Evolution of High-Affinity Peptide Probes to Detect the SH3 Domain of Cancer Biomarker BCR–ABL

Abstract

The BCR–ABL fusion protein is closely associated with the pathological progression of chronic myelogenous leukaemia and some other myeloproliferative diseases, which has long been recognized as one of the most important cancer biomarkers in the tumor diagnosis community. The SH3 domain of BCR–ABL is a small, conserved protein module that specifically recognizes and binds proline-rich peptide fragments. In the current study, we used a synthetic strategy to discover new peptide probes with high affinity binding to the BCR–ABL SH3 domain. In the procedure, a sequence-based machine learning predictor was developed based on a set of affinity-known SH3 binders, and the predictor was then used to guide the evolutional optimization of numerous virtual peptides to enrich high binding potency for the SH3 domain. Subsequently, a evolved peptide population was generated, from which ten peptides with the highest affinity scores were selected and their interaction free energies with SH3 domain were characterized systematically using a combination of molecular dynamics simulation and binding free energy analysis. Consequently, four peptides were suggested as promising candidates and their affinities toward SH3 domain were assayed; two peptides, APTYTPPPPP and APTYAPPPPP, were identified to have potent binding capability with dissociation constants K d of 3 and 8 μM, respectively. Further, the structural basis and energetic property of SH3 domain in complex with APTYTPPPPP were examined in detail, revealing a non-specific interaction in SH3–peptide recognition that should render a broad ligand spectrum for the domain.