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Research proposal

Title

Computational biology in proteomics

Abstract

The broad application of proteomics in different biological and medical fields, as well as the

diffusion of high-throughput platforms, leads to increasing volumes of available proteomics

data. Computational proteomics is the data science concerned with the identification and

quantification of proteins from numerous data and the biological interpretation of their

concentration changes, posttranslational modifications, interactions, and subcellular

localizations. Computational proteomics is a highly multidisciplinary endeavor attracting

scientists from many fields and incorporates other disciplines like statistics, machine learning,

efficient scientific programming, and network and time series analysis.

Introduction

Proteomics is a fundamental science in which many sciences in the world are directing their efforts.

The proteins play a key role in the biological function and their studies make possible to understand

the mechanisms that occur in many biological events (human or animal diseases, factor that

influence plant and bacterial grown). Due to the complexity of the investigation approach that

involve various technologies, a high amount of data is produced. In fact, proteomics has known a

strong evolution and now we are in a phase of unparalleled growth that is reflected by the amount

of data generated from each experiment. That approach has provided, for the first time,

unprecedented opportunities to address biology of humans, animals, plants as well as micro-

organisms at system level. Bioinformatics applied to proteomics offered the management, data

elaboration and integration of these huge amount of data. It is with this philosophy that this chapter

was born. Thus, the role of bioinformatics is fundamental in order to reduce the analysis time and

to provide statistically significant results. To process data efficiently, new software packages and

algorithms are continuously being developed to improve protein identification, characterization

and quantification in terms of high-throughput and statistical accuracy. However, many limitations

exist concerning bioinformatics spectral data elaboration. In particular, for the analysis of plant

proteins extensive data elaboration is necessary due to the lack of structural information in the

proteomic and genomic public databases. The main focus of this chapter is to describe in detail the

status of bioinformatics applied to proteomic studies. Moreover, the elaboration strategies and

algorithms that have been adopted to overcome the well-known limitations of the protein analysis

without database structural information are described and disclosed. This chapter will get rid of

light on recent developments in bioinformatics and data-mining approaches, and their limitations

when applied to proteomic data sets, in order to reinforce the interdependence between proteomic

technologies and bioinformatics tools. Proteomic studies involve the identification as well as

qualitative and quantitative comparison of proteins expressed under different conditions, together

with description of their properties and functions, usually in a large-scale, high-throughput format.

The high dimensionality of data generated from these studies will require the development of

improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis

of various biological systems (for reviews see, Li et al, 2009; Mathieson & Jensen, 2008; Wright

et al, 2009). After a rapid moving on the wide theme of the genomic and proteomic sciences, in

which bioinformatics find their wider applications for the studies of biological systems, the chapter

will focus on mass spectrometry that has become the prominent analytical method for the study of

proteins and proteomes in post-genome era. The high volumes of complex spectra and data

generated from such experiments represent new challenges for the field of bioinformatics. The past

decade has seen an explosion of informatics tools targeted towards the processing, analysis,

storage, and integration of mass spectrometry based proteomic data. In this chapter, some of the

more recent developments in proteome informatics will be discussed. This includes new tools for

predicting the properties of proteins and peptides which can be exploited in experimental

proteomic design, and tools for the identification of peptides and proteins from their mass spectra.

Similarly, informatics approaches are required for the move towards quantitative proteomics

which are also briefly discussed. Finally, the growing number of proteomic data repositories and

emerging data standards developed for the field are highlighted. These tools and technologies point

the way towards the next phase of experimental proteomic and informatics challenges that the

proteomics community will face. The majority of the chapter is devoted to the description of

bioinformatics technologies (hardware and data management and applications) with particular

emphasis on the bioinformatics improvements that have made possible to obtain significant results

in the study of proteomics. Particular attention is focused on the emerging statistic semantic,

network learning technologies and data sharing that is the essential core of system biology data

elaboration. Finally, many examples of bioinformatics applied to biological systems are distributed

along the different section of the chapter so to lead the reader to completely fill and understand the

benefits of bioinformatics applied to system biology (Creston and et al, 2011).