Protein Identification Techniques Research Articles

Protein classification by autofluorescence spectral shape analysis using machine learning

Depending on the relative numbers and spatial arrangement of Tryptophan (Trp; W) and Tyrosine (Tyr; Y) residues, different proteins produce distinct autofluorescence (AF) spectral shapes when excited at ∼280 nm. Yet, considering the vast number and heterogeneous forms in nature, visual analysis and precise identification of proteins based on their AF spectra is challenging and further compounded in cases when different proteins produce substantially similar AF spectral shapes. There is, thus, a serious need to develop a methodology to address this problem. The current study proposes a practical technology to quickly identify proteins using machine learning (ML) algorithms based on their AF spectra. Specifically, AF spectra of fifteen different standard proteins of varying origin with distinct structural and Trp/Tyr compositions were recorded; based on the spectral features selected by the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm, a multiclass Support Vector Machine (SVM) learning model with Radial Basis Function (RBF), Polynomial, and Linear kernels classified the proteins with high accuracy of 99.06%, 99.03%, and 98.29% respectively. Since protein identification is the key to understand biological functions and disease diagnosis, the proposed methodology could offer a viable alternative to and improve the existing protein identification techniques.

Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy.

The evaluation of the performance levels in athletic horses is of major importance to prevent sports injuries. Raman spectroscopy is an innovative technique that allows for a rapid evaluation of biomolecules in biological fluids. It also permits qualitative and quantitative sample analyses, which lead to the simultaneous determination of the components of the examined biological fluids. On the basis of this, the Raman spectroscopy technique was applied on serum samples collected from five Italian Saddle horses subjected to a standardized obstacle course preceded by a warm-up to evaluate the applicability of this technique for the assessment of central and peripheral fatigue in athletic horses. Blood samples were collected via jugular venipuncture in a vacutainer tube with a clot activator before exercise, immediately after exercise, and 30 min and 1 h after the end of the obstacle course. Observing the obtained Raman spectra, the major changes due to the experimental conditions appeared in the (1300-1360) cm-1 and (1385-1520) cm-1 bands. In the (1300-1360) cm-1 band, lipids and tryptophan were identified; in the (1385-1520) cm-1 band, leucine, glycine, isoleucine, lactic acid, tripeptide, adenosine, and beta carotene were identified. A significant effect of exercise was recorded on all the sub-bands. In particular, a change immediately after exercise versus before exercise was found. Moreover, the mean lactic concentration was positively correlated with the Raman area of the sub-band assigned to lactic acid. In this context, the application of Raman spectroscopy on blood serum samples represents a useful technique for secondary-structure protein identification to investigate the metabolic changes that occur in athletic horses during physical exercise.

Mass Spectrometric Identification of BSA Covalently Captured onto a Chip for Atomic Force Microscopy.

Mass spectrometry (MS) is one of the main techniques for protein identification. Herein, MS has been employed for the identification of bovine serum albumin (BSA), which was covalently immobilized on the surface of a mica chip intended for investigation by atomic force microscopy (AFM). For the immobilization, two different types of crosslinkers have been used: 4-benzoylbenzoic acid N-succinimidyl ester (SuccBB) and dithiobis(succinimidyl propionate) (DSP). According to the data obtained by using an AFM-based molecular detector, the SuccBB crosslinker was more efficient in BSA immobilization than the DSP. The type of crosslinker used for protein capturing has been found to affect the results of MS identification. The results obtained herein can be applied in the development of novel systems intended for the highly sensitive analysis of proteins with molecular detectors.

Identification of monoclonal antibody drug substances using non-destructive Raman spectroscopy

Monoclonal antibodies provide highly specific and effective therapies for the treatment of chronic diseases. These protein-based therapeutics, or drug substances, are transported in single used plastic packaging to fill finish sites. According to good manufacturing practice guidelines, each drug substance needs to be identified before manufacturing of the drug product. However, considering their complex structure, it is challenging to correctly identify therapeutic proteins in an efficient manner. Common analytical techniques for therapeutic protein identification are SDS-gel electrophoresis, enzyme linked immunosorbent assays, high performance liquid chromatography and mass spectrometry-based assays. Although effective in correctly identifying the protein therapeutic, most of these techniques need extensive sample preparation and removal of samples from their containers. This step not only risks contamination but the sample taken for the identification is destroyed and cannot be re-used. Moreover, these techniques are often time consuming, sometimes taking several days to process. Here, we address these challenges by developing a rapid and non-destructive identification technique for monoclonal antibody-based drug substances. Raman spectroscopy in combination with chemometrics were used to identify three monoclonal antibody drug substances. This study explored the impact of laser exposure, time out of refrigerator and multiple freeze thaw cycles on the stability of monoclonal antibodies. and demonstrated the potential of using Raman spectroscopy for the identification of protein-based drug substances in the biopharmaceutical industry.

A novel method for the component identification of human blood products: Mass spectrometric analysis of human fibrinogen digested after SDS-PAGE in-gel digestion

Human fibrinogen, as a blood product of special origin, is relatively simple to prepare and purify. Therefore, completely isolating and removing the relevant impurity proteins is difficult. Further, which impurity protein components are present is not clear. In this study, human fibrinogen products from seven enterprises were collected from the market, and the presence of impurity proteins was confirmed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis. Subsequently, the major 12 impurity proteins were identified and screened by in-gel enzymolysis mass spectrometry, and 7 major impurity proteins with different peptide coverage were identified by enzyme-linked immunosorbent assay, in agreement with the mass spectrometry results. The seven major impurity proteins included fibronectin, plasminogen, F-XIII, F-VIII, complement factor H, cystatin-A, and α-2-macroglobulin. The final test results were in the range of undetectable to 50.94 µg/mL, with correspondingly low levels of impurity proteins between different companies and a manageable risk. Moreover, we found that these impurity proteins existed in the form of polymers, which might also be an important cause of adverse reactions. This study established a protein identification technique applicable to fibrinogen products, which provided new ideas for studying the protein composition of blood products. In addition, it provided a new means of testing for companies to monitor the flow of proteomic fractions and improve the purification yield and product quality. It laid the foundation for reducing the risk of clinical adverse reactions.

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.

PepWise: Peptide Identification Algorithms for Tandem Mass Spectrometry Based on the Weight of Pair Amino Acid Fracture

Tandem mass spectrometry is the core of the high-throughput techniques for protein identification. Abundant of MS/MS data can be generated and need to be interpreted, although numerous of peptide identification algorithms have been proposed, most well-known algorithms have been prevailingly employed to predict fragment m/z value to assign peptide sequences to spectrum, such as X!Tandem, OMSSA, Sequest, SQID and ProVerB incorporate intensity information into algorithms to assist peptide identification. Hence, we can easily know, different algorithms would use different information from the same MS data sets. Here we describe a novel protein algorithm based on the weight of pair amino acid fracture, named PepWise, compared with Mascot, Sequest at 1% False Discovery Rate (FDR), which verified the more accuracy, robustness and compatibility.

Biopharmaceutical quality control with mass spectrometry.

Mass spectrometry (MS)is a powerful technique for protein identification, quantificationand characterization that is widely applied in biochemical studies, and which can provide data on the quantity, structural integrity and post-translational modifications of proteins. It is therefore a versatile and widely used analytic tool for quality control of biopharmaceuticals, especially in quantifying host-cell protein impurities, identifying post-translation modifications and structural characterization of biopharmaceutical proteins. Here, we summarize recent advances in MS-based analyses of these key quality attributes of the biopharmaceutical development and manufacturing processes.

Nano dot blot: An alternative technique for protein identification and quantification in a high throughput format

BackgroundDot blot technique has been used in a similar way to western blotting, with the major difference being the lack of protein separation with electrophoresis. Protein samples are spotted over a membrane paper, the identification and quantification of a protein is achieved by immunodetection procedures such as colorimetry, fluorescence or chemiluminescence. This technique is widely accepted, but it uses large amounts of sample and antibodies to reveal the presence of the target protein. Significant milestones have been reached to achieve better results with the use of less sample and reagents; however, the ninety-six-well format is still in use. New methodIn this work, we propose an innovation to this technique, reducing the amount of sample and antibodies to identify a specific protein when compared to the regular dot blot method. Procedure consists of using a sample volume of approximately 200 nanoliters deposited with a multineedle device developed by our group. ResultsFive samples of standard protein or antigen can be spotted in a Cartesian format to identify and quantify the protein involved in physiological or pathological conditions. In addition, at least five replicates of sample or antigen are used to enable better statistics to calculate the concentration of every standard and the protein present in a sample. ConclusionsHundreds of samples can be deposited in a few minutes and analyzed in a single experimental session. To validate this method, which we called nano dot blot, six proteins involved in the inflammation process were tested in acute and chronic rat models of seizures.

PIONEER: Pipeline for Generating High-Quality Spectral Libraries for DIA-MS Data.

Data-independent-acquisition mass spectrometry (DIA-MS) is a state-of-the-art proteomic technique for high-throughput identification and quantification of peptides and proteins. Interpretation of DIA-MS data relies on the use of a spectral library, which is optimally created from data acquired from the same samples in data-dependent acquisition (DDA) mode. As DIA-MS quantification relies on the spectral libraries, having a high-quality, non-redundant, and comprehensive spectral library is essential. This article describes the major steps for creating a high-quality spectral library using a combination of multiple complementary search engines. We discuss appropriate strategies to control the false discovery rate for the final spectral library as a result of merging multiple searches. © 2021 The Authors Current Protocols © 2021 Wiley Periodicals LLC. Basic Protocol 1: Searching DDA-MS files with multiple search engines Basic Protocol 2: Merging results from multiple search engines Basic Protocol 3: Creating spectral libraries from merged results Alternate Protocol: Using CLI for automating tasks Support Protocol: Creating concatenated FASTA files.

A comprehensive overview of proteomics approach for COVID 19: new perspectives in target therapy strategies.

World Health Organisation declared COVID-19 a pandemic on March 11, 2020. It was temporarily named as 2019-nCoV then subsequently named as COVID-19 virus. A coronavirus is a group of viruses, known to be zoonotic, causing illness ranging from acute to mild respiratory infections. These are spherical or pleomorphic enveloped particles containing positive sense RNA. The virus enters host cells, its uncoated genetic material transcribes, and translates. Since it has started spreading rapidly, protective measures have been taken all over the world. However, its transmission has been proved to be unstoppable and the absence of an effective drug makes the situation worse. The scientific community has gone all-out to discover and develop a possible vaccine or a competent antiviral drug. Other domains of biological sciences that promise effective results and target somewhat stable entities that are proteins, could be very useful in this time of crisis. Proteomics and metabolomics are the vast fields that are equipped with sufficient technologies to face this challenge. Various protein separation and identification techniques are available which facilitates the analysis of various types of interactions among proteins and their evolutionary lineages. The presented review aims at confronting the question: ‘how proteomics can help in tackling SARS-CoV-2?’ It deals with the role of upcoming proteome technology in these pandemic situations and discusses the proteomics approach towards the COVID-19 dilemma.

Identification of prothymosin alpha (PTMA) as a biomarker for esophageal squamous cell carcinoma (ESCC) by label-free quantitative proteomics and Quantitative Dot Blot (QDB)

BackgroundEsophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice.MethodsIn this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry.ResultsIn total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC.ConclusionIn this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

Accurate Estimation of Context-Dependent False Discovery Rates in Top-Down Proteomics

Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.

IAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins

Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.

Prediction of Nitrosocysteine Sites Using Position and Composition Variant Features

S-nitrosylation is one of the most prominent posttranslational modification among proteins. It involves the addition of nitrogen oxide group to cysteine thiols forming S-nitrosocysteine. Evidence suggests that S-nitrosylation plays a foremost role in numerous human diseases and disorders. The incorporation of techniques for robust identification of S-nitrosylated proteins is highly anticipated in biological research and drug discovery. The proposed system endeavors a novel strategy based on a statistical and computational intelligent methods for the identification of S-nitrosocystiene sites within a given primary protein sequence. For this purpose, 5-step rule was approached comprising of benchmark dataset creation, mathematical modelling, prediction, evaluation and web-server development. For position relative feature extraction, statistical moments were used and a multilayer neural network was trained adapting Gradient Descent and Adaptive Learning algorithms. The results were comparatively analyzed with existing techniques using benchmark datasets. It is inferred through conclusive experimentation that the proposed scheme is very propitious, accurate and exceptionally effective for the prediction of S-nitrosocystiene in protein sequences.

Role of Mass Spectrometry in Investigating a Novel Protein: The Example of Tumor Differentiation Factor (TDF).

Better understanding of central nervous system (CNS) molecules can include the identification of new molecules and their receptor systems. Discovery of novel proteins and elucidation of receptor targets can be accomplished using mass spectrometry (MS). We describe a case study of such a molecule, which our lab has studied using MS in combination with other protein identification techniques, such as immunohistochemistry and Western Blotting. This molecule is known as tumor differentiation factor (TDF), a recently-found protein secreted by the pituitary into the blood. TDF mRNA has been detected in brain; not heart, placenta, lung, liver, skeletal muscle, or pancreas. Currently TDF has an unclear function, and prior to our studies, its localization was only minimally understood, with no understanding of receptor targets. We investigated the distribution of TDF in the rat brain using immunohistochemistry (IHC) and immunofluorescence (IF). TDF protein was detected in pituitary and most other brain regions, in specific neurons but not astrocytes. We found TDF immunoreactivity in cultured neuroblastoma, not astrocytoma. These data suggest that TDF is localized to neurons, not to astrocytes. Our group also conducted studies to identify the TDF receptor (TDF-R). Using LC-MS/MS and Western blotting, we identified the members of the Heat Shock 70-kDa family of proteins (HSP70) as potential TDF-R candidates in both MCF7 and BT-549 human breast cancer cells (HBCC) and PC3, DU145, and LNCaP human prostate cancer cells (HPCC), but not in HeLa cells, NG108 neuroblastoma, or HDF-a and BLK CL.4 cells fibroblasts or fibroblast-like cells. These studies have combined directed protein identification techniques with mass spectrometry to increase our understanding of a novel protein that may have distinct actions as a hormone in the body and as a growth factor in the brain.

Sensitive Western-Blot Analysis of Azide-Tagged Protein Post Translational Modifications Using Thermoresponsive Polymer Self-Assembly.

Western-blot (WB) is a powerful analytical technique for protein identification in complex biological samples and has been widely used in biological studies for decades. Detection specificity and sensitivity of WB largely relies on quality of the antibodies and performance of the conjugated HRP. However, the application of WB analysis for the detection of protein post-translational modifications (PTMs) is hampered by the low abundance of protein PTMs and by the limited availability of antibodies that specifically differentiate various kinds of PTMs from their protein substrates. Therefore, new recognition mechanisms and signal amplification strategies for WB analysis of protein PTMs is in high demand. In this work, we prepared a soluble polymer that detects various azide-tagged PTM proteins in WB analysis using triarylphosphine and HRP modified thermoresponsive polymer. Specific and efficient detection of azide-tagged PTM protein is achieved via the bioorthogonal reaction between azide and triarylphosphine. More importantly, the chemiluminiscent signal in the WB analysis is largely amplified by the temperature induced self-assembly of numerous thermoresponsive polymer chains carrying multiple HRPs. As a result, approximately 100 times more sensitive detection than commercial antibodies is achieved by this method using standard PTM proteins. Though, this new reagent does not directly detect native PTMs in cell, tissue or blood samples, it still has important application potential in protein PTM studies, considering the wide availability of azide-tagging techniques to a variety of PTMs.

Protein expression in human cumulus cells as an indicator of blastocyst formation and pregnancy success.

The goal for the present study was to implement a technique for protein extraction and identification in human cumulus cells (CCs). Forty samples of CCs were collected after ovum pick-up from patients undergoing intracytoplasmic sperm injection (ICSI). Samples were split into the blastocyst group (n = 10), including patients in which all embryos converted into blastocysts, and the non-blastocyst group (n = 10), including patients in which none of the embryos reached the blastocyst stage or the positive-pregnancy (n = 10) and negative-pregnancy group (n = 10). Proteins were extracted and injected into a liquid chromatography system coupled to a mass spectrometer. The spectra were processed and used to search a database. There were 87 different proteins in samples from the blastocyst and non-blastocyst groups, in which 30 were exclusively expressed in the blastocyst group and 17 in the non-blastocyst group. Among the 72 proteins detected in the pregnancy groups, 19 were exclusively expressed in the positive, and 16 were exclusively expressed in the negative-pregnancy group. CC proteomics may be useful for predicting pregnancy success and the identification of patients that should be included in extended embryo culture programs.

A Simplified Workflow for Protein Quantitation of Rat Brain Tissues Using Label-Free Proteomics and Spectral Counting.

Mass spectrometry-based proteomics is an increasingly valuable tool for determining relative or quantitative protein abundance in brain tissues. A plethora of technical and analytical methods are available, but straightforward and practical approaches are often needed to facilitate reproducibility. This aspect is particularly important as an increasing number of studies focus on models of traumatic brain injury or brain trauma, for which brain tissue proteomes have not yet been fully described. This text provides suggested techniques for robust identification and quantitation of brain proteins by using molecular weight fractionation prior to mass spectrometry-based proteomics. Detailed sample preparation and generalized protocols for chromatography, mass spectrometry, spectral counting, and normalization are described. The rat cerebral cortex isolated from a model of blast-overpressure was used as an exemplary source of brain tissue. However, these techniques may be adapted for lysates generated from several types of cells or tissues and adapted by the end user.

Proteomics in Rheumatoid Arthritis Research.

Although rheumatoid arthritis (RA) is the most common chronic inflammatory autoimmune disease, diagnosis of RA is currently based on clinical manifestations, and there is no simple, practical assessment tool in the clinical field to assess disease activity and severity. Recently, there has been increasing interest in the discovery of new diagnostic RA biomarkers that can assist in evaluating disease activity, severity, and treatment response. Proteomics, the large-scale study of the proteome, has emerged as a powerful technique for protein identification and characterization. For the past 10 years, proteomic techniques have been applied to different biological samples (synovial tissue/fluid, blood, and urine) from RA patients and experimental animal models. In this review, we summarize the current state of the application of proteomics in RA and its importance in identifying biomarkers and treatment targets.