Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities

Abstract

Exploitation of protein structures for potential drug leads by molecular docking is critically dependent on methods for scoring putative protein-ligand interactions. An ideal function for scoring must exhibit predictive accuracy and high computational speed, and must be tolerant of variations in the relative protein-ligand molecular alignment and conformation. This paper describes the development of an empirically derived scoring function, based on the binding affinities of protein-ligand complexes coupled with their crystallographically determined structures. The function's primary terms involve hydrophobic and polar complementarity, with additional terms for entropic and solvation effects. The issue of alignment/conformation dependence was solved by constructing a continuous differentiable nonlinear function with the requirement that maxima in ligand conformation/alignment space corresponded closely to crystallographically determined structures. The expected error in the predicted affinity based on cross-validation was 1.0 log unit. The function is sufficiently fast and accurate to serve as the objective function of a molecular-docking search engine. The function is particularly well suited to the docking problem, since it has spatially narrow maxima that are broadly accessible via gradient descent.

Automatic identification and representation of protein binding sites for molecular docking

“…Such pockets, when they exist, offer great potential for strong ligand binding, and many known protein-ligand binding complexes are of this type. The bias towards hydrophobicity is in general agreement with the observation that ligand-protein binding affinity is largely due to hydrophobic interactions (Jain, 1996). This bias is accomplished by multiplicatively weighting the scores of steric probes, yielding weighted scores wi = 4 * si for steric probes, and wi = si for polar probes.…”

“…The probes are placed at distances and orientations that are optimal for the interaction with the given protein atom. Long range dipoles and other interactions not considered by the scoring function (Jain, 1996) are not represented by probes.…”

“…Next, each probe's position and orientation are adjusted to maximize its interaction with all protein atoms by following the gradient of the scoring function (Jain, 1996). At this point, each probe will have a score representing the binding contribution from a like atom on a ligand (scores are in units -loglo(Kd)).…”

“…Each probe has an associated score s, produced by the scoring function (Jain, 1996). The scores represent each probe's estimated (additive) contribution to the ligand-protein binding affinity.…”

Structural Modeling, Evolution and Ligand Interaction of KMP11 Protein of Different Leishmania Strains

“…In case of L. major, sulforaphane binds to KMP11 in three different poses. The interesting thing is that the involved amino acid responsible for formation of H-bond is lysine (LYS29, LYS85 and LYS92) with (Jain , 1996), PMF (Muegge and Martin, 1999). The se scoring functions tend to fall into two major classes emphasizing either: H-bonding interactions or van der Waals, hydrophobic as well as polar attractive/repulsive interactions.…”