Thermodynamic Additivity of Sequence Variations: An Algorithm for Creating High Affinity Peptides Without Large Libraries or Structural Information

被引:11
作者
Greving, Matthew P. [1 ,2 ]
Belcher, Paul E. [1 ,2 ]
Diehnelt, Chris W. [1 ,2 ]
Gonzalez-Moa, Maria J. [1 ,2 ]
Emery, Jack [1 ,2 ]
Fu, Jinglin [1 ,2 ]
Johnston, Stephen Albert [1 ,2 ,3 ]
Woodbury, Neal W. [1 ,2 ,4 ]
机构
[1] Arizona State Univ, Ctr BioOpt Nanotechnol, Tempe, AZ USA
[2] Arizona State Univ, Ctr Innovat Med, Biodesign Inst, Tempe, AZ USA
[3] Arizona State Univ, Sch Life Sci, Tempe, AZ USA
[4] Arizona State Univ, Dept Chem & Biochem, Tempe, AZ USA
来源
PLOS ONE | 2010年 / 5卷 / 11期
基金
美国国家科学基金会;
关键词
PROTEIN-PROTEIN INTERACTIONS; TNF-ALPHA; COMPUTATIONAL DESIGN; BINDING INTERFACES; BINARY-CODE; RECEPTOR; GENERATION; TARGET; ENERGY; RECOGNITION;
D O I
10.1371/journal.pone.0015432
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. Methodology/Principal Findings: 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-alpha 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/Significance: By using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-alpha 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.
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页数:9
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