title: Thermodynamic additivity of sequence variations: an algorithm for creating high affinity peptides without large libraries or structural information.
abstract: BACKGROUND
  There is a significant need for affinity reagents with high target affinity/specificity that can be developed rapidly and inexpensively. Existing affinity reagent development approaches, including protein mutagenesis, directed evolution, and fragment-based design utilize large libraries and/or require structural information thereby adding time and expense. Until now, no systematic approach to affinity reagent development existed that could produce nanomolar affinity from small chemically synthesized peptide libraries without the aid of structural information.
  
  
  RESULTS
  Based on the principle of additivity, we have developed an algorithm for generating high affinity peptide ligands. In this algorithm, point-variations in a lead sequence are screened and combined in a systematic manner to achieve additive binding energies. To demonstrate this approach, low-affinity lead peptides for multiple protein targets were identified from sparse random sequence space and optimized to high affinity in just two chemical steps. In one example, a TNF-α binding peptide with K(d) = 90 nM and high target specificity was generated. The changes in binding energy associated with each variation were generally additive upon combining variations, validating the basis of the algorithm. Interestingly, cooperativity between point-variations was not observed, and in a few specific cases, combinations were less than energetically additive.
  
  
  CONCLUSIONS
  By using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-α binding peptides reported to date was produced. Most importantly, high affinity was achieved from small, chemically-synthesized libraries without the need for structural information at any time during the process. This is significantly different than protein mutagenesis, directed evolution, or fragment-based design approaches, which rely on large libraries and/or structural guidance. With this algorithm, high affinity/specificity peptide ligands can be developed rapidly, inexpensively, and in an entirely chemical manner.
authors.affiliation: Center for BioOptical Nanotechnology and Center for Innovations in Medicine, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Greving
authors.forename: Paul E
authors.initials: PE
authors.lastname: Belcher
authors.forename: Chris W
authors.initials: CW
authors.lastname: Diehnelt
authors.forename: Maria J
authors.initials: MJ
authors.lastname: Gonzalez-Moa
authors.forename: Jack
authors.initials: J
authors.lastname: Emery
authors.forename: Jinglin
authors.initials: J
authors.lastname: Fu
authors.forename: Stephen Albert
authors.initials: SA
authors.lastname: Johnston
authors.forename: Neal W
authors.initials: NW
authors.lastname: Woodbury
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001667:Binding, Competitive; D002942:Circular Dichroism; D008024:Ligands; D008969:Molecular Sequence Data; D019151:Peptide Library; D010455:Peptides / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D011485:Protein Binding; D017433:Protein Structure, Secondary; D020349:Surface Plasmon Resonance; D013816:Thermodynamics; D014409:Tumor Necrosis Factor-alpha / Q000737:chemistry / Q000378:metabolism
chemical_list: D008024:Ligands; D019151:Peptide Library; D010455:Peptides; D014409:Tumor Necrosis Factor-alpha