finDr: A web server for in silico D-peptide ligand identification

Abstract

In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure (https://findr.biologie.uni-freiburg.de/). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.

Keywords: D-AA, dextrorotatory amino acid; D-peptide; EA, evolutionary algorithm; Evolutionary algorithm; L-AA, levorotatory amino acid; MD, molecular dynamics; MIEA, mirror-image evolutionary algorithm; MIPD, mirror-image phage display; MIVS, mirror-image virtual screening; MRSA, methicillin-resistant Staphylococcus aureus; Mirror-image phage display; Molecular docking; NCL, native chemical ligation; PD-1, receptor programmed death 1; PPI, protein-protein interaction; PSMα3, phenol soluble modulin alpha 3; Peptide design; SPPS, solid phase peptide synthesis; Web server.

Fig. 1

Schematic representation of the stereochemistry of a protein - peptide interaction. If an L-peptide ligand binds a protein's mirror-image (D-protein), then their corresponding mirror-images (D-peptide ligand and L-protein) will also bind to each other in exactly the same fashion.

Fig. 2

Selective binding of MIPD-derived phage clones to D-PSMα3. Results of a phage ELISA performed with isolated phages from MIPD displaying the indicated peptides on their surfaces. The absorbance resulting from phage binding to D-PSMα3 coated wells was normalized to that resulting from unspecific phage binding to D-PSMα3 free control wells. As negative control, a phage clone was randomly chosen from the phage library. Data represent mean + SEM of triplicates. P values were calculated by a two sided, unpaired Student's t-test ***: P ≤ 0.0001.

Fig. 3

Schematic representation of the MIPD and MIVS workflow. MIPD: A library of random L-peptides expressed on the surface of bacteriophages is selected via surface panning against a chemically synthesized D-analogue of the target L-protein. MIVS: A structural library of helical L-peptide segments extracted from the PDB is screened for binding affinity towards an in silico mirrored D-version of the target L-protein structure via molecular docking. Both methods yield L-peptide ligands to D-protein targets. Consequently, the corresponding D-peptides bind to the naturally occurring L-protein target (see Fig. 1).

Fig. 4

Mirror-image virtual screening oftheL-peptide library to D-PSMα3. A: Distribution of binding energies of 28.647 L-peptides, each docked to 5 different conformations of D-PSMα3. B: Histogram of the L-peptides’ mean binding energy to the 5 different D-PSMα3 conformers.

Fig. 5

Diagram of MIEA. The fitness of all peptides in a population P_n is evaluated by molecular docking. Based on this, a population P_n+1 is newly generated by copying the peptides with the lowest binding energy, with and without crossover recombination of their sequences, and introducing random mutations. Further, a number of X individual peptides with the best binding energy are directly copied into the population P_n+1 without alteration. This population P_n+1 is then again evaluated via docking to complete the MIEA cycle.

Fig. 6

Improvement of binding affinity of L-peptides to D-PSMα3 over 15 generations of MIEA. A: Binding energies of L-peptides to D-PSMα3 per generation of an MIEA, assessed by molecular docking using AutoDock Vina. Only the 20 best binding peptides of each generation are shown. B: Binding energy of all 88 peptides to D-PSMα3 in each generation. Mean binding energy of each peptide population and SEM are shown. Statistical significance of the difference from the initial population was determined by an unpaired, two-tailed t-test. C: Association of the L-peptide ligand L-EA2 (sequence FKWRYERDKKQS, shown in orange) to D-PSMα3 (shown in blue). D: Association of the D-peptide ligand D-EA2 (sequence all D-FKWRYERDKKQS, shown in orange) to L-PSMα3 (shown in blue). Bound states in C and D were obtained by Autodock Vina.

Fig. 7

SCORE binding assay of MIPD- and MIEA-derived peptide ligands to PSMα3. Real-time binding kinetics of L-MIPD27 and L-EA2 to D-PSMα3 as measured by SCORE. The mean intensities of four spots (EA2, MIPD27) and 2 spots (scrambled) with standard deviation are depicted. The association and dissociation phases are indicated; the dissociation kinetic starts shortly after induction of the washing step due to methodological reasons.

Fig. 8

D-peptide ligand identification for ErbB2 with finDr. A: MIVS - Histogram of the binding energies of all docked library peptides, screenshot from finDr results webpage. B: MIEA - Binding energy of L-peptides to D-ErbB2 per generation of the MIEA. Only the 20 best binding peptides are shown. C: MIEA - Mean binding energy per generation D: Binding energy of the best binding peptide in each generation of MIEA. E, F: L-ErbB2 in complex with its D-peptide ligand derived from MIVS (E) and MIEA (F) (visualized by PyMOL, Gridbox for docking is shown in black).