Methods in molecular biology最新文献

SNARE proteins play a pivotal role in membrane fusion and various cellular processes. Accurate identification of SNARE proteins is crucial for elucidating their functions in both health and disease contexts. This chapter presents a novel approach employing multiscan convolutional neural networks (CNNs) combined with position-specific scoring matrix (PSSM) profiles to accurately recognize SNARE proteins. By leveraging deep learning techniques, our method significantly enhances the accuracy and efficacy of SNARE protein classification. We detail the step-by-step methodology, including dataset preparation, feature extraction using PSI-BLAST, and the design of the multiscan CNN architecture. Our results demonstrate that this approach outperforms existing methods, providing a robust and reliable tool for bioinformatics research.

The bimolecular fluorescence complementation (BiFC) technique is a powerful tool for visualizing protein-protein interactions in vivo. It involves genetically fused nonfluorescent fragments of green fluorescent protein (GFP) or its variants to the target proteins of interest. When these proteins interact, the GFP fragments come together, resulting in the reconstitution of a functional fluorescent protein complex that can be observed using fluorescence microscopy. In this chapter, we provide a detailed overview of the BiFC method and its application in studying protein-protein interactions in mouse hippocampal neurons. We discuss experimental procedures, including virus construct design, neuronal transduction, and imaging optimization. Additionally, we explore complementary assays for result validation and address potential challenges associated with BiFC experiments in the neuronal system. Overall, the BiFC offers researchers a valuable approach for investigating the spatial and temporal dynamics of protein interactions in living neuronal cells.

Metabolic profiling (untargeted metabolomics) aims for a global unbiased analysis of metabolites in a cell or biological system. It remains a highly useful research tool used across various analytical platforms. Incremental improvements across multiple steps in the analytical process may have large consequences for the end quality of the data. Thus, this chapter concentrates on which aspects of quality assurance can be implemented by a lab in the (pre-)analytical stages of the analysis to improve the overall end quality of their data. The scope of this chapter is limited to liquid-chromatography-mass spectrometry (LC-MS)-based profiling, which is one of the most widely utilized platforms, although the general principles are applicable to all metabolomics experiments.

Untargeted analysis by LC-MS is a valuable tool for metabolic profiling (metabonomics/metabolomics), and applications of this technology have grown rapidly over the past decade. LC-MS offers advantages of speed, sensitivity, relative ease of sample preparation, and large dynamic range compared to other platforms in this role. However, like any analytical approach, there are still drawbacks and challenges that have to be overcome, some of which are being addressed by advances in both column chemistries and instrumentation. In particular, the combination of LC-MS with ion mobility offers many new possibilities for improved analyte separation, detection, and structural identification. There are many untargeted LC-MS approaches which can be applied to metabolic phenotyping, and these usually need to be optimized for the type of sample, the nature of the study, or the biological question. Some of the main LC-MS approaches for untargeted metabolic phenotyping are described in detail in the following protocol.

Influenza A virus (IAV) has an eight-segmented, single-stranded, negative-sense viral genomic RNA (vRNA). Each vRNA strand associates with nucleoproteins and an RNA-dependent RNA polymerase complex to form a viral ribonucleoprotein (vRNP) complex. IAV vRNPs adopt a flexible double-helical configuration that varies in length. Although the transcription and replication of vRNA take place in the context of vRNPs, the precise structural conformation of vRNPs during RNA synthesis remains partially elucidated. To unravel the intricate ultrastructure of the vRNP, it is necessary to purify it while preserving its native functionality. Herein, we introduce a comprehensive protocol for the purification of IAV vRNPs using glycerol gradient ultracentrifugation. Furthermore, we provide a method for the high-speed atomic force microscopy observation of vRNPs during viral RNA synthesis.

Influenza A viruses are a major health care burden, and their biology has been intensely studied for decades. However, many details of virus infection are still elusive, requiring the development of refined and advanced technologies. Super-resolution microscopy allows the study of virus replication at the scale of an infecting virus, offering an exciting perspective on previously unseen mechanistic details of infection. Here we describe the materials and procedures required to perform single-molecule imaging of virus-receptor interaction in live cells. We further provide hints and tips on how to analyze and visualize the obtained datasets.

F-type Adenosine triphosphate (ATP) synthase is a membrane-bound macromolecular complex, which is responsible for the synthesis of ATP, the universal energy source in living cells. This enzyme uses the proton- or sodium-motive force to power ATP synthesis by a unique rotary mechanism and can also operate in reverse, ATP hydrolysis, to generate ion gradients across membranes. The F₁F_o-ATP synthases from bacteria consist of eight different structural subunits, forming a complex of ~550 kDa in size. In the bacterium Ilyobacter tartaricus, the ATP synthase has the stoichiometry α₃β₃γδεab₂c₁₁. This chapter describes a wet-lab working protocol for the purification of several tens of milligrams of pure, heterologously (E. coli-) produced I. tartaricus Na⁺-driven F₁F_o-ATP synthase and its subsequent efficient reconstitution into proteoliposomes. The methods are useful for a broad range of subsequent biochemical and biotechnological applications.

Proteogenomics is a growing "multi-omics" research area that combines mass spectrometry-based proteomics and high-throughput nucleotide sequencing technologies. Proteogenomics has helped in genomic annotation for organisms whose complete genome sequences became available by using high-throughput DNA sequencing technologies. Apart from genome annotation, this multi-omics approach has also helped researchers confirm expression of variant proteins belonging to unique proteoforms that could have resulted from single-nucleotide polymorphism (SNP), insertion and deletions (Indels), splice isoforms, or other genome or transcriptome variations.A proteogenomic study depends on a multistep informatics workflow, requiring different software at each step. These integrated steps include creating an appropriate protein sequence database, matching spectral data against these sequences, and finally identifying peptide sequences corresponding to novel proteoforms followed by variant classification and functional analysis. The disparate software required for a proteogenomic study is difficult for most researchers to access and use, especially those lacking computational expertise. Furthermore, using them disjointedly can be error-prone as it requires setting up individual parameters for each software. Consequently, reproducibility suffers. Managing output files from each software is an additional challenge. One solution for these challenges in proteogenomics is the open-source Web-based computational platform Galaxy. Its capability to create and manage workflows comprised of disparate software while recording and saving all important parameters promotes both usability and reproducibility. Here, we describe a workflow that can perform proteogenomic analysis on a Galaxy-based platform. This Galaxy workflow facilitates matching of spectral data with a customized protein sequence database, identifying novel protein variants, assessing quality of results, and classifying variants along with visualization against the genome.

The journey from laboratory research to clinical practice is marked by significant advancements in the fields of single-cell technologies and non-coding RNA (ncRNA) research. This convergence may reshape our approach to personalized medicine, offering groundbreaking insights and treatments in various clinical settings. This chapter discusses advancements in (nc)RNAs in the clinics, innovations in single-cell technologies and algorithms, and the impact on actual precision medicine, showing the integration of single-cell and ncRNA research can have a tangible impact on precision medicine. Case studies in Oncology, Immunology, and other fields demonstrate how these technologies can guide treatment decisions, tailor therapies to individual patients, and improve outcomes. This approach is particularly potent in addressing diseases with high inter- and intra-tumor heterogeneity. The final sections address standardization, data integration, and analysis challenges because the complexity and volume of data generated by single-cell and ncRNA research poses significant challenges. Medical Informatics is not just a support tool but could be seen as a pivotal component in advancing clinical applications of single-cell and ncRNA research by bridging the gap between bench and bedside. The future of personalized medicine depends on our ability to harness the power of these technologies, and Medical Informatics in combination with ncRNA and single-cell technologies may stand at the forefront of this endeavor.