Polarity Index in Proteins-A Bioinformatics Tool

This chapter describes the structure of a group of polymers responsible for regulating the basic functions and inheritance of living organisms: the proteins. The characterization of these organic units involves a very complex problem due to its multi-factorial nature. To develop this topic I showed the difficulty when trying to differentiate living matter from non-living matter and then I proceeded to find distinctions at cellular level. At this stage I identified two main groups with three types of major macromolecular structures, where proteins are included, and four levels of structural complexity known as primary, secondary, tertiary and quaternary structures, from which proteins get their morphologic features.

This chapter introduces the covalent and non-covalent intermolecular bonds as major actors in the basic macromolecule structure. It states the importance of the ionization energy in bonds and it determines the electronegativity or polarity quantification as an effective discriminator for the type of bond. With this quantification amino acids are classified into four groups: polar basic, polar acidic, polar neutral and non-polar.

This chapter will describe one supervised algorithm that is frequently used to predict the function of proteins, the quantitative structure-activity relationship model (QSAR). I will discuss its advantages and disadvantages, and I will present a model called the polarity index method. This model shows a high degree of efficiency in predicting the main function of peptides and proteins by inspecting the linear sequence of macromolecules to evaluate a single physico-chemical property, polarity.

This chapter explains the mathematical background used for polarity index method described in Chapter 3 and its relation with the catastrophic bifurcation points located in the geometric representation of the relative frequencies of a protein group. I discuss the singularities and regularities of this geometric representation and how this metric identifies the main action of a protein with a high level of accuracy. I introduce the concepts of maximum points, minimum points and saddle points, I also calculate the smooth curve from matrix Qi + Σⁿ =1Qi (Sect. 3.3.2), and I justify the exhaustiveness of the metric.

This chapter discusses the computational profile required for the implementation of the polarity index method (Sect. 4) from two platforms: distributed computing and; serial computing. It also shows the advantages and disadvantages of each one of them; and the processing time used by the method. Finally it describes the degree of automation reached by the method.

This chapter includes the identification of a group of peptides known as antimicrobial peptides. They have an important role in the immune system of all living organisms. The identification of the main function associated to each peptide was made by calculating the polar profile of the peptide with the QSAR method called polarity index, already described in Sect. 3.3. The peptides computationally tested were taken from two different databases: Antimicrobial Peptides Database (APD2), and Uniprot Database, which included the set of peptides called selective cationic amphipatic antibacterial peptides.

This chapter describes the identification of a group of peptides called Cell penetrating peptides (CPP) that are featured by their ability to penetrate the membrane of different microorganisms. They were identified using the polarity index method described in Sect. 3.3. For this purpose two classifications were taken into account: non endocytic and endocytic pathway uptake mechanisms. The peptides studied were taken from CPPsite Database (CPPsite), and the set of selective cationic amphipatic antibacterial peptides (SCAAP) described in Sect. 6.3.3. The comparative study of these two groups made possible the identification of a particular reason for the toxicity of the selective cationic amphipatic antibacterial peptides.

This chapter describes the main results obtained when the polar profile of peptides associated to Amyloidosis is determined by polarity index method (Sect. 3.3), and the relation these peptides have with a group of proteins identified by their structure-function called natively folded proteins, partially folded proteins, and natively unfolded proteins. Amyloidosis is a term that groups a set of mental diseases with neurodegenerative characteristics, caused by the agglomeration of natively unfolded proteins on the neurons and lipoproteins. This chapter shows that the proteins that express neurons are natively folded proteins.

In this chapter, I discuss the main results obtained when calculating the polar profile of two important groups of proteins: globular and fibrous proteins, by polarity index method (Sect. 3.3). This classification, unlike other proteins mentioned in previous chapters of the book, is not a sub-division but a major classification from which all protein classifications come from.

This chapter describes the experiments related to the origin of life developed by Miller & Urey, Fox & Harada, and Rode oriented to the polymerization of the prebiotic proteins. It is explained how each experiment with a different number of amino acids, proportion and methodology, made it possible to find a polar profile for their dipeptides and proteins, and how this information was used to study the trend of each experiment. The results show a common polar profile with the most preserved genes from three microorganisms.

This final chapter describes what will be, in the opinion of the authors, the future trend for the construction of synthetic proteins in the bioinformatics field, their potential use in the pharmaceutical industry, and the impact that distributed computing will have on the algorithms designed to predict the main action of proteins.