Bioinformatics Resources for In Silico Proteome Analysis

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

A Novel In Silico Benchmarked Pipeline Capable of Complete Protein Analysis: A Possible Tool for Potential Drug Discovery

Simple Summary

Protein interactions govern the majority of an organism’s biological processes. Therefore, to fully understand the functionality of an organism, we must know how proteins work at a molecular level. This study assembled a protocol that enables scientists to construct a protein’s tertiary structure easily and subsequently to investigate its mechanism and function. Each step involved in prediction, validation, and functional analysis of a protein is crucial to obtain an accurate result. We have dubbed this the trifecta analysis. It was clear early in our research that no single study in the literature had previously encompassed the complete trifecta analysis. In particular, studies that recommend free, open-source tools that have been benchmarked for each step are lacking. The present study ensures that predictions are accurate and validated and will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could lead to drug development.

Abstract

Current in silico proteomics require the trifecta analysis, namely, prediction, validation, and functional assessment of a modeled protein. The main drawback of this endeavor is the lack of a single protocol that utilizes a proper set of benchmarked open-source tools to predict a protein’s structure and function accurately. The present study rectifies this drawback through the design and development of such a protocol. The protocol begins with the characterization of a novel coding sequence to identify the expressed protein. It then recognizes and isolates evolutionarily conserved sequence motifs through phylogenetics. The next step is to predict the protein’s secondary structure, followed by the prediction, refinement, and validation of its three-dimensional tertiary structure. These steps enable the functional analysis of the macromolecule through protein docking, which facilitates the identification of the protein’s active site. Each of these steps is crucial for the complete characterization of the protein under study. We have dubbed this process the trifecta analysis. In this study, we have proven the effectiveness of our protocol using the cystatin C and AChE proteins. Beginning with just their sequences, we have characterized both proteins’ structures and functions, including identifying the cystatin C protein’s seven-residue active site and the AChE protein’s active-site gorge via protein–protein and protein–ligand docking, respectively. This process will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could expand drug development.

Partial enzymatic reactions: A missed opportunity in proteomics research

RATIONALE

Biological studies are conducted at ever-increasing rates by relying on proteomic workflows. Although data acquisition by mass spectrometry is highly automated and rapid, sample preparation continues to be the bottleneck of developing high-throughput workflows. Enzymatic protein processing, in particular, involves time-consuming protocols that can extend from one day to another. To address this gap, we developed and evaluated simple, in-solution tryptic enzymatic reactions that unfold within a few minutes, and demonstrate the utility of the methodology for the rapid analysis of proteins originating from cancer cell extracts.

METHODS

Tryptic enzymatic reactions were conducted for 7-60 min, and the results were compared with that of a routine approach conducted for 18 h. No other reaction conditions were changed relative to the 18 h procedure. The reaction products were analyzed by nanospray high-performance liquid chromatography/tandem mass spectrometry (nano-HPLC/MS/MS), and the quality of the products was assessed in terms of peptide/protein identifications, sequence coverage, peptide length, missed-cleavage sites, quality of generated ions, and peptide hydrophilic/hydrophobic properties.

RESULTS

The results demonstrate that brief, and therefore incomplete, enzymatic processes lead to a large number of peptide fragments that improve protein sequence and proteome coverage, that the tandem mass spectra produced from these peptides are of high quality for reliable protein identifications, and that the physical properties of peptides are prone to supporting the development of alternative multi-dimensional separations and middle-down proteomics analysis strategies. The reproducibility of generating the same peptides within a few minutes of enzymatic digestion was remarkably close to that obtained from 18 h long reactions, and the combined results of short and long reactions increased proteome coverage by ~40%.

CONCLUSIONS

We demonstrate that partial enzymatic reactions conducted on short time-scales represent a valuable asset to proteomic studies, and propose their implementation either as simple, cost-effective, stand-alone protocols for substantially streamlining the analysis of biological samples, or as complementary protocols, for improving protein sequence and proteome coverage.

Unravelling peptidomes by in silico mining

Peptides of great number and diversity occur in all domains of life and exhibit a range of pharmaceutically relevant bioactivities. The complexity of biological samples including human cells or tissues, plant extracts or animal venom cocktails, often impedes the discovery of novel bioactive peptides using mass spectrometrybased peptidomics analysis. An increasing number of publicly available genome and transcriptome datasets, together with refined bioinformatics analysis, allows for rapid identification of novel peptides which may have been previously unrecognized. Moreover, a combination of information extracted from

Urinary proteomics evaluation in interstitial cystitis/painful bladder syndrome: a pilot study

PURPOSE

Interstitial cystitis/painful bladder syndrome (IC/PBS) is characterized by chronic pain, pressure and discomfort felt in the pelvis or bladder. An in-depth shotgun proteomics study was carried out to profile the urinary proteome of women with IC/PBS to identify possible specific proteins and networks associated with IC%PBS.

MATERIALS AND METHODS

Urine samples from ten female IC/PBS patients and ten female asymptomatic, healthy control subjects were analyzed in quadruplicate by liquid chromatography-tandem mass spectrometry (LC-MS/MS) on a hybrid linear ion trap-orbitrap mass spectrometer. Gas-phase fractionation (GPF) was used to enhance protein identification. Differences in protein quantity were determined by peptide spectral counting.

RESULTS

alpha-1B-glycoprotein (A1BG) and orosomucoid-1 (ORM1) were detected in all IC%PBS patients, and > or = 60% of these patients had elevated expression of these two proteins compared to control subjects. Transthyretin (TTR) and hemopexin (HPX) were detected in all control individuals, but > or = 60% of the IC/PBS patients had decreased expression levels of these two proteins. Enrichment functional analysis showed cell adhesion and response to stimuli were down-regulated whereas response to inflammation, wounding, and tissue degradation were up-regulated in IC/PBS. Activation of neurophysiological processes in synaptic inhibition, and lack of DNA damage repair may also be key components of IC%PBS.

CONCLUSION

There are qualitative and quantitative differences between the urinary proteomes of women with and without IC%PBS. We identified a number of proteins as well as pathways%networks that might contribute to the pathology of IC%PBS or result from perturbations induced by this condition.

Proteome informatics for cancer research: from molecules to clinic

Proteomics offers the most direct approach to understand disease and its molecular biomarkers. Biomarkers denote the biological states of tissues, cells, or body fluids that are useful for disease detection and classification. Clinical proteomics is used for early disease detection, molecular diagnosis of disease, identification and formulation of therapies, and disease monitoring and prognostics. Bioinformatics tools are essential for converting raw proteomics data into knowledge and subsequently into useful applications. These tools are used for the collection, processing, analysis, and interpretation of the vast amounts of proteomics data. Management, analysis, and interpretation of large quantities of raw and processed data require a combination of various informatics technologies such as databases, sequence comparison, predictive models, and statistical tools. We have demonstrated the utility of bioinformatics in clinical proteomics through the analysis of the cancer antigen survivin and its suitability as a target for cancer immunotherapy.

The case for well-conducted experiments to validate statistical protocols for 2D gels: different pre-processing = different lists of significant proteins

Background

The proteomics literature has seen a proliferation of publications that seek to apply the rapidly improving technology of 2D gels to study various biological systems. However, there is a dearth of systematic studies that have investigated appropriate statistical approaches to analyse the data from these experiments.

Results

Comparison of the effects of statistical pre-processing on the results of two sample t-tests suggests that the results of 2D gel experiments and by extension the conclusions derived from these experiments are not independent of the statistical protocol used.

Conclusions

This study suggests that there is a need for well-conducted validation studies to establish optimal statistical techniques to be used on such data sets.