Protein Function Annotation Research Articles

Identification of protein complexes and functional modules in E. coli PPI networks

BackgroundEscherichia coli always plays an important role in microbial research, and it has been a benchmark model for the study of molecular mechanisms of microorganisms. Molecular complexes, operons, and functional modules are valuable molecular functional domains of E. coli. The identification of protein complexes and functional modules of E. coli is essential to reveal the principles of cell organization, process, and function. At present, many studies focus on the detection of E. coli protein complexes based on experimental methods. However, based on the large-scale proteomics data set of E. coli, the simultaneous prediction of protein complexes and functional modules, especially the comparative analysis of them is relatively less.ResultsIn this study, the Edge Label Propagate Algorithm (ELPA) of the complex biological network was used to predict the protein complexes and functional modules of two high-quality PPI networks of E. coli, respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, most protein modules predicted in the two datasets matched highly with real protein complexes, cellular processes, and biological functions. Some novel and significant protein complexes and functional modules were revealed based on ELPA. Moreover, through a comparative analysis of predicted complexes with corresponding functional modules, we found the protein complexes were significantly overlapped with corresponding functional modules, and almost all predicted protein complexes were completely covered by one or more functional modules. Finally, on the same PPI network of E. coli, ELPA was compared with a well-known protein module detection method (MCL) and we found that the performance of ELPA and MCL is comparable in predicting protein complexes.ConclusionsIn this paper, a link clustering method was used to predict protein complexes and functional modules in PPI networks of E. coli, and the correlation between them was compared, which could help us to understand the molecular functional units of E. coli better.

Molecular Modelling of Oryza sativa Starch-branching Enzyme 1

Starch-branching enzymes (SBE) serves as the only enzyme generating glucan branches in green plants and consequently plays a significant role on the resulting starch final structure. Research on rice (Oryza sativa) SBE1 (OsSBE1) structural biology remain untapped. Therefore, there is a necessity for research on the enzyme molecular structure which could lead to the protein function annotation, starch production and energy booster drug design. Analysis of OsSBE1 secondary structure, domains and their interactions, enzyme 3D structure prediction and validation based on C-score were carried out. The OsSBE1 primary sequence was retrieved from GenBank and its secondary structure was predicted to be; α-helix (27.68%), extended strand (22.78%) and higher random coil (949.54%). Enzyme domains were found to be carbohydrate-binding module (CBM) 48 (isoamylase N-terminal domain), α-amylase catalytic domain and α-amylase C-terminal all-beta domain with active sites important amino acids asparagine and glutamic acid. From the five 3D models generated, model 3 displayed best prediction. The Ramachandran refinement has 97.3 amino acids residues in favoured region and 0.4 C-score. This bioinformatics study has elucidated on the OsSBE1 molecular model and first to report on its domain interaction.

Benchmarking gene ontology function predictions using negative annotations.

MotivationWith the ever-increasing number and diversity of sequenced species, the challenge to characterize genes with functional information is even more important. In most species, this characterization almost entirely relies on automated electronic methods. As such, it is critical to benchmark the various methods. The Critical Assessment of protein Function Annotation algorithms (CAFA) series of community experiments provide the most comprehensive benchmark, with a time-delayed analysis leveraging newly curated experimentally supported annotations. However, the definition of a false positive in CAFA has not fully accounted for the open world assumption (OWA), leading to a systematic underestimation of precision. The main reason for this limitation is the relative paucity of negative experimental annotations.ResultsThis article introduces a new, OWA-compliant, benchmark based on a balanced test set of positive and negative annotations. The negative annotations are derived from expert-curated annotations of protein families on phylogenetic trees. This approach results in a large increase in the average information content of negative annotations. The benchmark has been tested using the naïve and BLAST baseline methods, as well as two orthology-based methods. This new benchmark could complement existing ones in future CAFA experiments.Availability and ImplementationAll data, as well as code used for analysis, is available from https://lab.dessimoz.org/20_not.Supplementary information Supplementary data are available at Bioinformatics online.

Network pharmacology-based prediction of the active ingredients and mechanism of Shen Gui capsule for application to coronary heart disease

BackgroundShen Gui capsule (SGC) is a new national drug in China that is widely used in clinical practice and has significant therapeutic effects on coronary heart disease (CHD). However, its active ingredients and mechanism of action for treating coronary heart disease remain unknown. Therefore, the purpose of this paper is to systematically explore the mechanism and efficacy of SGC in the “multicomponent-multitarget- multipathway” treatment for CHD using network pharmacology technology. MethodsThe potential active ingredients of SGC were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and screened by pharmacokinetic parameters. Their possible targets were predicted using the TCMSP and DrugBank database. The CHD-related targets were identified from Comparative Toxicogenomics Database (CTD), UniProt, and PharmGKB database. The compound-target-disease network was constructed using Cytoscape for visualization. Additionally, the protein functional annotation and identification of signaling pathways of potential targets were performed by Gene Ontology (GO) and KEGG enrichment analysis using the Metascape platform. ResultsThe 61 active ingredients of SGC were found to regulate neuroactive ligand-receptor interaction, fluid shear stress and atherosclerosis, estrogen signaling pathway and other pathways through 62 targets. SGC is involved in regulating the circulatory system, nervous system and immune system and other aspects of the body, and thus plays a significant role in the treatment of CHD and its complications, showing the mechanism of SGC's “multicomponent, multitarget, and multipathway” prevention and treatment of CHD. In addition, three predictive components were first found to have potential biological activity by this method. ConclusionThe studies we have performed successfully predict the effective components and potential targets of SGC in the prevention and treatment of CHD, which helped to systematically clarify its mechanism of action and provided a direction for future research on the modern mechanism of SGC in the treatment of CHD.

EDock: blind protein\u2013ligand docking by replica-exchange monte carlo simulation

Protein–ligand docking is an important approach for virtual screening and protein function annotation. Although many docking methods have been developed, most require a high-resolution crystal structure of the receptor and a user-specified binding site to start. This information is, however, not available for the majority of unknown proteins, including many pharmaceutically important targets. Developing blind docking methods without predefined binding sites and working with low-resolution receptor models from protein structure prediction is thus essential. In this manuscript, we propose a novel Monte Carlo based method, EDock, for blind protein–ligand docking. For a given protein, binding sites are first predicted by sequence-profile and substructure-based comparison searches with initial ligand poses generated by graph matching. Next, replica-exchange Monte Carlo (REMC) simulations are performed for ligand conformation refinement under the guidance of a physical force field coupled with binding-site distance constraints. The method was tested on two large-scale datasets containing 535 protein–ligand pairs. Without specifying binding pockets on the experimental receptor structures, EDock achieves on average a ligand RMSD of 2.03 Å, which compares favorably with state-of-the-art docking methods including DOCK6 (2.68 Å) and AutoDock Vina (3.92 Å). When starting with predicted models from I-TASSER, EDock still generates reasonable docking models, with a success rate 159% and 67% higher than DOCK6 and AutoDock Vina, respectively. Detailed data analyses show that the major advantage of EDock lies in reliable ligand binding site predictions and extensive REMC sampling, which allows for the implementation of multiple van der Waals weightings to accommodate different levels of steric clashes and cavity distortions and therefore enhances the robustness of low-resolution docking with predicted protein structures.

Carboxypeptidase B-Assisted Charge-Based Fractional Diagonal Chromatography for Deep Screening of C-Terminome.

The determination of protein C-termini is of great significance for protein function annotation and proteolysis research. However, the progress of C-terminomics is still far behind its counterpart, N-terminomics, because of the low reactivity of the carboxyl group. Herein, we developed a negative selection strategy, termed carboxypeptidase B-assisted charge-based fractional diagonal chromatography (CPB-ChaFRADIC), to achieve a global C-terminome analysis. The highly reactive carboxypeptidase B cleavage was utilized to reduce the charge state of non-C-terminal peptides. Together with high-performance charge-based fractional diagonal chromatography, the C-terminal peptides could be isolated. Such a strategy was applied for profiling C-termini from Escherichia coli cell lysates and 441 canonical C-termini and 510 neo-C-termini originating from proteolytic processing were identified. These findings represent 2-fold and 5.8-fold that of identified C-termini via direct analysis, respectively. Using parallel digestion with trypsin and LysC, such a strategy enabled the identification of 604 canonical C-termini and 818 neo-C-termini, representing the largest C-terminome data set of E. coli, and no deficiency in His/Lys/Arg-containing C-terminal peptides was observed. The presented CPB-ChaFRADIC strategy is therefore a highly efficient and unbiased strategy for large-scale C-terminome analysis. Furthermore, using the CPB-ChaFRADIC strategy, we identified 107 cleavage sites and 102 substrates of caspase-3 in Jurkat cells, demonstrating that the CPB-ChaFRADIC strategy shows great promise in promoting proteolysis research. Data are available via ProteomeXchange with identifier PXD018520.

Molecular characterization and expression analysis of pitaya (Hylocereus polyrhizus) HpLRR genes in response to Neoscytalidium dimidiatum infection

BackgroundCanker disease caused by Neoscytalidium dimidiatum is a devastating disease resulting in a major loss to the pitaya industry. However, resistance proteins in plants play crucial roles to against pathogen infection. Among resistance proteins, the leucine-rich repeat (LRR) protein is a major family that plays crucial roles in plant growth, development, and biotic and abiotic stress responses, especially in disease defense.ResultsIn the present study, a transcriptomics analysis identified a total of 272 LRR genes, 233 of which had coding sequences (CDSs), in the plant pitaya (Hylocereus polyrhizus) in response to fungal Neoscytalidium dimidiatum infection. These genes were divided into various subgroups based on specific domains and phylogenetic analysis. Molecular characterization, functional annotation of proteins, and an expression analysis of the LRR genes were conducted. Additionally, four LRR genes (CL445.Contig4_All, Unigene28_All, CL28.Contig2_All, and Unigene2712_All, which were selected because they had the four longest CDSs were further assessed using quantitative reverse transcription PCR (qRT-PCR) at different fungal infection stages in different pitaya species (Hylocereus polyrhizus and Hylocereus undatus), in different pitaya tissues, and after treatment with salicylic acid (SA), methyl jasmonate (MeJA), and abscisic acid (ABA) hormones. The associated protein functions and roles in signaling pathways were identified.ConclusionsThis study provides a comprehensive overview of the HpLRR family genes at transcriptional level in pitaya in response to N. dimidiatum infection, it will be helpful to understand the molecular mechanism of pitaya canker disease, and lay a strong foundation for further research.

A novel sequence-based prediction method for ATP-binding sites using fusion of SMOTE algorithm and random forests classifier

Correctly identifying the protein-ATP binding site is valuable for both protein function annotation and new drug discovery. However, the number of non-ATP-binding residues is much more than the number of ATP-binding residues, which makes the prediction a classical imbalanced learning problem. Previous studies often apply the under-sampling technique to construct a relatively balanced dataset, but some information is inevitably lost during the sample process. In this work, we utilize the SMOTE algorithm, which generates the balanced dataset by generating ATP-binding sites with the idea of interpolation. The Random Forest is selected as classifier to ensure the acceptable training speed. With the combination of complementary template-based method, the prediction performance of the proposed method is further improved. After comparing with other sequence-based predictors, our proposed method achieves satisfying performance and proved to be efficient for ATP-binding sites prediction.

ORF3a mutation associated with higher mortality rate in SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has recently caused acute respiratory distress syndrome affecting more than 200 countries with varied mortality rate. Successive genetic variants of SARS-CoV-2 become evident across the globe immediately after its complete genome sequencing. Here, we found a decent association of SARS-CoV-2 ORF3a mutation with higher mortality rate. Extensive in silico studies revealed several amino acid changes in ORF3a protein which ultimately leads to diverse structural modifications like B cell epitope loss, gain/loss of phosphorylation site and loss of leucine zipper motif. We could further relate these changes to the enhanced antigenic diversity of SARS-CoV-2. Through protein−protein network analysis and functional annotation studies, we obtained a close federation of ORF3a protein with host immune response via divergent signal transduction pathways including JAK-STAT, chemokine and cytokine-related pathways. Our data not only unveil the fairly appreciable association of ORF3a mutation with higher mortality rate, but also suggest a potential mechanistic insight towards the immunopathogenic manifestation of SARS-CoV-2 infection.

Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features

Genes are termed to be essential if their loss of function compromises viability or results in profound loss of fitness. On the genome scale, these genes can be determined experimentally employing RNAi or knockout screens, but this is very resource intensive. Computational methods for essential gene prediction can overcome this drawback, particularly when intrinsic (e.g. from the protein sequence) as well as extrinsic features (e.g. from transcription profiles) are considered. In this work, we employed machine learning to predict essential genes in Drosophila melanogaster. A total of 27,340 features were generated based on a large variety of different aspects comprising nucleotide and protein sequences, gene networks, protein-protein interactions, evolutionary conservation and functional annotations. Employing cross-validation, we obtained an excellent prediction performance. The best model achieved in D. melanogaster a ROC-AUC of 0.90, a PR-AUC of 0.30 and a F1 score of 0.34. Our approach considerably outperformed a benchmark method in which only features derived from the protein sequences were used (P < 0.001). Investigating which features contributed to this success, we found all categories of features, most prominently network topological, functional and sequence-based features. To evaluate our approach we performed the same workflow for essential gene prediction in human and achieved an ROC-AUC = 0.97, PR-AUC = 0.73, and F1 = 0.64.In summary, this study shows that using our well-elaborated assembly of features covering a broad range of intrinsic and extrinsic gene and protein features enabled intelligent systems to predict well the essentiality of genes in an organism.

A Novel Prediction Method for ATP-Binding Sites From Protein Primary Sequences Based on Fusion of Deep Convolutional Neural Network and Ensemble Learning

Accurately identifying protein-ATP (Adenosine-5’-triphosphate) binding sites is significant for protein function annotation and new drug invention. Previous studies often utilize classical machine learning classification algorithms to predict protein-ATP binding sites based on protein primary sequence. However, deep learning as a newly developed technique has shown outstanding performance in various fields. In this work, we introduce the deep convolutional neural network for protein-ATP binding sites prediction based on sequence information. Two classification networks are developed including a residual-inception-based predictor and a multi-inception-based predictor, then the ensemble learning is applied by giving optimized weights to each network architecture to combine them for more superior performance. We examine the performance of our proposed method on two groups of independent testing sets including a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. As a result, our proposed method outperforms other state-of-art sequence-based predictors with the AUC of 0.922 and 0.896 respectively which illustrates the efficacy of deep learning technique in protein-ATP binding sites prediction. The source code and benchmark datasets can be downloaded at https://github.com/tlsjz/ATPbinding .

Deep Learning-based Ab Initio Protein Structure Prediction and Structure-based Protein Function Annotation

Deep Learning-based Ab Initio Protein Structure Prediction and Structure-based Protein Function Annotation

PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.

Interactions between proteins and DNAs play essential roles in many biological processes. DNA binding proteins can be classified into two categories. Double-stranded DNA-binding proteins (DSBs) bind to double-stranded DNA and are involved in a series of cell functions such as gene expression and regulation. Single-stranded DNA-binding proteins (SSBs) are necessary for DNA replication, recombination, and repair and are responsible for binding to the single-stranded DNA. Therefore, the effective classification of DNA-binding proteins is helpful for functional annotations of proteins. In this work, we propose PredPSD, a computational method based on sequence information that accurately predicts SSBs and DSBs. It introduces three novel feature extraction algorithms. In particular, we use the autocross-covariance (ACC) transformation to transform feature matrices into fixed-length vectors. Then, we put the optimal feature subset obtained by the minimal-redundancy-maximal-relevance criterion (mRMR) feature selection algorithm into the gradient tree boosting (GTB). In 10-fold cross-validation based on a benchmark dataset, PredPSD achieves promising performances with an AUC score of 0.956 and an accuracy of 0.912, which are better than those of existing methods. Moreover, our method has significantly improved the prediction accuracy in independent testing. The experimental results show that PredPSD can significantly recognize the binding specificity and differentiate DSBs and SSBs.

SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function.

Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein-protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities. Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe. Supplementary data are available at Bioinformatics online.

Bisindolemethane derivatives as highly potent anticancer agents: Synthesis, medicinal activity evaluation, cell-based compound discovery, and computational target predictions

In recent years chemical and biological interest in Turbomycin B and its various analogues have motivated researchers to develop new bioactive indole scaffolds. In this work, we focused on the development of indole alkaloids, especially bioindolemethane derivatives and their anticancer potential. Based on the excellent IC50 value against HeLa and A549 cell, cyano-substituted Turbomycin B (CN-TBM) was selected to understand the mechanism behind cancer cell death. The potential targets involved in CN-TBM mediated apoptotic cell death were predicted by comparing the results of two chemoinformatic approaches, i.e., PharmMapper and IdTarget. Four targets were predicted using these tools and the targets were further subjected to molecular docking to obtain a single target for CN-TBM. Serine/threonine-protein kinase (Pim-1) was identified as the direct target of CN-TBM with a pharmacophore model complementing well with the molecular features of CN-TBM. The interaction between CN-TBM and Pim-1 was well supported by high fit-score, Z-score, idTarget dock score and excellent binding affinity. Further, the present study also provides an insight into co-expression, shared protein domains, functional annotation and relationship within the ten putative targets of CN-TBM predicted by PharmMapper.

Differential Proteome Analysis of Hybrid Bamboo (Bambusa pervariabilis\u2009\xd7\u2009Dendrocalamopsis grandis) Under Fungal Stress (Arthrinium phaeospermum)

In this study, TMT (tandem mass tag)-labeled quantitative protein technology combined with LC–MS/MS (liquid chromatography-mass spectrometry/mass spectrometry) was used to isolate and identify the proteins of the hybrid bamboo (Bambusa pervariabilis × Dendrocalamopsis grandis) and the bamboo inoculated with the pathogenic fungi Arthrinium phaeospermum. A total of 3320 unique peptide fragments were identified after inoculation with either A. phaeospermum or sterile water, and 1791 proteins were quantified. A total of 102 differentially expressed proteins were obtained, of which 66 differential proteins were upregulated and 36 downregulated in the treatment group. Annotation and enrichment analysis of these peptides and proteins using the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases with bioinformatics software showed that the differentially expressed protein functional annotation items were mainly concentrated on biological processes and cell components. The LC–PRM/MS (liquid chromatography-parallel reaction monitoring/mass spectrometry) quantitative analysis technique was used to quantitatively analyze 11 differential candidate proteins obtained by TMT combined with LC–MS/MS. The up–down trend of 10 differential proteins in the PRM results was consistent with that of the TMT quantitative analysis. The coincidence rate of the two results was 91%, which confirmed the reliability of the proteomic results. Therefore, the differentially expressed proteins and signaling pathways discovered here may be the further concern for the bamboo-pathogen interaction studies.

Proteome analysis and differential expression by JA driven elicitation in Andrographis paniculata (Burm. f.) Wall. ex Nees using Q-TOF–LC–MS/MS

Andrographis paniculata plantlets were elicited with 50 µM jasmonic acid (JA) in vitro for 1 week and proteome analysis was carried out. HPLC analysis revealed enhanced andrographolide content in JA treated plantlets. Isolated protein samples from both JA treated and untreated control plantlets were analysed by Q-TOF–LC–MS/MS. JA elicitation influenced expression of 22 proteins involved in the isoprenoid pathway in treated and three with untreated plantlets. 3308 proteins in control and 3994 from JA elicited were identified with 1393 found common in both. The differential expression study of proteins revealed a pattern with 621 decreased and 201 enhanced. About 40 metabolic processes identified by functional annotation of proteins and highly elevated (5.7%) post-translational modifications were observed in JA treated. KEGG analysis unveiled appearance of additional secondary metabolism related proteins; primarily phenylpropanoids, isoprenoids and flavonoids from both elicited and untreated. Various transcription factors such as DREB’s, MYC’s, WRKY, and MED which uniquely influenced terpenoid biosynthesis were identified through differential expression study following JA elicitation. The total proteome and differential expression of proteins in JA elicited and untreated in vitro grown A. paniculata plantlets are reported for the first time. These finding will be of significance in understanding elicitation mechanism, gene regulation, metabolic network changes in post elicitation scenario, search of new/novel compounds and developing strategies for manipulating yield enhancement of plant-derived bioactive molecules in general and diterpene lactones in particular. Andrographis paniculata proteome showed 3308 control proteins, 3994 in JA elicited tissue. Differential expression study indicated 621 downregulated, 201 upregulated proteins. KEGG revealed terpenoid biosynthesis upregulation attributed to enhanced andrographolide.

MGnify: the microbiome analysis resource in 2020.

MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations.

Co-regulation map of the human proteome enables identification of protein functions.

Assigning functions to the vast array of proteins present in eukaryotic cells remains challenging. To identify relationships between proteins, and thereby enable functional annotation of proteins, we determined changes in abundance of 10,323 human proteins in response to 294 biological perturbations using isotope-labeling mass spectrometry. We applied the machine learning algorithm treeClust to reveal functional associations between co-regulated human proteins from ProteomeHD, a compilation of our own data and datasets from the Proteomics Identifications database. This produced a co-regulation map of the human proteome. Co-regulation was able to capture relationships between proteins that do not physically interact or colocalize. For example, co-regulation of the peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us to discover an organelle interface between peroxisomes and mitochondria in mammalian cells. We also predicted the functions of microproteins that are difficult to study with traditional methods. The co-regulation map can be explored at www.proteomeHD.net .

Evolutionary significance and functional characterization of streptomycin adenylyltransferase from Serratia marcescens

Complete functional annotations of proteins are essential to understand the role and mechanisms in pathogenesis. Aminoglycoside nucleotidyltransferases are the subclasses of aminoglycosides modifying enzymes conferring resistance to organisms. Insight into the structural and functional understanding of nucleotidyltransferase family protein provides vital information to combat pathogenesis. Phylogenetic analysis is employed to identify the evolutionary significance and common motif’s present among the homologs of nucleotidyltransferase family protein. Structure, sequence based approaches and molecular docking were implemented to predict the exact function of the protein. Wide distribution of the nucleotidyltransferase family protein in gram-positive and gram-negative organisms are evidenced from phylogenetic analysis. Five common motifs were present in all the homolog’s of nucleotidyltransferase family protein. Sequence-structure based functional annotations predicts that the targeted protein function as ATP-Mg dependent streptomycin adenylyltransferase. Structural comparisons and docking studies correlate well with the identified function. The complete function of nucleotidyltransferase family protein was identified as Streptomycin adenylyltransferase and it could be targeted as a potential therapeutic target to overcome antibiotic resistance.Communicated by Ramaswamy H. SarmaAbbreviationsAACaminoglycoside acetyltransferasesAMEaminoglycoside modifying enzymeANTaminoglycoside nucleotidyltransferasesAPHaminoglycoside phosphotransferasesATPadenosine triphosphateCASTpcomputer atlas and surface topography of proteinsDUFdomains of unknown functionGlidegrid-based ligand docking with energeticHMMhidden Markov modelMASTmotif alignment and search toolMEGAmolecular evolutionary genetics analysisMEMEmultiple Em for motif elicitationMSAmultiple sequence alignmentNMPnucleoside monophosphateNTPnucleoside triphosphateNTnucleotidyltransferaseOPLSoptimized potential for liquid simulationXPextra precision