Rapid Identification Of Proteins Research Articles

Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning.

Protein aggregation (PA) is a critical phenomenon associated with Alzheimer's and Parkinson's disease. Recent studies have suggested that factors like aggregation-prone regions (APRs) and β-strand interactions are crucial in understanding such behavior. While experimental methods have provided valuable insights, there has been a shift towards computational strategies, particularly machine learning, for their efficacy and speed. The challenge, however, lies in effectively incorporating structural information into these models. This study constructs a Graph Convolutional Network (GCN) to predict PA scores with the expanded and refined Protein Data Bank (PDB) and AlphaFold2.0 dataset. We employed AGGRESCAN3D 2.0 to calculate PA propensity and to enhance the dataset, we systematically separated multi polypeptide chains within PDB data into single polypeptide chains, removing redundancy. This effort resulted in a dataset comprising 302 032 unique PDB entries. Subsequently, we compared sequence similarity and obtained 22 774 Homo sapiens data from AlphaFold2.0. Using this expanded and refined dataset, the trained GCN model for PA prediction achieves a remarkable coefficient of determination (R 2) score of 0.9849 and a low mean absolute error (MAE) of 0.0381. Furthermore, the efficacy of the active learning process was demonstrated through its rapid identification of proteins with high PA propensity. Consequently, the active learning approach achieved an MAE of 0.0291 in expected improvement, surpassing other methods. It identified 99% of the target proteins by exploring merely 29% of the entire search space. This improved GCN model demonstrates promise in selecting proteins susceptible to PA, advancing protein science. This work contributes to the development of efficient computational tools for PA prediction, with potential applications in disease diagnosis and therapy.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.

Digerati – A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins

The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which are thought to be involved in host-pathogen interactions, virulence, and disease pathogenicity. Members of this family are genetically divergent and challenging to both identify and classify using conventional computational tools. Thus, advanced in silico methods are needed to identify proteins of this family for subsequent functional annotation efficiently. In this study, we developed the first deep learning-based approach, termed Digerati, for the rapid and accurate identification of PE and PPE family proteins. Digerati was built upon a multipath parallel hybrid deep learning framework, which equips multi-layer convolutional neural networks with bidirectional, long short-term memory, equipped with a self-attention module to effectively learn the higher-order feature representations of PE/PPE proteins. Empirical studies demonstrated that Digerati achieved a significantly better performance (∼18–20%) than alignment-based approaches, including BLASTP, PHMMER, and HHsuite, in both prediction accuracy and speed. Digerati is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE/PPE family members. The webserver and source codes of Digerati are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/Digerati/.

Mass spectrometry in snake venom research

Mass spectrometry allows rapid and reliable identification and characterisation of proteins and peptides in snake venoms. With the increasing availability of transcriptomic and genomic data, there is a growing database of protein sequences that is essential for protein identification. Snake venoms are analysed using a multi-dimensional proteomic approach known as ‚venomics‘. Proteins are first separated by one- or two-dimensional gel electrophoresis or reversed-phase liquid chromatography. The individual protein spots or fractions are digested enzymatically and the resulting peptides are analysed by mass spectrometry. The proteins are identified by comparing the mass spectra of the peptides with those in the database. High-performance mass spectrometers allow the analysis of venoms even without prior separation of the protein mixture. We have analysed the protein composition (proteome) of two European snake venoms of greatest medical interest, the nose-horned viper (Vipera a. ammodytes) and the common adder (Vipera b. berus). The nose-horned viper is the most venomous European snake. Although its bite is rarely fatal, a human wictim often needs to be observed in hospital and treated with an antivenom. The adder is the most widespread European venomous snake and its bite causes milder symptoms than the bite of the nose-horned viper in most cases. To explain the observed differences in the effects of the two venoms at the molecular level, a proteomic study was performed. We also analysed the proteome of the venom of the meadow viper (Vipera ursinii), the most threatened snake species in Europe. It does not pose a threat to humans. In the wild, it feeds mainly on insects, while in captivity it is fed on mice. A comparison of the proteome of the venom of snakes in the wild and snakes in captivity showed clear differences. Thus, the composition of snake venom is diet-dependent. Mass spectrometry is also a very useful tool in the characterisation of antivenoms (antivenomics) to determine their specificity and neutralising power.

Identification of adaptor proteins using the ANOVA feature selection technique.

The adaptor proteins play a crucially important role in regulating lymphocyte activation. Rapid and efficient identification of adaptor proteins is essential for understanding their functions. However, biochemical methods require not only expensive experimental costs, but also long experiment cycles and more personnel. Therefore, a computational method that could accurately identify adaptor proteins is urgently needed. To solve this issue, we developed a classifier that combined the support vector machine (SVM) with the composition of k-Spaced Amino Acid Pairs (CKSAAP) and the amino acid composition (AAC) to identify adaptor proteins. Analysis of variance (ANOVA) was used to select the optimized features which could generate the maximum prediction performance. By examining the proposed model on independent data, we found that the 447 optimized features could achieve an accuracy of 92.39% with an AUC of 0.9766, demonstrating the powerful capabilities of our model. We hope that the proposed model could provide more clues for studying adaptor proteins.

Protein Informatics and Vaccine Development: Cancer Case Study.

Clinical translation is a challenging step in the development of cancer vaccines and is found to be related to the complex nature of cancer immunology. Vaccine-based therapeutic strategies for cancer have gained consideration with the advent of vaccine technology as well as an understanding of cancer immunology. Immunotherapy has been widely used in the treatment of cancer. Some promising candidates have been identified to engineer cancer vaccines like Glycoprotein, Mucin 1, MHC protein, etc. It has benefited from the availability of advanced techniques for rapid identification and selection of proteins for precision engineering. Simultaneously, nanovaccines have been focused on target delivery and artificial intelligence-based approaches for personalized vaccine development. The manuscript summarizes the advances in the development of structurebased cancer vaccines along with the status of clinical studies and applications.

Rapid and easy identification of genes associated with nanoparticles from plant protein structure database

The study provides simple protocol for identifying unknown genes (DNA and RNA) and proteins present in the plants. The exact mechanism of nanoparticle synthesis from plant extracts is still unclear. Although functionalization of nanoparticle has been achieved by binding biomolecules and stabilizing through exchange of ligands and electrostatic interactions. This study will help in case of difficulty in extraction of proteins and DNA from nanoparticles due to less sample collection or degradation of active biomolecules. Plant extracts holds various biomolecules and can be used to presume the basic formulae of plant proteins by using software. The presence of DNA traces on silver nanoparticles confirmed and DNA sequence was extracted from NCBI gene bank. Homology modeling is an alternative method to predict 3D structure of proteins in the absence of experimental results. DNA gel electrophoresis and quantification of biomolecules and 3D protein structures have built from known sequences of conforming family members of protein using SWISS-MODEL. A major advantage of automated homology modeling is creating protein models that are easy to design the structure. Protein sequencing is continuously evolving nowadays. Experts are improving SWISS-MODEL to enhance the efficient transition. The study will help in easy and rapid identification of genes and proteins present on the surface of biological nanoparticles. Structural analysis of protein (proteomics) helps to identify novel functions that could facilitate designing of efficient biosensors.

Isobaric Tags for Relative and Absolute Quantitation Identification of Blood Proteins Relevant to Paroxetine Response in Patients With Major Depressive Disorder.

ObjectivesIsobaric tags for relative and absolute quantitation (iTRAQ) is a proteomic investigation that could be utilized for rapid identification and quantification of proteins, which we would use to identify differentially expressed proteins in treatment responsive patients with major depressive disorder (MDD).MethodsSix treatment responsive patients of MDD were recruited, and their peripheral blood mononuclear cell (PBMC) were collected before and after 4 weeks of paroxetine treatment. iTRAQ and Mascot search engine were used to detect differentially expressed proteins, which were then validated by Western blot.ResultsTwo thousand one hundred and fifty three proteins were screened, and seven proteins showed differences of more than two-fold and 62 proteins with a differences of less than two-fold. Six proteins with commercially available antibodies were identified, and were validated by Western blot in 10 paroxetine responsive MDD patients. Putative hydroxypyruvate isomerase (HYI), eukaryotic translation initiation factor 4H (eIF4H), and RNA binding motif 8A (RBM8A) had statistically significant differences before and after treatment in the validation. Data are available via ProteomeXchange with identifier PXD028947.ConclusionsBy using iTRAQ and Western blot, we were able to identify HYI, eIF4H, and RAM8a to be the potential predictors of paroxetine treatment response in patients with MDD. This finding could help establish future individualized medicine.

Snapshot imprinting: rapid identification of cancer cell surface proteins and epitopes using molecularly imprinted polymers

Proteomic mapping of cell surfaces is an invaluable tool for drug development and clinical diagnostics. This work describes a new ‘snapshot imprinting’ method designed to obtain proteomic maps of cell surfaces, with the aim of identifying cell surface markers and epitopes for diagnostic and therapeutic applications. The analysis of two cancer cell lines, HN5 and MDA-MB-468, is described herein as a proof of concept, along with the selective targeting of three identified epitopes of epidermal growth factor receptor using molecularly imprinted polymer nanoparticles. 438 proteins were identified using this technique, with 283 considered to be transmembrane or extracellular proteins. The major advantage of the molecular imprinting approach developed here is the ability to analyse cell surface proteins without tedious fractionation, affinity separation or labelling. We believe that this system of protein analysis may provide a basic molecular diagnostics toolbox for precise, personalised treatment of cancer and other diseases.

Strategies for Rapid Identification of Acinetobacter baumannii Membrane Proteins and Polymyxin B's Effects.

Acinetobacter baumannii, especially multidrug resistant Acinetobacter baumannii, is a notable source of pressure in the areas of public health and antibiotic development. To overcome this problem, attention has been focused on membrane proteins. Different digestion methods and extraction detergents were examined for membrane proteome sample preparation, and label-free quantitative and targeted proteome analyses of the polymyxin B-induced Acinetobacter baumannii ATCC 19606 membrane proteome were performed based on nano LC-MS/MS. Ultracentrifugation of proteins at a speed of 150,000×g, digestion by trypsin, filter-aided sample preparation, and detergents such as lauryldimethylamine-N-oxide were proved as a fast and effective way for identification of membrane proteome by nano LC-MS/MS. Upon treatment with polymyxin B, expression levels of 15 proteins related to membrane structure, transporters, cell surface, and periplasmic space were found to be significantly changed. Furthermore, targeted proteome was also used to confirm these changes. A relatively rapid membrane proteome preparation method was developed, and a more comprehensive view of changes in the Acinetobacter baumannii membrane proteome under polymyxin B pressure was obtained.

Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion.

Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Development of a miniature protein mass spectrometer capable of analyzing native proteins

Current miniature mass spectrometers were usually designed for the detection of small and medium size molecules, including volatile (semi-volatile) compounds, drugs and lipids. In this study, a miniature protein mass spectrometer was developed in this work, which could serve as a biosensor for the rapid identification of proteins as well as their conformations. A linear ion trap with a field radius of 2.5 mm was designed to extend mass range of the instrument to over 6500 Th. Mass resolution and sensitivity of the instrument were also optimized for protein ions by increasing the buffer gas pressure and using a high-gain Faraday detector. It is then demonstrated that the mass spectra of native proteins, such as IgG1, could be acquired by coupling the instrument with a soft electrospray ionization source. As a proof-of-concept demonstration, results suggest that the current instrument could be used to identify target proteins and probe/distinguish their conformations in solutions.

Identification of Antibacterial Immunity Proteins in <em>Escherichia coli</em> using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

Here we present a protocol for the rapid identification of proteins produced by genomically sequenced pathogenic bacteria using MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. Metastable protein ions fragment because of the aspartic acid effect and this specificity is exploited for protein identification.

In vivo discovery of RNA proximal proteins via proximity-dependent biotinylation

ABSTRACT RNA molecules function as messenger RNAs (mRNAs) that encode proteins and noncoding transcripts that serve as adaptor molecules, structural components, and regulators of genome organization and gene expression. Their function and regulation are largely mediated by RNA binding proteins (RBPs). Here we present RNA proximity labelling (RPL), an RNA-centric method comprising the endonuclease-deficient Type VI CRISPR-Cas protein dCas13b fused to engineered ascorbate peroxidase APEX2. RPL discovers target RNA proximal proteins in vivo via proximity-based biotinylation. RPL applied to U1 identified proteins involved in both U1 canonical and noncanonical functions. Profiling of poly(A) tail proximal proteins uncovered expected categories of RBPs and provided additional evidence for 5ʹ-3ʹ proximity and unexplored subcellular localizations of poly(A)+ RNA. Our results suggest that RPL allows rapid identification of target RNA binding proteins in native cellular contexts, and is expected to pave the way for discovery of novel RNA–protein interactions important for health and disease.

Hevea brasiliensis latex proteomics: a review of analytical methods and the way forward.

Natural rubber or latex from the Hevea brasiliensis is an important commodity in various economic sectors in today's modern society. Proteins have been detected in latex since the early twentieth century, and they are known to regulate various biological pathways within the H. brasiliensis trees such as the natural rubber biosynthesis, defence against pathogens, wound healing, and stress tolerance. However, the exact mechanisms of the pathways are still not clear. Proteomic analyses on latex have found various proteins and revealed how they fit into the mechanisms of the biological pathways. In the past three decades, there has been rapid latex protein identification due to the improvement of latex protein extraction methods, as well as the emergence of two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS). In this manuscript, we reviewed the methods of latex protein extraction that keeps on improving over the past three decades as well as the results of numerous latex protein identification and quantitation.

Label-Free Multiple Reaction Monitoring, a Promising Method for Quantification Analyses of Specific Proteins in Bacteria.

Bacillus subtilis produces eight industrially important exo-proteases. For the detection of proteases, the activity- and antibody-based assays are normally used. Current activity-based assays require expensive multiplex chemical substrates which allow specificity determination of each enzyme. In this study, we provide evidences pertaining to the usefulness of the label-free multiple reaction monitoring (MRM) assay for a rapid identification and quantitation of specific proteins in bacteria. We used wild-type B. pumilus cells producing at least two serine proteases, subtilisin-like protease (AprBp) and glutamyl endopeptidase (GseBp), as well as optimized recombinant B. subtilis cells containing the same protease genes under control of the LIKE expression system. The Skyline software was used for the selection of three specific proteotypic peptides and their fragment ions for quantification and confirmation of AprBp and GseBp in complex mixtures. MRM indicated that the production of AprBp and GseBp exo-enzymes were respectively 0.9- and 26.6-fold higher in the culture medium of B. pumilus strain in comparison to the recombinant B. subtilis strains carrying optimized LIKE expression systems under identical conditions. The developed procedure in this study is fast, easy to perform and dependable. Additionally, it achieves accurate proteins identification and quantification in complex mixture.

Identification of Target Protein for Bio-active Small Molecule Using Photo-cross Linked Beads and MALDI-TOF Mass Spectrometry

Development of methods for protein identification is one of the important aspects of proteomics. Here, we report a protocol for the preparation of compound conjugated beads by photo-crosslinking, affinity purification, gel electrophoresis, and highly sensitive mass spectrometric assay for drug-target identification. Although there are several other methods used for drug-target identification, such as biochemical fractionation or radioactive ligand binding assay, affinity purification is widely used for its straight-forward and easy approach. To identify the target protein of an inhibitor of cancer cell-accelerated fibroblast migration, we prepared the inhibitor-conjugated beads by photo-crosslinking. Proteins were pulled down from cell lysates by the compound beads and separated by SDS-PAGE, and a specifically pulled down protein was cut out, trypsin-digested, analyzed using matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF-MS) and identified by peptide mass fingerprinting (PMF) method. Since the photo-crosslinking enables the immobilization of ligands on an affinity matrix in a functional group-independent manner, we do not have to determine the functional group of the compound to conjugate the matrix. In addition, as compared to other MS techniques such as electrospray ionization, MALDI offers a less complex sample preparation procedure and higher sensitivity, and thus is better suited for the rapid identification of proteins isolated by gel electrophoresis.

Aggregation-induced emission based one-step "lighting up" sensor array for rapid protein identification.

Based on the distinct fingerprint-like fluorescence responses generated by different electrostatic and hydrophobic interactions between three kinds of self-designed water-soluble aggregation-induced emission (AIE) fluorogens (AIEgens) and proteins, a fast responsive (10 min) and one-step "lighting up" fluorescent sensor array for rapid protein discrimination was developed.

Ion-Ion Proton Transfer and Parallel Ion Parking for the Analysis of Mixtures of Intact Proteins on a Modified Orbitrap Mass Analyzer

We have enabled parallel ion parking on a modified Orbitrap Elite™ as a way to control ion-ion proton transfer reactions via selective activation of a range of ions. The result is the concentration of the majority of ion current from multiple charge states of each precursor proteoform into a single charge state, maximizing signal intensity and increasing effective sensitivity compared to conventional MS1 spectra. These techniques were applied in an on-line HPLC, data-dependent MS/MS analysis of intact E. coli ribosomal proteins with HCD fragmentation. With one injection, all but two ribosomal proteins were selected for fragmentation and subsequently identified. The techniques described facilitate rapid identification of intact proteins in complex mixtures and an enhanced ability to observe proteins of low abundance.

In Silico Analysis of Bioactive Peptides Released from Giant Grouper (Epinephelus lanceolatus) Roe Proteins Identified by Proteomics Approach.

Major proteins contained in dried giant grouper roe (GR) such as vitellogenin (from Epinephelus coioides; NCBI accession number: AAW29031.1), apolipoprotein A-1 precursor (from Epinephelus coioides; NCBI accession number: ACI01807.1) and apolipoprotein E (from Epinephelus bruneus; NCBI accession number: AEB31283.1) were characterized through compiled proteomics techniques (SDS-PAGE, in-gel digestion, mass spectrometry and on-line Mascot database analysis). These proteins were subjected to in silico analysis using BLAST and BIOPEP-UWM database. Sequence similarity search by BLAST revealed that the aligned vitellogenin sequences from Epinephelus coioides and Epinephelus lanceolatus share 70% identity, which indicates that the sequence sample has significant similarity with proteins in sequence databases. Moreover, prediction of potential bioactivities through BIOPEP-UWM database resulted in high numbers of peptides predominantly with dipeptidyl peptidase-IV (DPP-IV) and angiotensin-I-converting enzyme (ACE-I) inhibitory activities. Pepsin (pH > 2) was predicted to be the most promising enzyme for the production of bioactive peptides from GR protein, which theoretically released 82 DPP-IV inhibitory peptides and 47 ACE-I inhibitory peptides. Overall, this work highlighted the potentiality of giant grouper roe as raw material for the generation of pharmaceutical products. Furthermore, the application of proteomics and in silico techniques provided rapid identification of proteins and useful prediction of its potential bioactivities.