Using player generated data to elucidate molecular docking

Abstract

Molecular docking prediction, used to design new drugs and identify biological function, can be represented as a motion planning problem in high dimensional space. This complex task can benefit from human guidance, i.e., interactive simulations combining visual and haptic feedback from atomic forces [4]. Using human-generated data to find solutions to complex problems has been used in protein folding with FoldIt [3], showing that crowdsourced data collection is viable to help solve difficult problems in biology. However, existing interactive molecular docking systems that typically use haptic devices employ devices that are not widely available to the public, limiting the potential to crowdsource solutions [1]. This user study tested 4 input devices to find potentially bound ligand-receptor states and biologically feasible paths via motion planning. On a PC laptop, players used one of the following: a 6 degree-of-freedom (DOF) haptic feedback device, a 3 DOF haptic feedback device, a game controller with vibration feedback, and a mouse and keyboard, with our in-house interactive rigid body molecular docking program, DockAnywhere [1, 2]. Players tried to dock a known inhibitor of HIV Protease (PDB ID 1AJX). The goal is to move the ligand around the receptor to find the lowest potential energy state possible. Players get feedback as a positive integer score based on the potential energy and, for haptic devices, a force feedback calculated from atomic forces. During gameplay, ligand positions and orientations are recorded as it is moved through the environment. In total, 439,832 states were collected from 32 players (8 per device). These states were divided into 4 sets, one per device. and Motion planning queries found a feasible path to the goal state in all sets, with the mouse showing more pronounced energy barriers. We found that exploration varied among different devices, with players on haptic devices exploring the interaction energy landscape more uniformly. However, all 4 devices yielded low energy ligand states with comparable values and similar closest distance to the binding site. In summary, force feedback showed no clear improvement to finding low potential energy states or getting closer to the known binding state. However, haptic devices appear to enable a more thorough exploration of the state space, creating more samples and generating smoother paths. The nature of this guidance should be the subject of future investigation.