On the Regularities of the Polar Profiles of Proteins Related to Ebola Virus Infection and their Functional Domains

Abstract

The number of fatalities and economic losses caused by the Ebola virus infection across the planet culminated in the havoc that occurred between August and November 2014. However, little is known about the molecular protein profile of this devastating virus. This work represents a thorough bioinformatics analysis of the regularities of charge distribution (polar profiles) in two groups of proteins and their functional domains associated with Ebola virus disease: Ebola virus proteins and Human proteins interacting with Ebola virus. Our analysis reveals that a fragment exists in each of these proteins-one named the "functional domain"-with the polar profile similar to the polar profile of the protein that contains it. Each protein is formed by a group of short sub-sequences, where each fragment has a different and distinctive polar profile and where the polar profile between adjacent short sub-sequences changes orderly and gradually to coincide with the polar profile of the whole protein. When using the charge distribution as a metric, it was observed that it effectively discriminates the proteins from their functional domains. As a counterexample, the same test was applied to a set of synthetic proteins built for that purpose, revealing that any of the regularities reported here for the Ebola virus proteins and human proteins interacting with Ebola virus were not present in the synthetic proteins. Our results indicate that the polar profile of each protein studied and its corresponding functional domain are similar. Thus, when building each protein from its functional domai-adding one amino acid at a time and plotting each time its polar profile-it was observed that the resulting graphs can be divided into groups with similar polar profiles.

Bioinformatics-based Characterization of the Sequence Variability of Zika Virus Polyprotein and Envelope Protein (E)

“…The metric of the PIM system is named PIM profile. The acronym stands for Polarity Index Method (PIM) 10 - 12 ; this is an automatic set of programs and Linux scripts that evaluates the 16 polarity/charge interactions identified when reading the sequence of the protein pairs of residues (amino acid by amino acid), from left to right or from the amino-terminus to the carboxy-terminus. The PIM system has 3 steps ( Table 3 , A-C steps).…”

“…Some of these features include biases in amino acid locations, where IDPs/IDPRs are automatically enriched in some specific disorder-promoting amino acid, such as A, E, G, K, P, Q, and S, being automatically short of several types of amino acid, known as order-promoting residues (C, F, I, L, N, V, and W, Y ),. [10][11][12][16][17][18] These remarkable sequence differences are used by computational programs to identify the IDPD of specific proteins. 19,20 The characteristics of the intrinsic disorder distribution in the sequences of Zika virus envelope proteins (E) were studied by a per-residue disorder predictor, PONDR VSL2.…”

“…The 4 structural sets (Table 1; rows UNF, PAR, CPP, and NCP), unfolded and partially folded proteins were taken from Oldfield et al 's work 14 and the cell-penetrating peptides and non-cell-penetrating peptides were taken from CPPSite A [LKCNKLVPLF] [i,j] = P -0 pos 5 (14) 0 pos 6 (13) 0 pos 7 (12) 0 pos 8 (11) N 1 pos 9 (4) 0 pos 10 (10) 1 pos 11 (3) 0 pos 12 (9) NP 1 pos 13 (2) 0 pos 14 (8) 0 pos 15 (7) 4 pos 16 (1)…”

“…P + P -N NP P + 0.000 pos 1 (16) 0.000 pos 2 (15) 0.111 pos 3 (6) 0.111 pos 4 (5) A [LKCNKLVPLF] [i,j] = P -0.000 pos 5 (14) 0.000 pos 6 (13) 0.000 pos 7 (12) 0.000 pos 8 (11) N 0.111 pos 9 (4) 0.000 pos 10 (10) 0.111 pos 11 (3) 0.000 pos 12 (9) NP 0.111 pos 13 (2) 0.000 pos 14 (8) 0.000 pos 15 (7) 0.444 pos…”

“…That is why we consider our computational approach useful and we have taken advantage of a set of in-house algorithms that can infer protein structure-function relationships based solely on the linear representation of a query protein. To do that, we use the Polarity Index Method (PIM) system 10 , 11 to calculate the PIM profile of each Zika virus envelope protein (E), and simultaneously we use a set of predictors of intrinsic disorder to generate the disorder predisposition profiles 12 , 13 of these same protein groups (the corresponding programs are defined in the Materials and Methods section). A new parameter is introduced, which is the average of those previous parameters, enabling better discrimination and clarification among the different protein groups studied.…”