Computational Design of Ligand-binding Proteins with High Affinity and Selectivity Liping Xu Literature Report

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

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 3

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

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

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-gliadin peptidase JACS, 2012, 134, Principles for designing ideal protein structures Nature, 2012, 491, 222 Computational design of self-assembling protein nanomaterials with 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 6

Problem Being Raised 7 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. USA 2009, 106, 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

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

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

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

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

Affinity Maturation 1.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 DIG Tyr knockouts suggest that the designed hydrogen bonds each contribute ~2kcal/mol to binding energy. Table 1. K d values of designs ND, not determined Computational model of DIG10.1 (blue), DIG10.2 (orange) and DIG10.3 (green). Binding thermodynamics determined by ITC 12

Crystal Structure 13 DIG Å resolution DIG Å resolution

Crystal Structure and Computational Design 1. r.m.s.d = 0.54 Å 2. high shape-complementarity on ligand-protein interface 3. no water molecules in the binding pocket Binding site superposition crystal structure (magenta) computational model (grey) Backbone superposition 1. r.m.s.d = 0.99 Å 2. three hydrogen bonds as designed DIG10.2-DIG 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) 14 Atomic-level agreement

Binding Selectivity 34 2 Losing O2 HBond; 2 fits better. 15

Binding Selectivity 34 2 Losing O3 HBond; 3 fits good. Losing All HBonds; More hydrophobic compounds fit good. 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. 16

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. 17

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. Rosetta is freely available to academic and government laboratories, with over 10,000 free licenses already in use. It has grown to offer a wide variety of effective sampling algorithms to explore backbone, side-chain and sequence space. University of Washington Johns Hopkins University New York Universiy Rosetta Design GroupStanford University China Three Gorges University … 18

Summary 1.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. 19

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