Protein Primary Sequence Research Articles

Targeting the TLR signalosome with TIR domain-derived cell-permeable decoy peptides: the current state and perspectives.

The ability to engineer pharmaceuticals that target the signal-dependent interactions of signaling proteins should revolutionize drug development. One approach to the rational design of protein interaction inhibitors uses decoy peptides, i.e. segments of protein primary sequence, which are derived from interfaces that mediate functional protein interactions. Decoy peptides often retain the ability of the full-length prototype to bind the docking site of the folded protein and thereby block the signal transduction. This review summarizes advances made in the last decade in the development of cell-permeable decoy peptide (CPDP) inhibitors to target the Toll/IL-1R resistance (TIR) domain-mediated protein interactions in TLR signaling, in connection with the recent progress in understanding of the TLR signalosome assembly mechanisms. We present a large collection of currently available, TIR-targeting CPDPs and propose their classification based on the types of TIR–TIR interactions they target. The binding behavior of different CPDP-TIR pairs, studied in cell-based assays and in binary in vitro systems using recombinant TIR domains, is also reviewed. The available affinity data provide benchmarks for rapid preliminary evaluation of future inhibitors. We review literature that evaluates the in vivo potency of select CPDPs and attempt to outline the areas of forthcoming progress, towards the development of CPDP-based TLR inhibitors of pharmaceutical grade.

Single-residue physicochemical characteristics kinetically partition membrane protein self-assembly and aggregation.

Ninety-five percent of all transmembrane proteins exist in kinetically trapped aggregation-prone states that have been directly linked to neurodegenerative diseases. Interestingly, the primary sequence almost invariably avoids off-pathway aggregate formation, by folding reliably into its native, thermodynamically stabilized structure. However, with the rising incidence of protein aggregation diseases, it is now important to understand the underlying mechanism(s) of membrane protein aggregation. Micromolecular physicochemical and biochemical alterations in the primary sequence that trigger the formation of macromolecular cross-β aggregates can be measured only through combinatorial spectroscopic experiments. Here, we developed spectroscopic thermal perturbation with 117 experimental variables to assess how subtle protein sequence variations drive the molecular transition of the folded protein to oligomeric aggregates. Using the Yersinia pestis outer transmembrane β-barrel Ail as a model, we delineated how a single-residue substitution that alters the membrane-anchoring ability of Ail significantly contributes to the kinetic component of Ail stability. We additionally observed a stabilizing role for interface aliphatics, and that interface aromatics physicochemically contribute to Ail self-assembly and aggregation. Moreover, our method identified the formation of structured oligomeric intermediates during Ail aggregation. We show that the self-aggregation tendency of Ail is offset by the evolution of a thermodynamically compromised primary sequence that balances folding, stability, and oligomerization. Our approach provides critical information on how subtle changes in protein primary sequence trigger cross-β fibril formation, with insights that have direct implications for deducing the molecular progression of neurodegeneration and amyloidogenesis in humans.

Identification of membrane protein types via multivariate information fusion with Hilbert–Schmidt Independence Criterion

Membrane proteins perform a variety of functions vital to the survival of organisms, such as oxidoreductase, transferase or hydrolase. If the type of membrane protein can be detected, the function of protein can be quickly determined. Many existing computational methods not only use the autocorrelation function on the hydrophobicity index of amino acids, but also consider the evolutionary conservatism information of the primary protein sequences. In this study, we employ Average Blocks (AvBlock), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Histogram of Oriented Gradient (HOG) and Pseudo-PSSM (PsePSSM) to extract evolution characteristics from Position-Specific Score Matrix (PSSM). Then, we construct five kernels from above five corresponding feature sets. Finally, we propose a novel Multiple Kernel Support Vector Machine (MKSVM) classifier based on Hilbert Schmidt Independence Criterion (HSIC) to integrate five kernels for identifying membrane proteins. For the performance evaluation, our method is tested on four benchmark datasets of membrane proteins. The comparative results demonstrate that our prediction model achieves the best performance among all existing outstanding approaches.

Enhanced Characterization of Membrane Protein Complexes by Ultraviolet Photodissociation Mass Spectrometry.

Development of chemical chaperones to solubilize membrane protein complexes in aqueous solutions has allowed for gas-phase analysis of their native-like assemblies, including rapid evaluation of stability and interacting partners. Characterization of protein primary sequence, however, has thus far been limited. Ultraviolet photodissociation (UVPD) generates a multitude of sequence ions for the E. coli ammonia channel (AmtB), provides improved localization of a possible post-translational modification of aquaporin Z (AqpZ), and surpasses previous reports of sequence coverage for mechanosensitive channel of large conductance (MscL). Variations in UVPD sequence ion abundance have been shown to correspond to structural changes induced upon some perturbation. Preliminary results are reported here for elucidating increased rigidity or flexibility of MscL when bound to various phospholipids.

Multiscale simulation unravel the kinetic mechanisms of inflammasome assembly

In the innate immune system, the host defense from the invasion of external pathogens triggers the inflammatory responses. Proteins involved in the inflammatory pathways were often found to aggregate into supramolecular oligomers, called ‘inflammasome’, mostly through the homotypic interaction between their domains that belong to the death domain superfamily. Although much has been known about the formation of these helical molecular machineries, the detailed correlation between the dynamics of their assembly and the structure of each domain is still not well understood. Using the filament formed by the PYD domains of adaptor molecule ASC as a test system, we constructed a new multiscale simulation framework to study the kinetics of inflammasome assembly. We found that the filament assembly is a multi-step, but highly cooperative process. Moreover, there are three types of binding interfaces between domain subunits in the ASCPYD filament. The multiscale simulation results suggest that dynamics of domain assembly are rooted in the primary protein sequence which defines the energetics of molecular recognition through three binding interfaces. Interface I plays a more regulatory role than the other two in mediating both the kinetics and the thermodynamics of assembly. Finally, the efficiency of our computational framework allows us to design mutants on a systematic scale and predict their impacts on filament assembly. In summary, this is, to the best of our knowledge, the first simulation method to model the spatial-temporal process of inflammasome assembly. Our work is a useful addition to a suite of existing experimental techniques to study the functions of inflammasome in innate immune system.

R3D Prediction Model: Remote Homology Prediction using Protein Contact Map with Dimensionality Reduction

Bioinformatics is one of a developing field that utilizations evaluation to extort information from Biological Data. Bioinformatics approaches are regularly utilized for significant activities that create expansive informational collections. Two basic tremendous scale practices that use bioinformatics are genomics and proteomics. Proteins are the far reaching, complex atoms that are essential for normal working of cells. 20% of the human body is involved proteins. Proteins are involved smaller units called amino acids, which are building squares of proteins. Protein remote homology identification and recognition are focal issues in bioinformatics. Sequence homologies are a vital source of data about proteins. In this research, the framework propose different strategy that diminishes the high dimensionality of the vector representation in remote homology detection by utilizing models that are characterized at the 3D level and consequently are very structurally and practically related. Subsequently, the 3D models are mapped from the protein primary sequence. The framework proposes to address the issue of remote homology identification by reducing 3D structure models. The new technique, called remote homology identification by the Reduction of 3D models (remote-R3D), is introduced and tested on various protein families.

What limits the primary sequence space of natural proteins?

Number of naturally occurring primary sequences of proteins is an infinitesimally small subset of the possible number of primary sequences that can be synthesized using 20 amino acids. Prevailing views ascribe this to slow and incremental mutational/selection evolutionary mechanisms. However, considering the large number of avenues available in form of diversity of emerging/evolving and/or disappearing living systems for exploring the primary sequence space over the evolutionary time scale of ∼3.5 billion years, this remains a conjecture. Therefore, to investigate primary sequence space limitations, we carried out a systematic study for finding primary sequences absent in nature. We report the discovery of the smallest peptide sequence “Cysteine-Glutamine-Tryptophan-Tryptophan” that is not found in over half-a-million curated protein sequences in the Uniprot (Swiss-Prot) database. Additionally, we report a library of 83605 pentapeptides that are not found in any of the known protein sequences. Compositional analyses of these absent primary sequences yield a remarkably strong power relationship between the percentage occurrence of individual amino acids in all known protein sequences and their respective frequency of occurrence in the absent peptides, regardless of their specific position in the sequences. If random evolutionary mechanisms were responsible for limitations to the primary sequence space, then one would not expect any relationship between compositions of available and absent primary sequences. Thus, we conclusively show that stoichiometric constraints on amino acids limit the primary sequence space of proteins in nature. We discuss the possibly profound implications of our findings in both evolutionary and synthetic biology.Communicated by Ramaswamy H. Sarma

Prediction of apoptosis protein subcellular location based on position-specific scoring matrix and isometric mapping algorithm.

Apoptosis proteins are related to many diseases. Obtaining the subcellular localization information of apoptosis proteins is helpful to understand the mechanism of diseases and to develop new drugs. At present, the researchers mainly focus on the primary protein sequences, so there is still room for improvement in the prediction accuracy of the subcellular localization of apoptosis proteins. In this paper, a new method named ERT-ECT-PSSM-IS is proposed to predict apoptosis proteins based on the position-specific scoring matrix (PSSM). First, the local and global features of different directions are extracted by evolutionary row transformation (ERT) and cross-covariance of evolutionary column transformation (ECT) based on PSSM (ERT-ECT-PSSM). Second, an improved isometric mapping algorithm (I-SMA) is used to eliminate redundant features. Finally, we adopt a support vector machine (SVM) to classify our results, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results show that the proposed method not only extracts more abundant feature expression but also has better predictive performance and robustness for the subcellular localization of apoptosis proteins in ZD98, ZW225, and CL317 databases. Graphical abstract Framework of the proposed prediction model.

Physicochemical n-Grams Tool: A tool for protein physicochemical descriptor generation via Chou's 5-step rule.

Physicochemical n-Grams Tool (PnGT) is an open-source standalone software for calculating physicochemical descriptors of protein. PnGT was developed using the Python scripting language and developed the user interface using Tkinter. The software currently calculates 33 physicochemical descriptors along with the sequence length for the given protein primary sequence. The descriptor generated by this tool can be directly utilized as the feature vector for the development of proteomics statistical or machine learning predictive model.

Intact caveolae are required for proper extravillous trophoblast migration and differentiation.

Caveolae constitute membrane domains critical for the organization and synchronization of different signaling molecules related to numerous cell processes such as cell migration, invasion, and differentiation. Caveolin-1 (Cav-1) is the main integral membrane protein of these domains. Recently, it was found that a normal expression of aquaporin-3 (AQP3) is required for extravillous trophoblast (EVT) cell migration. Our aim was to investigate the role of caveolae in the migration, invasion, and endovascular differentiation of human EVT cells during placentation and its interaction with AQP3. EVT cells (Swan 71 cell line) were cultured in complete Dulbecco's modified Eagle's medium-nutrient mixture F12 and treated with 5 mM methyl-β-cyclodextrin (MβCD) to disrupt caveolae. We found that after MβCD treatment, Cav-1 protein was undetectable. In this condition, the ability of the cells to migrate was significantly decreased compared with the control cells, while no differences were observed in the number of invading cells and the metalloproteinases activity between control and MβCD-treated cells. Surprisingly, the disruption of caveolae significantly enhanced EVT endovascular differentiation. On the contrary, the silencing of AQP3, negatively affected tube-like formation. The theoretical analysis of the primary sequence of AQP3 protein revealed a putative Cav-1-binding site. In addition, immunoprecipitation and double immunofluorescence assays showed that AQP3 colocalized with Cav-1. These results showed that during placentation an intact caveola in EVT cells may be necessary for AQP3 and Cav-1 interaction and any perturbations might result in serious pregnancy disorders.

The energetic basis for hydroxyapatite mineralization by amelogenin variants provides insights into the origin of amelogenesis imperfecta

Small variations in the primary amino acid sequence of extracellular matrix proteins can have profound effects on the biomineralization of hard tissues. For example, a change in one amino acid within the amelogenin protein can lead to drastic changes in enamel phenotype, resulting in amelogenesis imperfecta, enamel that is defective and easily damaged. Despite the importance of these undesirable phenotypes, there is very little understanding of how single amino acid variation in amelogenins can lead to malformed enamel. Here, we aim to develop a thermodynamic understanding of how protein variants can affect steps of the biomineralization process. High-resolution, in situ atomic force microscopy (AFM) showed that altering one amino acid within the murine amelogenin sequence (natural variants T21 and P41T, and experimental variant P71T) resulted in an increase in the quantity of protein adsorbed onto hydroxyapatite (HAP) and the formation of multiple protein layers. Quantitative analysis of the equilibrium adsorbate amounts revealed that the protein variants had higher oligomer-oligomer binding energies. MMP20 enzyme degradation and HAP mineralization studies showed that the amino acid variants slowed the degradation of amelogenin by MMP20 and inhibited the growth and phase transformation of HAP. We propose that the protein variants cause malformed enamel because they bind excessively to HAP and disrupt the normal HAP growth and enzymatic degradation processes. The in situ methods applied to determine the energetics of molecular level processes are powerful tools toward understanding the mechanisms of biomineralization.

PSICA: a fast and accurate web service for protein model quality analysis.

This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.

DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, about 6-7% of these proteins are utilized for genes encoding. DBPs shape the DNA into a compact structure known chromatin while some of these proteins regulate the chromosome packaging and transcription process. In the pharmaceutical industry, DBPs are used as a key component of antibiotics, steroids, and cancer drugs. These proteins also involve in biophysical, biological, and biochemical studies of DNA. Due to the crucial role in various biological activities, identification of DBPs is a hot issue in protein science. A series of experimental and computational methods have been proposed, however, some methods didn't achieve the desired results while some are inadequate in its accuracy and authenticity. Still, it is highly desired to present more intelligent computational predictors. In this work, we introduce an innovative computational method namely DP-BINDER based on physicochemical and evolutionary information. We captured local highly decisive features from physicochemical properties of primary protein sequences via normalized Moreau-Broto autocorrelation (NMBAC) and evolutionary information by position specific scoring matrix-transition probability composition (PSSM-TPC) and pseudo-position specific scoring matrix (PsePSSM) using training and independent datasets. The optimal features were selected by the support vector machine-recursive feature elimination and correlation bias reduction (SVM-RFE + CBR) from fused features and were fed into random forest (RF) and support vector machine (SVM). Our method attained 92.46% and 89.58% accuracy with jackknife and ten-fold cross-validation, respectively on the training dataset, while 81.17% accuracy on the independent dataset for prediction of DBPs. These results demonstrate that our method attained the highest success rate in the literature. The superiority of DP-BINDER over existing approaches due to several reasons including abstraction of local dominant features via effective feature descriptors, utilization of appropriate feature selection algorithms and effective classifier.

Fingerprinting of Proteins that Mediate Quagga Mussel Adhesion using a De Novo Assembled Foot Transcriptome

The European freshwater mollusk Dreissena bugensis (quagga mussel), an invasive species to North America, adheres to surfaces underwater via the byssus: a non-living protein ‘anchor’. In spite of its importance as a biofouling species, the sequence of the majority of byssal proteins responsible for adhesion are not known, and little genomic data is available. To determine protein sequence information, we utilized next-generation RNA sequencing and de novo assembly to construct a cDNA library of the quagga mussel foot transcriptome, which contains over 200,000 transcripts. Quagga mussel byssal proteins were extracted from freshly induced secretions and analyzed using LC-MS/MS; peptide spectra were matched to the transcriptome to fingerprint the entire protein primary sequences. We present the full sequences of fourteen novel quagga mussel byssal proteins, named Dreissena bugensis foot proteins 4 to 17 (Dbfp4–Dbfp17), and new sequence data for two previously observed byssal proteins Dbfp1 and Dbfp2. Theoretical masses of the newly discovered proteins range from 4.3 kDa to 21.6 kDa. These protein sequences are unique but contain features similar to glue proteins from other species, including a high degree of polymorphism, proteins with repeated peptide motifs, disordered protein structure, and block structures.

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.

Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features.

The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou’s pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books.

The Genetic Code is Read by an Idiosyncratic and Highly-Connected Decoding Network

The relationship between the synonymous codons in the genome and the isoacceptor tRNAs that decode them defines how the genetic code is read. Deciphering this relationship in vivo is considered challenging because decoding with distinct isoacceptors yields the same primary protein sequence. We solve this problem by creating chimeras between the anticodon stem loop (ASL, the primary determinant of decoding properties) of endogenous tRNAs and the body of an orthogonal tRNA. We use these chimeras to tag the proteome with unnatural amino acids in response to the codons each ASL decodes. By identifying the sites of proteome tagging and quantitatively comparing the extent of tagging by each ASL in response to each codon we profile tRNA usage for nearly 50,000 decoding events in the E. coli genome. We demonstrate that for the amino acids that are encoded entirely within a family codon box, each isoacceptor can read all four codons in the box; this reveals third base decoding that is not predicted by the extended wobble rules. Moreover for amino acids which are encoded by six codons, we reveal that the isoacceptors believed to only decode codons within a family codon box also decode the other two codons for the same amino acid in a distinct box via ‘cross box decoding’; this demonstrates that non-canonical base pairing is used to decode the first and/or second position of certain codon anticodon interactions. Thus, in contrast to the standard view, Watson Crick base pairs are not required at the first and second position of codon anticodon interactions. Finally we reveal that the cell preferentially uses different isoacceptors to decode different occurrences of the same triplet codon sequence at different positions in the genome.

Computational and experimental analysis of bioactive peptide linear motifs in the integrin adhesome.

Therapeutic modulation of protein interactions is challenging, but short linear motifs (SLiMs) represent potential targets. Focal adhesions play a central role in adhesion by linking cells to the extracellular matrix. Integrins are central to this process, and many other intracellular proteins are components of the integrin adhesome. We applied a peptide network targeting approach to explore the intracellular modulation of integrin function in platelets. Firstly, we computed a platelet-relevant integrin adhesome, inferred via homology of known platelet proteins to adhesome components. We then computationally selected peptides from the set of platelet integrin adhesome cytoplasmic and membrane adjacent protein-protein interfaces. Motifs of interest in the intracellular component of the platelet integrin adhesome were identified using a predictor of SLiMs based on analysis of protein primary amino acid sequences (SLiMPred), a predictor of strongly conserved motifs within disordered protein regions (SLiMPrints), and information from the literature regarding protein interactions in the complex. We then synthesized peptides incorporating these motifs combined with cell penetrating factors (tat peptide and palmitylation for cytoplasmic and membrane proteins respectively). We tested for the platelet activating effects of the peptides, as well as their abilities to inhibit activation. Bioactivity testing revealed a number of peptides that modulated platelet function, including those derived from α-actinin (ACTN1) and syndecan (SDC4), binding to vinculin and syntenin respectively. Both chimeric peptide experiments and peptide combination experiments failed to identify strong effects, perhaps characterizing the adhesome as relatively robust against within-adhesome synergistic perturbation. We investigated in more detail peptides targeting vinculin. Combined experimental and computational evidence suggested a model in which the positively charged tat-derived cell penetrating part of the peptide contributes to bioactivity via stabilizing charge interactions with a region of the ACTN1 negatively charged surface. We conclude that some interactions in the integrin adhesome appear to be capable of modulation by short peptides, and may aid in the identification and characterization of target sites within the complex that may be useful for therapeutic modulation.

Identification of coenzyme-binding proteins with machine learning algorithms

The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on the theory of Star Graph Topological Indices (SGTIs) of protein primary sequences, we proposed a method to develop a first classification model for predicting protein with coenzyme-binding properties. To simulate the properties of coenzyme-binding proteins, we created a dataset containing 2897 proteins, among 456 proteins functioned as coenzyme-binding activity. The SGTIs of peptide sequence were calculated with Sequence to Star Network (S2SNet) application. We used the SGTIs as inputs to several classification techniques with a machine learning software - Weka. A Random Forest classifier based on 3 features of the embedded and non-embedded graphs was identified as the best predictive model for coenzyme-binding proteins. This model developed was with the true positive (TP) rate of 91.7%, false positive (FP) rate of 7.6%, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.971. The prediction of new coenzyme-binding activity proteins using this model could be useful for further drug development or enzyme metabolism researches.

PNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC.

Closely related to causes of various diseases such as rheumatoid arthritis, septic shock, and coeliac disease; tyrosine nitration is considered as one of the most important post-translational modification in proteins. Inside a cell, protein modifications occur accurately by the action of sophisticated cellular machinery. Specific enzymes present in endoplasmic reticulum accomplish this task. The identification of potential tyrosine residues in a protein primary sequence, which can be nitrated, is a challenging task. To counter the prevailing, laborious and time-consuming experimental approaches, a novel computational model is introduced in the present study. Based on data collected from experimentally verified tyrosine nitration sites feature vectors are formed. Later, an adaptive training algorithm is used to train a back propagation neural network for prediction purposes. To objectively measure the accuracy of the proposed model, rigorous verification and validation tests are carried out. Through verification and validation, a promising accuracy of 88%, a sensitivity of 85%, a specificity of 89.18% and Mathew's Correlation Coefficient of 0.627 is achieved. It is concluded that the proposed computational model provides the foundation for further investigation and be used for the identification of nitrotyrosine sites in proteins.