Structural and Functional Modeling of Artificial Bioactive Proteins

A total of 32 synthetic proteins designed by Michael Hecht and co-workers was investigated using standard bioinformatics tools for the structure and function modeling. The dataset consisted of 15 artificial α-proteins (Hecht_α) designed to fold into 102-residue four-helix bundles and 17 artificial six-stranded β-sheet proteins (Hecht_β). We compared the experimentally-determined properties of the sequences investigated with the results of computational methods for protein structure and bioactivity prediction. The conclusion reached is that the dataset of Michael Hecht and co-workers could be successfully used both to test current methods and to develop new ones for the characterization of artificially-designed molecules based on the specific binary patterns of amino acid polarity. The comparative investigations of the bioinformatics methods on the datasets of both de novo proteins and natural ones may lead to: (1) improvement of the existing tools for protein structure and function analysis; (2) new algorithms for the construction of de novo protein subsets; and (3) additional information on the complex natural sequence space and its relation to the individual subspaces of de novo sequences. Additional investigations on different and varied datasets are needed to confirm the general applicability of this concept.

[1]  Michael J. E. Sternberg,et al.  3DLigandSite: predicting ligand-binding sites using similar structures , 2010, Nucleic Acids Res..

[2]  C. DeLisi,et al.  Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins. , 1987, Journal of molecular biology.

[3]  Michael H Hecht,et al.  Cofactor binding and enzymatic activity in an unevolved superfamily of de novo designed 4‐helix bundle proteins , 2009, Protein science : a publication of the Protein Society.

[4]  Kyle A. Barlow,et al.  A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design , 2015, PloS one.

[5]  L. Serpell,et al.  Modular Design of Self-Assembling Peptide-Based Nanotubes. , 2015, Journal of the American Chemical Society.

[6]  Edward R. Dougherty,et al.  Detecting Periodic Genes from Irregularly Sampled Gene Expressions: A Comparison Study , 2008, EURASIP J. Bioinform. Syst. Biol..

[7]  Jean-Michel Claverie,et al.  Phylogeny.fr: robust phylogenetic analysis for the non-specialist , 2008, Nucleic Acids Res..

[8]  U. Dietrich,et al.  Novel Phylogenetic Algorithm to Monitor Human Tropism in Egyptian H5N1-HPAIV Reveals Evolution toward Efficient Human-to-Human Transmission , 2013, PloS one.

[9]  Michael H Hecht,et al.  Directed evolution of the peroxidase activity of a de novo-designed protein. , 2012, Protein engineering, design & selection : PEDS.

[10]  U. Dietrich,et al.  Application of the EIIP/ISM bioinformatics concept in development of new drugs. , 2007, Current medicinal chemistry.

[11]  P. Wardęga,et al.  A Simple Three-Step Method for Design and Affinity Testing of New Antisense Peptides: An Example of Erythropoietin , 2014, International journal of molecular sciences.

[12]  Pierre Baldi,et al.  COBEpro: a novel system for predicting continuous B-cell epitopes. , 2009, Protein engineering, design & selection : PEDS.

[13]  D. Baker,et al.  The coming of age of de novo protein design , 2016, Nature.

[14]  Harinder Singh,et al.  Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence , 2013, PloS one.

[15]  Pierre Baldi,et al.  SOLpro: accurate sequence-based prediction of protein solubility , 2009, Bioinform..

[16]  A New Rule-Based System for the Construction and Structural Characterization of Artificial Proteins , 2013 .

[17]  M. Hecht,et al.  Rationally designed mutations convert de novo amyloid-like fibrils into monomeric β-sheet proteins , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Pierre Baldi,et al.  A machine learning information retrieval approach to protein fold recognition. , 2006, Bioinformatics.

[19]  Pierre Baldi,et al.  High-throughput prediction of protein antigenicity using protein microarray data , 2010, Bioinform..

[20]  D. Woolfson,et al.  Controlling the Assembly of Coiled–Coil Peptide Nanotubes , 2015, Angewandte Chemie.

[21]  T. Petersen,et al.  A generic method for assignment of reliability scores applied to solvent accessibility predictions , 2009, BMC Structural Biology.

[22]  L. H. Bradley,et al.  Protein design by binary patterning of polar and nonpolar amino acids. , 1993, Methods in molecular biology.

[23]  P. Konjevoda,et al.  Prediction of secondary protein structure with binary coding patterns of amino acid and nucleotide physicochemical properties , 2003 .

[24]  M. Botta,et al.  Discovery of new therapeutic targets by the informational spectrum method. , 2008, Current protein & peptide science.

[25]  Yinan Wei,et al.  Stably folded de novo proteins from a designed combinatorial library , 2003, Protein science : a publication of the Protein Society.

[26]  C DeLisi,et al.  Periodic variation in side-chain polarities of T-cell antigenic peptides correlates with their structure and activity. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Damian Counsell,et al.  Bioinformatics and Molecular Evolution , 2005, Comparative and Functional Genomics.

[28]  Michael J E Sternberg,et al.  The Phyre2 web portal for protein modeling, prediction and analysis , 2015, Nature Protocols.

[29]  Andrew R Thomson,et al.  A monodisperse transmembrane α-helical peptide barrel. , 2017, Nature chemistry.

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

[31]  Geoffrey I. Webb,et al.  Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli , 2016, Scientific Reports.

[32]  H. S. Struijker Boudier,et al.  Anginex, a designed peptide that inhibits angiogenesis. , 2001, The Biochemical journal.

[33]  M. Hecht,et al.  De novo proteins from combinatorial libraries. , 2001, Chemical reviews.

[34]  Michael H. Hecht,et al.  De Novo Designed Proteins from a Library of Artificial Sequences Function in Escherichia Coli and Enable Cell Growth , 2011, PloS one.

[35]  M. Hecht,et al.  Screening combinatorial libraries of de Novo proteins by hydrogen- deuterium exchange and electrospray mass spectrometry , 1999 .

[36]  Michael H Hecht,et al.  A protein constructed de novo enables cell growth by altering gene regulation , 2016, Proceedings of the National Academy of Sciences.

[37]  Ian Kimber,et al.  Immunogenicity of therapeutic proteins: Influence of aggregation , 2013, Journal of immunotoxicology.

[38]  Ø. Hammer,et al.  PAST: PALEONTOLOGICAL STATISTICAL SOFTWARE PACKAGE FOR EDUCATION AND DATA ANALYSIS , 2001 .

[39]  M. Hecht,et al.  De Novo Proteins with Life-Sustaining Functions Are Structurally Dynamic. , 2016, Journal of molecular biology.

[40]  Eric J. Schnitzer,et al.  Escherichia coli L-Serine Deaminase Requires a [4Fe-4S] Cluster in Catalysis* , 2004, Journal of Biological Chemistry.

[41]  Sergey V. Petoukhov.  The system-resonance approach in modeling genetic structures , 2016, Biosyst..

[42]  Michael H Hecht,et al.  Novel proteins: from fold to function. , 2011, Current opinion in chemical biology.

[43]  J. Richardson,et al.  Betadoublet: de novo design, synthesis, and characterization of a beta-sandwich protein. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[44]  David Baker,et al.  Accurate de novo design of hyperstable constrained peptides , 2016, Nature.

[45]  M. Hecht,et al.  Binary patterning of polar and nonpolar amino acids in the sequences and structures of native proteins , 1995, Protein science : a publication of the Protein Society.

[46]  Phillip I. Good,et al.  Analyzing the Large Number of Variables in Biomedical and Satellite Imagery , 2011 .

[47]  M. Hecht,et al.  De novo amyloid proteins from designed combinatorial libraries. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[48]  Michael H Hecht,et al.  A Non-natural Protein Rescues Cells Deleted for a Key Enzyme in Central Metabolism. , 2017, ACS synthetic biology.

[49]  L. H. Bradley,et al.  De novo proteins from designed combinatorial libraries , 2004, Protein science : a publication of the Protein Society.

[50]  Lisa Pakstis,et al.  Responsive hydrogels from the intramolecular folding and self-assembly of a designed peptide. , 2002, Journal of the American Chemical Society.

[51]  Gail J. Bartlett,et al.  New currency for old rope: from coiled-coil assemblies to α-helical barrels. , 2012, Current opinion in structural biology.

[52]  Michael J E Sternberg,et al.  Prediction of ligand binding sites using homologous structures and conservation at CASP8 , 2009, Proteins.

[53]  K. Hocke,et al.  Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram , 2008 .

[54]  Pierre Baldi,et al.  SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity , 2014, Bioinform..

[55]  R. Dargel.  The Resonant Recognition Model of Macromolecular Bioactivity Theory and Applications , 1998 .

[56]  D. C. Young.  Computational drug design , 2009 .

[57]  Aimee L. Boyle,et al.  A basis set of de novo coiled-coil peptide oligomers for rational protein design and synthetic biology. , 2012, ACS synthetic biology.

[58]  J. Claverie,et al.  BLAST-EXPLORER helps you building datasets for phylogenetic analysis , 2010, BMC Evolutionary Biology.

[59]  M. Hecht,et al.  Self-Assembling Nano-Architectures Created from a Protein Nano-Building Block Using an Intermolecularly Folded Dimeric de Novo Protein. , 2015, Journal of the American Chemical Society.

[60]  H. F. Coronel-Brizio,et al.  Codon information value and codon transition-probability distributions in short-term evolution , 2016 .

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

[62]  Yujiang Shi,et al.  Metabolic enzymes and coenzymes in transcription--a direct link between metabolism and transcription? , 2004, Trends in genetics : TIG.

[63]  I. Cosic.  The resonant recognition model of macromolecular bioactivity : theory and applications , 1997 .

[64]  Md. Imtaiyaz Hassan,et al.  Design, Principles, Network Architecture and Their Analysis Strategies as Applied to Biological Systems , 2016 .

[65]  Michael H Hecht,et al.  De novo proteins from binary-patterned combinatorial libraries. , 2006, Methods in molecular biology.

[66]  Morten Nielsen,et al.  Improved method for predicting linear B-cell epitopes , 2006, Immunome research.

[67]  C. Hoogland,et al.  In The Proteomics Protocols Handbook , 2005 .

[68]  Olga Golosova,et al.  Unipro UGENE: a unified bioinformatics toolkit , 2012, Bioinform..

[70]  Andrew R Thomson,et al.  De novo protein design: how do we expand into the universe of possible protein structures? , 2015, Current opinion in structural biology.

[71]  D. Eisenberg,et al.  The hydrophobic moment detects periodicity in protein hydrophobicity. , 1984, Proceedings of the National Academy of Sciences of the United States of America.