A Wide Antimicrobial Peptides Search Method Using Fuzzy Modeling ...

A Wide Antimicrobial Peptides Search Method Using Fuzzy Modeling Fabiano C. Fernandes, William F. Porto, and Octavio L. Franco Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília - DF, Brazil, 70790-160 {fabianofernandesdf,williamfp7,ocfranco}@gmail.com

Abstract. The search for novel antimicrobial peptides in free databases is a key element to design new antibiotics. Their amino acid physicochemical features impact into the antimicrobial peptides activities. The relationship between the amino acid physicochemical properties and the antimicrobial target might have a fuzzy behavior. This study proposes a sequence similarity and physicochemical search method followed by a fuzzy inference system to find the most appropriated antimicrobial peptides for each domain. The proposed system was tested with NCBI’s NR protein data file and the obtained peptide sub dataset will be tested in vitro. Keywords: antimicrobial peptides, fuzzy modeling and drug design.

1 Introduction Antimicrobial peptides (AMPs) are found in eukaryotes and they are used by the immune system to control bacterial infection [1]. A sequence similarity wide search for AMPs common patterns within protein sequences could be utilized to discover novel sequences that are useful for new drug design [2]. The physicochemical amino acids similarity may assist the search for common features in peptide sequence data. Moreover, investigation of common physicochemical rules in amino acid sequences with some similarities degree is important for elucidation of new sequences [2]. The antimicrobial activity can be affected by some intrinsic characteristics, such as peptide length, hidrophobicity and others [3]. In the present study, we developed a new method to search common patterns in protein sequences based on sequence similarity and amino acid physicochemical characteristics using fuzzy modeling. The Section 2 presents the proposed method. In Section 3 the proposed method is used to select the best putative sequences and Section 4 shows the conclusion.

2 Materials and Methods The proposed method comprises the following steps: (i) select only the sequences with small size, between 6 and 59 amino acids residues from the NCBI’s (National K.S. Guimarães, A. Panchenko, T.M. Przytycka (Eds.): BSB 2009, LNBI 5676, pp. 147–150, 2009. © Springer-Verlag Berlin Heidelberg 2009

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Center for Biotechnology Information) protein non redundant dataset (NR), (ii) based on a “seed” sequence and its suitable variation, select all matches within this subset (sequence similarity), (iii) calculate the physicochemical properties of all selected sequences, and (iv) using a Fuzzy Inference System, predict what are the most suitable primary structure. The search method will be complemented with in vitro testing to validate it. The Figure 1 shows the proposed method.

Fig. 1. Peptide search method using fuzzy modeling

Based on previous research, three novel antimicrobial peptides were isolated from green coconut water and used as a peptide “seed” and further used as a variation pattern filter [8]. After this filter, we searched for sequences with a length size earlier proposed obtaining a NR’s file subset containing only the most suited AMPs candidates for this given “seed”. After obtaining the NR’s file subset with only the AMPs candidates, the average hydrophobicity of complete sequences were calculated in order to be used as a first physicochemical property. This property was chosen since hydrophobicity is an important attribute for antimicrobial peptide membrane interactions [5]. The second physicochemical property used was the hydrophobic to charged ratio residues sequences that can vary from 1:1 to 2:1 for adequate AMPs [3]. The Mathworks Matlab Fuzzy Inference System (FIS) [7] was used as a fuzzy modeling tool to find the most suited candidate for AMPs function. Firstly, we were not looking for the strongest candidates to AMPs, but prototypes that showed some weakness degree, leading some uniqueness sequences and structure. This action was carried out in order to filter obvious and conventional AMPs and reduce unspecific activities [5]. The FIS variables used Table 1. Defined FIS rules Rules If Hydrophobicity is low or the Ratio is lower than 1 Then AMP is weak If Hydrophobicity is medium and Ratio is adequate Then AMP is Specific If Hydrophobicity is High Then Amp is strong

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are Hydrophobicity, previously described Ratio and AMP property. The defined FIS rules are shown in Table 1. The membership functions for hydrophobicity, ratio and AMPs are Gaussian MF, and Triangular MF. The surface plot for the FIS rules shows that the best residues sequences will have a degree of fuzziness between hydrophobicity and the ratio of hydrophobic to charged amino acids, as shown in Figure 2.

Fig. 2. The surface fuzziness between average hydrophobicity and the ratio of hydrophobic to charged amino acids. The circle shows the best peptides.

3 Results In order to evaluate the proposed system accuracy, we used the “seed” SVAGRAQGM and the variations [A-Z][AVLIPMFWY][A-Z]G[KRH][A-Z][A-Z]G[A-Z] since it showed higher antibacterial activity when compared to TESYFVFSVGM and YCSYTMEA and they are promising AMPs [8]. The seed variations were based on hydrophobicity common patterns and side chains [2]. The Table 2 shows a sample result of the first and second filtering scripts. From the original NR file with 7.153.872 amino acid residues sequences we obtained 1.510 amino acid residues sequences following the size between 6 to 59 and the “seed” variations. The calculations of hydrophobicity, the ratio of hydrophobic to charged amino acid residues sequences and the fuzzy output are also shown in Table 2. The FIS output variable was modeled to range from 0 indicating the putative non specific AMPs, to 0.6 indicating the probably most specific AMPs.

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F.C. Fernandes, W.F. Porto, and O.L. Franco Table 2. A peptide sample of search for patterns and sequence size gi 16304083 1432012 163733248

Sequence VDSVVGKEDGLGVENI HGSAAIASAYS SIYLCSVDGRGTGELFF GEGSRLTVL MLAVGRIGGNLNQIAQ WLNRAMLAGRTDLDA LTVARRMLTIERQLAQI VEAARRC

Hydrophobicity 0.226

Ratio 2.0

Fuzzy 0.605

0.369

2.0

0.603

0.114

2.1

0.605

4 Conclusion In this paper we have proposed a new method for searching and scoring amino acid residues sequences with AMPs similarities. This fuzzy method allows the choice of a small number of antimicrobial peptides for a better examination and therefore for in vitro testing. Other strategies have been used, such as the development of a novel peptide grammar to find unnatural active AMPs [1], which is complementary to strategy here utilized to find natural AMPs. In a near future, more physicochemical characteristics will be added in the fuzzy system, calibrate the membership functions and add more fuzzy rules, in order to clear validate our methodology. Together with the in vitro testing we can achieve a good accuracy system for searching and predicting new anti microbial peptides.

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