Computational Design of Ligand-binding Proteins with High Affinity and Selectivity

1. Literature Report Computational Design of Ligand-binding Proteins with High Affinity and Selectivity Liping Xu 2013-11-22

2. We can make… Complex natural product Anti-cancer drug Taxol Small medicine molecule Penicillin Can we make? Cyclic peptide Cyclosporine Folded, functionalized unnatural protein

3. Contents • Introduction of protein design and David Baker • Computational design of DIG-binding protein • Problem being raised • Computational methodology • Experimental binding validation • Affinity Maturation • Crystal Structure • Binding Selectivity • Rosetta • Summary

4. Introduction Protein design is the rational design of new protein molecules to fold to a target protein structure. Protein design has many applications in medicine, enzyme catalysis, and bioengineering. De novo design Protein redesign Challenges Structural flexibility “side chain and backbone flexibility” Known protein structure Making calculated variations Energy (scoring) function “both accurate and simple for computational calculations” New protein structure

5. David Baker Computer game to design new proteins Running the Rosetta program on your computer while you don't need it David Baker Computational biologist University of Washington Full-chain protein structure prediction

6. David Baker Recent publications on protein design: Computational design of ligand-binding proteins with high affinity and selectivity Nature, 2013, 501, 212 Computational design of an α-gliadinpeptidase JACS, 2012, 134, 20513 Principles for designing ideal protein structures Nature, 2012, 491, 222 Computational design of self-assembling protein nanomaterialswith atomic level accuracy Science, 2012, 336, 1171 Atomic model of the type III secretion system needle Nature, 2012, 486, 276 Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis Nature chemical biology, 2012, 8, 294

7. Problem Being Raised Rational design of ligand-binding proteins have met with little success. Schreier, B. et al. Computational design of ligand binding is not a solved problem. Proc. Natl. Acad. Sci. USA2009, 106, 18491 Baker’s group has developed a computational method for designing ligand- binding proteins with three properties characteristic of naturally occurring binding sites: 1. Specific energetically favorable hydrogen-bonding and van der Waals interactions with the ligand; 2. High overall shape complementarity to the ligand; 3. Structural pre-organization in the unbound protein state

8. Problem Being Raised However… 1. “narrow therapeutic window” (margin between effectiveness and toxicity) 2. Easily being overdosed Nausea, dizziness, depression Digoxigenin (DIG) 1. Heart disease drug 2. Non-radioactive biomolecular labelling reagent So, anti-digoxigenin antibodies are needed to treat overdoses of digoxin.

9. Computational Methodology Linker-modified DIG Pre-chosen hydrogen bonding interaction side chains; Rotamers for each interaction side chain Place ligand and interacting residues in scaffolds and design binding site sequence

10. Experimental Binding Validation ZZ(-): negtive control ZZ(+): positive control DIG10 + DIG: with unlabelled DIG Experimental characterization of the selected 17 designs. Two of them perform better: DIG10 and DIG5. 1Z1S: original scaffold

11. Experimental Binding Validation Yeast-surface expression of DIG10 Interface residues are shown. Substitutions of DIG10-designed interface residues reduce binding signals.

12. Affinity Maturation Optimization of DIG10 by site-saturation mutagenesis increases binding affinity 75-fold, yielding DIG10.1; 2. Mutations of Ala37Pro and His41Tyr generates DIG10.2; 3. Further consideration of more residues in combination leads to DIG10.3. 4. Tyr knockouts suggest that the designed hydrogen bonds each contribute ~2kcal/mol to binding energy. Computational model of DIG10.1 (blue), DIG10.2 (orange) and DIG10.3 (green). Table 1. Kd values of designs ND, not determined Binding thermodynamics determined by ITC

13. Crystal Structure DIG10.2 2.05 Åresolution DIG10.3 3.2 Åresolution

14. Crystal Structure and Computational Design crystal structure (magenta) computational model (grey) DIG10.2-DIG Backbone superposition Binding site superposition 1. r.m.s.d= 0.54 Å 2. high shape-complementarity on ligand-protein interface 3. no water molecules in the binding pocket 1. r.m.s.d= 0.99 Å 2. three hydrogen bonds as designed Atomic-level agreement The structure and binding mode is nearly identical in the X-ray structure and the design model; The structure of DIG10.3-DIG also agree closely with the design model (r.m.s.d = 0.68 Å)

15. Binding Selectivity 2 3 4 Losing O2 HBond; 2 fits better.

16. Binding Selectivity 1. The selectivity is conferred through the designed HBond Interactions; 2. This feature can be programmed using positive design alone through the explicit placement of designed polar and hydrophobic interactions. 2 3 4 Losing All HBonds; More hydrophobic compounds fit good. Losing O3 HBond; 3fits good.

17. Methods Design calculations were performed using RosettaMatch to incorporate five pre- defined interactions to DIG into a set of 401 scaffolds. Rosettadesign was then used to optimize each binding sequence for maximal ligand-binding affinity. Designs having low interface energy, high shape complementarity, and high binding site pre-organization were selected for experimental characterization.

18. Rosetta Rosetta – The premier software suite for macromolecular modeling As a flexible, multi-purpose application, it includes tools for structure prediction, design, and remodeling of proteins and nucleic acids. It has consistently been a strong performer in Critical Assessment of Structure Prediction (CASP) competitions. It has grown to offer a wide variety of effective sampling algorithms to explore backbone, side-chain and sequence space.  Rosetta is freely available to academic and government laboratories, with over 10,000 free licenses already in use. Stanford University Rosetta Design Group University of Washington … China Three Gorges University New York Universiy Johns Hopkins University

19. Summary The binding affinity of designed protein DIG10.3 is similar to those of anti-digoxin antibodies. 2. It is stable for extended periods and can be expressed at high levels in bacteria, so it could provide a more cost-effective alternative for biotechnological and for therapeutic purposes. 3. Computational protein design should provide an increasingly powerful approach to creating small molecule receptors for synthetic biology.

20. Thanks for your attention!