Using Neural Networks to Identify Features Associated with HIV Nef Protein and Cancer

HIV Nef protein has a known association with cancer, yet the specific features associated with this relationship remain poorly characterized. Using a dataset of well curated Nef sequences, we trained evolved neural networks to classify sequences as having originated from cancer or non-cancer samples. This process allows us to identify features that are possibly associated with Nef protein in HIV and its relation to cancer. We review the methods and features identified for their biological significance.

[1]  D. Eisenberg,et al.  Analysis of membrane and surface protein sequences with the hydrophobic moment plot. , 1984, Journal of molecular biology.

[2]  J. Janin,et al.  Surface and inside volumes in globular proteins , 1979, Nature.

[3]  R Cowan,et al.  Hydrophobicity indices for amino acid residues as determined by high-performance liquid chromatography. , 1990, Peptide research.

[4]  M. Reitz,et al.  Structure and expression of tat-, rev-, and nef-specific transcripts of human immunodeficiency virus type 1 in infected lymphocytes and macrophages , 1990, Journal of virology.

[5]  P. Y. Chou,et al.  Prediction of the secondary structure of proteins from their amino acid sequence. , 2006 .

[6]  H. Hofmann,et al.  On the theoretical prediction of protein antigenic determinants from amino acid sequences. , 1987, Biomedica biochimica acta.

[7]  T. Smithgall,et al.  Conserved residues in the HIV-1 Nef hydrophobic pocket are essential for recruitment and activation of the Hck tyrosine kinase. , 2004, Journal of molecular biology.

[8]  Gary B. Fogel,et al.  Evolved neural networks for HIV-1 co-receptor identification , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[9]  R. Jernigan,et al.  Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. , 1996, Journal of molecular biology.

[10]  D. Eisenberg,et al.  Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. , 1983, Journal of molecular biology.

[11]  Gary B. Fogel,et al.  Identification of dual-tropic HIV-1 using evolved neural networks , 2015, Biosyst..

[12]  D. Mould,et al.  Development of hydrophobicity parameters to analyze proteins which bear post- or cotranslational modifications. , 1991, Analytical biochemistry.

[13]  Akintola A. Aboderin,et al.  An empirical hydrophobicity scale for α-amino-acids and some of its applications , 1971 .

[14]  Hector Zenil,et al.  Evolving Neural Networks through a Reverse Encoding Tree , 2020, 2020 IEEE Congress on Evolutionary Computation (CEC).

[15]  Dong Ling Tong,et al.  gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data , 2015, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[16]  Ruurd van der Zee,et al.  Prediction of sequential antigenic regions in proteins , 1985, FEBS letters.

[17]  K. R. Woods,et al.  Prediction of protein antigenic determinants from amino acid sequences. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[18]  M. O. Dayhoff,et al.  Atlas of protein sequence and structure , 1965 .

[19]  Márcio Dorn,et al.  Microarray Classification and Gene Selection with FS-NEAT , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[20]  O. Yang,et al.  Immune Selection In Vitro Reveals Human Immunodeficiency Virus Type 1 Nef Sequence Motifs Important for Its Immune Evasion Function In Vivo , 2012, Journal of Virology.

[21]  S. Jameel,et al.  Biology of the HIV Nef protein. , 2005, The Indian journal of medical research.

[22]  Gang Zhao,et al.  An amino acid “transmembrane tendency” scale that approaches the theoretical limit to accuracy for prediction of transmembrane helices: Relationship to biological hydrophobicity , 2006, Protein science : a publication of the Protein Society.

[23]  H. Bull,et al.  Surface tension of amino acid solutions: a hydrophobicity scale of the amino acid residues. , 1974, Archives of biochemistry and biophysics.

[24]  J. M. Zimmerman,et al.  The characterization of amino acid sequences in proteins by statistical methods. , 1968, Journal of theoretical biology.

[25]  C. Chothia The nature of the accessible and buried surfaces in proteins. , 1976, Journal of molecular biology.

[26]  C. Tanford Contribution of Hydrophobic Interactions to the Stability of the Globular Conformation of Proteins , 1962 .

[27]  Y. Ghiglione,et al.  Nef performance in macrophages: the master orchestrator of viral persistence and spread. , 2011, Current HIV research.

[28]  G. Rose,et al.  Hydrophobicity of amino acid residues in globular proteins. , 1985, Science.

[29]  M. Levitt Conformational preferences of amino acids in globular proteins. , 1978, Biochemistry.

[30]  Márcio Dorn,et al.  Neuroevolution as a tool for microarray gene expression pattern identification in cancer research , 2019, J. Biomed. Informatics.

[31]  L. LamersSusanna,et al.  On the Physicochemical and Structural Modifications Associated with HIV-1 Subtype B Tropism Transition. , 2016 .

[32]  S Solomon,et al.  The isolation of peptides by high-performance liquid chromatography using predicted elution positions. , 1982, Analytical biochemistry.

[33]  G. Fogel,et al.  Brain-specific HIV Nef identified in multiple patients with neurological disease , 2018, Journal of NeuroVirology.

[34]  R. Grantham Amino Acid Difference Formula to Help Explain Protein Evolution , 1974, Science.

[35]  Á. Holguín,et al.  Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach , 2017, Genome biology and evolution.

[36]  R. Hodges,et al.  New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. , 1986, Biochemistry.

[37]  H. Guy Amino acid side-chain partition energies and distribution of residues in soluble proteins. , 1985, Biophysical journal.

[38]  O. Pybus,et al.  HIV Maintains an Evolving and Dispersed Population in Multiple Tissues during Suppressive Combined Antiretroviral Therapy in Individuals with Cancer , 2016, Journal of Virology.

[39]  P M Cullis,et al.  Affinities of amino acid side chains for solvent water. , 1981, Biochemistry.

[40]  V I L F M Y W P G D N S A C E H K Q R T,et al.  Amino Acid , 2020, Definitions.

[41]  Hongyu Zhao,et al.  Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality , 2018, Clinical Epigenetics.

[42]  Stefano Nolfi,et al.  Robust optimization through neuroevolution , 2019, PloS one.

[43]  K J Wilson,et al.  The behaviour of peptides on reverse-phase supports during high-pressure liquid chromatography. , 1981, The Biochemical journal.

[44]  R D Appel,et al.  Protein identification and analysis tools in the ExPASy server. , 1999, Methods in molecular biology.

[45]  G. Fogel,et al.  HIV-1 Nef in Macrophage-Mediated Disease Pathogenesis , 2012, International reviews of immunology.

[46]  J. Meek Prediction of peptide retention times in high-pressure liquid chromatography on the basis of amino acid composition. , 1980, Proceedings of the National Academy of Sciences of the United States of America.

[47]  G. Fogel,et al.  Predicted coreceptor usage at end-stage HIV disease in tissues derived from subjects on antiretroviral therapy with an undetectable plasma viral load. , 2017, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

[48]  P. Ponnuswamy,et al.  Hydrophobic character of amino acid residues in globular proteins , 1978, Nature.

[49]  D. D. Jones,et al.  Amino acid properties and side-chain orientation in proteins: a cross correlation appraoch. , 1975, Journal of theoretical biology.

[50]  J. Lillard,et al.  HIV Nef-M1 Effects on Colorectal Cancer Growth in Tumor-induced Spleens and Hepatic Metastasis. , 2009, Molecular and cellular pharmacology.

[51]  M. Salemi,et al.  Single Genome Sequencing of Expressed and Proviral HIV-1 Envelope Glycoprotein 120 (gp120) and nef Genes. , 2017, Bio-protocol.

[52]  C. Sander,et al.  Antiparallel and parallel beta-strands differ in amino acid residue preferences. , 1979, Nature.

[53]  P Argos,et al.  Oligopeptide biases in protein sequences and their use in predicting protein coding regions in nucleotide sequences , 1988, Proteins.

[54]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[55]  D. Baltimore,et al.  Kinetics of expression of multiply spliced RNA in early human immunodeficiency virus type 1 infection of lymphocytes and monocytes. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[56]  G. Fogel,et al.  On the Physicochemical and Structural Modifications Associated with HIV-1 Subtype B Tropism Transition. , 2016, AIDS research and human retroviruses.

[57]  M. Geyer,et al.  Extracellular HIV-1 Nef increases migration of monocytes. , 2006, Experimental cell research.

[58]  A. Leo,et al.  Extension of the fragment method to calculate amino acid zwitterion and side chain partition coefficients , 1987, Proteins.

[59]  P Argos,et al.  A conformational preference parameter to predict helices in integral membrane proteins. , 1986, Biochimica et biophysica acta.

[60]  B. Peterlin,et al.  Structure–function relationships in HIV‐1 Nef , 2001, EMBO reports.

[61]  G. Fogel,et al.  Prediction of R5, X4, and R5X4 HIV-1 Coreceptor Usage with Evolved Neural Networks , 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[62]  Shneior Lifson,et al.  Antiparallel and parallel β-strands differ in amino acid residue preferences , 1979, Nature.

[63]  B. Sawaya,et al.  HIV-1 Nef promotes cell proliferation and microRNA dysregulation in lung cells , 2019, Cell cycle.

[64]  M A Roseman,et al.  Hydrophilicity of polar amino acid side-chains is markedly reduced by flanking peptide bonds. , 1988, Journal of molecular biology.

[65]  G Deléage,et al.  An algorithm for protein secondary structure prediction based on class prediction. , 1987, Protein engineering.

[66]  P. Y. Chou,et al.  Prediction of protein conformation. , 1974, Biochemistry.