Translational Modification Sites Research Articles

Investigating the structural and functional consequences of germline single nucleotide polymorphisms located in the genes of the alternative lengthening of telomere (ALT) pathway

BackgroundThe Alternative Lengthening of Telomeres (ALT) pathway represents a non-canonical mechanism of telomere maintenance that operates independently of the conventional telomerase activity. The three biologically significant proteins, designated as SMARCAL1 (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A-like protein 1), DAXX (Death domain-associated protein 6) and ATRX (alpha-thalassemia/mental retardation, X-linked) are associated with ALT in certain cancer types. The purpose of this study was to identify the most high-risk nsSNPs (non-synonymous Single Nucleotide Polymorphisms) within these three genes and assess their impacts on the structure and function of the proteins they encode. MethodsThe reported genetic polymorphisms of SMARCAL1, DAXX and ATRX genes were retrieved from the Ensembl database. Later, various computational tools like PROVEAN, PolyPhen2, SNPs and GO, SNAP2, Predict-SNP, Panther and PMut were used to predict the most deleterious nsSNPs. MutPred was used to understand the underlying molecular reasons of those nsSNPs being deleterious, followed by prediction of Post Translational Modification Sites (PTMs) using ModPred. I-Mutant and MUpro were used to predict the effect of SNP on energy stability. Later, 3D clustering analysis was done using Mutation 3D server. Moreover, ConSurf was utilized to identify the conservation scores of wild-type amino acids. Additionally, the NCBI conserved domain search tool was employed to pinpoint conserved domains within these three proteins. Project-Hope helped for biophysical validation, followed by prediction of these genes’ interaction and function by using GeneMANIA. ResultAnalysis on SMARCAL1 protein revealed that among 665 nsSNPs, four were identified as the most deleterious: L578S, T581S, P582A, and P582S. Similarly, within the DAXX protein, among a pool of 480 nsSNPs, P284S, R230C, and R230S were found out to be the most deleterious variants. In case of ATRX protein, V178D, R246C, and V277G, from the total of 1009 nsSNPs, were predicted to be the most deleterious. All these nsSNPs were found to occur at residue positions that are 100 % conserved within protein domains and were predicted to be most damaging from both structural and functional perspectives and highly destabilizing to their corresponding proteins. ConclusionComputational investigation on the 3 proteins-SMARCAL1, DAXX and ATRX through different bioinformatics analysis tools concludes that the identified high risk nsSNPs of these proteins are pathogenic SNPs. These variants potentially exert functional and structural influences, thus making them valuable candidates for future genetic studies.

Identifying the impact of structurally and functionally high-risk nonsynonymous SNPs on human patched protein using in-silico approach

BackgroundHuman PTCH1 is a negative receptor of Hedgehog (HH) signaling required to sustain cancer stem-like cells (CSCs) in a tumor microenvironment to drive tumor growth. If mutations are present in the PTCH1 gene results in the autocrine signaling and switches pathway on even in the absence of HH ligand. Further functional reduction in native PTCH1 protein may result in the impaired function of the protein. ObjectiveTo identify the functionally deleterious SNPs in human PTCH1 protein. MethodsVarious computational tools like SIFT, PolyPhen2, PROVEAN, SNAP2, SNPs&GO, PhD-SNP, I-Mutant, iPTREE-STAB and MUpro were used to predict most deleterious SNPs of PTCH1. ConSurf and NCBI conserved domain search tool was used to find conserved domain. Post translational modification sites were predicted using ModPred. SPARKS-X was used to generate 3D structure of the PTCH1 protein. ResultsIn human PTCH1 gene, a total of 60 nonsynonymous SNPs (nsSNPs) were identified to be deleterious or non-tolerable by SIFT. Further, out of 60 nsSNPs, P298L, V907G, R1024H, R134Q, W235R, D259A, and R570Q were predicted to be present in the conserved domain of PTCH1 protein and potentially damaging by all the prediction tools. Amongst them, P298, V907 and R570 were predicted as a site for post-translational modifications (PTM) while R1024 to be involved in a ligand-binding residue. ConclusionCurrent study demonstrated that most deleterious nsSNPs found is arginine to histidine at position 1024 responsible for disease occurrence which might be helpful to eliminate CSCs in a tumor bulk.

Complementary and Integrated Studies of Longevity and Healthy Aging

Five NIH-funded studies, the Long Life Family Study (LLFS, U19), the Longevity Consortium (LC, U19), Longevity Genomics (U24), and Protective Omics Profiles in Centenarians (UH2) work together to triangulate on mechanisms of extreme longevity and healthy aging with the ultimate goal of discovering predictors and targetable pathways. Linkage analyses by LLFS identified extremely strong genetic linkage peaks for cross-sectional as well as longitudinal trajectory rates-of-change phenotypes. Deep sequencing suggests these peaks are driven by rare, protective variants in selected pedigrees. In cross-species studies (UH2, LC), genomics, metabolomics and proteomics are used to exploit many-fold variances in natural life spans to discover protective mechanisms that explain some of these differences. Proteome analysis reveal several longevity-related proteins such as Cip1/p21, FOXO3, TOP2A, AKT1, RICTOR, INSR and SIRT6 harboring post translational modification sites that preferentially appear in short- versus long-lived species. The U24 effort developed a tool using genetically-mediated gene expression to prioritize genes for longevity translational efforts. We found that BLOC1S1 was associated with longevity and protection from atrial fibrillation and hearing loss without being associated with adverse events. This novel target is undergoing functional characterization. A proteomic assay (4,131 proteins, Somascan) annotated by genome-wide association study results in a total of 1,797 centenarians and 3,685 controls divided into independent discovery and replication sets, discovered significant and replicated over-expression (thus, pro-longevity) of BIRC2 and under-expression of APOB in carriers of the APOE ɛ-2 allele. A novel protein signature of rs2184061 (CDKN2a/CDKN2B in chromosome 9) was also associated with slower aging.

In silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions

Ras association domain-containing protein 5 (RASSF5), one of the prospective biomarkers for tumors, generally plays a crucial role as a tumor suppressor. As deleterious effects can result from functional differences through SNPs, we sought to analyze the most deleterious SNPs of RASSF5 as well as predict the structural changes associated with the mutants that hamper the normal protein–protein interactions. We adopted both sequence and structure based approaches to analyze the SNPs of RASSF5 protein. We also analyzed the putative post translational modification sites as well as the altered protein–protein interactions that encompass various cascades of signals. Out of all the SNPs obtained from the NCBI database, only 25 were considered as highly deleterious by six in silico SNP prediction tools. Among them, upon analyzing the effect of these nsSNPs on the stability of the protein, we found 17 SNPs that decrease the stability. Significant deviation in the energy minimization score was observed in P350R, F321L, and R277W. Besides this, docking analysis confirmed that P350R, A319V, F321L, and R277W reduce the binding affinity of the protein with H-Ras, where P350R shows the most remarkable deviation. Protein–protein interaction analysis revealed that RASSF5 acts as a hub connecting two clusters consisting of 18 proteins and alteration in the RASSF5 may lead to disassociation of several signal cascades. Thus, based on these analyses, our study suggests that the reported functional SNPs may serve as potential targets for different proteomic studies, diagnosis and therapeutic interventions.

Assessing predictors for new post translational modification sites: Acase study on hydroxylation.

Post-translational modification (PTM) sites have become popular for predictor development. However, with the exception of phosphorylation and a handful of other examples, PTMs suffer from a limited number of available training examples and sparsity in protein sequences. Here, proline hydroxylation is taken as an example to compare different methods and evaluate their performance on new experimentally determined sites. As a guide for effective experimental design, predictors require both high specificity and sensitivity. However, the self-reported performance may often not be indicative of prediction quality and detection of new sites is not guaranteed. We have benchmarked seven published hydroxylation site predictors on two newly constructed independent datasets. The self-reported performance is found to widely overestimate the real accuracy measured on independent datasets. No predictor performs better than random on new examples, indicating the refined models do not sufficiently generalize to detect new sites. The number of false positives is high and precision low, in particular for non-collagen proteins whose motifs are not conserved. As hydroxylation site predictors do not generalize for new data, caution is advised when using PTM predictors in the absence of independent evaluations, in particular for highly specific sites involved in signalling.

Prediction and Analysis of Three-Dimensional Structure of the p7- Transactivated Protein1 of Hepatitis C Virus.

The p7-transactivated protein1 of Hepatitis C virus is a small integral membrane protein of 127 amino acids, which is crucial for assembly and release of infectious virions. Ab initio or comparative modelling, is an essential tool to solve the problem of protein structure prediction and to comprehend the physicochemical fundamental of how proteins fold in nature. Only one domain (1-127) of p7-transactivated protein1 has been predicted using the systematic in silico approach, ThreaDom. I-TASSER was ranked as the best server for full-length 3-D protein structural predictions of p7-transactivated protein1 where the benchmarked scoring system such as C-score, TM-score, RMSD and Z-score are used to obtain quantitative assessments of the I-TASSER models. Scanning protein motif databases, along with secondary and surface accessibility predictions integrated with post translational modification sites (PTMs) prediction revealed functional and protein binding motifs. Three protein binding motifs (two Asp/Glutamnse, CTNNB1- bd_N) with high sequence conservation and two PTMs prediction: Camp_phospho_site and Myristyl site were predicted using BLOCKS and PROSITE scan. These motifs and PTMs were related to the function of p7-transactivated protein1 protein in inducing ion channel/pore and release of infectious virions. Using SCOP, only one hit matched protein sequence at 71-120 was classified as small proteins and FYVE/PHD zinc finger superfamily. Integrating this information about the p7-transactivated protein1 with SCOP and CATH annotations of the templates facilitates the assignment of structure-function/ evolution relationships to the known and the newly determined protein structures.

Computational analysis for the determination of deleterious nsSNPs in human MTHFR gene

Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme involved in folate metabolism and plays a central role in DNA methylation and biosynthesis. MTHFR mutations may alter the cellular folate supply which in turn affects nucleic acid synthesis, DNA methylation and chromosomal damage. The identification of number of SNPs in the human genome growing nowadays and hence, the evaluation of functional & structural consequences of these SNPs is very laborious by means of experimental analysis. Therefore, in the present study, recently developed various computational algorithms have been used which can predict the functional and structural consequences of the SNPs. Various computational tools like SIFT, PolyPhen2, PROVEAN, SNAP2, nsSNPAnalyzer, SNPs&GO, PhD-SNP, PMut, I-Mutant, iPTREE-STAB and MUpro were used to predict most deleterious SNPs. Additionally, ConSurf was used to find amino acids conservation and NCBI conserved domain search tool to find conserved domains in MTHFR. Post translational modification sites were predicted using ModPred. SPARKS-X was used to generate 3D structure of the native and mutant MTHFR protein, ModRefiner for further refinement, Varify3D and RAMPAGE to validate structure. Ligand binding sites were predicted using FTsite, RaptorX binding and COACH. Three SNPs i.e. R157Q, L323P and W500C predicted the most deleterious in all the tools used for functional and stability analysis. Moreover, both residues R157, L323 and W500 were predicted highly conserved, buried and structural residues by ConSurf. Post translational modification sites were also predicted at R157 and W500. The ligand binding sites were predicted at R157, L323 and W500.

Mutli-Features Prediction of Protein Translational Modification Sites.

Post translational modification plays a significiant role in the biological processing. The potential post translational modification is composed of the center sites and the adjacent amino acid residues which are fundamental protein sequence residues. It can be helpful to perform their biological functions and contribute to understanding the molecular mechanisms that are the foundations of protein design and drug design. The existing algorithms of predicting modified sites often have some shortcomings, such as lower stability and accuracy. In this paper, a combination of physical, chemical, statistical, and biological properties of a protein have been ulitized as the features, and a novel framework is proposed to predict a protein's post translational modification sites. The multi-layer neural network and support vector machine are invoked to predict the potential modified sites with the selected features that include the compositions of amino acid residues, the E-H description of protein segments, and several properties from the AAIndex database. Being aware of the possible redundant information, the feature selection is proposed in the propocessing step in this research. The experimental results show that the proposed method has the ability to improve the accuracy in this classification issue.

Aggregation prone regions in human proteome: Insights from large-scale data analyses.

Protein aggregation leads to several burdensome human maladies, but a molecular level understanding of how human proteome has tackled the threat of aggregation is currently lacking. In this work, we survey the human proteome for incidence of aggregation prone regions (APRs), by using sequences of experimentally validated amyloid-fibril forming peptides and via computational predictions. While approximately 30 human proteins are currently known to be amyloidogenic, we found that 260 proteins (∼1% of human proteome) contain at least one experimentally validated amyloid-fibril forming segment. Computer predictions suggest that more than 80% of the human proteins contain at least one potential APR and approximately two-thirds (65%) contain two or more APRs; spanning 3-5% of their sequences. Sequence randomizations show that this apparently high incidence of APRs has been actually significantly reduced by unique amino acid composition and sequence patterning of human proteins. The human proteome has utilized a wide repertoire of sequence-structural optimization strategies, most of them already known, to minimize deleterious consequences due to the presence of APRs while simultaneously taking advantage of their order promoting properties. This survey also found that APRs tend to be located near the active and ligand binding sites in human proteins, but not near the post translational modification sites. The APRs in human proteins are also preferentially found at heterotypic interfaces rather than homotypic ones. Interestingly, this survey reveals that APRs play multiple, often opposing, roles in the human protein sequence-structure-function relationships. Insights gained from this work have several interesting implications towards novel drug discovery and development. Proteins 2017; 85:1099-1118. © 2017 Wiley Periodicals, Inc.

Analysis of nucleotide sequences and genetic polymorphisms of UL138 gene of low passage human cytomegalovirus strains in Guangzhou

Objective To analyze the nucleotide sequences and genetic polymorphisms of UL138 gene of low passage human cytomegalovirus (HCMV) strains isolated from infants in Guangzhou province. Methods The low passage strains of HCMV were isolated from urine samples of 10 infants with HCMV infection in Guangzhou province and identified by multiplex PCR. The UL138 genes were amplified, cloned and identified with sequencing. The sequences were analyzed together with the homologous sequences of 10 clinical isolates published in GenBank. The sequences of UL138 genes were analyzed by using bioinformatics softwares for investigation of the post-translational modification sites, isoelectric points and second structures of UL138 proteins. Results Three low passage strains of HCMV (D2, D3 and D52) were isolated from infants with congenital HCMV infection. The complete sequences of UL138 genes of the three strains were submitted to GenBank after sequencing identification with the GenBank accession numbers of DQ180375, DQ180387 and DQ180359, respectively. The UL138 gene sequences of the three clinical isolates were highly conservative. Among the 841 base pairs of the UL138 gene sequences, mutations were identified in 16 sites with base substitution, no any insertion and deletion mutation was found. The 16 mutations resulted in 7 amino acid changes. No additional or deleted sites were found with regard to the post translational modification sites of UL138 protein in all clinical isolates except the Toledo strain. The isoelectric point of UL138 protein was 6.51 for all clinical isolates. Conclusion The UL138 genes and the deduced amino acid sequences of HCMV strains isolated from infants in Guangzhou were highly conservative, regardless of the polymorphism of UL138 gene. This study paved the way for further investigation on HCMV infection and its pathogenic mechanism. Key words: Human cytomegalovirus; Clinical isolate; UL138; Gene sequence; Polymorphism

Intracellular signaling, molecular modeling and docking of human Dectin-1 receptor and its microbial and non-microbial ligands: a novel gateway to control inflammatory and immune diseases

Human Dectin-1 receptor, a type of pattern recognition receptor, is expressed in dendritic cells, monocytes, macrophages and a subset of T-cells. It comprises an extracellular C-type lectin domain (CTLD), transmembrane region and immunoreceptor tyrosine based activation motif (ITAM) containing cytoplasmic domain. Stimulation of CTLD by fungal β-glucans initiates various cellular pro-inflammatory and immune responses that are controlled by downstream signaling components (Syk and Src kinases and PKCδ) of Dectin-1 receptor. Using immunoblotting, FACS analysis, fluorescence microscopy and SPR technique, we confirmed Syk and Src association with Cytopalsmic Domain of Dectin-1 Receptor (CDDR) is dependent on novel down-stream signaling kinase PKCδ expression and activity that directly bind with the Tyr-15 of ITAM motif. Additionally, we have identified osteopontin (OPN) as a non-microbial endogenous ligand that signals through Dectin-1 receptor for activation of NADPH oxidase in human monocytes. It is an acidic secreted glycoprotein highly expressed by immune cells and involved in pathogenesis of atherosclerosis and vascular remodeling. To combat these pathophysiological conditions and drug designing homology model of CTLD, CDDR and OPN have been constructed using bioinformatics tools. The interaction analysis of regulatory molecular complex of Dectin-1, i.e., CDDR and PKCδ is done through protein-protein docking strategy using HADDOCK webserver. OPN and Dectin-1 interaction pattern were also predicted in addition with post translational modification sites: some myristoylation, N-glycosylation sites, tyrosine protein kinases sites and cell attachment sequence. These findings will be a milestone in control of bacterial and fungal infections and intervention of atherosclerosis.