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Sampling and energy evaluation challenges in ligand binding protein design.

ABSTRACT: The steroid hormone 17?-hydroxylprogesterone (17-OHP) is a biomarker for congenital adrenal hyperplasia and hence there is considerable interest in development of sensors for this compound. We used computational protein design to generate protein models with binding sites for 17-OHP containing an extended, nonpolar, shape-complementary binding pocket for the four-ring core of the compound, and hydrogen bonding residues at the base of the pocket to interact with carbonyl and hydroxyl groups at the more polar end of the ligand. Eight of 16 designed proteins experimentally tested bind 17-OHP with micromolar affinity. A co-crystal structure of one of the designs revealed that 17-OHP is rotated 180° around a pseudo-two-fold axis in the compound and displays multiple binding modes within the pocket, while still interacting with all of the designed residues in the engineered site. Subsequent rounds of mutagenesis and binding selection improved the ligand affinity to nanomolar range, while appearing to constrain the ligand to a single bound conformation that maintains the same "flipped" orientation relative to the original design. We trace the discrepancy in the design calculations to two sources: first, a failure to model subtle backbone changes which alter the distribution of sidechain rotameric states and second, an underestimation of the energetic cost of desolvating the carbonyl and hydroxyl groups of the ligand. The difference between design model and crystal structure thus arises from both sampling limitations and energy function inaccuracies that are exacerbated by the near two-fold symmetry of the molecule.

SUBMITTER: Dou J

PROVIDER: S-EPMC5699494 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Sampling and energy evaluation challenges in ligand binding protein design.

Dou Jiayi J Doyle Lindsey L Jr Greisen Per P Schena Alberto A Park Hahnbeom H Johnsson Kai K Stoddard Barry L BL Baker David D

Protein science : a publication of the Protein Society 20171030 12

The steroid hormone 17α-hydroxylprogesterone (17-OHP) is a biomarker for congenital adrenal hyperplasia and hence there is considerable interest in development of sensors for this compound. We used computational protein design to generate protein models with binding sites for 17-OHP containing an extended, nonpolar, shape-complementary binding pocket for the four-ring core of the compound, and hydrogen bonding residues at the base of the pocket to interact with carbonyl and hydroxyl groups at th ...[more]

PMID: 28980354

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Project description:Accurate prediction of the binding affinities of small-molecule ligands to their biological targets is fundamental for structure-based drug design but remains a very challenging task. In this paper, we have performed computational studies to predict the binding models of 31 small-molecule Smac (the second mitochondria-derived activator of caspase) mimetics to their target, the XIAP (X-linked inhibitor of apoptosis) protein, and their binding affinities. Our results showed that computational docking was able to reliably predict the binding models, as confirmed by experimentally determined crystal structures of some Smac mimetics complexed with XIAP. However, all the computational methods we have tested, including an empirical scoring function, two knowledge-based scoring functions, and MM-GBSA (molecular mechanics and generalized Born surface area), yield poor to modest prediction for binding affinities. The linear correlation coefficient (r(2)) value between the predicted affinities and the experimentally determined affinities was found to be between 0.21 and 0.36. Inclusion of ensemble protein-ligand conformations obtained from molecular dynamic simulations did not significantly improve the prediction. However, major improvement was achieved when the free-energy change for ligands between their free- and bound-states, or "ligand-reorganization free energy", was included in the MM-GBSA calculation, and the r(2) value increased from 0.36 to 0.66. The prediction was validated using 10 additional Smac mimetics designed and evaluated by an independent group. This study demonstrates that ligand reorganization free energy plays an important role in the overall binding free energy between Smac mimetics and XIAP. This term should be evaluated for other ligand-protein systems and included in the development of new scoring functions. To our best knowledge, this is the first computational study to demonstrate the importance of ligand reorganization free energy for the prediction of protein-ligand binding free energy.

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Sampling and energy evaluation challenges in ligand binding protein design.

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Sampling and energy evaluation challenges in ligand binding protein design.

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