Investigating Oncological Properties of Transactivation Domain of the p53 using Computerized Digital Signal Processing-based Bioinformatics Technique: A Demonstration
Published Date: September 05, 2018
Investigating Oncological Properties of Transactivation Domain of the p53 using Computerized Digital Signal Processing-based Bioinformatics Technique: A Demonstration
Nwankwo N1*, Njoku N1, Okafor IS2, Adikwu M3, Ibezim E4, and Ibezim N5
12419 Triston Street, Anna Texas, 75409, USA
2Dept of Pharm Tech and Pharmaceutics, University of Jos, Nigeria
3Vice Chancellor, University of Abuja, Nigeria
4Dept of Pharm Tech and Pharmaceutics, University of Nigeria, Nsukka, Nigeria
5Dept of Computer and Robotics Education, University of Nigeria, Nsukka, Nigeria
*Corresponding author: Norbert Nwankwo, 2419 Triston Street, Anna Texas, 75409, USA, E-mail: email@example.com
Citation: Nwankwo N, Njoku N, Okafor IS, Adikwu M, Ibezim E, Ibezim N (2018) Investigating Oncological Properties of Transactivation Domain of the p53 using Computerized Digital Signal Processing based Bioinformatics Technique: A Demonstration. J Bioinf Com Sys Bio 2(1):102.
Background: We preliminarily advocated for multidisciplinary-based American centers for the calculation of biological bio-functionalities where robust computerized techniques will be used to investigate varieties of illnesses including Cancer; and further design and develop therapeutic interventions. Genetic variations in p53 have been associated with Cancer as well as conferment of novel features. Understanding these variations will help proffer therapeutic interventions.
Aim: To help validate the reliability of these procedures, mutation-induced (L22Q, W23S, W53Q, and F54S) abrogation of transcriptional activity, clinically observed from the Transactivation Domain (TAD1 and TAD2) of the p53 was computationally re-assessed. Steps involved in investigating this oncological characteristic using this computerized bioinformatics-based procedure are demonstrated as a guide.
Method: A Digital Signal Processing procedure called Informational Spectrum Method (ISM) is employed on the protein sequences of p53 and its motif, TAD using 7 Amino Acid Scales (AASs). ISM entails translation of the protein sequences into numerical sequences (signals) using appropriate AASs and subsequent analysis by means of Discrete Fourier Transform.
Results: Our findings revealed that different AASs provided different consensus frequencies or positions of common interactions. Similarly, the oncogene, p53 and its fragment TAD both provided different positions of common interaction. Additionally, concomitant engagement of all mutants provided 100% activity. Other ASSs are envisaged to provide complementary results.
Discussions: These findings appear to suggest that the steps needed in oncological examinations require the engagement of the proteins involved (and not their fragments or whole genome). The results favored the concomitant use of all mutants. This procedure has earlier proffered investigations into over 1000 proteins, designed pro-active drugs and vaccines, online bioinformatics tools (such as ISTREE) and biomedical devices including Computer-Aided Drug Resistance Calculator (US20150370964).
Conclusion: This computerized approach rationalizes, may revolutionize oncological investigations and provide a guide for all biological investigations. Because it depends on protein sequence information, it will further help design and assess the activities and suitability of monoclonal antibodies, a major source of emerging therapy for cancer, COPD, and others, including reports, show that single point mutation (S49R) in the EC domain of the EGFR abrogates binding interaction with a monoclonal antibody therapy (Cetuximab) unlike another called Panitumumab, rendering Cetuximab less effective. Establishment Onco-informatics specialty has become paramount.
Keywords: Bioinformatics; Digital signal processing; Informational spectrum method; p53; Transactivation domain
Good understanding of disease processes is needed to proffer therapeutic solutions. Earlier solutions including Anti-Microbial Resistance (AMR), which engage assay techniques such as Culture and Sensitivity tests have been provided. These techniques utilize slow and resources-consuming clinical techniques [1-3]. However, computerized procedures including Discrete Fourier Transform (DFT)-based, Digital Signal Processing (DSP) techniques are known to be faster, resource-saving , and have taken over assessments in several fields. They are now being incorporated into several specialties resulting in new fields like Immuno-informatics , Nano-informatics , Geo-informatics  and others.
According to a study , over 50 genes have been implicated in the p53-regulated bio-functionalities including cell cycling, cell death (apoptosis), cell differentiation, cellular senescence (cessation of cell replication), angiogenesis (growth of new blood vessels) and elimination of the damaged DNA, resulting in over 20,000 tumors . Mutations associated with transcription are investigated in this study.
Transcription refers to the process of replicating genetic information kept in the DNA strand into messenger (matching) RNA by means of RNA polymerase enzyme . Wild-type p53 is known as an effective transcriptional activator. However, p53 mutants have been recognized to trans-activate multitudes of genes , providing opportunities for diverse interactions with other genes. Studies have shown that wild-type p53 concentrate its transcriptional activities on the promotion of cell cycle arrest or programmed cell death  while the activities of the p53 mutants transcend into other biological activities, which have targets (protein or genes) or genes expressing the interactions.
Most of the oncogenic mutations have been recognized to be entrenched in the DNA-binding domain (DBD). However, few have been isolated from other motifs including the Transactivation domain (TAD) [8–10]. These motifs regulate transcriptional activities. The motifs compose of the 1-61 amino acid lengths of the p53 [8,9]. p53 (wild-type) depends on the four essential hydrophobic components (L22, W23, W53 and F54) of the TAD1 and TAD2 to exert its transactivation characteristic [8,9]. These hydrophobic protein residues with polar amino acids and bring about abrogation of transactivation property, by means of the four mutations (L22Q, W23S, W53Q, and F54S) [8,9].
Out of the 565 available Amino Acid Scales , seven are engaged. They are Kyte-Doolittle, Electron Ion Interaction Potential (EIIP), CHAM830107, CHAM830108, KLEP840101, FAUJ880111, FAUJ880112 . EIIP accounts for binding interaction [12–14,] while Kyte-Doolittle represents Hydrophobicity interaction . CHAM830107, CHAM830108, KLEP840101, FAUJ880111, FAUJ880112 express Charge Transfer in the form of Positive, Negative and Net Charges, respectively [12,15]. These AASs are chosen because they govern physio-chemical properties involved (Hydrophobicity and Polar Charges) [8,9]. To unveil the totality of this biological feature, all the parameters involved in the interactions must be engaged, and the results aggregated . This is because it has been reported that at one-point mutation, several AASs . AAS refers to the degree of participation by each of the 20 essential amino acids in an interaction.
By translating protein residues into signals and subsequently decomposing them using DSP technique (Discrete Fourier Transform (DFT)), some researchers ushered in a novel procedure to uncovering biological characteristics hidden in proteins . This invention presented two physio-mathematical techniques called Resonant Recognition Method (RRM) [14–17] and Informational Spectrum Method (ISM) [13,18-40].
Decomposition of numerical sequences (signals) using DFT brings about numerous peaks, each representing a biological characteristic. Both techniques had fetched investigations into more than 1000 proteins including oncogenes  such as interactions that exist between the pro-oncogene refer to p53 and (its mediator) MDM2 ; antitumor regulatory activities of TNF, IL-2, IFN-beta, and human M-CSF were investigated using RRM . ISM has provided a common position of interaction for p53 and its mediators .
As a guide to ISM-based computerized approach to assessing oncological assessment, this preliminarily and clinically verified abrogation of transcriptional activity found in the Transactivation Domains (TAD1 and TAD2) of the p53 resulting from four mutations (L22Q, W23S, W53Q, and F54S)  is therefore computationally re-examined.
The similar study has been carried out using Plasmodial protein. Abrogation of pharmacological inactivity resulting from a single mutation in the Duffy Antigen Receptor Chemokines (nDARC Y41F) protein found in the Plasmodial peptide (nDARC) peptide has computationally been verified , Preliminarily, this study has clinically been ascertained . Computational re-examination using the five affected AASs revealed that the decreased activity (84.4%) demonstrated by the wild-type nDARC was restored to 100% activity due to the introduction of a single mutation nDARC Y41F .
As huge sequence information is daily obtained especially in the field of cancer, transforming them into oncological properties using bioinformatics approaches (Translational Bioinformatics) has become vital in the assessment, designing and development of both anti-cancer therapy and personalized medicine . Employing these procedures especially in monoclonal antibodies is needful as they have become a major source of therapies in diseases like Cancer, Diabetes, Rheumatoid diseases, COPD. Reports have shown that though single point mutation (S49R) in the EC domain of the EGFR abrogates binding interaction with a Monoclonal Antibody therapy (Cetuximab), this mutation does not have effect on another Monoclonal Antibody therapy (Panitumumab) [44–46]. This may explain why Cetuximab is less effective . Establishment of a new field that will concentrate on information technology-oriented studies using these computerized procedures on cancer studies (Onco-informatics) has also become necessary.
The results are presented in Section 3.
The materials consist of 393 and 61 amino acid lengths of the p53 and its Transactivation Domain (TAD), respectively (Figure 1) [9,10], as well as seven Amino Acid Scales (AASs). The Consensus sequences are obtained from UNIPROT database . The AASs are Kyte-Doolittle, EIIP, CHAM830107, CHAM830108, KLEP840101, FAUJ880111, FAUJ880112 . Table 1 is the corresponding EIIP numerical values for the 20 essential amino acids.
Informational Spectrum Method (ISM) is employed here. The procedure is briefly described here. It has already been detailed in [13,18–23,25,29,33,34]. ISM consists of three major steps.
Conversion of the Protein Primary structure (Amino Acid Sequences) into Numerical Sequences (Signals) using seven Amino Acid Parameters: This is the initial step in the ISM process. Each Amino Acid Scales (AASs) engaged in the interactions has numerical values representing the contribution offered by each of the 20 essential amino acids. Each amino acid sequence, therefore, is converted into numerical sequences using the corresponding contributions provided by each of the 20 essential amino acids. The numerical sequences are called signals. As signals, they are qualified for decomposition and analysis using any Digital Signal Processing technique such as Discrete Fourier Transform used here. In a situation where the sequences are unequal in length, zero values are added to bring the sequences to equal length. This is called Zero-padding.
Discrete Fourier Transform-based Processing of the Signals (Informational Spectrum): This step entails decomposing these series of signals using Discrete Fourier Transform (DFT). The DFT decomposition is expressed as:
n is the discrete time index that runs from 0 to N-1. k is the discrete frequency index that traverses 0-N/2 as one part of the mirror (symmetric) image is used. X(k) expresses them member of the numerical series. N is the length of the numerical sequence. X(n) represents the coefficient of the DFT.
DFT decompositions usually give rise to both real and imaginary component. This is because they are sum of both sine and cosine waves. Absolute components are derived as:
X(n) = [R(n) + I(N)j] Equation 2
Where n = 1, 2, ..., N/2, R and I express the Real and Imaginary parts each.
The Absolute Spectrum representing the magnitude of interaction as amplitudes is expressed as:
Sa(n) = X(n)X*(n), which is equal to ?X(n)2? Equation 3
Where Sa represents the Absolute Spectrum, X(n) the DFT coefficient, and X*(n) indicates the conjugate component, while n ranges from 0-N/2.
Plots of the magnitude of interaction (amplitudes) on the y-axis and frequencies on the x-axis are referred to as Informational Spectra (IS). Each peak represents the degree of interaction with other bio-molecules with regards to the bio-functionality investigated as typified by the AAS engaged.
Point-wise Multiplication of the Signals (Common Informational Spectrum): The position of common interaction is determined by point-wise multiplication of the Informational Spectra (IS), which are derived by processing the numerical sequences (signals) using Discrete Fourier Transform. Point-wise (element-wise) multiplication is a mathematical operation (multiplication) that occurs with regards to individual points in the space (in this case, sequences) . It is expressed as:
Ca = ∏ S(a)(m) Equation 4
Ca is the Common Informational Spectrum characteristics. ∏ is the point-wise multiplication function.
S(a) is still the Absolute Spectrum. m = 1, 2, 3, ...M and M represent the number of protein sequences involved.
According to the principles of ISM  and RRM , biomolecules with common biological characteristics demonstrate a maximum peak at the common point of interaction. This position is called Consensus Frequency (CF), which is calculated as:
CF = PP/N
PP is the Peak Position and N is the length of the longest protein investigated.
The degree of contribution by each sequence at the CF with respect to the biological characteristic are then calculated (numerically obtained) as the magnitude of interaction. To obtain total bio-functionality, and in this case, oncological properties, all mutants and wild types, as well as the AASs, must be engaged as several AASs have been implicated in a single sequence change (11).
Informational Spectrum Method-based analysis of both p53, which is composed of 393 protein residues and its 61 membered fragments, TAD, using seven Amino Acid Scales resulted in hundreds of peaks (Tables 2 and 3, and Figures 2–7). Each peak represents an interaction or intra-action with other bio-molecules, a bio-functionality, which could be a physio-chemical or structural as depicted by the AAS in use. Peaks including those at the point of common interaction are examined.
The results are presented in tables 2 and 3, and figures 2–7. Oncogene p53 is processed using the seven AASs. EIIP and Kyte-Doolittle only are applied on TAD.
Consensus Frequency (CF)/Position of Common Interaction:
EIIP and Kyte-Doolittle: As shown in Tables 2 and 3, the Consensus Frequencies (CFs) or common positions of interaction for TAD and p53 using EIIP are 0.278 and 0.031, respectively. Similarly, by means of Kyte-Doolittle, TAD and p53 presented CFs at 0.230 and 0.252, respectively.
Other AASs: Table 2 and 3 indicate that while CHAM830107 and CHAM830108 displayed CFs at 0.473 and 0.252 each, KLEP840101, FAUJ880111 and FAUJ880112 demonstrated CFs at 0.313, 0.204 and 0.018, respectively.
Percentage biological activities as expressed by wild types and mutants
Table 3 is the percentage biological activities in terms physio-chemical properties (binding interaction, hydrophobicity and charge transfer) using seven AASs, and as expressed by the p53 and its motif, TAD, at positions including the points of common interactions. Except position 78 of the EIIP-based results of the analysis of p53, others are the positions of common interactions (Table 3, column 4). The result shows that only an individual mutant, W23S demonstrated 100% activity. All mutants yielded 95.5%.
All AASs but CHAM830107, KLEP840101, FAUJ880111 and FAUJ880112 demonstrated differences in the level of interactions at the positions of common interaction. Both the wild-type and the mutants showed same level of interaction (100%). Except the Kyte-Doolittle, all other AASs demonstrated the highest interaction (100%) at the CFs when the four mutations are engaged simultaneously.
In this section, the interpretation of the peaks generated using the ISM procedure and seven AASs on 61 and 393 amino acid lengths of the TAD and p53, respectively are presented. Each peak represents a biological functionality based on the proteins that share the CF and the AAS engaged.
There are eight highlights that guided how this computerized procedure would be employed on oncological evaluations:
1. At peak (frequency = 0.0373 or position 18), CD4s of the HIV hosts including Human and Chimpanzee, as preliminarily studied, demonstrated point of common interaction with the HIV gp120 obtained from several isolates [17,25,34,38]. Based on this, several biological characteristics were examined including the mechanism by which HIV infection translates to AIDS [25,38] and the prediction of HIV tropism [25,34]. Similarly, this signifies that common positions of interaction with other proteins will help unearth other biological characteristics of the oncogenes.
2. As shown in figure 8, at another peak (frequency = 0.14 or position 68) both CD4s of Human and Chimpanzee demonstrated maximum binding interaction . Both species have earlier been found to originate from same source. This biological characteristic is observed in other HIV strains including 98US_MSC5016 and 98US_MSC5007 isolated from American soldiers on Foreign Service. This property helped identify that they may have originated from Zaire and Nigeria, respectively . This is because the gp120 of 98US_MSC5007 and a Nigerian isolate (92NG083) were found share same position of maximum interaction . Similarly, 98US_MSC5016 and the Zairian isolates (ELI, MAL, and Z2/CDC-34) have very close position of maximum interaction.
For engagement in the oncological investigations, it is therefore proposed that every peak will be considered for a bio-functionality.
3. It was revealed in this study that EIIP- and Kyte-Doolittle-based analysis of p53 and its motif, TAD demonstrated different positions of common interaction or CFs (Table 2 and Figures 2 and 3). For example, EIIP revealed a position of common interaction (CF) at 0.278 and 0.031 for TAD and p53, respectively. In the case of Kyte-Doolittle, TAD and p53 presented CFs of 0.230 and 0.252, respectively. It must be noted that TAD is only a domain of the oncogene, p53. It is therefore important to note that the clinical investigation of oncological property is directed to the oncogene, p53 and not its fragment.
4. We have earlier shown that the CFs of HIV Envelope protein (gp160) and its two components Surface (gp120) and Transmembrane proteins (gp41) share different positions of common interaction (Figure 9). The only CF that corresponded to the CD4 belongs to the Surface protein. Clinically, interaction between the HIV gp120 and CD4 has been verified . Computationally, this has also been validated using RRM  and ISM . Since the ISM-based analysis of the oncogene, p53 and its segment TAD demonstrated different CFs, it is recommended that ISM-based oncological studies be channeled towards sequences of the genes and proteins involved rather than fragments. Additionally, it can also be deduced that engagement of whole sequence is discouraged.
5. It is disclosed in this study that all AASs, but Kyte-Doolittle demonstrated the highest interaction (100%) at the CFs when all the four mutations are engaged concomitant (Table 3 Row 8). The levels of interaction at the CFs by TAD and p53 demonstrated by simultaneous engagement of all mutants using Kyte-Doolittle are 82% and 97.0%, respectively. Engaging the mutants individually did not demonstrate consistently increased activities unlike when they are concurrently utilized. This appears to symbolize that associated oncological, hence biological characteristics arising from sequence changes requires the concomitant engagement of all mutations, where there are more than one mutation.
6. The findings revealed that except for position 78 of the EIIP-based results of the analysis of p53, all other positions studied are the points of common interactions (Table 3, column 4). The result shows that all mutants did not yield 100% rather 95.5%. However, an individual mutant, W23S provided 100% activity. This study therefore recommends that oncological examinations should focus on activities at the position of common interaction (CF).
7. All AASs excluding CHAM830107, KLEP840101, FAUJ880111 and FAUJ880112 demonstrated differences in the level of interactions at the positions of common interaction. ISM-based analysis of the sequences of both wild-type and the mutants showed same level of interaction (100%). This is as shown in table 3, Columns 7, 9, 10 and 11. It was observed that for these AASs, though there are sequence alterations, the AAS values did not change. This is because in these AASs, only two values (0 and 1) are involved and most have zero values. As a result, the AAS value of the amino acids belonging to the consensus and mutated sequences (L, Q, W, S, and F) involved in the four mutations is same (zero). As a result, there was no change in activity signifying insensitivity of the AASs. It is recommended that in the oncological evaluations, all AASs must be engaged to compliment these results.
8. Similar assessment using the Plasmodial protein nDARC  provided similar results. Abrogation of pharmacological property of a plasmodial protein nDARC caused by a single mutation nDARC Y41F demonstrated a restoration 100% from decreased pharmacological activity (84.4%) (Figure 10). This appears to signify that this uncovered bio-functionality applies to all proteins including oncogenes.
This study provides a guide for a computerized bioinformatics-based approach that may in future take over the assessment of oncological characteristics and help rationalize resources and time invested in preliminary and fruitful clinical cancer researches that had produced vast information on genetic variations (mutations) .
We preliminarily proposed multi-disciplinary centers where diseases need be rationally investigated, therapeutic interventions and biomedical devices designed using sequence information-based bioinformatics techniques. This novel computerized Informational Spectrum Method-based procedure requires simply, a Discrete Fourier Transform process, all Amino Acid Scales and protein sequences involved. As a robust, resource- and time-saving approach, it is envisaged to assist immeasurably, clinical investigations including oncological assessment.
In this study, protein residues of an oncogene, p53 and its motif, Transactivation Domain (TAD) are processed using ISM procedure and seven out of the 565 Amino Acid Scales. Several peaks were generated, each signaling a bio-functionality. The results revealed that the oncogene, p53 and the motif have different positions of interaction implying that neither the protein sequence of fragment (motif such as the TAD) nor the whole genome but only the protein involved in the interaction is appropriate for use in this computerized technique. The outcome also disclosed that complete reversal of the abrogation of activity by the mutants was achieved when all the mutants are concomitantly engaged. Additionally, consistency was observed at the position of common interaction rather than another chosen position indicating oncological assessment and others should make use of positions of common interaction.
This study, which is a demonstration of the employment of a Fourier Transform-based, Digital Signal Processing technique that has fetched reliable technologies like Radar, Image Processing and Speech Detectors in investigating the oncological properties of the Transactivation Domain of p53 serves as a guide for future assessments of all biological functionalities including oncological properties.
Conflict of Interest
All the authors declared that they have no conflict of interest.
- Cavalieri SJ, Harbeck RJ, McCarter YS. Manual of Antimicrobial Susceptibility Testing. American Society for Microbiology. 2005.
- W. O. F. A. H. (OIE). International Standards on Antimicrobial Resistance. 2008.
- W. O. F. A. H. (OIE). Laboratory Methodologies for Bacterial Antimicrobial Susceptibility Test. 2008.
- SW. The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Publishing. 2002.
- Maojo V, Fritts M, Martin-Sanchez F, De la Iglesia D, Cachau RE, Garcia-Remesal M, et al. Nanoinformatics: developing new computing applications for nanomedicine. Comput Sci Eng. 2012;94(6):521–539.
- Dikou A, Papapanagiotou E, Troumbis A. Integrating landscape ecology and geoinformatics to decipher landscape dynamics for regional planning. Environ Manage. 2011;48(3):523-38. doi: 10.1007/s00267-011-9714-4.
- Evans MC. Recent advances in immunoinformatics: application of in silico tools to drug development. Curr Opin Drug Discov Devel. 2008;11(2):233-41.
- Olivier M, Hollstein M, Hainaut P. TP53 Mutations in Human Cancers: Origins, Consequences, and Clinical Use. Cold Spring Harb Perspect Biol. 2010;2(1):a001008. doi: 10.1101/cshperspect.a001008.
- Freed-Pastor WA, Prives C. Mutant p53: One Name, Many Proteins. Genes Dev. 2012;26(12):1268-86. doi: 10.1101/gad.190678.112.
- Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Molecular Biology of the Cell. New York: Garland Science. 2002.
- Hoj L, Kjaer J, Winther O, Cozzi-Lepri A, Lundgren JD. In silico identification of physicochemical properties at mutating positions relevant to reducing susceptibility to amprenavir. XVII International HIV Drug Resistance Workshop. Poster No.113. 2008.
- Tomii K, Kanehisa M. Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins. Protein Engineering. 1966;9(1):27-36.
- Veljkovic V, Cosic I, Dimitrijevic B, Lalovic 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 Transactions on Biomedical Engineering. 1994;41(I):1101-1114.
- Cosic I, Cosic D, Lazar K. Environmental Light and Its Relationship with Electromagnetic Resonances of Biomolecular Interactions, as Predicted by the Resonant Recognition Model. Int J Environ Res Public Health. 2016;13(7).pii: E647. doi: 10.3390/ijerph13070647.
- Cosic I, Pirogova E, Akay M. Application of the Resonant Recognition Model to Analysis of Interaction between Viral and Tumor Suppressor Proteins. 2003.
- Cosic I. Analysis of HIV proteins using DSP techniques. Proc. of IEEE EMBS. 2002(3): 2886-2889, 2001.
- Nwankwo N, Njoku N. Amino Acid Scales: The Driving Force in the Translation of Genomic Information into Phenotypic Characteristics. J Bioinf Com Sys Bio. 2017;1(2):1-6.
- Veljkovic V, Niman HL, Glisic S, Veljkovic N, Perovic V, Muller CP. Identification of hemagglutinin structural domain and polymorphisms which may modulate swine h1n1 interactions with human receptor. BMC Struct Biol. 2009;9:62. doi: 10.1186/1472-6807-9-62.
- Veljkovic V, Metlas R. Identification of nanopeptide from HTLV-111, ARV-2 and LAVBRU envelope gp120 determining binding to t4 cell surface protein. Cancer Biochem Biophys. 1988;10(2):91-106.
- Veljkovic V, Veljkovic N, Muller CP, Müller S, Glisic S, Perovic V, et al. Characterization of conserved properties of hemagglutinin of h5n1 and human influenza viruses: possible consequences for therapy and infection control. BMC Struct Biol. 2009;9:21. doi: 10.1186/1472-6807-9-21.
- Doliana R, Veljkovic V, Prljic J, Veljkovic N, De Lorenzo E, Mongiat M, et al. Emilins interact with anthrax protective antigen and inhibit toxin action in vitro. Matrix Biol. 2008;27(2):96-106.
- Veljkovic N, Glisic S, Prljic J, Perovic V, Botta M, Veljkovic V. Discovery of New Therapeutic Targets by the Informational Spectrum Method. Curr Protein Pept Sci. 2008;9(5):493-506.
- Nwankwo NI, inventor; NWANKWO NORBERT IKECHUKWU, assignee. Computer Aided Drug Resistance Calculator Calculating Drug Resistance Using Amprenavir as a Case Study. United States Patent Publication No: 20150370964 A1. 2015.
- Nwankwo N. Signal processing-based Bioinformatics methods for characterization and identification of Bio-functionalities of proteins. 2012.
- Nwankwo N, Njoku N. American Center for Calculation of Biological Functionalities: A Multidisciplinary Forum for Designing Therapeutic Interventions and Determining Pathogenesis. Journal of Microbial Pathogenesis. 2017.
- Nwankwo N, Seker H. Assessment of the Binding Characteristics of Human Immunodeficiency Virus Type 1 Glycoprotein120 and Host Cluster of Differentiation4 Using Digital Signal Processing. Conf. Proc. IEEE BIBE 2010:289-290.
- Nwankwo N, Njoku N. Digital Signal Processing-based Techniques: Providing Pharmacists with computerized procedures for investigations. 6th World Pharmacists and Clinical Pharmaceutical Annual Congress, Chicago, USA. 22nd–23rd May, 2017.
- Nwankwo N, Okafor I, Ibezim E. Njoku N, 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):101.
- Nwankwo N. Computer-Aided Drug Potency Decoder: A Biomedical/Bioengineering Device for Determining and Distinguishing Drug Efficacies. J Bioengineer & Biomedical Sci. 2015;6(1):176. doi: 10.4172/2155- 9538.1000176.
- Nwankwo N. Computer-Aided Pharmaco-Investigator: A Biomedical/Bioengineering Device for Pharmacological Investigations. Austin Virol and Retrovirology. 2015;2(2):1015.
- 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.
- Nwankwo N, Godwin M, Ngozika 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, Adikwu M, Okafor I. HIV Tropism Prediction: Digital Signal Processing-Based Bioinformatics Approach is Non-Sequence Alignment Dependent. Computational Biology and Bioinformatics. 2015;3(2):21-30.
- Nwankwo N. Direct Computerized Translation of Biological data into Biological Information is now feasible: The Gains of Digital Signal Processing-based Bioinformatics Techniques. Virol-mycol. 2015;4:2.
- Nwankwo N. Digital Signal Processing Techniques: Can They Analyze Vaccines Containing Mixtures of Proteins? 8th Vaccine and ISV Congress, Philadelphia, USA. 2014.
- 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 N, Seker H. HIV Progression to AIDS: Bioinformatics Approach to Determining the Mechanism of Action. Curr HIV Res. 2013;11(1):30-42.
- Nwankwo N, Seker H. A signal processing-based bioinformatics approach to assessing drug resistance: human immunodeficiency virus as a case study. Conf. Proc. IEEE Eng Med Biol Soc. 2010:1836-1839.
- 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. 2017.
- Hickman ES, Moroni MC, Helin K. The role of p53 and pRB in apoptosis and cancer. Curr Opin Genet Dev. 2002;12(1):60-6.
- Chitnis CE, Sharma A. Targeting the plasmodium vivax duffy-binding protein. Trends Parasitol. 2008;24(1):29-34.
- Qing Y. Translational Bioinformatics Methods for Drug Discovery and Development. Boston American Press. 2017;97-110.
- Leto SM, Trusolino L. Primary and acquired resistance to EGFR-targeted therapies in colorectal cancer: impact on future treatment strategies. J Mol Med (Berl). 2014;92(7):709-22. doi: 10.1007/s00109-014-1161-2.
- Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6(224):224ra24. doi: 10.1126/scitranslmed.3007094.
- Montagut C, Dalmases A, Bellosillo B, Crespo M, Pairet S, Iglesias M, et al. Identification of a mutation in the extracellular domain of the epidermal growth factor receptor conferring cetuximab resistance in colorectal cancer. Nat Med. 2012;18(2):221-3. doi: 10.1038/nm.2609.
- 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;10:136-154.
- Corbeil J, Richman DD. 1995. “Productive infection and subsequent interaction of cd4-gp120 at the cellular membrane is required for HIV-induced apoptosis of cd4+ T cells. J Gen Virol. 1995;76 ( Pt 3):681-90.
- Muller PAJ, Vousden KH. Mutant p53 in Cancer: New Functions and Therapeutic Opportunities. Cancer Cell. 2014;25(3):304–317.
Copyright: © 2018 Nwankwo N, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.