Amino Acid Scales: The Driving Force in the Translation of Genomic Information into Phenotypic Characteristics
Published Date: August 30, 2018
Amino Acid Scales: The Driving Force in the Translation of Genomic Information into Phenotypic Characteristics
Norbert Nwankwo1* and Ngozika Njoku2
1Department of Clinical Pharmacy, University of Port Harcourt, Nigeria
2Nova Psychiatric Services, Quincy, USA
*Corresponding author: Norbert Nwankwo, Department of Clinical Pharmacy, Faculty of Pharmacy, University of Port Harcourt, Nigeria, E-mail: firstname.lastname@example.org.
Citation: Nwankwo N, Njoku N (2017) Amino Acid Scales: The Driving Force in the Translation of Genomic Information into Phenotypic Characteristics. J Bioinf Com Sys Bio 1(2): 107.
Anti-Microbial Resistance (AMR) Challenge for a point-of-care diagnostic device, which was rolled out recently may have centered on in-vivo or in-vitro assays. This may be as a result of the fact that it is unclear how computerized procedures translate genomic information into phenotypic features. These translations may not have been possible without the role played by Amino Acid Scale (AASs).
AASs are biological parameters that represent the level of involvement of each of the 20 essential amino acids that constitute protein. These biological parameters depict the physiochemical and structural interactions. In combination with Discrete Fourier Transform (DFT)-based technique, AASs has helped translate and quantify biological information embedded in protein sequences (genomic information) into phenotypic characteristics. This Digital Signal Processing (DSP)-based technique is called Informational Spectrum Method (ISM).
To understand this role, interpretation of the mechanism by which AAS translates genomic information into phenotypic characteristics is needed. In this study, explanation of the role of AAS is provided using those associated with binding interaction called Electron–Ion Interaction Potential (EIIP) and Charge Transfer. Their roles are studied as it concerns resistance offered to the HIV Protease Enzyme (PE) by the Amprenavir, and ERG11 gene of the Candida albicans by Fluconazole have utilized as well as druggability of antiretroviral agents (Enfuvirtide and Sifuvirtide) and Malaria vaccine candidates (P18 and P32), respectively.
With this understanding, it is envisaged that direct and computerized dry-laboratory approaches to biological evaluations may in future take over wet-laboratory investigations.
Keywords: Amino acid scales; Charge transfer; Digital signal processing; Electron–Ion interaction potential; Informational spectrum method
List of Abbreviations
AASs: Amino Acid Scales; AMR: Anti-Microbial Resistance; CS: Circum-sporozoite; DSP: Digital Signal Processing; DFT: Discrete Fourier Transform; EIIP: Electron–Ion Interaction Potential; GAG: Glycosaminoglycan; HSGP: Heparan Sulfate Gyco-proteins; ISM: Informational Spectrum Method.
Recently, the Anti-Microbial Resistance (AMR) Challenge for a point-of-care diagnostic device was rolled out. This challenge requested for submissions that may have focused on the clinical procedure (in vivo or in vitro assay). This may be as a result of the fact that little is understood about the ability of some procedures such as Digital Signal Processing (DSP) techniques to translate genomic information embedded in proteins into phenotypic (in vivo or in vitro) characteristics. Deciphering outcomes of clinical experiments (the phenotypic characteristics) from their sequence (genomic) information using DSP techniques had preliminarily been considered infeasible. However, these practices are now known to be practicable [1,2]. One such DSP-procedure is called Informational Spectrum Method (ISM) [1–10].
ISM first translates the genomic information (such as binding interaction, hydrophobicity, helicity, etc.) embedded in the protein sequence information into signals (numerical sequence) using the Amino Acid Scale (AAS) involved. These numerical sequences representing the translated genomic information from each interaction are then processed using Discrete Fourier Transform (DFT). The end-product is a phenotypic characteristic including antibiotic resistance was demanded by AMR Challenge. This translation is unachievable except with the aid of AASs, and a DFT procedure.
AASs are biological parameters representing the degree of phenotypic participation of each of the 20 essential amino acids that constitute protein . There are over 565 AASs. Some of them are deposited in a database . In this study, AAS associated with the binding interaction (EIIP) and five others that based on Charge Transfer are used to explain the role played by AASs in the translation of genomic information into phenotypic features.
Electron-Ion Interaction Potential (EIIP)
EIIP expresses the degree of participation of the 20 amino acids in a binding interaction that exists between bio-molecules  (Table 1). EIIP is represented by two sets of 20 numerical values [3,4,11]. EIIP is the first interaction that brings bio-molecules together. It occurs in all bio-molecular interactions. It has been identified that proteins are not only amino acids in linear formation  but have unrestricted electrons and charges . These charges, in turn, generate short-lived polarization of the side-chain groups, hence electromagnetic oscillation between parts of the protein residues . It has been identified that during the process of electromagnetic oscillation and reverberation, molecules that share a resemblance in their biological activity are found to resonate at same frequency bringing about the increase in the attraction (binding interaction) .
It has been identified  that in a given sequence where the amino acid side chain is specifically bulky at a given sequence position, a replacement with a small side chain (mutation) will bring about a compensatory measure that preserves the general structural motif and physio-chemical quantities. The compensatory measure that results in the preservation of structural motif and physiochemical characteristics came from changes in amino acid charge, hydrogen bonding, hydrophobicity, amphilicity, etc. that resulted from replacement with a small side chain (mutation) . This is to say that mutations are the products of structural motif and physio-chemical modification arising from responses to changes in the numerical sequences (signals) as presented by the AASs involved such as Charge Transfer. The AASs involved in the Charge Transfer include CHAM830107, CHAM830108, FAUJ880111, FAUJ880112, and KLEP840101. While CHAM830107 accounts for charge transfer capability, CHAM830108 describes charge transfer donor capability (Table 2).
In this study, the resistance offered to the HIV Protease Enzyme (PE) by the Amprenavir  and also ERG11 gene of the Candida albicans by Fluconazole  were utilized to demonstrate how genomic information is transformed into phenotypic characteristics. Triazole has been identified to resist fungal growth by interfering with Ergosterol, a cell wall component, which it blocks through enzymatic activities of CYP51A1. CYP51A1 is encoded by ERG11 gene [17,18]. In addition, druggability of two antiretroviral agents and Malaria vaccine candidates are also engaged in the demonstration of the relevance of AASs in the translation of genomic information into phenotypic features. Druggability has been defined as a level of fitness or complementariness of drugs, vaccines, etc. to their binding sites that guarantee their efficacy .
The materials engaged in this study of resistance offered to Amprenavir by the HIV Protease Enzyme include 22 AASs involved. Other materials include the consensus sequence of the enzyme and the sequences arising from the 295 mutations . To investigate the druggability of two antiretroviral agents (Enfuvirtide and Sifuvirtide), and Malaria vaccine candidates (P18 and P32), the amino acid sequences of these proteins and AASs involved in the interactions are utilized [7–9]. The materials for the study of Fluconazole-resistance in Candida albicans  include EIIP, the consensus sequence of the ERG11 as well as its mutant, K143R.
All consensus sequences were retrieved from a database  and their corresponding mutants are constructed from the consensus sequences. Both consensus and mutated sequences were then subjected to ISM procedure.
The Procedure: Informational Spectrum Method (ISM)
Informational Spectrum Method (ISM) has been detailed [1–10]. In this study, the procedure is briefly provided.
Translation of the alphabetic code of the proteins into numerical sequences (signals): The sequences which were first derived from a database  were translated into numerical sequences (signals) using AASs involved. Figure 1 is the translation process as achieved using the sequence of P18 and EIIP.
The decomposition of the signals using discrete fourier transform (DFT): The signals were then processed by means of Discrete Fourier Transform (DFT) and presented as plots. The plots represent the magnitudes of phenotypic characteristics on the y-axis and the positions of interaction on X-axis. This plot is called Informational Spectrum (IS).
Point-wise multiplication of the informational spectrum (CIS): For proteins with common functionality, it has been disclosed that point-wise multiplication of the IS reveals a common position of interaction called Consensus Frequency (CF). This process is called Common Informational Spectrum (CIS). By aggregating results from all the AASs involved, the entire bio-functionality will be derived [6–9].
As demonstrated in these studies, each AAS recorded a particular phenotypic characteristic. For example, figure 2 shows the CIS of the Malaria vaccine candidates P18 and P32 as assessed using EIIP, representing their vaccine candidature or potency as calculated by means of binding interaction. This displays a CF at position 18. The potencies recorded by P18 and P32, respectively are 100% each (Figure 3 and 4). As preliminarily recorded [7–9], engaging all the nine AASs involved in the physiochemical and structural interactions, the totality of druggability presented by P18 and P32 were derived as 93.40 and 95.15% respectively (Table 2).
Figure 5 shows the level of susceptibility at the Consensus Frequency of 0.4149 belonging to the sequence from mutant N88A, which has maximum activity 0.31 or 100%. The mutant, N88F displayed 0.22 or 71% (Figure 6) leaving a resistance of 29%. In this study, AAS with a descriptor, Ra was engaged. The totality of resistance calculated using all mutants and AASs is obtained as 5.06%.
In the case of Fluconazole resistance, the Consensus Frequency is derived at position 154 (Figure 7). The level of susceptibility displayed by consensus sequence is identified as 2.054 or 100% (Figure 7) while the mutant (K143R), which is associated with reduced susceptibility, demonstrated 2.04 or 99.3% (Figure 8). EIIP-based resistance offered to Fluconazole by ERG11 of the Candida albicans is therefore calculated as 0.7% 
Based on this procedure, druggability proffered by anti-retroviral agents (Enfuvirtide and Sifuvirtide) using nine AASs engaged was derived as 73.2% and 85.4%, respectively [7–9].
As earlier indicated, AAS-based phenotypic assessments (physiochemical and structural characteristics) including resistance offered to the target proteins (HIV-1 Protease Enzyme and ERG11) by various agents (Amprenavir and Fluconazole); druggability of antiretroviral agents as well as of Malaria vaccine candidates have been unraveled [6–9]. These assessments were achieved by evaluating inter-molecular and intra-molecular interactions involved. This demonstrates that phenotypic characteristics such as resistance offered by drugs must not only be obtained from clinical laboratories. In this study, the vital roles played by ASSs in the transformation of genomic information into phenotypic features are explained using only two AASs. They are AAS associated with the binding interaction (EIIP) and five others that are Charge Transfer-based.
Electron-Ion Interaction Potential (EIIP)
EIIP is the AAS descriptor for binding interaction. This is the earliest interaction that occurs between bio-molecules. After the molecules have bound and become mono-molecule, they engage other physio-chemical or structural property-based AASs in further intra-molecular interactions in order to mingle. All bio-molecules must first engage inter-molecular interaction.
As shown in table 2 as well as figures 3 and 4, EIIP-based translation of signals of P18 and P32 (genomic information) provided 100% druggability, respectively. It also demonstrated druggability of 81.50% (Enfuvirtide) and 100% (Sifuvirtide), respectively. In the case of Fluconazole, 0.7% reduced susceptibility is observed in ERG11 as a result of K143R mutation using EIIP  (Figure 9 and 10).
Malaria vaccine candidates (P18 and P32) provided 100% druggability each, which appears to signify their maximum ability to recognize and bind to the appropriate antigen. This vital property of vaccine is term "specificity". Additionally, Sifuvirtide demonstrated greater specificity (100%) than Enfuvirtide (81.50%). The overall result reveals that Sifuvirtide (85.42%) is more potent than Enfuvirtide (72.33%). This outcome is in accord with earlier clinical studies [7–9]. For the Fluconazole, about 60 mutations have been associated with reduced susceptibility including Y132F, Y132H, K143R and K143Q . The total resistance offered to ERG11 by Fluconazole will be obtained using all the mutations and AASs involved.
The differences in the amino acid composition and alignment, existing between the two Malaria vaccine candidate (P18 and P32), demonstrates differences in their general structural motifs and physio-chemical quantities. These physiochemical quantities, which are Charge Transfer-based, are calculated using CHAM830107, CHAM830108, FAUJ880111, FAUJ880112, and KLEP840101 (Table 2). These Charge Transfer-based AASs are engaged because it has been identified that interaction between Malaria vaccine candidates (P18 and P32) obtained from the Plasmodial protein called Circum-sporozoite (CS) and target protein Glycosaminoglycan (GAG) of the Heparan Sulfate Gyco-proteins (HSGP) uses negatively charged carboxyl group .
The biological meanings of the AASs are as shown in table 2. CHAM830107 is defined as a parameter of Charge Transfer capability while CHAM830108 describes charge transfer donor capability. FAUJ880111, FAUJ880112, and KLEP840101 are descriptors for a positive charge, negative charge and net charge, respectively. Engaging each AAS, the level of druggability contributed by each AAS is as displayed in table 2. As shown in that result case the differences in the amino acid composition and arrangement of the P18 and P32 altered the Charge transfer pattern in these signals (numerical sequences) demonstrated druggability of 93.40% and 95.15%, respectively. This signifies that individual contribution by each AAS can be derived and elucidated.
Because it is presumed that bio-functionalities cannot be extracted from genomic information, biological assessments were expected to remain clinical laboratory-based. In this study, we demonstrated that it is now practicable to decipher phenotypic characteristics from their genomic information using Bioinformatics procedure and also highlighted the role played by Amino Acid Scale using two AASs. They are Electron–Ion Interaction Potential (EIIP) and Amino Acid Charge Transfer.
The study focused on the resistance offered to the HIV Protease Enzyme (PE) and ERG11 of Candida albicans by Amprenavir and Fluconazole, respectively. Additionally, druggability of two antiretroviral agents and Malaria vaccine candidates were also utilized. Six AASs associated with the interactions involved, (EIIP and Charge-Transfer-based) out of the currently available 565 AASs were used. EIIP, which is associated with the binding interaction, provided 100% druggability for both Malaria vaccine candidates signifying maximum Specificity, hence appropriately targeting Malaria and providing specific immunity against Malaria. This property is vital as it demonstrates optimal ability to bio-identify and bio-affine to their targets. Five other Charge Transfer-based AASs demonstrated different levels of intra-interaction in terms of charge transfer capability, charge transfer donor capability, positive, negative, and net charges.
A good interpretation of all the 565 AASs as they relate to phenotypic assessments and provided in this study remains vital in the understanding of the role played by AASs in the translation of genomic information into phenotypic characteristics. With this understanding, it becomes paramount to engage these rational dry-laboratory procedures in biological assays such as the AMR challenge instead of resource-consuming wet-laboratory approaches.
- Nwankwo N. Direct Computerized Translation of Biological data into Biological Information is now feasible: the Gains of Digital Signal Processing-based Bioinformatics Techniques. International conference on Retroviruses and Novel Drugs. Chicago, USA. 2015.
- Nwankwo N. Can Bio-functionalities be deciphered from Protein Sequence Information using Computational Approaches? 29th Annual Symposium of the Protein Society Book Abstract. 2015. p 407.
- Veljkovi? V, Cosi? I, Dimitrijevi? B, Lalovi? D. Is it possible to analyze DNA and protein sequence by the method of digital signal processing. IEEE Trans Biomed Eng. 1985;32(5):337-41.
- Cosic I. Macromolecular Bioactivity: Is It Resonant Interaction between Macromolecules?-Theory and Applications. IEEE Trans Biomed Eng. 1994;41(12):1101-14.
- Nwankwo N. A Digital Signal Processing-based Bioinformatics Approach to Identifying the Origins of HIV-1 non B subtypes infecting US Army Personnel serving abroad. Curr HIV Res. 2013;11(4):271-80.
- Nwankwo NI, inventor. NWANKWO NORBERT IKECHUKWU, assignee. Computer Aided Drug Resistance Calculator Calculating Drug Resistance Using Amprenavir as a Case Study. United States Patent Application: 20150370964 A1. 2015.
- Nwankwo N, Molokwu G, Njoku N. Novel Computerized Approaches to Investigating Pharmacological Activities. Computational Biology and Bioinformatics. 2015;3(4):5264. doi: 10.11648/j.cbb.20150304.12.
- Nwankwo N, Okafor I, Ibezim E, Njoku G, Nwankwo C, Chinasa C, et al. Computer-Aided Druggability Detector: A Computerized Apparatus for Quickening Drug Discovery Processes. J Bioinf Com Sys Bio. 2016;1(1):1–9.
- Nwankwo N. Signal processing-based Bioinformatics methods for characterization and identification of Bio-functionalities of proteins. Open thesis. 2012.
- Veljkovic V, Veljkovic N, Muller CP, Müller S, Glisic S, Perovic V, et al. Characterization of conserved properties of hemagglutinin of h5n1 and human in?uenza viruses: possible consequences for therapy and infection control. BMC Struct Biol. 2009;9:21. doi: 10.1186/1472-6807-9-21.
- Wilce MC, Aguilar MI, Hearn MT. Physicochemical basis of amino acid hydrophobicity scales: evaluation of four new scales of amino acid hydrophobicity coe?cients derived from rp-hplc of peptides. J Anal Chem. 1995;67:1210–1219.
- Lodish H, Berk A, Zipursky SL, Matsudaira P, Baltimore D, Darnell J. Molecular Cell Biology. 4th ed. New York: W. H Freeman; 2000.
- Nair AS, Sreenadhan SP. A coding measure scheme employing electron-ion interaction pseudopotential (EIIP). Bioinformation. 2006;1(6):197–202.
- Frohlich H. Long-range coherence and energy storage in biological systems. J Quantum Chem. 1968;11: 641–649.
- Biro JC. Amino Acid Size, Charge Hydropathy indices and matrices for protein structure analysis. Theor Biol Med Model. 2006;3:15. doi: 10.1186/1742-4682-3-15.
- Nwankwo N, Njoku N. Computer-Aided Drug Resistance Calculator: A Rational and Rapid Point-Of-Care Diagnostic Device. Executive Summaries for AMR Phase I Solvers (unpublished). 2017.
- Xiang MJ, Liu JY, Ni PH, Wang S, Shi C, Wei B, et al. ERG11 mutations associated with azole resistance in clinical isolates of candida albican. FEMS Yeast Res. 2013;13(4):386-93. doi: 10.1111/1567-1364.12042.
- Wang H, Kong F, Sorell TC, Wang B, McNicholas P, Pantarat N, et al. Rapid detection of ERG11 gene mutations in clinical canidida albicans isolates with reduced susceptibility to fluconazole by rolling circle amplification and DNA sequencing. BMC Microbiol. 2009;9:167. doi: 10.1186/1471-2180-9-167.
- Jain E, Bairoch A, Duvaud S, Phan I, Redaschi N, Suzek BE, et al. Infrastructure for the life sciences: design and implementation of the UNIPROT website. BMC Bioinformatics. 2009;10:136. doi: 10.1186/1471-2105-10-136.
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