(110e) Mod Designer: A Robust Optimization Framework for the Design of Proteins with Post-Translational Modifications and Unnatural Amino Acids and Its Applications to Cancer

Most proteins being used for drug applications contain post-translational modifications (PTMs) [1]. It can become prohibitively expensive and time-consuming to experimentally modify and test the effects of large numbers of modifications. To enable academic and industrial researchers to minimize time and monetary cost searching for new functional designs, in this work we introduce a method to in silico design proteins and peptides with post-translational modifications and unnatural amino acids.

We develop a new integer linear optimization design formulation and complementary parallelized algorithm exploiting MPI to introduce the modified amino acids, drawing on our recently developed forcefields. Given a ligand or an ensemble of ligand conformations of a peptide bound in a receptor protein, the formulation maximizes the favorable (Van der Waals and hydrogen bond) contacts and minimizes steric clashes while maintaining or enhancing the binding free energy. To reduce the combinatorial complexity of the problem, logical restraints to preserve the residue-wise charge and hydrophobicity are applied to reduce the number of allowed modifications in each position. For each configuration, the initial contacts, clashes, and hydrogen bonds are populated. Next, for each configuration, each design position is modified, a local energy minimization is performed, and the new contacts, clashes, hydrogen bonds and interaction energy are populated. Finally, the ILP is solved to global optimality, generating a rank-ordered list of designs using integer cuts. The importance of using a flexible template in using these design metrics is demonstrated using an ensemble of conformers generated from a molecular dynamics simulation.

The algorithm is designed to serve as Stage 3: Modification Selection of our generalized de novo design framework [2], with Stage 1 being an optimization driven Sequence Selection, and Stage 2 being Fold Specificity and statistical mechanically-based Approximate Binding Affinity calculations. We describe our efforts to use the method to design new variants of Compstatin to inhibit Complement C3c and new variants of peptide inhibitors targeting the methyltransferase EZH2. For Compstatin, we begin design using the extremely potent Compstatin analog, Variant E1. It was found as one of the top ten solutions in the rank-ordered list of designs [3]. In all efforts we begin with template sequences and structures containing only the 20 amino acids discovered to experimentally bind and inhibit their target receptors [4-6]. In this way, the modified amino acids serve to fine-tune specificity and potentially enhance the affinity of a given inhibitor.

References:

 

1.         Walsh, C., Posttranslational modification of proteins: expanding nature's inventory2006, Englewood, Colo.: Roberts and Co. Publishers. xxi, 490 p.

2.         Bellows, M.L., H.K. Fung, and C.A. Floudas, Recent Advances in De Novo Protein Design, in Process Systems Engineering2011, Wiley-VCH Verlag GmbH & Co. KGaA. p. 207-232.

3.         Mallik, B., et al., Design and NMR Characterization of Active Analogues of Compstatin Containing Non-Natural Amino Acids. Journal of Medicinal Chemistry, 2004. 48(1): p. 274-286.

4.         Bellows, M.L., et al., Discovery of Entry Inhibitors for HIV-1 via a New De Novo Protein Design Framework. Biophysical Journal, 2010. 99(10): p. 3445-3453.

5.         Bellows, M.L., et al., New Compstatin Variants through Two De Novo Protein Design Frameworks. Biophysical Journal, 2010. 98(10): p. 2337-2346.

6.         López de Victoria, A., et al., A New Generation of Potent Complement Inhibitors of the Compstatin Family. Chemical Biology & Drug Design, 2011. 77(6): p. 431-440.