Computational Protein Design Force Field Optimization:A Negative Design Approach

• Main Author: Alvizo, Oscar
• Format: Others
• Published: 2007
• Online Access: https://thesis.library.caltech.edu/5192/1/Alvizo_Thesis_052307.pdf; Alvizo, Oscar (2007) Computational Protein Design Force Field Optimization:A Negative Design Approach. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/NYVY-7Z76. https://resolver.caltech.edu/CaltechETD:etd-05212007-164114 <https://resolver.caltech.edu/CaltechETD:etd-05212007-164114>

<p>An accurate force field is essential to computational protein design and protein folding studies. Proper force field tuning is problematic, however, due in part to the incomplete modeling of the unfolded state. The first part of this thesis discusses the optimization of a protein design force field by constraining the amino acid composition of the designed sequences to that of the wild-type protein. According to the random energy model, the unfolded state energies of amino acid sequences with the same composition are identical. Under these constraints, unfolded state energies are inconsequential and any discrepancies between computational predictions and experimental results can be directly attributed to flaws in the force field’s ability to properly account for folded state sequence energies. This aspect of fixed composition design allows for force field optimization by focusing solely on the interactions in the folded state. In addition, the fixed composition requirement imposes a large negative design constraint that is used to ensure fold specificity. Several rounds of fixed composition optimization of the beta-1 domain of protein G yielded force field parameters with significantly greater predictive power: optimized sequences exhibited higher wild-type sequence identity in critical regions of the structure and the wild-type sequence showed an improved Z score. Experimental studies revealed a 24-fold mutant to be stably folded with a melting temperature comparable to that of the wild-type protein.</p> <p>The second part of the thesis discusses the optimization of HIV protease substrate specificity using a combination of positive and negative design. HIV protease is a homodimeric protein with a symmetrical binding region that recognizes and cleaves asymmetrical substrates that exhibit little sequence homology. The designs attempt to increase specificity towards one of HIV protease’s wild-type targets by optimizing hydrogen bonds and electrostatic interactions using a positive design approach. Explicit negative design is incorporated by modeling predicted mutations on multiple substrates. A scoring function that selects for mutations that pack favorably with the target substrate but result in large steric clashes in alternate substrates is used. A three point mutant was designed and experimentally shown to have increased specificity towards the target substrate.</p>