DIRseq: a method for predicting drug-interacting residues of intrinsically disordered proteins from sequences

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• Intrinsically disordered proteins (IDPs) are increasingly recognized as crucial drug targets.
• Identifying drug-interacting residues (DIRs) is vital for drug optimization and mechanistic studies.
• Current methods like NMR and molecular dynamics (MD) simulations are resource-intensive.

Purpose of the Study:

• To develop a rapid, sequence-based method for predicting DIRs in IDPs.
• To provide a computational tool that complements experimental approaches for IDP-drug interaction studies.

Main Methods:

• Developed DIRseq, a novel computational method utilizing amino-acid sequence information.
• DIRseq considers the contribution of all residues to drug interaction propensity, with factors attenuating by sequence distance.
• Validated predictions against experimental data from NMR chemical shift perturbation and other established methods.

Main Results:

• DIRseq accurately predicts DIRs in IDPs, demonstrating strong agreement with experimental findings.
• Successfully identified key DIRs in the tumor suppressor protein p53, including L22WK24 and Q52WFT55.
• The method shows promise in deciphering the sequence-based code governing IDP-drug binding.

Conclusions:

• DIRseq offers a fast and effective computational approach for predicting DIRs in IDPs.
• The method has significant applications in virtual screening and designing IDP fragments for further study.
• DIRseq facilitates a deeper understanding of IDP-drug interactions and accelerates drug discovery pipelines.