TY - JOUR
T1 - Genetic Algorithm Approach for the Optimization of Protein Antifreeze Activity Using Molecular Simulations
AU - Kozuch, Daniel J.
AU - Stillinger, Frank H.
AU - Debenedetti, Pablo G.
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1656466 awarded to D.J.K.
Publisher Copyright:
©
PY - 2020/12/8
Y1 - 2020/12/8
N2 - Antifreeze proteins (AFPs) are of much interest for their ability to inhibit ice growth at low concentrations. In this work, we present a genetic algorithm for the in silico design of AFP mutants with improved antifreeze activity, measured as the predicted thermal hysteresis at a fixed concentration, ΔTC. Central to the algorithm is our recently developed neural network method for predicting ΔTC from molecular simulations [Kozuch et al., PNAS, 115, 13252 (2018)]. Applying the algorithm to three structurally diverse AFPs, wfAFP, rQAE, and RiAFP, we find that significantly improved mutants are discovered for rQAE and RiAFP. Testing of the optimized mutants shows an increase in ΔTC of 0.572 ± 0.11 K (262 ± 50.6%) and 1.33 ± 0.14 K (39.9 ± 4.19%) over the native structures for rQAE and RiAFP, respectively. Structural analysis of the optimized mutants reveals that the algorithm is able to exploit two pathways for enhancing the predicted antifreeze activity of the mutants: (1) increasing the local order of surface waters by encouraging the formation of internal water channels in the protein and (2) increasing the total ice-binding area by improving the planar structure of the ice-binding surface. Additionally, analysis of all mutants explored by the algorithm reveals that a subset of residues, mainly nonpolar, are particularly helpful in improving antifreeze activity at the ice-binding surface.
AB - Antifreeze proteins (AFPs) are of much interest for their ability to inhibit ice growth at low concentrations. In this work, we present a genetic algorithm for the in silico design of AFP mutants with improved antifreeze activity, measured as the predicted thermal hysteresis at a fixed concentration, ΔTC. Central to the algorithm is our recently developed neural network method for predicting ΔTC from molecular simulations [Kozuch et al., PNAS, 115, 13252 (2018)]. Applying the algorithm to three structurally diverse AFPs, wfAFP, rQAE, and RiAFP, we find that significantly improved mutants are discovered for rQAE and RiAFP. Testing of the optimized mutants shows an increase in ΔTC of 0.572 ± 0.11 K (262 ± 50.6%) and 1.33 ± 0.14 K (39.9 ± 4.19%) over the native structures for rQAE and RiAFP, respectively. Structural analysis of the optimized mutants reveals that the algorithm is able to exploit two pathways for enhancing the predicted antifreeze activity of the mutants: (1) increasing the local order of surface waters by encouraging the formation of internal water channels in the protein and (2) increasing the total ice-binding area by improving the planar structure of the ice-binding surface. Additionally, analysis of all mutants explored by the algorithm reveals that a subset of residues, mainly nonpolar, are particularly helpful in improving antifreeze activity at the ice-binding surface.
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U2 - 10.1021/acs.jctc.0c00773
DO - 10.1021/acs.jctc.0c00773
M3 - Article
C2 - 33201707
AN - SCOPUS:85096579692
SN - 1549-9618
VL - 16
SP - 7866
EP - 7873
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
IS - 12
ER -