Method For Identification Of Proteins Research Articles

Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry

Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment.

Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.

Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable to meet the needs of modern society. Machine learning methods can make up for the deficiencies of biological experimental techniques to a certain extent, but they are not as simple and fast as deep learning for data processing. In this paper, a deep learning framework based on parallel long and short-term memory(LSTM) and convolutional neural networks(CNN) was proposed to identify DNA-binding protein. This model can not only further extract the information and features of protein sequences, but also the features of evolutionary information. Finally, the two features are combined for training and testing. On the PDB2272 dataset, compared with PDBP_Fusion model, Accuracy(ACC) and Matthew's Correlation Coefficient (MCC) increased by 3.82% and 7.98% respectively. The experimental results of this model have certain advantages.

Star topology convolution for graph representation learning

We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and can share weights across graphs. To test the method, STC was compared with the state-of-the-art graph convolutional methods in a supervised learning setting on nine node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Arxiv, MAG, ACM, DBLP, and IMDB. The experimental results showed that STC achieved the state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed the state-of-the-art essential protein identification methods. An application of using pretrained STC as the embedding for feature extraction of some downstream classification tasks was introduced. The experimental results showed that STC can share weights across different graphs and be used as the embedding to improve the performance of downstream tasks.

Identification of alveolar proteins associated with claudin‐18 using BioID

Tight junctions are protein complexes that form at sites of cell contact that function to regulate paracellular flow of small molecules, water, and ions. Distinct protein components contribute to the barrier function of tight junctions including the claudin family of transmembrane proteins and scaffolding proteins such as Zonula Occludens‐1(ZO‐1). Changes in tight junction morphology from the established smooth cobblestone patterning to a spiked junction are associated with an increase in barrier permeability (leak). Tight junction spike formation is associated with changes in claudin‐18 interacting partners, including ZO‐1. This suggests that protein‐claudin‐18 interactions are essential for tight junction morphology and therefore function. Using rat alveolar epithelial cells (rAECs), we used BioID to investigate the proximal claudin‐18 proteome. BioID uses a promiscuous biotinylating enzyme, BirA, which we fused to claudin‐18 to biotinylate proximal and interacting proteins. To determine that BirA‐claudin‐18 localized to tight junctions, rAECs were transduced with adenovirus to express BirA‐claudin‐18 and stained with claudin‐18 and streptavidin‐Cy3. This showed that the assay was functional and our chimera trafficked to tight junctions and colocalized to proximal proteins. To identify biotinylated proteins, we used tandem mass spectrometry (MS/MS) and identified 3771 protein matches within the BirA‐claudin‐18 sample. Proteins of interest were vetted, and a list of protein candidates were compiled based on peptide spectrum matches (PSMs) and known protein localization. Proteins of interest include retinoic acid and palladin, which are of specific interest because they are associated with a spike like structure found in the testis, the tubulobulbar complex, which we hypothesize behaves as a signaling platform in the testis and is comparable to tight junction spikes found in the lung. These findings show that BioID is a valid method for identification of proteins to claudin‐18, providing a discovery‐based approach to identify novel proteins that play a role in barrier function and tight junction regulation.

A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies.

Mass spectrometry (MS)-based proteomic measurements are uniquely poised to impact the development of cell and gene therapies. With the adoption of rigorous instrumental performance qualifications (PQs), large-scale proteomics can move from a research to a manufacturing control tool. Especially suited, data-independent acquisition (DIA) approaches have distinctive qualities to extend multiattribute method (MAM) principles to characterize the proteome of cell therapies. Here, we describe the development of a DIA method for the sensitive identification and quantification of proteins on a Q-TOF instrument. Using the improved acquisition parameters, we defined a control strategy and highlighted some metrics to improve the reproducibility of SWATH acquisition-based proteomic measurements. Finally, we applied the method to analyze the proteome of Jurkat cells that here serves as a model for human T-cells. Raw and processed data were deposited in PRIDE (PXD029780).

A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem.

Machine learning has been widely used to solve complex problems in engineering applications and scientific fields, and many machine learning-based methods have achieved good results in different fields. SNAREs are key elements of membrane fusion and required for the fusion process of stable intermediates. They are also associated with the formation of some psychiatric disorders. This study processes the original sequence data with the synthetic minority oversampling technique (SMOTE) to solve the problem of data imbalance and produces the most suitable machine learning model with the iLearnPlus platform for the identification of SNARE proteins. Ultimately, a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the cross-validation dataset, and a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the independent dataset (the adaptive skip dipeptide composition descriptor was used for feature extraction, and LightGBM with proper parameters was used as the classifier). These results demonstrate that this combination can perform well in the classification of SNARE proteins and is superior to other methods.

Shotgun Proteomics as a Powerful Tool for the Study of the Proteomes of Plants, Their Pathogens, and Plant-Pathogen Interactions.

The interaction between plants and pathogenic microorganisms is a multifaceted process mediated by both plant- and pathogen-derived molecules, including proteins, metabolites, and lipids. Large-scale proteome analysis can quantify the dynamics of proteins, biological pathways, and posttranslational modifications (PTMs) involved in the plant–pathogen interaction. Mass spectrometry (MS)-based proteomics has become the preferred method for characterizing proteins at the proteome and sub-proteome (e.g., the phosphoproteome) levels. MS-based proteomics can reveal changes in the quantitative state of a proteome and provide a foundation for understanding the mechanisms involved in plant–pathogen interactions. This review is intended as a primer for biologists that may be unfamiliar with the diverse range of methodology for MS-based shotgun proteomics, with a focus on techniques that have been used to investigate plant–pathogen interactions. We provide a summary of the essential steps required for shotgun proteomic studies of plants, pathogens and plant–pathogen interactions, including methods for protein digestion, identification, separation, and quantification. Finally, we discuss how protein PTMs may directly participate in the interaction between a pathogen and its host plant.

Two-dimensional electrophoresis-cellular thermal shift assay (2DE-CETSA) for target identification of bioactive compounds.

Identification of target molecules of new bioactive compounds is still a challenge in drug development. Various proteomics-based methods have been developed to analyze the interaction between compounds and target proteins. Among these methods, cellular thermal shift assay (CETSA) has been frequently applied in recent years for validation studies of compound-protein interactions using antibodies. Combining CETSA with comprehensive proteomic analysis has been successful in narrowing down the target(s) of a new compound from the enormous number of proteins in cell. In this chapter, we introduce 2DE-CETSA, which combines CETSA with proteome analysis using two-dimensional electrophoresis as a method for identification of target proteins.

LA IMPORTANCIA DE LAS PROTEÍNAS DEL PLASMA SEMINAL BOVINO EN LA FUNCIÓN ESPERMÁTICA Y LA FERTILIDAD

Bovine livestock is one of the most important economic and social sectors for many countries. In this sense, the development of strategies to improve reproductive bull fertility and reproduction rates is relevant. It's highlighted the role of seminal plasma proteins (SPP) in reproductive fertility, so it has found close relationships among studies on the structure and biological activity of SPP, with seminal quality, including viability, sperm motility, and morphology. In addition, they have been found to regulate sperm functions such as capacitation, acrosome reaction, and they are even related to protecting sperm against thermal and oxidative stress. Moreover, the methods of separation and protein identification and their contribution to characterizing the bovine SP proteome should be also highlighted. In this sense, the most recent studies have been directed towards developing supplements with SPP that improve quality sperm subjected to cryopreservation processes. Research has begun and should forward to establish how the networks or sets of proteins are related to the functioning and fertility of sperm, the search for biomarkers of fertility, and the use of proteins in biotechnological processes, to increase efficiency reproductive.

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

Essential Protein Recognition via Community Significance.

Essential protein plays a vital role in understanding the cellular life. With the advance in high-throughput technologies, a number of protein-protein interaction (PPI) networks have been constructed such that essential proteins can be identified from a system biology perspective. Although a series of network-based essential protein discovery methods have been proposed, these existing methods still have some drawbacks. Recently, it has been shown that the significance-based method SigEP is promising on overcoming the defects that are inherent in currently available essential protein identification methods. However, the SigEP method is developed under the unrealistic Erdös-Rényi (E-R) model and its time complexity is very high. Hence, we propose a new significance-based essential protein recognition method named EPCS in which the essential protein discovery problem is formulated as a community significance testing problem. Experimental results on four PPI networks show that EPCS performs better than nine state-of-the-art essential protein identification methods and the only significance-based essential protein identification method SigEP.

Capture of the newly transcribed RNA interactome using click chemistry.

Application of synthetic nucleoside analogues to capture newly transcribed RNAs has unveiled key features of RNA metabolism. Whether this approach could be adapted to isolate the RNA-bound proteome (RNA interactome) was, however, unexplored. We have developed a new method (capture of the newly transcribed RNA interactome using click chemistry, or RICK) for the systematic identification of RNA-binding proteins based on the incorporation of 5-ethynyluridine into newly transcribed RNAs followed by UV cross-linking and click chemistry-mediated biotinylation. The RNA-protein adducts are then isolated by affinity capture using streptavidin-coated beads. Through high-throughput RNA sequencing and mass spectrometry, the RNAs and proteins can be elucidated globally. A typical RICK experimental procedure takes only 1 d, excluding the steps of cell preparation, 5-ethynyluridine labeling, validation (silver staining, western blotting, quantitative reverse-transcription PCR (qRT-PCR) or RNA sequencing (RNA-seq)) and proteomics. Major advantages of RICK are the capture of RNA-binding proteins interacting with any type of RNA and, particularly, the ability to discern between newly transcribed and steady-state RNAs through controlled labeling. Thanks to its versatility, RICK will facilitate the characterization of the total and newly transcribed RNA interactome in different cell types and conditions.

In silico assessment of a novel single-molecule protein fingerprinting method employing fragmentation and nanopore detection

SummaryThe identification of proteins at the single-molecule level would open exciting new venues in biological research and disease diagnostics. Previously, we proposed a nanopore-based method for protein identification called chop-n-drop fingerprinting, in which the fragmentation pattern induced and measured by a proteasome-nanopore construct is used to identify single proteins. In the simulation study presented here, we show that 97.1% of human proteome constituents are uniquely identified under close to ideal measuring circumstances, using a simple alignment-based classification method. We show that our method is robust against experimental error, as 69.4% can still be identified if the resolution is twice as low as currently attainable, and 10% of proteasome restriction sites and protein fragments are randomly ignored. Based on these results and our experimental proof of concept, we argue that chop-n-drop fingerprinting has the potential to make cost-effective single-molecule protein identification feasible in the near future.

A Cross-entropy-based Method for Essential Protein Identification in Yeast Protein-protein Interaction Network

Background: Research has shown that essential proteins play important roles in the development and survival of organisms. Because of the high costs of traditional biological experiments, several computational prediction methods based on known protein-protein interactions (PPIs) have been recently proposed to detect essential proteins. Objective: Here, a novel prediction model called IoMCD is proposed to identify essential proteins by combining known PPIs with a variety of biological information about proteins, including gene expression data and homologous information of proteins. Methods: Compared to the traditional state-of-the-art prediction models, IoMCD involves two kinds of weights that are obtained, respectively, by extracting topological features of proteins from the original known protein–protein interaction (PPI) networks and calculating the Pearson correlation coefficients (PCCs) between the gene expression data of proteins. Based on these two kinds of weights and adopting a cross-entropy method, a unique weight is assigned to each protein. Subsequently, the homologous information of proteins is used to calculate an initial score for each protein. Finally, based on the unique weights and initial score of proteins, an iterative method is designed to measure the essentialities of proteins. Results: Intensive experiments were performed, and simulation results showed that the prediction accuracy of IoMCD, based on the dataset downloaded from the DIP and Gavin databases, was 92.16% and 89.71%, respectively, in the top 1% of the predicted essential proteins. Conclusion: Both simulation results demonstrated that IoMCD can achieve excellent prediction accuracy and could be an effective method for essential protein prediction.

Ultrafast and Reproducible Proteomics from Small Amounts of Heart Tissue Enabled by Azo and timsTOF Pro.

Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are time-consuming, typically including overnight tryptic digestion, extensive sample cleanup to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

A novel essential protein identification method based on PPI networks and gene expression data

BackgroundSome proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins.ResultsIn this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression.ConclusionsWe demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Identification of lncRNA-Protein Interactions by CLIP and RNA Pull-Down Assays.

The emerging data indicates that long noncoding RNAs (lncRNAs) are involved in fundamental biological processes, and their deregulation may lead to oncogenesis and other diseases. LncRNA fulfil its biological functions at least in part by interacting with distinctive proteins. Here, we described two methods to identify the direct or indirect interactions between lncRNA and proteins: cross-linking and immunoprecipitation (CLIP) and RNA pull-down assay. CLIP methods enable yield a list of lncRNAs that directly interact target protein in living cells, whereas immunoprecipitation of biotin-labeled RNA (RNA pull-down) assay represents a method for identification of proteins that directly and indirectly bind with a particular target lncRNA of interest.

Machine learning-aided protein identification from multidimensional signatures.

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of a novel method for protein identification in a microfluidic format.

Mapping the morphological identifiers of distinct conformations via the protein translocation current in nanopores

Conformational changes of proteins play a vital role in implementing their functions and revealing the underlying mechanisms in various biological processes. It is still challenging to monitor protein conformations with temporal fingerprints of current-resistance pulses in the nanopore technique. Here the low-resolution morphologies of different conformations of a typical integrin, αxβ2, were estimated via relative blockade currents simulated from all-atom molecular dynamics (MD). Distinct conformational states of αxβ2 were directly explained by the volume and shape identifiers. Protein modulation in ionic current was analyzed from the conductivity distribution inside the protein-blocked nanopore. Combining a discrete model with spheroidal approximation, a MD-based approach was developed to theoretically predict the volume and shape of the nanopore for sensing αxβ2. This method was also applicable in specifying morphological identifiers of six other proteins, and the theoretical predictions are in good agreement with the experimental measurements. These results potentiated the validity of this method for the conformational identification of proteins in nanopores.

Chop-N-Drop: <i>In silico</i> Assessment of a Novel Single-Molecule Protein Fingerprinting Method Employing Fragmentation and Nanopore Detection

The identification of proteins at the single-molecule level would open exciting new venues in biological research and disease diagnostics. Previously we proposed a nanopore-based method for protein identification called chop-n-drop fingerprinting, in which the fragmentation pattern induced and measured by a proteasome-nanopore construct is used to identify single proteins. However whether such fragmentation patterns are sufficiently characteristic of proteins to identify them in complex samples remained unclear. In the simulation study presented here, we show that 97.9% of human proteome constituents are uniquely identified under close to ideal measuring circumstances, using a simple alignment-based classification method. We show that our method is robust against experimental error, as 78.8% can still be identified if the resolution is twice as low as currently attainable and 10% of proteasome restriction sites and protein fragments are randomly ignored. Based on these results and our experimental proof-of-concept, we argue that chop-n-drop fingerprinting has the potential to make cost-effective single-molecule protein identification feasible in the near future.