water molecules are a key component of 
 biological systems and act 
 as ordered structural elements at binding interfaces .
the mediation of ligand binding by water molecules can 
 have important consequences for binding affinity and specificity . 

numerous examples of water - mediated protein  ligand interactions 
 are known , including peptide binding in tyrosine kinase ( src ) , binding of inhibitors to proteases , and carbohydrate - binding proteins .
the consideration of individual water molecules 
 in ligand design hinges on an accurate assessment of opposing thermodynamic 
 contributions .
this includes the entropic gain of displacing a highly 
 ordered water molecule and the enthalpic loss of breaking water 
however , assessing the 
 role of individual water molecules at the binding interface is a complex 
 problem , as highlighted by the prediction that there is no direct 
 correlation between the free energy of water molecules in the binding 
 site and the affinity of bound ligands . despite the difficulty in predicting and interpreting the 
 roles 
 of such water molecules ,
several attempts have been made at classifying 
 binding site water molecules with respect to the likelihood of their 
  displaceability . 
high displaceability in this context 
 corresponds to displacement of the water molecule by a suitable chemical 
 group on the ligand with an accompanying favorable change in the binding 
 affinity .
there are a variety of methods for identifying and ranking 
 water molecules in binding sites , including physics - based methods 
 and empirical methods .
a physical method based on the double decoupling 
 method employing thermodynamic integration ( ti ) to replica exchange 
 monte carlo simulations was found to be successful in classifying 
 water molecules as displaceable or conserved .
however , such an approach requires extremely time - intensive calculations 
 that must be performed on each water molecule individually .
this drawback 
 also affects free - energy perturbation ( fep ) techniques , which have 
 also been used to successfully predict the ease of displacement of 
 ordered water molecules in protein binding sites .
fep predictions were found to correlate with the change 
 in affinity for structural modifications that displaced the water 
 molecules .
however , the study also noted that a complete thermodynamic 
 analysis is needed in order to accurately compute the effects of ligand 
 modifications . another approach employed machine learning to create 
 a probabilistic water classifier .
this 
 was found to be very good at predicting the location of water molecules 
 and shown to have reasonable predictive power in classifying water 
 molecules as displaceable or conserved .
the structural features of 
 water molecules in x - ray crystallographic structures , such as b - factors , 
 accessibility to bulk solvent , number , and strength of protein - water 
 hydrogen bonds , were used to develop multivariate logistic regression 
 models for predicting the displacement of water molecules , yielding 
 a prediction efficiency of 67% .
an empirical method has also been 
 developed that employs pseudo - bayesian statistical analysis on predictions 
 from the hint scoring function and the 
 rank algorithm , which is based on the 
 number and geometric quality of hydrogen bonds for each water molecule . 

the method was found to be particularly useful for identifying strongly 
 conserved water molecules .
an alternative 
 to the approaches described above is the use of 
 inhomogeneous fluid solvation theory ( ifst ) .
ifst can be used to create a thermodynamic profile of the solvent 
 surrounding a protein , which can be used to identify 
 binding hotspots .
the free energies calculated 
 by ifst are the contributions of regions of three - dimensional space 
 to the solvation free energy of the solute .
when this region is a highly occupied hydration site , g can be thought of as the contribution of a specific water 
 molecule to the solvation free energy of the solute .
watermap has been used to explore the hydrophobic effect , understand sar , explain kinase selectivity , and predict 
 binding affinities .
the advantage of 
 ifst over other physics - based methods such as ti and fep is that one 
 simulation yields predictions for all the water molecules in a system . 

recent work on ifst has focused on binding site prediction , discretization on a cartesian grid , and investigating the effect of using different 
 water models . in this study , we employ 
 ifst to predict the experimentally observed displacement of water 
 molecules in a protein binding site .
when displacing a water 
 molecule from a binding site in molecular 
 design , consideration must be given to the functional group that replaces 
 it .
multiple copy simultaneous search ( mcss ) is one of the oldest 
 methods to predict energetically favorable positions of functional 
 groups in protein binding sites .
the 
 mcss scoring function is based on the charmm force field with interaction energies calculated from the 
 difference between the bound and unbound states .
the solvent effects 
 are commonly taken into account by an approximation of dielectric 
 screening from a distance - dependent dielectric model .
an alternative 
 approach to account for solvent effects is to couple the molecular 
 mechanical energy component with polar and nonpolar solvation free 
 energies derived from implicit solvent formalism in mm - pb / sa or gb / sa 
 methods .
implicit solvent approaches have gained 
 widespread application in virtual screening , rescoring of docking 
 poses , and estimation of ligand binding energies in ligand series .
the sorting of mcss minima based on mm - pb / sa derived free energy 
 estimates has yielded encouraging results .
mcss analysis using probe molecules with different chemical characters 
 provides what have been termed functionality maps .
these functionality 
 maps have been used for construction of ligands for numerous protein 
 targets .
other approaches have 
 also been developed for computational solvent 
 mapping and probe analysis .
ftmap searches for favorable probe positions 
 on protein surfaces using an empirical energy function including a 
 desolvation term .
a clustering procedure 
 is used to identify consensus probe binding sites which are identified 
 as fragment - binding hotspots .
the integral 
 equation theory of liquids , known as the three - dimensional reference 
 interaction site model ( 3d - rism ) has also been 
 used to find most probable positions of functional groups and small 
 ligands on protein surfaces .
this method 
 has been applied to thermolysin with solvent probes for which experimental 
 data were available , and reasonable agreement with experimental results 
 was obtained .
more recently , methods 
 employing molecular dynamics ( md ) simulations using a mixture of explicit 
 water and functional group probes have been introduced .
guvench and mackerell used a solvent mixture 
 consisting of propane  benzene  water to construct probability 
 density maps of probe binding preferences which corresponded to known 
 ligand functional group preferences .
in other studies , more diverse sets of probe 
 molecules have been used to assess the druggability of allosteric 
 protein targets and binding hotspots .
an important feature of these developments was 
 the combined use of probe clustering and water displacement as indicators 
 of hotspots .
as summarized above , md simulation based protocols using 
 solvent - probe mixtures have been used previously to identify binding 
 hotspots and predict target druggability . however ,
a direct assessment 
 of relative displaceability of ordered water molecules has not been 
 addressed in these studies .
an effective method for providing such 
 assessments would prove valuable in the hit - to - lead and lead optimization 
 stages of drug development .
in addition , an accurate ranking of water 
 molecules making bridging interactions between the protein and the 
 ligand can be also be used to make judicious choices about keeping 
 or removing water molecules prior to molecular docking methods for 
 virtual screening .
the estimation of thermodynamic properties of water molecules 
 at 
 binding interfaces can provide very useful information for ligand 
 design .
the main aim of this study was to investigate the combination 
 of hydration site free energies calculated by ifst with the mcss scores 
 of probe molecules in order to predict the observed displacement of 
 water molecules . to calculate an experimental measure of displaceability
, 
 we used a large collection of ligand - bound structures of heat shock 
 protein 90 ( hsp90 ) . the observed
displacement was calculated from 
 the frequency with which water molecules from the apo structure were 
 displaced by a ligand .
hsp90 is well - known for its function as a molecular 
 chaperone . due to its important role in assisting protein folding , 
 preventing self - aggregation , and cell cycle progression ,
the 
 structure of hsp90 contains a highly conserved n - terminal domain that 
 is linked , via a highly flexible linker , to a middle domain and a 
 c - terminal domain .
the n - terminal domain 
 contains an adenosine binding pocket , responsible for its atpase activity , 
 and has an unusual motif known as bergerat fold , which is characterized 
 by a rigid adenosine binding site and a flexible loop in phosphate 
 binding region .
the atpase activity of 
 the n - terminal domain drives structural transitions required for chaperone 
 functioning , and therefore it has been targeted in several drug discovery 
 campaigns .
water molecules have been 
 a key aspect of structural studies on hsp90 , and a key consideration in ligand design programs has been the role 
 of four key water molecules in the binding site that are highly conserved 
 in several complex structures .
their systematic displacement 
 and the resulting consequences on ligand optimization have also been 
 the subject of recent investigations .
these factors 
 make hsp90 an ideal test case to assess the predictions of ifst .
a data set consisting of 
 103 ligand - bound x - ray crystallographic structures of hsp90 ( n - terminal 
 domain ) was compiled , based on roughley and hubbard s work .
the structures can be organized into four groups : 
 those based on a resorcinol substructure ( 41 ) , those based on a ( benz)-amide 
 substructure ( 14 ) , those based on an aminopyr(im)idine substructure 
 ( 39 ) , and those based on  second - site  fragments ( 9 ) . 
 due to modifications around central scaffolds , this data set
the set of crystal structures used in this work is reported in the supporting information .
in addition , a crystal 
 structure of the unbound closed - state conformation of hsp90 ( 1yer ) was used as the 
 apo form of the receptor .
all the structures were moved to the same 
 reference frame by a rigid - body superimposition onto the ligand - bound 
 crystal structure from 1uy6 . from the superimposition ,
any water molecule in the 
 apo structure lying within 2.0  of any of the ligand atoms in 
 any structure was selected for subsequent analysis .
for each of these 
 reference water molecules , the observed displacement fraction ( d ) was defined from the fractional conservation ( f).12 f is obtained by counting 
 the number of ligand - bound structures where a corresponding water 
 molecule was present within 1.0  ( nhydrated ) and then representing it as a fraction of total structures in the 
 data set ( ntotal ) .
the orientations 
 of asparagine and glutamine residues were then checked using schrodinger s 
 preparation wizard .
residues asn40 , asn105 , 
 gln123 , gln133 , and gln194 were altered by switching the nitrogen 
 and oxygen atoms to improve the hydrogen - bonding patterns .
histidine 
 residues were also checked for orientation and protonation state using 
 schrodinger s preparation wizard .
residues his154 , his189 , 
 and his210 were flipped and assigned as epsilon protonated .
all remaining 
 histidines were assigned as delta protonated , but residue his77 was 
 flipped .
the residues lysine , arginine , aspartate , glutamate , cysteine , 
 and tyrosine were also analyzed to check their protonation state . 

there was no evidence of any unusual protonation states , and thus 
 all lysine and arginine residues were assigned as positively charged , 
 all aspartate and glutamate residues were assigned as negatively charged , 
 and all cysteine and tyrosine residues were assigned as neutral .
the 
 hydrogen - atom positions were then built using the hbuild facility 
 of charmm with the charmm27 energy function , and the atomic charges were assigned from the charmm27 force field .
the prepared protein structure was 
 then subjected to mcss calculations using eight different probe molecules . 

the probes were chosen to ensure that energetically favorable positions 
 of polar , charged , hydrophobic , and aromatic functional groups were 
 sampled .
two representative probes from each group were selected : 
 methanol and ammonia ( polar ) , ammonium ions and acetate ions ( charged ) , 
 methane and propane ( hydrophobic ) , plus phenol and benzene ( aromatic ) . 

the binding site region for mcss 
 calculations was defined as a 12.0  sphere around the centroid 
 of all overlaid ligands . for each probe molecule ,
500 copies were 
 randomly placed in this sphere where any copy within 1.2  of 
 any protein atom was removed .
energy minimizations were carried out 
 in stages , and duplicate copies ( within 0.2  of another copy ) 
 were removed at the end of each stage .
the first stage was 500 steps 
 of steepest descent ; the second stage was 300 steps of steepest descent , 
 and the final stages were 20 repetitions of 500 steps of conjugate 
 gradient minimization .
all minimizations were performed using a distance - dependent 
 dielectric model with the default dielectric constant of 1.0 . at the 
 end of the mcss run ,
probe copies with charmm interaction energies 
 greater than + 10.0 kcal / mol were removed .
finally , probe copies within 
 1.0  of each other were clustered together , and the copy with 
 the lowest interaction energy was selected as the cluster representative . 
 all of the above calculations were performed using the mcss implementation 
 in accelrys discovery studio 3.1 .
the raw probe scores from 
 mcss consist of the charmm interaction energy between the probe molecule 
 and protein .
these raw scores were size - normalized by multiplying 
 by the ratio of water to probe molecular volume .
this allowed us to 
 compare the scores of probes with different sizes together . in two 
 variations of the protocol , the normalized mcss scores were combined 
 with two different desolvation penalties : the experimental hydration 
 enthalpy ( mcss - he ) and the experimental hydration free energy ( mcss - hfe ) . 

the experimental values of hydration enthalpy and hydration free energy 
 for each probe were obtained from the literature .
the mcss derived probe positions 
 were compared with the reference water molecules using the following 
 procedure .
a probe position was identified as corresponding to a reference 
 water molecule if any of the probe heavy atoms was less than 1.5  
 from the oxygen atom of the water molecule .
when multiple probe positions 
 were obtained for a given water molecule , the best scoring pose was 
 selected . in a variation of the scoring 
 protocol for mcss - derived probe positions ,
the binding energy of each 
 probe was calculated using an mm - gbsa approach:3 the free energy of 
 each of the above 
 terms is calculated from4 emm is 
 the energy calculated from the 
 standard charmm energy function , which includes bonded and nonbonded 
 terms .
gelec represents the electrostatic 
 component of solvation energy and was calculated using a variation 
 of the standard gbsa model , referred to as the generalized born method 
 with molecular volume integration ( gbmv ) . the nonpolar contribution 
 ( gnp ) to solvation free energy
was calculated 
 on the basis of the surface area ( sa ) model , which assumes a linear 
 relationship between gnp and the solvent 
 accessible surface area .
a value of 5.0 
 cal / mol /  was used for the surface area coefficient 
 in the sa model . the change in the solute entropy ( ts ) terms was assumed 
 to be zero in this case .
the mm - gbsa scores were also size - normalized 
 using the method detailed above . for 
 the md simulation ,
solvation was performed with 
 the solvate program , version 1.0 from 
 the max planck institute to generate a sphere of radius 50  
 around the protein center .
the system was then cut to form a rhombic 
 dodecahedron ( rhdo ) with an edge length of 60  using the charmm 
 program ( version 34b1 ) .
water molecules 
 were modeled with the tip3p - ewald , tip4p-2005 , and tip5p - ewald water models in three separate systems .
the water model has been 
 shown to affect free - energy calculations in recent studies .
six sodium ions were included in all three systems to yield a net 
 charge of zero .
the ions were placed far from the hsp90 binding site . during all md simulations , the heavy 
 atoms were harmonically restrained using a 1.0 kcal / mol /  harmonic force , the rhdo was treated using periodic boundary 
 conditions , and the electrostatics were modeled using the particle 
 mesh ewald method .
the system was first 
 subjected to md equilibration for 100 ps in an npt ensemble .
this 
 stage of preparation was undertaken to equilibrate the density of 
 the water molecules .
the density of the water molecules plays an important 
 role in ifst and is thus important to equilibrate .
the system was 
 then subjected to md equilibration for 1 ns in an nvt ensemble .
we 
 ensured that the system was brought to equilibrium before continuing 
 by verifying that the system reached a point where the energy fluctuations 
 were stable .
production simulations 
 were performed 
 for 5.0 ns at 300 k. all md simulations were performed using the namd 
 program version 2.8 with the charmm27 
 force field allowing an md time step of 2.0 
 fs .
electrostatic interactions were modeled with a uniform dielectric 
 and a dielectric constant of 1.0 throughout the setup and production 
 runs .
van der waals interactions were truncated at 11.0  with 
 switching from 9.0  .
all md simulations were performed using 
 namd compiled for use with the cuda - accelerated gpus .
the 5.0 ns md 
 simulations required approximately 15 , 20 , and 25 h for the tip3p - ewald , 
 tip4p-2005 , and tip5p - ewald water models on two four - processor gpu 
 cores .
the first stage of the 
 ifst analysis was to identify regions within the binding site with 
 a high number density of water , termed hydration sites .
we defined the center of the binding site as 
 the centroid of the two water molecules w331 and w440 and considered 
 the region within 12.5  of this centroid .
we employed a radius 
 of 1.2  for these hydration sites , in line with prior work .
the first method was to place hydration sites at crystallographically 
 determined water positions , and the second was to cluster water molecules 
 from the md simulation .
for clustering , hydration sites were selected 
 by sampling 5000 equally spaced snapshots from the md trajectory and 
 calculating the number density at cartesian gridpoints within the 
 binding site . a grid with a resolution 0.5  was generated around 
 the centroid of the binding site .
the number of water molecules within 
 1.2  from all 5000 snapshots was used to calculate the number 
 density , and hydration sites were selected iteratively , starting at 
 the gridpoint with the highest number density .
no gridpoints were 
 selected within 2.4  of an existing gridpoint , and the iteration 
 terminated when the remaining gridpoints had a number density lower 
 than 50% of the bulk value .
the hydration site positions were then 
 calculated as the mean position of all water molecules within 1.2 
  of each selected cartesian gridpoint .
the clustering required 
 approximately 21 , 27 , and 34 h for the tip3p - ewald , tip4p-2005 , and 
 tip5p - ewald water models on a single processor .
the interaction energy of each 
 hydration site was calculated by sampling 1000 snapshots with one 
 every 5.0 ps from the 5.0 ns simulation .
the average interaction energy 
 of every water molecule within the site was computed from the interaction 
 with the solute ( esw ) and the other water 
 molecules ( eww ) .
this was then combined 
 with the average interaction energy of a water molecule determined 
 from the bulk water simulation ( ebulk ) 
 to calculate the energy difference ( e ) and 
 binding energy difference ( ebind ) as shown in eqs 5 and 6 , respectively:56 the average 
 number of water molecules 
 in a hydration site is termed n. from a corresponding 
 simulation of bulk water , ebulk takes 
 values of 9.81 kcal / mol , 11.33 kcal / mol , and 9.67 
 kcal / mol for the tip3p - ewald , tip4p-2005 , and tip5p - ewald water models . 

the binding energy can also be termed the normalized energy or the 
 per water energy .
the entropy 
 of each hydration 
 site was calculated by sampling 250  000 snapshots with one 
 every 20 fs from the 5.0 ns simulation .
the entropy difference was 
 calculated from the contributions of the solute  water term 
 ( ssw ) and the water 
the variable r represents 
 the position of the water molecule with respect to the center of the 
 hydration site , and the set of angles  represents the orientation 
 of the water molecule in the fixed protein reference frame . as in 
 previous work , only contributions from two particle correlations were 
 considered , and vibrational entropy changes 
 are not considered .
furthermore , the solute - water term was separated 
 into translational ( ssw , trans ) and orientational 
 ( ssw , orient ) contributions , and the orientational 
 distributions were assumed to be independent of the position within 
 the sites .
these terms can be calculated 
 for each hydration site as shown in eqs 7 and 8 , respectively:78 in these equations , k is boltzmann s 
 constant ,  is the number density 
 of bulk solvent , and  is the integral over the angles . 
 the solute 
water translational correlation functions were calculated 
 using a bin size of 0.03  for the radial component , 10 
 for the  angle and 1/18 for cos . the solute 
water 
 orientational correlation functions were calculated using a bin size 
 of 10 for the  and  angles and 1/18 for cos . 
 the water  water reorganization term was also separated into 
 translational ( sww , trans ) and orientational 
 ( sww , orient ) contributions .
the water  water 
 translational pair probability densities were calculated as previously 
 between water molecules in the hydration site and a set of subvolumes 
 surrounding the hydration site .
a sphere 
 of radius 3.6  around the hydration site was split into subvolumes 
 using a bin size of 0.1  for the radial component , 12 
 for the  angle and 1/15 for cos . the 3.6  cutoff 
 is used because contributions to the translational entropy outside 
 the first solvation shell are expected to be small . due to the vast amount of data required for calculation 
 of the solute  water 
water triplet correlation function , 
 a bulk water  water radial distribution function gww(r ) was assumed as previously.9 gsw(r ) 
 is 
 the translational probability density with respect to bulk water within 
 a subvolume . for each hydration site ,
sww , orient was calculated from the mutual information ( iww ) between the orientations of water molecules in the hydration 
 site and the orientations of water molecules in subvolumes surrounding 
 the hydration site .
a sphere of radius 3.6  around the hydration 
 site was split into subvolumes using a bin size of 1/4 for cos  
 and a bin size of 45 for the other euler angles .
the mutual 
 information was then calculated between the hydration site and each 
 of the subvolumes:101112 sww(rel ) is 
 the water  water 
 relative orientational entropy , where rel is the 
 relative orientation of two water molecules using a bin size of 10. 
 the full five - dimensional relative orientational entropies were estimated 
 by using the second order entropy approximation generated by a truncation 
 of the mutual information expansion .
water terms were then compared with the entropy of 
 a water molecule determined from the bulk water simulation ( sbulk ) due to other water molecules within 3.6 
  to calculate the entropy difference ( s ) and entropy of binding ( sbind ) using eqs 13 and 14 , respectively:1314 tsbulk takes values of + 3.54 kcal / mol , + 3.94 kcal / mol , 
 and + 3.85 kcal / mol for the tip3p - ewald , tip4p-2005 , and tip5p - ewald 
 water models based on the calculations described here .
it is important 
 to note that the values of a half in eq 13 have 
 been removed from the definitions for sww , trans and sww , orient in eqs 9 and 10 ( which has been the convention 
 in previous work ) such that eqs 13 and 14 correspond to eqs 5 and 6 . for each 
 hydration site ,
the 
 difference in free energy ( g ) and the free 
 energy of binding ( gbind ) is then 
 calculated using eqs 15 and 16 , respectively:1516 e is assumed 
 to be a good approximation for h , as the pv 
 term is expected to be negligible .
we hypothesized that the 
 combination of best probe score and the corresponding water score 
 should be reliable predictors of the observed displacement measure 
 defined in this study . in order to assess this relationship ,
multiple 
 linear regression analysis was performed in the r statistical computing 
 package:17where o , 1 , and 
 2 are coefficients and e represents 
 the error term .
several models of this form were constructed 
 using different formulations of the probe and hydration site scores . 

the probe scores corresponded to mcss , mcss - he , mcss - hfe , and mm - gbsa 
 scores .
for the hydration site score , seven ifst calculated properties 
 were used : g , gbind , e , ebind , s , sbind , and esw .
the predictive power 
 for each model was expressed as the adjusted coefficient of determination 
 ( r ) , which is given by18where n is the number of 
 samples and p is the number of variables in the model . 

the use of r corrects for improvement 
 in correlation arising just by the increase in number of variables . 
 in all other instances , where correlation between any two variables 
 is reported , we used the standard r values .
in the initial ifst analysis , hydration sites were 
 placed on each 
 crystallographic water molecule .
the 
 results of this analysis using water model tip4p-2005 are summarized 
 in table 1 .
all of the crystallographic water 
 positions bound favorably to the protein surface , as indicated by 
 the corresponding hydration sites with negative g values .
favorable g values have also been 
 noted in previous applications of ifst to proteins and model systems . the free 
 energy of hydration sites exhibited a moderately strong correlation 
 with the observed displacement as indicated by an r value of 0.57 .
interestingly , some of the hydration 
 sites with a highly favorable free energy corresponded to water molecules 
 ( e.g. , w301 and w323 ) that are involved in bridging interactions and 
 are retained by most of the ligands ( figure 1 ) . among the other thermodynamic properties of hydration sites calculated 
 from ifst , esw and e also demonstrated strong correlation with the observed displacement , 
 with r values of 0.62 and 0.58 , respectively . 

this is not unexpected , 
 as the binding quantities do not consider the occupancy of the hydration 
 site , which can be low or high .
however , the 
 predictions are that enthalpic contributions tend to outweigh entropic 
 contributions in determining the free energy .
this is an interesting 
 result that has also been noted in previous applications of ifst to 
 proteins and model systems .
however , it may not be general for all systems 
 or for all force fields .
it is interesting to note that one of the 
 hydration sites ( w336 ) consistently appeared as an outlier in all 
 correlation plots ( figure 2 ) .
the removal of 
 this hydration site from the data set resulted in substantial improvement 
 of correlation between observed displacement and thermodynamic properties , 
 as indicated by r values of 0.78 , 0.82 , 
 and 0.81 for g , esw , and e , respectively . a clear explanation 
 for why this hydration site is an outlier can be found by comparing 
 w336 with w301 .
w301 is very close to w336 and hydrogen bonds to the 
 same aspartate residue ( asp93 ) .
interactions with asp93 yield strongly 
 favorable free energies for both hydration sites in comparison with 
 hydration sites near uncharged residues .
however , w301 makes a significantly 
 more favorable contribution to the solvation free energy than w336 
 ( 11.81 kcal / mol versus 8.28 kcal / mol ) .
this corresponds 
 to a significantly lower observed displacement for w301 compared to 
 w336 ( 0.05 versus 0.99 ) .
importantly , displacement of w336 is only 
 achieved by a positively charged ligand moiety , presumably because 
 the strong electrostatic interactions are necessary to overcome the 
 strong interactions made by the water molecule .
thus , displacement 
 of w301 must overcome even stronger water interactions and is rarely 
 observed .
this explanation provides the rationale for employing mcss 
 calculations , to allow the strength of interactions with water to 
 be compared with the strength of interactions with probe molecules .
water 
 molecules in the binding site of hsp90 n - terminal domain 
 in the apo form ( 1yer ) .
each water molecule is labeled with the observed displacement 
 fraction based on the analysis of 103 ligands considered in this study . 

a subset of four highly conserved water molecules ( labeled w1w4 ) 
 is involved in several hydrogen bonds with the binding site .
one of 
 these water molecules ( w2 ) was not seen in 1yer , and its position was derived by superposition 
 of a liganded structure ( 1uy6 ) .
correlation plots between 
 the observed displacement from eq 2 and g calculated from eq 15 .
ifst predictions 
 were made using the water models 
 tip3p - ewald , tip4p-2005 , and tip5p - ewald .
seven different ifst 
 predictions are reported for hydration sites determined from crystallographic 
 water molecule locations and the ifst free energy prediction is reported 
 for hydration sites determined by clustering of water molecules from 
 the md trajectory .
we also compared 
 the effect of other water models in obtaining 
 these quantities for hydration sites .
the detailed results for each 
 water model are given in the supporting information ( table s2 ) .
a comparison of the linear correlation between observed 
 displacement and g values from different models 
 is shown in figure 2 .
all three water models 
 produced very similar results , with tip3p - ewald producing a very slightly 
 lower correlation and tip4p-2005 producing a very slightly higher 
 correlation . from this point
onward , the discussion of ifst results 
 is based entirely on the tip4p-2005 model for this reason . in 
 addition to placing hydration sites at crystallographic water 
 positions
the density cutoff was set to half the bulk density in order to identify 
 all hydration sites .
it was noted that some crystallographically observed 
 water molecules have approximately the same number density as bulk 
 water and were not predicted using a higher cutoff .
such hydration 
 sites exhibit clustering of water molecules , leading to translational 
 ordering and an unfavorable entropy contribution , but have a weakly 
 favorable enthalpic component and thus a free energy that is approximately 
 the same as bulk water . in terms of reproducing the crystallographic 
 water positions from the simulation , the success rate ( expressed as 
 a percentage of crystallographic water molecules reproduced within 
 1.0  ) was similar across different water models with tip4p-2005 
 having the highest success rate of 80% ( table 1 and table s2 ) .
clustering identified 
 18 of the 20 displaceable water molecules within 1.5  and 16 
 within 1.0  . the failures all corresponded to crystallographic 
 water positions with low occupancy and small free energy contributions 
 as computed by ifst from the experimentally determined positions . 

this resulted in a slightly lower correlation with observed displacement 
 ( r of 0.50 ) and suggests that placing 
 hydration sites at crystallographic water positions may be more predictive .
the results of the mcss calculations are summarized in table 2 . for each water position ,
the raw mcss scores 
 reflect the charmm interaction energy between the probe and the protein , 
 and desolvation penalties were applied to these raw scores .
we tested 
 the use of the hydration enthalpy ( mcss - he ) and the hydration free 
 energy ( mcss - hfe ) as desolvation penalties .
the resulting scores were 
 then normalized according to the relevant probe molecular volume . 

the original 
 mcss output indicated a strong preference for charged groups for the 
 majority of the hydration sites .
this is most likely due to the crude 
 approximation of solvent - screened electrostatic interactions in the 
 distance - dependent dielectric model .
the addition of desolvation penalties 
 somewhat corrected for this trend by replacing nonpolar or polar probes 
 in at least eight water positions .
the mm - gbsa scores demonstrated 
 the most significant differences , identifying polar molecules such 
 as methanol and phenol as the most suitable probes for the majority 
 of the hydration sites .
the important point to note from 
 the mcss analysis was that none 
 of the different formulations had a significant correlation with observed 
 displacement ( table 3 ) .
this is not unexpected , 
 since capturing displaceability of water molecules solely from probe 
 binding is unlikely , especially when probe scores contain crude approximations 
 for solvation and entropic effects .
the correlation between the different 
 formulations of probe score and the predicted thermodynamic properties 
 of water molecules was also mostly weak or insignificant .
volume - corrected 
 mcss scores showed r values of 0.29 and 
 0.39 with g and gbind of the corresponding hydration site . when best scoring 
 mcss probes are divided into polar and nonpolar categories ,
the correlation 
 of scores with most of the ifst based properties is in opposite directions . 

for instance , polar probes have a correlation of 0.21 and 0.24 with 
 g and h , respectively . 

on the other hand nonpolar probes have a correlation of 0.60 
 and 0.59 with g and h , respectively .
this partially explains the nearly insignificant 
 correlation when probes are considered together . in general ,
it was 
 noticed that hydration sites with a large enthalpic component were 
 associated with charged or polar probes whereas hydration sites with 
 a small enthalpic component were associated with nonpolar probes . in order to qualitatively assess 
 the mcss results , we compared 
 the distribution of probes in the binding site with that of the ligands . 
 for each hydration site ,
ligand atoms within 1.0  of the crystallographic 
 water molecule were obtained , which provided the distribution of atoms 
 that most likely displaced it .
these atoms were then divided into 
 polar ( n , o , s , p ) and nonpolar ( c ) groups .
the same procedure was 
 then repeated for probes which provided the predicted distribution 
 of functional groups around each hydration site . in figure 3 , these distributions are represented as an occupancy 
 map on a 1  grid , where each grid point contains the amount 
 of time it was occupied by an atom , represented as a fraction of the 
 total number of atoms within 1  of the hydration site .
first , 
 we selected a set of hydration sites with highly favorable free energy 
 ( figure 3a and b ) and relatively high observed 
 displacement . for at least three of these hydration sites ( w325 , w346 , 
 and w412 ) , polar atoms comprised more than a 1/3 fraction of the displacing 
 groups . w324 and
w529 were displaced less frequently by polar groups . 
 when compared with probe occupancies ,
an important difference was that probe polar atoms more frequently 
 occupied w324 and w529 . on the other hand
, probe polar atoms were 
 less frequently observed for w346 , which is frequently displaced by 
 a polar ligand atom .
the overlap between the ligand and probe occupancy 
 maps was more pronounced for a set of less favorable hydration sites 
 ( figure 3c and d ) , which were most frequently 
 displaced by nonpolar functional groups ( both in crystallographic 
 ligands and probes ) .
this can be rationalized on the basis that the 
 optimal van der waals interactions between ( ligand / probe atoms and 
 protein atoms ) are more accurately reproduced by mcss than the electrostatic 
 interactions .
taken together , these results showed that hydration 
 sites with highly favorable free energy ( owing mostly to a strong 
 enthalpic component ) were displaced frequently by polar ligand atoms , 
 and mcss - based predictions for probes showed a similar trend .
conversely , 
 hydration sites with less favorable free energy ( with a decreased 
 enthalpic but comparable or even large entropic component ) were mostly 
 displaced by nonpolar ligand and probe atoms .
the number of distinct 
 probes occupying the same energetically favorable site has been used 
 before as an indicator of binding hot spots . prior to selection of the best scoring probe
, we obtained the number 
 of distinct probes identified at each hydration site and compared 
 it with the observed displacement .
hence , 
 a clear relationship between diversity of probe binding and the likelihood 
 of water displacement was not established from these data . a comparison 
 of polar and nonpolar atom densities derived from 
 crystallographic ligands ( a and b ) and mcss - derived probe positions 
 ( c and d ) in the hsp90 binding site . atoms around a given hydration 
 site are represented as an occupancy map on a 1  grid , with 
 grid points contoured at points where 30% of the total number of atoms 
 within a 1  region around the hydration site are of the given 
 type ) .
( a and c ) a set of highly favorable hydration sites with corresponding 
 polar - atom occupancy maps compared across crystallographic ligands 
 and probes .
( b and d ) a set of relatively less favorable hydration 
 sites with corresponding nonpolar atom occupancy maps compared across 
 crystallographic ligands and probes .
the results obtained from ifst calculations showed that the 
 predicted 
 free energy of hydration sites was a fairly reliable predictor of 
 observed displacement . in order to assess whether the combination 
 of ifst and mcss probe analysis could improve the predictions , we 
 used multiple linear regression models based on different combinations 
 of hydration site thermodynamic properties and probe scores .
the resulting 
 data are summarized in table 4 , where the adjusted 
 coefficient of determination is reported for each model . in most cases , 

the use of desolvation 
 penalties derived from experimental data also did not lead to any 
 improvement in predictions .
however , as noted above , when the outlier 
 w336 is removed from the data set , the adjusted r values increased in some cases .
for example , the highest 
 value of adjusted r ( 0.80 ) was obtained 
 from a model combining esw values from 
 ifst and probe gbsa scores .
this also indicated that the use of gbsa 
 to estimate probe desolvation penalties provided better results than 
 constant desolvation penalties based on experimental hydration enthalpy 
 or free energy values .
each model is derived from two 
 predictors , indicated by row and column titles , and the value reflects 
 strength of correlation with observed displacement . for comparison , r s for ifst values ( without mcss ) are 
 also given . an important 
 consideration in ligand design
is the prioritization 
 of ordered water molecules in the binding site for displacement and 
 concomitant affinity gains .
we selected a set of four water positions 
 to assess the applicability of solvation thermodynamics and probe 
 analysis in order to address such questions ( figure 1 ) .
three of these water positions are already included in 
 the set of reference water molecules derived from the apo structure 
 ( 1yer ) . however , 
 one additional water molecule is visible only in ligand bound hsp90 
 ( e.g. , 1uy6 ) . 

for consistency with previous studies , we refer to these water positions 
 as w1 , w2 , w3 , and w4 . the free energy 
 of hydration sites corresponding to these water molecules , the best 
 scoring probes ( based on gbsa scores ) , and associated data are summarized 
 in table 5 .
these water molecules form a buried 
 and tightly coordinated network of hydrogen bonds with the protein 
 and/or ligand ( figure 1 ) . the combined regression 
 model based on ifst and mcss predicts w2 to be the most displaceable 
 in this group , whereas w2 and w3 are predicted to be moderately displaceable , 
 and w1 is predicted to be very difficult to displace .
the probe analysis 
 predicts that w2 and w4 could be replaced by methane , whereas the 
 w1 and w3 sites were most favorably filled by ammonia and methanol , 
 respectively ( figure 4b and d ) .
a collective 
 picture emerges from the probe analysis that the binding site region 
 containing w2 and w4 accommodates functional groups of nonpolar character , 
 but w3 needs to retain hydrogen bonding interactions made by the displaced 
 water molecules .
this is also suggested by the good interaction energy 
 ( esw ) for the w3 site .
( b ) 
 w2 and w3 were predicted to be replaced by methane and methanol as 
 most favorable probes .
( c ) in x - ray crystallographic ligands , w2 and 
 w3 have been displaced by nitrile and methyl groups , respectively . 

( d ) the best scoring probes corresponding to w1 and w4 were ammonia 
 and methane , respectively . ( e ) in x - ray crystallographic ligands , 

w4 has been replaced by an ethyl group , but no reported examples were 
 found for the displacement of w1 ( it is not present in some structures 
 but never overlaps with the ligand ) .
w1 , w3 , and 
 w4 correspond to 
 301 , 328 , and 303 from 1yer , whereas w2 is missing from 1yer .
thus , all ifst 
 results are from corresponding hydration sites using the clustering 
 protocol .
the ifst predictions 
 agree well with experimental data on binding 
 affinities for a pyrrolopyrimidine scaffold .
commensurate with this 
 is a high predicted displacement , and indeed displacement of w2 by 
 a nitrile group was shown to increase binding affinity approximately 
 250-fold ( figure 4c ) .
mcss suggests a methyl probe is the most favorable replacement for 
 w2 ( with an mmgbsa score of 4.55 kcal / mol ) , but the methanol 
 probe also scores highly ( with an mmgbsa score of 4.13 kcal / mol ) , 
 and this is consistent with displacement by a nitrile group that makes 
 a hydrogen bond with asn51 .
thus , the experimental observations can 
 be explained by ifst and mcss due to the low g for w2 in concert with a good probe score ( table 5 ) . for the nitrile derivative , further displacement of w3 
 by a methyl group did not increase binding affinity .
this was attributed 
 to the attenuation of affinity gain by the loss of hydrogen bonding 
 interactions for w3 and the clash of the new side chain .
the rationalization 
 based on ifst predictions is that the lack of significant gain in 
 affinity is because w3 is not as easy to displace as w2 and that a 
 nonpolar group is not the optimal replacement .
. further 
 displacement of w4 by extending the methyl group to an ethyl group 
 was shown to increase binding affinity 3-fold ( figure 4c ) .
ifst and mcss again rationalize this , with w4 predicted 
 to be moderately displaceable by a nonpolar group such as methane . 

in addition , there is a small nonpolar cavity behind w3 that does 
 not contain a water molecule .
such evacuated regions can provide unexpected 
 boons in binding affinity , and extension 
 of the ethyl group to an isopropyl group may prove beneficial .
it 
 is interesting to note that the model predicts that w2 , w3 , and w4 
 are significantly more displaceable than has been observed experimentally . 

the recent experimental data suggest 
 that ifst is able to identify untapped potential at these sites
. this 
 would be very useful information in other established drug development 
 projects . in order to further investigate the relationship between 
 x - ray 
 crystallographic ligands and thermodynamic properties of hydration 
 sites along with associated functional groups
, we selected a subset 
 of unfavorable hydration sites including w338 , w357 , w379 , w381 , w385 , 
 w405 , w435 , w476 , w536 , and w547 .
the choice of these hydration sites 
 was based on the fact that most of these are almost always displaced 
 by the ligands ( table 1 ) , and their calculated 
 free energies lie in the range 0.5 kcal / mol to 2.5 
 kcal / mol .
the positions of the corresponding x - ray crystallographic 
 water molecules and the most favored mcss probes , based on mm - gbsa 
 scores , are shown in figure 5 .
the majority 
 of sites are occupied by nonpolar groups , though some lower energy 
 hydration sites are occupied by polar groups .
this suggests an overall 
 trend of prioritizing lower energy hydration sites for displacement 
 with polar groups and high energy hydration sites with nonpolar groups . 

we performed a retrospective analysis of the different ligand series 
 used in this study to see if a similar trend could be observed .
first , we observe that w338 and w547 are commonly displaced by nonpolar 
 groups in known inhibitors , which is consistent with the nonpolar 
 mcss probe positions .
w338 is displaced by a nonpolar group in figures 6a  c and 7a , b , whereas 
 w547 is displaced by a nonpolar group in figures 6c , 7a , and 8a  c .
similarly , for w357 the methanol probe position is consistent 
 with the polar functionalities of known inhibitors in figures 6a  c , 7a , b , and 8a , b .
a subset of crystallographic water molecules in the hsp90 
 binding 
 site , labeled with the ifst - based solvation free energies in kcal / mol 
 and colored from yellow to red representing low to high solvation 
 free energy .
representative ligands 
 from aminopyri(mi)dine series overlaid on 
 the crystallographic water molecules labeled with ifst - based solvation 
 free energies in kcal / mol and colored from yellow to red representing 
 low to high solvation free energy .
these compounds represent hits 
 from fragment and virtual screening ( a and b ) and an optimized clinical 
 candidate ( c ) .
representative ligands 
 from resorcinol ( a , b ) and benzamide ( c , 
 d ) series overlaid on the crystallographic water molecules labeled 
 with ifst - based solvation free energies in kcal / mol and colored from 
 yellow to red representing low to high solvation free energy .
ligand 
 series overlaid on crystallographic water molecules labeled with ifst - based 
 solvation free energies in kcal / mol and colored from yellow to red 
 representing low to high solvation free energy .
in addition , we noticed that w405 
 and w435 , which are relatively 
 favorable hydration sites in this subgroup , are located in a hydrophobic 
 cavity lined by ala55 , ile96 , gly97 , and ile107 . in the x - ray structure , 

the only hydrogen bond observed for this pair of water molecules is 
 between w435 and the backbone carbonyl of leu107 .
the mcss predicted 
 probes reproduce this pattern by favoring a methane in place of w405 
 and a phenol group in place of w435 , retaining the hydrogen bond of 
 w435 with the backbone carbonyl of leu107 .
such binding motifs where 
 hydrogen bonding functionalities are enclosed in a hydrophobic environment 
 can provide binding hotspots .
the combination 
 of ifst and mcss can provide valuable information in this regard . 

many of the potent hsp90 inhibitors displace this pair of water molecules 
 with a combination of polar and nonpolar groups , as predicted by probe 
 analysis ( figure 6b , 7c , d ) .
figures 68 provide different scenarios in ligand design where predicted 
 displacement 
 of water alongside probe analysis could prove useful .
we emphasize 
 that binding site hydration was not explicitly addressed during ligand 
 design in these examples from the literature .
however , the results 
 are useful in understanding the utility of ifst and mcss . in figure 6 ,
representative ligands from various stages of 
 design ( fragment / virtual screening hits 6a 
 and b plus the clinical candidate 6c ) are shown , 
 which highlight the observation that stepwise disruption of binding 
 site hydration ( although not attempted in this particular ligand series ) 
 is a suitable route toward optimization .
it is notable that all of 
 the ligands in this study displace w357 , which ifst predicts to make 
 the least favorable contribution the solvation free energy among the 
 20 waters studied .
w536 is predicted to make the second least favorable 
 contribution to the solvation free energy and also appears to be a 
 clear binding hotspot .
the weaker inhibitors in figure 6a and b do not displace it , whereas the high affinity clinical 
 candidate in figure 6c does displace it .
this 
 suggests that further affinity can be gained by molecules that do 
 not displace w536 , such as the inhibitor in figure 8b . a systematic approach to ligand
optimization should involve 
 an assessment of binding site water molecules and the chemical functionalities 
 suitable for displacing them .
figure 7 provides 
 an example where core scaffolds from two distinct chemotypes ( resorcinol 
 in figure 7a and b plus benzamide in figure 7c and d ) bind at similar locations , displacing the 
 same hydration sites , w357 and w405 , representing potent hydration 
 sites for displacement .
apart from these two sites , different functional 
 groups in each of these optimized candidates displace different water 
 molecules .
consequently , the similarities between these functional 
 groups and mcss - predicted probes are interesting , e.g. , methanol probes 
 at w385 and w536 compared against the ligand in figure 7a and phenol at w381 compared against the ligand in figure 7c .
finally , in fragment linking approaches ( shown 
 in figure 8a and b ) , the choice of linker can 
 benefit from consideration of probes favorably displacing a water 
 molecule .
for example , the methylsulfonamide linker between the two 
 fragments shown in figure 8a displaces w381 
 in figure 8b .
mcss favors a polar phenol probe 
 at this site ( figure 6b ) , and the overlap of 
 polar atoms between ligand and probe is noteworthy to summarize , 
 the results suggest that ifst and mcss can be favorably 
 combined to provide a useful binding site profile . the clustering 
 of probes at a given location on the protein surface has been used 
 as an indicator of binding hotspots , which can give rise to substantial 
 gains in affinity for protein  ligand interactions .
however , 
 the use of probe calculations alone can result in false positives 
 as they do not accurately consider the effects of solvation .
ifst 
 provides explicit accounting for water molecules , and hence a combination 
 of both approaches can lead to better identification of hotspots in 
 concert with advantageous functional groups for binding .
conversely , 
 high scoring probe positions from mcss that correspond to energetically 
 favorable hydration sites can indicate strategically important sites 
 where designing bridging interactions will be more appropriate .
in this study , we have combined ifst calculations of solvent thermodynamics 
 with mcss probe analysis to predict the observed displacement of water 
 molecules from protein hydration sites on hsp90 and the best type 
 of functional moiety to replace them , in the context of small molecule 
 ligand design .
ifst calculations of the hydration site free energies 
 yielded a moderate correlation with the observed displacement , an 
 encouraging result given the number and variety of ligands considered . 
 interestingly , the correlation with the total interaction energy ( e ) is slightly better than the correlation with the free 
 energy ( g ) , and the correlation with the protein 
 interaction energy ( esw ) is even better . 

this can be rationalized on the basis that the enthalpic contributions 
 to the solvation free energies are calculated to be significantly 
 higher than the entropic contributions and that the loss of water  water 
 interactions is less significant in the context of a ligand in the 
 binding site that has already displaced the neighboring water molecules . 

this result suggests that approaches such as mcss , which can calculate 
 the interaction energy of water molecules with the protein , should 
 work well in predicting observed displacement . however , there are 
 two issues with this .
the first issue is that the protein interaction 
 energy from ifst is calculated from an explicit water simulation , 
 and calculations based on a single water molecule that is not part 
 of an ensemble will be different .
the second issue is that the protein 
 interaction energy from ifst is scaled by the occupancy of the site , 
 and a calculation of the highest scoring single water molecule is 
 not .
such a calculation is more analogous to the ifst binding quantities , 
 which we have shown to have a weaker correlation with observed displacement . to explore the application of ifst , we also calculated predictions 
 based on three different water models , and in this case the results 
 are very similar .
all three water models predict hydration sites at 
 very similar locations , and the r between 
 free energy predictions for the different water models is greater 
 than 0.9 in all cases .
in addition to predicting the observed displacement 
 of water molecules , we considered whether md simulations can reproduce 
 crystallographic water positions .
for tip4p-2005 , 18 out of the 20 
 crystallographic water positions were reproduced within 1.5  
 ( table 1 ) .
it is important to note that a complete reproduction 
 of crystallographic water positions is not always to be expected , 
 as crystallographic water positions rely on an assignment of density 
 from sometimes ambiguous data .
the first was to place hydration sites at the positions 
 of crystallographic water molecules , and the second was to place hydration 
 sites by clustering the positions of water molecules from the md simulations . 

the two approaches yield hydration sites at very similar positions 
 with almost identical thermodynamic properties .
this is shown by the 
 high correlation between the free energies of the set of 18 equivalent 
 hydration sites that are common between the two approaches ( r = 0.99 ) . however , placing hydration sites 
 at crystallographic water positions is slightly more predictive of 
 the displacement , because the clustering procedure failures to identify 
 all the crystallographic hydration sites .
the ifst calculations show 
 another interesting result , which is that the enthalpic contributions 
 to the total free energy are commonly of greater magnitude than the 
 entropic contributions . this has been noted previously and
in contrast to 
 the results of ifst , mcss proved unsuccessful at predicting displacement 
 and was unable to improve the predictions of ifst when the two were 
 used in concert .
however , mcss does demonstrate a synergy with ifst 
 when applied to ligand design .
when ifst identifies a hydration site 
 as highly displaceable , mcss can predict the ligand functionality 
 that should be used to displace it .
such information is harder to 
 glean from ifst calculations alone , although two points should be 
 noted .
the first is that the positions of hydration sites predicted 
 to have a highly favorable free energy are commonly found to overlap 
 with polar ligand atoms .
this is easily rationalized , as strong hydrogen 
 bonding is the cause of both effects .
in addition , the predicted orientation 
 of the hydrogen bonding interactions for a water molecule is often 
 recapitulated by the geometry of ligands , and this observation could 
 be used in design .
conversely , hydration sites predicted to have a 
 small free energy are commonly found to overlap with nonpolar ligand 
 atoms .
water displaceability is a difficult metric to capture 
 from experimental 
 data because displacement is boolean for any given complex .
in fact , 
 almost every water molecule should be displaceable by some ligand , 
 but this may be deleterious to the binding affinity .
for example , 
 displaceability data may be confounded if medicinal chemists have 
 deliberately attempted to displace particular water molecules in cases 
 where this is deleterious to the binding affinity .
this will increase 
 the frequency of displacement for a given water molecule in a case 
 where displacement is not advantageous . for this reason
, thermodynamic 
 data may prove more useful in quantifying displaceability , but one 
 still can not quantify the specific contribution of a single water 
 molecule to the binding affinity .
thus , we consider that the frequency 
 of displacement from a large and varied data set of complexes is the 
 best experimental data available
. however , such data are still imperfect 
 due to the bias of design by medicinal chemists .
in addition , there 
 are many cases where water molecules are subtly shifted rather than 
 displaced , and this blurs such data . despite these issues
, ifst has 
 proven to be a useful method for studying solvation thermodynamics , 
 providing a perspective that is not available using other computational 
 methods by decomposing solvation free energies into contributions 
 from specific spatial regions . a similar perspective
can be gained 
 from fep , by decomposing solvation free energies into contributions 
 from specific species , including the water molecules .
fep can also be used to selectively excite the individual degrees 
 of freedom of water molecules and assess their contribution to the 
 physicochemical characteristics of water . in this work
, we have shown that ifst can be used to identify 
 binding 
 hotspots and predict which water molecules should be advantageously 
 displaced from a protein binding site . while a larger study would 
 be needed to show that this is a general result , early results are 
 promising and suggest that such an analysis performed on a new drug 
 target would provide very useful information for ligand design .
it 
 may also show utility in identifying untapped potential for well - known 
 drug targets . as noted previously , accurately predicting the effect 
 of a ligand modification upon binding affinity can not be achieved 
 by considering any one factor and requires a comprehensive thermodynamic 
 analysis of the ligand binding .
however , 
 given the vast number of potential ligands to be considered , there 
 is a significant advantage in deriving useful information from ifst , 
 which can be calculated from a single md simulation .
 intermolecular 
 interactions in the aqueous phase must compete with 
 the interactions between the two binding partners and their solvating 
 water molecules . in biological systems , water molecules in protein 
 binding sites cluster at well - defined hydration sites and can form 
 strong hydrogen - bonding interactions with backbone and side - chain 
 atoms . 
 displacement of such water molecules is only favorable when 
 the ligand can form strong compensating hydrogen bonds . 
 conversely , 
 water molecules in hydrophobic regions of protein binding sites make 
 only weak interactions , and the requirements for favorable displacement 
 are less stringent . 
 the propensity of water molecules for displacement 
 can be identified using inhomogeneous fluid solvation theory ( ifst ) , 
 a statistical mechanical method that decomposes the solvation free 
 energy of a solute into the contributions from different spatial regions 
 and identifies potential binding hotspots . in this study , we employed 
 ifst to study the displacement of water molecules from the atp binding 
 site of hsp90 , using a test set of 103 ligands . 
 the predicted contribution 
 of a hydration site to the hydration free energy was found to correlate 
 well with the observed displacement . additionally , we investigated 
 if this correlation could be improved by using the energetic scores 
 of favorable probe groups binding at the location of hydration sites , 
 derived from a multiple copy simultaneous search ( mcss ) method . 
 the 
 probe binding scores were not highly predictive of the observed displacement 
 and did not improve the predictivity when used in combination with 
 ifst - based hydration free energies . 
 the results show that ifst alone 
 can be used to reliably predict the observed displacement of water 
 molecules in hsp90 . 
 however , mcss can augment ifst calculations by 
 suggesting which functional groups should be used to replace highly 
 displaceable water molecules 
 . such an approach could be very useful 
 in improving the hit - to - lead process for new drug targets . 
Introduction
Materials
and Methods
Results
Discussion