HINT, a code for understanding the interaction between biomolecules: a tribute to Donald J. Abraham

Front Mol Biosci. 2023 Jun 7:10:1194962. doi: 10.3389/fmolb.2023.1194962. eCollection 2023.

Abstract

A long-lasting goal of computational biochemists, medicinal chemists, and structural biologists has been the development of tools capable of deciphering the molecule-molecule interaction code that produces a rich variety of complex biomolecular assemblies comprised of the many different simple and biological molecules of life: water, small metabolites, cofactors, substrates, proteins, DNAs, and RNAs. Software applications that can mimic the interactions amongst all of these species, taking account of the laws of thermodynamics, would help gain information for understanding qualitatively and quantitatively key determinants contributing to the energetics of the bimolecular recognition process. This, in turn, would allow the design of novel compounds that might bind at the intermolecular interface by either preventing or reinforcing the recognition. HINT, hydropathic interaction, was a model and software code developed from a deceptively simple idea of Donald Abraham with the close collaboration with Glen Kellogg at Virginia Commonwealth University. HINT is based on a function that scores atom-atom interaction using LogP, the partition coefficient of any molecule between two phases; here, the solvents are water that mimics the cytoplasm milieu and octanol that mimics the protein internal hydropathic environment. This review summarizes the results of the extensive and successful collaboration between Abraham and Kellogg at VCU and the group at the University of Parma for testing HINT in a variety of different biomolecular interactions, from proteins with ligands to proteins with DNA.

Keywords: HINT; LogP; hydrophatic interactions; protein–DNA complexes; protein–ligand; protein–protein; water thermodynamics.

Publication types

  • Review

Grants and funding

This work was supported by the University of Salerno (grant numbers ORSA199808, ORSA208455, and ORSA219407); MIUR (grant FFABR2017 and PRIN 2017 program, grant number 2017483NH8); BANCA D’ITALIA (AMa); and the University of Turin (Ricerca Locale 2020, 2021) SPY_RILO_20_01, SPY_RILO_21_01 (FS).