Water Networks in Complexes between Proteins and FDA-Approved Drugs

Abstract

Water molecules at protein–ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.

OneOPES, a Combined Enhanced Sampling Method to Rule Them All

“…Collective variable-based enhanced sampling MD simulations rely on optimal CVs that approximate the reaction coordinate and encapsulate all the relevant slow DOFs so that they can accelerate their sampling. A serious drawback of these approaches is the effort required to define optimal CVs that capture complex processes such as folding, the flexibility of a receptor , or the role of water at a ligand/protein’s interface. − Methods based on machine learning are increasingly successful in providing optimal CVs, but they need significant amount of data that is not always available. ,,− …”

“…A serious drawback of these approaches is the effort required to define optimal CVs that capture complex processes such as folding, the flexibility of a receptor 89 , 90 or the role of water at a ligand/protein’s interface. 91 − 101 Methods based on machine learning are increasingly successful in providing optimal CVs, but they need significant amount of data that is not always available. 10 , 12 , 14 − 29 …”

OneOPES, a combined enhanced sampling method to rule them all

“…Collective variable-based enhanced sampling MD simulations rely on optimal CVs that approximate the reaction coordinate and encapsulate all the relevant slow DOFs so that they can accelerate their sampling. A serious drawback of these approaches is the effort required to define optimal CVs that capture complex processes such as folding, the flexibility of a receptor 81,82 or the role of water at a ligand/protein's interface [83][84][85][86][87][88][89][90][91][92][93] . Methods based on machine learning are increasingly successful in providing optimal CVs, but they need significant amount of data that is not always available 10,12,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] .…”

ARIP: A Tool for Precise Interatomic Contact Area and Volume Calculation in Proteins

“…In reality, it is not reasonable to assume that every protein atom is in contact with a water molecule, with the solvent layer defined as the van der Waals (vdW) radius of the atom plus 1.4 Å [20][21][22][23]. To address this issue, we analyzed the aforementioned X-ray structures to estimate the mosaic distance between water oxygen atoms and protein atoms.…”