more closely related to α-amylase genes from fungi than to bacterial, plant, or animal α-amylase genes. Restriction mapping of the gene showed it can be digested with restriction enzymes like NcoI, PstI, SmaI, and BcLI among others but not with EcoRI and EcoRV. Its protein product had a hydrophobicity score of - 0.43 but no transmembrane helix. The CDF_Amyl protein was subcellularly localized in the secretory pathway, an indication of its release into extracellular space after secretion. Also, the 3D structure of the CDF-Amyl protein was barrel-shaped with domains characteristic of α-amylases. The encoded α-amylase Vmax is 6.90 U/mg protein and Km is 6.70 mg/ml. It was concluded that the unique characteristics of the CDF_Amyl gene and its deduced protein could find applications in biotechnological, food and pharmaceutical industries where cloning and further modification of this gene would be required for product development and improvement.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking

Understanding protein-protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance. Protein-protein docking can afford the atomic structural details of protein-protein complexes, but the accurate prediction of the three-dimensional structures for protein-protein systems is still notoriously difficult due in part to the lack of an ideal scoring function for protein-protein docking. Compared with most scoring functions used in protein-protein docking, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) methodologies are more theoretically rigorous, but their overall performance for the predictions of binding affinities and binding poses for protein-protein systems has not been systematically evaluated. In this study, we first evaluated the performance of MM/PBSA and MM/GBSA to predict the binding affinities for 46 protein-protein complexes. On the whole, different force fields, solvation models, and interior dielectric constants have obvious impacts on the prediction accuracy of MM/GBSA and MM/PBSA. The MM/GBSA calculations based on the ff02 force field, the GB model developed by Onufriev et al. and a low interior dielectric constant (εin = 1) yield the best correlation between the predicted binding affinities and the experimental data (rp = -0.647), which is better than MM/PBSA (rp = -0.523) and a number of empirical scoring functions used in protein-protein docking (rp = -0.141 to -0.529). Then, we examined the capability of MM/GBSA to identify the possible near-native binding structures from the decoys generated by ZDOCK for 43 protein-protein systems. The results illustrate that the MM/GBSA rescoring has better capability to distinguish the correct binding structures from the decoys than the ZDOCK scoring. Besides, the optimal interior dielectric constant of MM/GBSA for re-ranking docking poses may be determined by analyzing the characteristics of protein-protein binding interfaces. Considering the relatively high prediction accuracy and low computational cost, MM/GBSA may be a good choice for predicting the binding affinities and identifying correct binding structures for protein-protein systems.

Ligandomics: a paradigm shift in biological drug discovery

As productivity of pharmaceutical research and development (R&D) for small-molecule drugs declines, the trend in drug discovery strategies is shifting towards biologics, which predominantly target secreted or cell surface proteins. Receptors and ligands are the most-valuable drug targets. In contrast to conventional approaches of discovering one ligand at a time, the emerging technology of ligandomics can systematically map disease-selective cellular ligands in the absence of molecular probes. Biologics targeting these ligands with disease selectivity have the advantages of high efficacy, minimal adverse effects, wide therapeutic indices, and low safety-related attrition rates. Therefore, ligandomics represents a paradigm shift to address the bottleneck of target discovery for biologics development.

Computational and Experimental Approaches to Predict Host-Parasite Protein-Protein Interactions

In host-parasite systems, protein-protein interactions are key to allow the pathogen to enter the host and persist within the host. The study of host-parasite molecular communication improves the understanding the mechanisms of infection, evasion of the host immune system and tropism across different tissues. Current trends in parasitology focus on unraveling host-parasite protein-protein interactions to aid the development of new strategies to combat pathogenic parasites with better treatments and prevention mechanisms. Due to the complexity of capturing experimentally these interactions, computational approaches integrating data from different sources (mainly "omics" data) become key to complement or support experimental approaches. Here, we focus on the application of experimental and computational methods in the prediction of host-parasite interactions and highlight the potential of each of these methods in specific contexts.

Prediction of Protein-Protein Interactions

The authors provide an overview of physical protein-protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field-highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Background

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis.

Results

Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous.

Conclusions

The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0346-4) contains supplementary material, which is available to authorized users.

LC-MS/MS-based targeted proteomics quantitatively detects the interaction between p53 and MDM2 in breast cancer

In breast cancer, p53 could be functionally compromised by interaction with several proteins. Among those proteins, MDM2 serves as a pivotal negative regulator and counteracts p53 activation. Thus, the ability to quantitatively and accurately monitor the changes in level of p53-MDM2 interaction with disease state can enable an improved understanding of this protein-protein interaction (PPI), provide a better insight into cancer development and allow the emergence of advanced treatments. However, rare studies have evaluated the quantitative extent of PPI including p53-MDM2 interaction so far. In this study, a LC-MS/MS-based targeted proteomics assay was developed and coupled with co-immunoprecipitation (Co-IP) for the quantification of p53-MDM2 complex. A p53 antibody with the epitope residing at 156-214 residues achieved the greatest IP efficiency. 321KPLDGEYFTLQIR333 (p53) and 327ENWLPEDK334 (MDM2) were selected as surrogate peptides in the targeted analysis. Stable isotope-labeled synthetic peptides were used as internal standards. An LOQ (limit of quantification) of 2ng/mL was obtained. Then, the assay was applied to quantitatively detect total p53, total MDM2 and p53-MDM2 in breast cells and tissue samples. Western blotting was performed for a comparison. Finally, a quantitative time-course analysis in MCF-7 cells with the treatment of nutlin-3 as a PPI inhibitor was also monitored.

BIOLOGICAL SIGNIFICANCE

Proteins do not function as single entities but rather as a team player that has to communicate. Protein-protein interaction (PPI), normally by means of non-covalent contact among binary or large protein complex, is essential for many cellular processes including cancer progression. Thus, the ability to quantitatively and accurately monitor the changes in level of PPI with disease state can enable an improved understanding of PPI, provide a better insight into cancer development and allow the emergence of advanced treatments. However, rare studies have evaluated the quantitative extent of PPI so far. The major issue of current available approaches is the trade-off between sensitivity and specificity. Thus, techniques with the ability to quantify PPIs with both high sensitivity (low false-negative rate) and high specificity (low false-positive rate) are eagerly desired. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based targeted proteomics has shown its potential to study biomolecules because of its high sensitivity, high selectivity and wide dynamic range. In this study, we made an effort to develop a LC-MS/MS-based targeted proteomics assay for the quantitative detection of p53-MDM2 interaction in breast cells and tissue samples.

α-Mangostin Extraction from the Native Mangosteen (Garcinia mangostana L.) and the Binding Mechanisms of α-Mangostin to HSA or TRF

In order to obtain the biological active compound, α-mangostin, from the traditional native mangosteen (Garcinia mangostana L.), an extraction method for industrial application was explored. A high yield of α-mangostin (5.2%) was obtained by extraction from dried mangosteen pericarps with subsequent purification on macroporous resin HPD-400. The chemical structure of α-mangostin was verified mass spectrometry (MS), nuclear magnetic resonance (¹H NMR and ¹³C NMR), infrared spectroscopy (IR) and UV-Vis spectroscopy. The purity of the obtained α-mangostin was 95.6% as determined by HPLC analysis. The binding of native α-mangostin to human serum albumin (HSA) or transferrin (TRF) was explored by combining spectral experiments with molecular modeling. The results showed that α-mangostin binds to HSA or TRF as static complexes but the binding affinities were different in different systems. The binding constants and thermodynamic parameters were measured by fluorescence spectroscopy and absorbance spectra. The association constant of HSA or TRF binding to α-mangostin is 6.4832×10⁵ L/mol and 1.4652×10⁵ L/mol at 298 K and 7.8619×10⁵ L/mol and 1.1582×10⁵ L/mol at 310 K, respectively. The binding distance, the energy transfer efficiency between α-mangostin and HSA or TRF were also obtained by virtue of the Förster theory of non-radiation energy transfer. The effect of α-mangostin on the HSA or TRF conformation was analyzed by synchronous spectrometry and fluorescence polarization studies. Molecular docking results reveal that the main interaction between α-mangostin and HSA is hydrophobic interactions, while the main interaction between α-mangostin and TRF is hydrogen bonding and Van der Waals forces. These results are consistent with spectral results.

Integrative analysis of human omics data using biomolecular networks

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

Analysis of the Bile Salt Export Pump (ABCB11) Interactome Employing Complementary Approaches

The bile salt export pump (BSEP, ABCB11) plays an essential role in the formation of bile. In hepatocytes, BSEP is localized within the apical (canalicular) membrane and a deficiency of canalicular BSEP function is associated with severe forms of cholestasis. Regulation of correct trafficking to the canalicular membrane and of activity is essential to ensure BSEP functionality and thus normal bile flow. However, little is known about the identity of interaction partners regulating function and localization of BSEP. In our study, interaction partners of BSEP were identified in a complementary approach: Firstly, BSEP interaction partners were co-immunoprecipitated from human liver samples and identified by mass spectrometry (MS). Secondly, a membrane yeast two-hybrid (MYTH) assay was used to determine protein interaction partners using a human liver cDNA library. A selection of interaction partners identified both by MYTH and MS were verified by in vitro interaction studies using purified proteins. By these complementary approaches, a set of ten novel BSEP interaction partners was identified. With the exception of radixin, all other interaction partners were integral or membrane-associated proteins including proteins of the early secretory pathway and the bile acyl-CoA synthetase, the second to last, ER-associated enzyme of bile salt synthesis.