Method For Identification Of Proteins Research Articles

A generalized protein identification method for novel and diverse sequencing technologies.

Protein sequencing is a rapidly evolving field with much progress towards the realization of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Based on a hypothetical sequencing device that can simulate several novel technologies, we show that on the human protein database (N = 20181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments, and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.

LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning.

The recognition of DNA Binding Proteins (DBPs) plays a crucial role in understanding biological functions such as replication, transcription, and repair. Although current sequence-based methods have shown some effectiveness, they often fail to fully utilize the potential of deep learning in capturing complex patterns. This study introduces a novel model, LGC-DBP, which integrates Long Short-Term Memory (LSTM), Gated Inception Convolution, and Improved Channel Attention mechanisms to enhance the prediction of DBPs. Initially, the model transforms protein sequences into Position Specific Scoring Matrices (PSSM), then processed through our deep learning framework. Within this framework, Gated Inception Convolution merges the concepts of gating units with the advantages of Graph Convolutional Network (GCN) and Dilated Convolution, significantly surpassing traditional convolution methods. The Improved Channel Attention mechanism substantially enhances the model's responsiveness and accuracy by shifting from a single input to three inputs and integrating three sigmoid functions along with an additional layer output. These innovative combinations have significantly improved model performance, enabling LGC-DBP to recognize and interpret the complex relationships within DBP features more accurately. The evaluation results show that LGC-DBP achieves an accuracy of 88.26% and a Matthews correlation coefficient of 0.701, both surpassing existing methods. These achievements demonstrate the model's strong capability in integrating and analyzing multi-dimensional data and mark a significant advancement over traditional methods by capturing deeper, nonlinear interactions within the data.

Classifying alkaliphilic proteins using embeddings from protein language model

Alkaliphilic proteins have great potential as biocatalysts in biotechnology, especially for enzyme engineering. Extensive research has focused on exploring the enzymatic potential of alkaliphiles and characterizing alkaliphilic proteins. However, the current method employed for identifying these proteins that requires web lab experiment is time-consuming, labor-intensive, and expensive. Therefore, the development of a computational method for alkaliphilic protein identification would be invaluable for protein engineering and design. In this study, we present a novel approach that uses embeddings from a protein language model called ESM-2(3B) in a deep learning framework to classify alkaliphilic and non-alkaliphilic proteins. To our knowledge, this is the first attempt to employ embeddings from a pre-trained protein language model to classify alkaliphilic protein. A reliable dataset comprising 1,002 alkaliphilic and 1,866 non-alkaliphilic proteins was constructed for training and testing the proposed model. The proposed model, dubbed ALPACA, achieves performance scores of 0.88, 0.84, and 0.75 for accuracy, f1-score, and Matthew correlation coefficient respectively on independent dataset. ALPACA is likely to serve as a valuable resource for exploring protein alkalinity and its role in protein design and engineering.

A Tandem-Affinity Purification Method for Identification of Primary Intracellular Drug-Binding Proteins.

In the field of drug discovery, understanding how small molecule drugs interact with cellular components is crucial. Our study introduces a novel methodology to uncover primary drug targets using Tandem Affinity Purification for identification of Drug-Binding Proteins (TAP-DBP). Central to our approach is the generation of a FLAG-hemagglutinin (HA)-tagged chimeric protein featuring the FKBP12(F36V) adaptor protein and the TurboID enzyme. Conjugation of drug molecules with the FKBP12(F36V) ligand allows for the coordinated recruitment of drug-binding partners effectively enabling in-cell TurboID-mediated biotinylation. By employing a tandem affinity purification protocol based on FLAG-immunoprecipitation and streptavidin pulldown, alongside mass spectrometry analysis, TAP-DBP allows for the precise identification of drug-primary binding partners. Overall, this study introduces a systematic, unbiased method for identification of drug-protein interactions, contributing a clear understanding of target engagement and drug selectivity to advance the mode of action of a drug in cells.

Proteomic Analysis of Dictyostelium discoideum by Mass Spectrometry.

The large-scale proteomic analysis of Dictyostelium discoideum has contributed to our understanding of intracellular as well as secreted proteins in this versatile model eukaryote. Mass spectrometry-based proteomic analysis is a robust, sensitive, and rapid analytical method for identification and characterization of proteins extracted from tissues, cells, cell fractions, or pull-down assays. The availability of core facilities which make proteomics inexpensive and easy to do has facilitated a wide range of research projects. In this chapter, we present a simple standard methodology to extract proteins and prepare samples from D. discoideum for mass spectrometry and methods to analyze the identified proteins.

Protein nanobarcodes enable single-step multiplexed fluorescence imaging.

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel.

PRIONS AND AMYLOIDS AS SPATIAL TEMPLATES OF THE PROTEOME

Until recently, studies of amyloids were aimed exclusively at revealing their role in the occurrence of dangerous diseases in humans and animals. However, they are widely distributed in nature and are involved in the regulation of essential vital processes in representatives of all three domains of the living world: archaea, bacteria and eukaryotes. The question of the biological significance of the prions – a special class of amyloids, isstill under discussion. The discovery of new functional amyloids became possible due to the development of the bioinformatic and proteomic methods for identification of amyloid-forming proteins. The review describes the way from the study of pathological amyloid structures to the investigation of adaptive amyloids inbacteria, plants, and animals. The importance of the amyloid structure, based on the principle of conformation template copying, as one of the most important forms of supramolecular organization of proteins isshown.

Alzheimer's Disease Protein Relevance Analysis Using Human and Mouse Model Proteomics Data.

The principles governing genotype-phenotype relationships are still emerging(1-3), and detailed translational as well as transcriptomic information is required to understand complex phenotypes, such as the pathogenesis of Alzheimer's disease. For this reason, the proteomics of Alzheimer disease (AD) continues to be studied extensively. Although comparisons between data obtained from humans and mouse models have been reported, approaches that specifically address the between-species statistical comparisons are understudied. Our study investigated the performance of two statistical methods for identification of proteins and biological pathways associated with Alzheimer's disease for cross-species comparisons, taking specific data analysis challenges into account, including collinearity, dimensionality reduction and cross-species protein matching. We used a human dataset from a well-characterized cohort followed for over 22 years with proteomic data available. For the mouse model, we generated proteomic data from whole brains of CVN-AD and matching control mouse models. We used these analyses to determine the reliability of a mouse model to forecast significant proteomic-based pathological changes in the brain that may mimic pathology in human Alzheimer's disease. Compared with LASSO regression, partial least squares discriminant analysis provided better statistical performance for the proteomics analysis. The major biological finding of the study was that extracellular matrix proteins and integrin-related pathways were dysregulated in both the human and mouse data. This approach may help inform the development of mouse models that are more relevant to the study of human late-onset Alzheimer's disease.

PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer.

As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification. In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence "images". Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins. The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.

Identification of membrane proteins regulated by ADAM15 by SUSPECS proteomics.

ADAM15 is a member of the disintegrin-metalloproteinase family of sheddases, which plays a role in several biological processes including cartilage homeostasis. In contrast with well-characterized ADAMs, such as the canonical sheddases ADAM17 and ADAM10, little is known about substrates of ADAM15 or how the enzyme exerts its biological functions. Herein, we used "surface-spanning enrichment with click-sugars (SUSPECS)" proteomics to identify ADAM15 substrates and/or proteins regulated by the proteinase at the cell surface of chondrocyte-like cells. Silencing of ADAM15 by siRNAs significantly altered membrane levels of 13 proteins, all previously not known to be regulated by ADAM15. We used orthogonal techniques to validate ADAM15 effects on 3 of these proteins which have known roles in cartilage homeostasis. This confirmed that ADAM15-silencing increased cell surface levels of the programmed cell death 1 ligand 2 (PDCD1LG2) and reduced cell surface levels of vasorin and the sulfate transporter SLC26A2 through an unknown post-translational mechanism. The increase of PDCD1LG2 by ADAM15 knockdown, a single-pass type I transmembrane protein, suggested it could be a proteinase substrate. However, shed PDCD1LG2 could not be detected even by a data-independent acquisition mass spectrometry, a highly sensitive method for identification and quantification of proteins in complex protein samples, suggesting that ADAM15 regulates PDCD1LG2 membrane levels by a mechanism different from ectodomain shedding.

Genetically encoded photocatalytic protein labeling enables spatially-resolved profiling of intracellular proteome

Mapping the subcellular organization of proteins is crucial for understanding their biological functions. Herein, we report a reactive oxygen species induced protein labeling and identification (RinID) method for profiling subcellular proteome in the context of living cells. Our method capitalizes on a genetically encoded photocatalyst, miniSOG, to locally generate singlet oxygen that reacts with proximal proteins. Labeled proteins are conjugated in situ with an exogenously supplied nucleophilic probe, which serves as a functional handle for subsequent affinity enrichment and mass spectrometry-based protein identification. From a panel of nucleophilic compounds, we identify biotin-conjugated aniline and propargyl amine as highly reactive probes. As a demonstration of the spatial specificity and depth of coverage in mammalian cells, we apply RinID in the mitochondrial matrix, capturing 477 mitochondrial proteins with 94% specificity. We further demonstrate the broad applicability of RinID in various subcellular compartments, including the nucleus and the endoplasmic reticulum (ER). The temporal control of RinID enables pulse-chase labeling of ER proteome in HeLa cells, which reveals substantially higher clearance rate for secreted proteins than ER resident proteins.

Toxin and phage production from pathogenic E. coli by antibiotic induction analyzed by chemical reduction, MALDI-TOF-TOF mass spectrometry and top-down proteomic analysis.

Shiga toxin-producing Escherichia coli (STEC) are an on-going threat to public health and agriculture. Our laboratory has developed a rapid method for identification of Shiga toxin (Stx), bacteriophage and host proteins produced from STEC. We demonstrate this technique on two genomically sequenced STEC O145:H28 strains linked to two major outbreaks of foodborne illness occurring in 2007 (Belgium) and 2010 (Arizona). Our approach is to induce expression of stx, prophage and host genes by antibiotic exposure, chemically reduce samples and identify protein biomarkers from unfractionated samples using MALDI-TOF-TOF, tandem mass spectrometry (MS/MS) and post-source decay (PSD). The protein mass and prominent fragment ions were used to identify protein sequences using top-down proteomic software developed in-house. Prominent fragment ions are the result of polypeptide backbone cleavage (PBC) resulting from the aspartic acid effect fragmentation mechanism. The B-subunit of Stx and acid-stress proteins HdeA and HdeB were identified in both STEC strains in their intramolecular disulfide bond-intact and reduced states. In addition, two cysteine-containing phage tail proteins were detected and identified from the Arizona strain but only under reducing conditions which suggests that bacteriophage complexes are bound by intermolecular disulfide bonds. An acyl carrier protein (ACP) and a phosphocarrier protein (HPr) were also identified from the Belgium strain. ACP was post-translationally modified with attachment of a phosphopantetheine linker at residue S36. The abundance of ACP (plus linker) was significantly increased upon chemical reduction suggesting the release of fatty acids bound to the ACP + linker at a thioester bond. MS/MS-PSD revealed dissociative loss of the linker from the precursor ion as well as fragment ions with and without the attached linker consistent with its attachment at S36. This study demonstrates the advantages of chemical reduction in facilitating the detection and top-down identification of protein biomarkers of pathogenic bacteria.

Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19

BackgroundSARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool.MethodsPatients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a “particles in exhaled air” (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92.ResultsSignificant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs.ConclusionOur results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.

Key protein identification by integrating protein complex information and multi-biological features.

Identifying key proteins based on protein-protein interaction networks has emerged as a prominent area of research in bioinformatics. However, current methods exhibit certain limitations, such as the omission of subcellular localization information and the disregard for the impact of topological structure noise on the reliability of key protein identification. Moreover, the influence of proteins outside a complex but interacting with proteins inside the complex on complex participation tends to be overlooked. Addressing these shortcomings, this paper presents a novel method for key protein identification that integrates protein complex information with multiple biological features. This approach offers a comprehensive evaluation of protein importance by considering subcellular localization centrality, topological centrality weighted by gene ontology (GO) similarity and complex participation centrality. Experimental results, including traditional statistical metrics, jackknife methodology metric and key protein overlap or difference, demonstrate that the proposed method not only achieves higher accuracy in identifying key proteins compared to nine classical methods but also exhibits robustness across diverse protein-protein interaction networks.

A conceptual electrophoresis-mediated protein identification method by frictional coefficient during motion under electrical and magnetic interference

Protein Identification (PI) based on conventional Mass Spectrometry (MS) is prone to errors. These errors are a result of the inability to solubilize the protein, interference of detergents, usage of wrong protease enzymes, inability to cleavage of the target protein by proteases, and interference of endogenous or exogenous contaminant molecules and calibrants. Conventional Mass Spectrometric PI is solely based on ionization and mass trajectory of protein in a vacuum. Therefore, it is crucial to develop and modify the existing technique not to be entirely dependent on the ionization and mass of the protein molecule in a vacuum. Other than the mass and an overall electrostatic charge of a protein, characteristic steric hindrance on a protein while travelling through a Polyacrylamide Gel matrix, can be used for accurate identification of proteins in Mass Spectrometry. A concept for a modified Mass Spectrometric method based on a Polyacrylamide Gel platform, to utilize the steric hindrance and friction coefficient of the moving protein under the influence of electric field and magnetic fields is proposed in the article. The theoretical approach of the concept is based on the laws of motion, electrical fields, electromagnetism, and Fleming's Left Hand Rule. In this concept, on a Polyacrylamide Gel platform, in an electric field, protein is accelerated to a constant velocity characteristic of the protein introduced to a magnetic field where the radius of the circular trajectory and ratio of mass and overall surface charge of the accelerated protein is measured. This article directs attention to the modificati on of Mass Spectrometry to improve reliability and accuracy in protein identification.

Placental macrophage responses to viral and bacterial ligands and the influence of fetal sex.

Bacterial and viral infections of the placenta are associated with inflammation and adverse pregnancy outcomes. Hofbauer cells (HBCs) are fetal-origin macrophages in the placenta, proposed to protect the fetus from vertical pathogen transmission. We performed quantitative proteomics on term HBCs under resting conditions and following exposure to bacterial and viral pathogen-associated molecular patterns (PAMPs), and investigated the contribution of fetal sex. Resting HBCs expressed proteins pertinent to macrophage function, including chemokines, cytokines, Toll-like receptors, and major histocompatibility complex class I and II molecules. HBCs mounted divergent responses to bacterial versus viral PAMPs but exhibited protein expression changes suggestive of a more pro-inflammatoryphenotype. A comparison between male and female HBCs showed that the latter mounted a stronger and wider response. Here, we provide a comprehensive understanding of the sex-dependent responses of placental macrophages to infectious triggers, which were primarily associated with lipid metabolism in males and cytoskeleton organization in females.

Optimized Method for Cell Surface Protein Identification on Primary B-Cell Precursor Acute Lymphoblastic Leukemia Cells

Optimized Method for Cell Surface Protein Identification on Primary B-Cell Precursor Acute Lymphoblastic Leukemia Cells

Synthesis of Surface-Functionalized Molybdenum Disulfide Nanomaterials for Efficient Adsorption and Deep Profiling of the Human Plasma Proteome by Data-Independent Acquisition.

Blood is one of the most important clinical samples for protein biomarker discovery, as it provides rich physiological and pathological information and is easy to obtain with low invasiveness. However, the discovery of protein biomarkers in the blood by mass spectrometry (MS)-based proteomic strategies has been shown to be highly challenging due to the particularly large concentration range of proteins and the strong interference by the high-abundant proteins in the blood. Therefore, developing sensitive methods for low-abundant biomarker protein identification is a key issue that has received great attention. Here, we report the synthesis and characterization of surface-functionalized magnetic molybdenum disulfide (MoS2) for the large-scale adsorption of low-abundant plasma proteins and deep profiling by MS. MoS2 nanomaterials resulted in the coverage of more than 3400 proteins (including a single-peptide hit) in a single LC-MS analysis without peptide prefractionation using pooled plasma samples, which were five times more than those obtained by the direct analysis of the plasma proteome. A detection limit in the low ng L-1 range was obtained, which is rare compared with previous reports.

A protein identification method for proteomics using amino acid composition analysis with IoT-based remote control

In the present study, we developed a protein identification method using low-cost and easy-to-operate amino acid composition analysis. The identification program automatically compares the quantitative result for each amino acid concentration obtained from the amino acid analysis to the amino acid composition data retrieved from the UniProt protein database. We found that the accuracy of protein identification using amino acid composition analysis was comparable to that of mass spectrometry analysis. The method was able to distinguish and identify differences in amino acid substitutions of several residues between proteins with high sequence homology. The identification accuracy of proteins was also improved by correcting the concentrations in the program for Cys, Trp, and Ile residues, which cannot be quantified by general sample preparation for amino acid analysis. Moreover, the amino acid analyzer was remotely controlled in accordance with the growing demand for remote work. The measured amino acid data were automatically uploaded to the IoT portal within a few minutes of each measurement, allowing researchers to download data and analyze them using the identification program anywhere and at any time by connecting to a network. The results indicated that the present method is useful for protein identification.