Water PMF for predicting the properties of water molecules in protein binding site

Water is an important component in living systems and deserves better understanding in chemistry and biology. However, due to the difficulty of investigating the water functions in protein structures, it is usually ignored in computational modeling, especially in the field of computer‐aided drug design. Here, using the potential of mean forces (PMFs) approach, we constructed a water PMF (wPMF) based on 3946 non‐redundant high resolution crystal structures. The extracted wPMF potential was first used to investigate the structure pattern of water and analyze the residue hydrophilicity. Then, the relationship between wPMF score and the B factor value of crystal waters was studied. It was found that wPMF agrees well with some previously reported experimental observations. In addition, the wPMF score was also tested in parallel with 3D‐RISM to measure the ability of retrieving experimentally observed waters, and showed comparable performance but with much less computational cost. In the end, we proposed a grid‐based clustering scheme together with a distance weighted wPMF score to further extend wPMF to predict the potential hydration sites of protein structure. From the test, this approach can predict the hydration site at the accuracy about 80% when the calculated score lower than ?4.0. It also allows the assessment of whether or not a given water molecule should be targeted for displacement in ligand design. Overall, the wPMF presented here provides an optional solution to many water related computational modeling problems, some of which can be highly valuable as part of a rational drug design strategy. © 2012 Wiley Periodicals, Inc.     

An unusual coordination polymer containing Cu+ ions and featuring possible Cu...Cu `cuprophilic' interactions: poly[di‐μ‐chlorido‐(μ4‐3,5‐diaminobenzoato‐κ4O:O′:N:N′)tricopper(I)(3 Cu—Cu)]

Compounds containing copper(I) are of interest for their role in biological processes. The nature of short (< ∼3.2 Å) Cu...Cu contacts within these compounds has been debated, being either described as weakly attractive (bonding) `cuprophilic' interactions, or simply as short metal–metal distances constrained by ligand geometry or largely ionic in nature. The title three‐dimensional Cu<sup>+</sup>‐containing coordination polymer, [Cu<sub>3</sub>(C<sub>7</sub>H<sub>7</sub>N<sub>2</sub>O<sub>2</sub>)Cl<sub>2</sub>]<sub>n</sub>, was formed from the in situ reduction of CuCl<sub>2</sub> in the presence of 3,5‐diaminobenzoic acid and KOH under hydrothermal conditions. Its complex crystal structure contains ten distinct Cu<sup>I</sup> atoms, two of which lie on crystallographic inversion centres. The copper coordination geometries include near‐linear CuOCl and CuN<sub>2</sub>, T‐shaped CuOCl<sub>2</sub> and distorted tetrahedral CuOCl<sub>3</sub> groups. Each Cu<sup>I</sup> atom is also associated with two adjacent metal atoms, with Cu...Cu distances varying from 2.7350 (14) to 3.2142 (13) Å; if all these are regarded as `cuprophilic' interactions, then infinite [01] zigzag chains of Cu^I atoms occur in the crystal. The structure is consolidated by N—H...Cl hydrogen bonds.     

Fast and accurate determination of the relative binding affinities of small compounds to HIV‐1 protease using non‐equilibrium work

The fast pulling ligand (FPL) out of binding cavity using non‐equilibrium molecular dynamics (MD) simulations was demonstrated to be a rapid, accurate and low CPU demand method for the determination of the relative binding affinities of a large number of HIV‐1 protease (PR) inhibitors. In this approach, the ligand is pulled out of the binding cavity of the protein using external harmonic forces, and the work of pulling force corresponds to the relative binding affinity of HIV‐1 PR inhibitor. The correlation coefficient between the pulling work and the experimental binding free energy of shows that FPL results are in good agreement with experiment. It is thus easier to rank the binding affinities of HIV‐1 PR inhibitors, that have similar binding affinities because the mean error bar of pulling work amounts to . The nature of binding is discovered using the FPL approach. © 2016 Wiley Periodicals, Inc.