binding - pocket analysis 
 is an active area of research that includes 
 pocket detection and characterization , druggability prediction , and 
 the study of binding - site flexibility .
the advent of the protein data bank ( pdb ) spurred the creation of a number of software packages aimed at 
 facilitating the analysis of macromolecular pockets . in recent years
, additional programs have been developed with improved 
 accuracy and increasingly advanced pocket - characterization algorithms , as reviewed by zheng et al .
one can get a good sense of the full gamut of possible binding - pocket 
 conformational states by obtaining multiple structures from x - ray 
 crystallography , nmr spectroscopy , or molecular dynamics ( md ) simulations 
 and comparing pocket volumes and , in particular , shapes .
these comparisons 
 facilitate the identification of novel , pharmacologically relevant 
 binding - pocket conformations , as well as transient binding pockets 
 that are not evident when a limited number of static structures are 
 considered .
additionally , pocket analysis can also be applied 
 to computer - aided 
 drug discovery ( cadd ) . among the many complex factors that govern 
 molecular recognition , pocket volume and shape are perhaps 
 the most straightforward .
simply put , a ligand will not generally 
 bind to a receptor if it can not physically fit within the confines 
 of the binding pocket , and receptor / ligand shape complementarity plays 
 a key role in molecular recognition .
consequently , 
 pocket characterization has been used to inform cadd efforts aimed 
 at predicting ligand binding , whether through virtual screening , qsar , 
 or volumetric similarity searching .
given the astounding 
 variety of pocket geometries possible , this characterization is no trivial task . to address this 
 challenge , both ligand- and receptor - centric approaches 

ligand - based methods such as openeye s 
 rapid overlay of chemical structures ( rocs ) algorithm seek to identify novel small - molecule binders by querying 
 a compound database for entries with three - dimensional shapes that 
 are similar to that of a known template ligand , as assessed by the degree of volume - overlap mismatch .
these 
 techniques perform comparably to more traditional virtual - screening 
 methods and have been used to successfully 
 identify a number of experimentally validated ligands ( see , for example , 
 refs ( 5153 ) ) . while ligand - based approaches 
 will certainly continue to have high 
 utility , a more receptor - centric methodology 

is sometimes advantageous . bound ligands often occupy only a portion 
 of their respective pockets , on average perhaps 
 as little as a third of the total space available .
analysis of ligand volume and shape alone can not account 
 for potential interactions with pocket regions that are not occupied 
 by the template ligand itself .
in contrast , receptor - based pocket 
 analysis elucidates the volume and shape of the entire cavity , including 
 regions not yet exploited by existing pharmacophores .
receptor - centric 
 techniques can also be used to select diverse 
 pocket shapes for use in subsequent virtual - screening efforts .
it 
 is often helpful to dock a library of small molecules into multiple 
 receptor conformations in order to account for receptor flexibility . 
 carefully selecting conformations with unique pocket geometries
has 
 been shown to enhance hit rates and subsequent ligand diversity . to simplify binding - pocket characterization , we recently developed 
 an algorithm called povme ( pocket volume measurer ) .
povme floods a pocket - encompassing region with equidistant 
 points , removes those points that are near receptor atoms , and calculates 
 the volume from the remaining points .
the points can themselves be 
 saved , providing a specific description of the pocket shape as well . 

inspired by the fairly widespread adoption of our program ( at least 
 43 citations as of june 2014 ) , we have now created a second , much 
 improved version .
povme 2.0 is over an order of magnitude faster than 
 povme 1.0 , includes a graphical user interface ( figure 1 ) that greatly improves usability , can calculate volumetric 
 density maps to facilitate analysis , and has improved accuracy .
povme 2.0 has been tested 
 on all major operating systems with various 
 versions of python , numpy , and scipy ( table 1 ) .
a copy of the program , which is released under the 
 terms of the gnu general public license , can be obtained from http://nbcr.ucsd.edu/povme .
we are hopeful that povme will 
 be a useful tool for the computational- and medicinal - chemist community .
povme 2.0 has been successfully 
 tested on all major operating systems with various versions of python , numpy , and scipy .
trajectory alignment , the construction 
 of a pocket - encompassing region , and the subsequent identification 
 of the pocket - occupying space are required .
optionally , the user can 
 also instruct povme to eliminate subregions that fall outside the 
 receptor s convex hull and/or are noncontiguous with the primary 
 pocket .
we expect that 
 md simulations will be the most common source of these files , but 
 multiple crystal structures or nmr conformations can also be used . 
 we have found that the computer program visual molecular dynamics 
 ( vmd ) is useful for aligning trajectories 
 and converting files to the pdb format , but other software packages 
 can also be used for this purpose .
alignment is necessary because 
 the povme algorithm assumes the pocket being measured does not translate 
 or rotate in space .
different alignment methodologies can subtly alter 
 how this requirement is met , as discussed in the results and discussion . a tutorial showing how to align and 
 convert trajectory files using vmd is included in the supporting information ( text s1 ) .
we note also 
 that single - frame pdb files can likewise serve as povme input if the 
 user wishes only to characterize a single pocket .
( figure 2a ) and  exclusion  ( figure 2b ) regions , respectively .
both of these regions 
 are constructed from a combination of user - specified spheres and rectangular 
 prisms .
the required inclusion region should entirely encompass all 
 the binding - pocket conformations of the trajectory .
povme includes 
 a helper script called  povme pocket i d ,  described 
 in greater detail below , to assist in identifying this region if necessary . 

the optional exclusion region defines portions of the inclusion region 
 that should be ignored , perhaps because they are not truly associated 
 with the pocket . to generate a grid of equidistant points that encompasses 
 all the binding - pocket conformations of the trajectory ( spaced 1.0 
  apart by default ) ,
povme first floods the user - specified inclusion 
 region with points and then removes any points also contained in the 
 optional exclusion region ( figure 2c ) . a graphical 
 summary of the povme 2.0 algorithm .
c ) the 
 portion of the inclusion region that is not also in the exclusion 
 region is flooded with equidistant points .
d ) any of the points that 
 are close to receptor atoms are deleted .
g ) all points that are not contiguous 
 with that region are similarly deleted .
as the purpose of povme is to measure the volume of the binding - pocket 
 cavity , the program next removes any points that are close to receptor 
 atoms , leaving only those points that are likely to be located within 
 the binding pocket itself ( figure 2d ) .
specifically , 
 the pairwise distances between all atoms and povme points are calculated . 

any point that is closer to a given atom than that atom s van 
 der waals radius , plus a user - specified tolerance ( 1.09  , the 
 radius of a hydrogen atom , by default ) , is removed .
as povme is written 
 in python , users can easily modify the default radii or add new radius 
 values for novel constituent atom types . if the user wishes to ignore 
 all receptor hydrogen atoms when calculating pocket volumes , the hydrogen 
 radius specified in the script can be set to 0.0  .
povme 2.0 introduces an optional new feature 
 for removing points that lie entirely outside the binding pocket . 

specifically , the gift - wrapping algorithm is used in combination with 
 the akl - toussaint heuristic to define 
 the convex hull of receptor atoms near the user - defined inclusion 
 region . as the gift - wrapping algorithm runs in o(n ) time , where n is the number of atoms 
 in the receptor structure , it is not necessarily the fastest algorithm 
 for computing the convex hull .
however , by coupling it with the alk - toussaint 
 heuristic , the expected running time is lowered to o(n ) .
ultimately , any points that fall outside the convex hull are removed 
 ( figure 2e ) .
this feature is particularly useful 
 when the user defines an inclusion region that protrudes into the 
 surrounding solvent - occupying space . like the original povme program ,
version 
 2.0 retains the optional ability to remove isolated patches of points 
 that are not contiguous with the primary binding pocket .
this feature 
 requires that the user define a third region , again using spheres 
 and rectangular prisms , that always falls within the primary binding - pocket 
 region regardless of the trajectory frame considered ( figure 2f ) .
all pocket - occupying points within or contiguous 
 to this region are retained , but isolated patches of points that are 
 not directly connected are deleted ( figure 2 g ) . by default , povme writes a number of files 
 to the disk .
the calculated pocket volumes , as well as user - defined 
 parameters and progress messages , are saved to a simple text - based 
 log file .
povme can also be instructed to save the volume measurements 
 to a second file in a simple tabular format that can be easily pasted 
 into popular spreadsheet programs .
pocket - occupying points are equidistant 
 ( 1.0  by default ) , so each point is associated with an identical 
 cubical volume ( e.g. , 1.0  ) .
the volume of a whole 
 pocket is calculated by simply summing the individual volumes associated 
 with each unique point .
povme also optionally saves the pocket - occupying 
 points of each frame to pdb file(s ) on the disk .
the user can instruct 
 the program to save these points to separate files and/or to a single 
 pdb trajectory . some visualization programs ( e.g. , vmd ) are only compatible 
 with trajectories that have the same number of atoms in each frame . 

povme can optionally write extra points to the origin ( 0.0 , 0.0 , 0.0 ) 
 on a frame - by - frame basis to satisfy this requirement .
as these povme 
 frames are formatted similarly to those produced by sitemap , they 
 are also compatible with the pocket - shape volumetric - overlap clustering 
 tools produced by schrdinger .
finally , povme 
 also optionally saves a volumetric density map in 
 the data explorer ( dx ) format , similar to the mdpocket algorithm .
a volumetric density value is associated with 
 each of the pocket - occupying points by calculating the fraction of 
 all trajectory pockets that include the given point .
if the density 
 map is displayed as an isosurface , the value of the isosurface expresses 
 the fraction of time ( e.g. , over the course of the simulation ) that 
 the pocket included the displayed volume .
the povme distribution 
 file also includes povme pocket i d , a simple script that identifies 
 binding pockets and generates appropriate povme inclusion regions . 

after loading the heavy atoms from a pdb file , the algorithm identifies 
 pockets by 1 ) covering the entire protein in a low - resolution 3d grid 
 of equidistant points ( spaced 4.0  apart by default ) , 2 ) removing 
 points that come within a user - specified distance of any protein atom 
 ( 3.0  by default ) , and 3 ) removing points that fall outside 
 the convex hull defined by the protein alpha carbons .
the remaining 
 points tend to congregate in binding pockets . these points are 
 then replaced with smaller higher - resolution 3d grids of equidistant 
 points ( spaced 1.0  apart by default ) .
these higher - resolution 
 points are subjected to the same protocol ( i.e. , points are removed 
 if they are too close to protein atoms or fall outside the convex 
 hull ) , thus providing a more detailed description of binding - pocket 
 geometries . as stray ,
isolated points often remain , the algorithm 
 iteratively removes points that have fewer than a user - specified number 
 of neighbors ( 4 by default ) until no such points remain .
finally , 
 stretches of contiguous high - resolution points are grouped 
 together , thus partitioning the points according to their associated 
 pockets .
the points of each pocket are further divided into a user - defined 
 number of clusters ( 5 by default ) using the k - means clustering algorithm , 
 and encompassing spheres are generated for each cluster .
the 
 script outputs a separate pdb file for the points of each pocket . 

within each of these files , each cluster of points is assigned a unique 
 pdb chain i d .
the pdb header describes the inclusion - region sphere 
 associated with each chain / cluster , formatted for use in a povme input 
 file .
once the user has determined via visualization which of the 
 identified pockets is of greatest interest , he or she can select which 
 calculated spheres are required to entirely encompass that pocket . the 1xdn crystal 
 structure , which includes enzyme residues 
 52365 as well as an active - site atp molecule and magnesium 
 ion , was obtained from the protein data bank .
the amber leap module was used to submerge 
 the protein in a rectangular box of water molecules that extended 
 10  beyond the system atoms in all three cartesian dimensions . 

monovalent ions were added to neutralize the system and to bring it 
 to a 0.1 m salt concentration .
the protein and water atoms were parameterized 
 using the amber99sb force field and the 
 tip4p - ew water model , respectively . additionally , 
 the parameters for atp , magnesium , and monovalent ions developed by 
 meagher et al . , allner et al . , and joung and cheatham were used , respectively .
the rel1 system was subjected to 
 five 5000-step energy minimizations using the namd molecular - dynamics 
 simulation package to gradually introduce full flexibility . 

we first allowed only hydrogen atoms to move , second released all 
 water molecules , third released ions and atp , fourth released the 
 protein amino - acid side chains , and fifth removed all constraints . 
 the system was then heated from 0 to 310 k in an nvt ensemble for 
 500 ps , with the protein backbone restrained .
equilibration was achieved 
 in two segments , each consisting of a 250 ps simulation in the npt 
 ensemble . in the first segment ,
the protein backbone was restrained ; 
 in the second , no restraints were applied .
a total of 650 ns were simulated ( one simulation 
 of 250 ns and four of 100 ns ) .
different random seeds were used for 
 each productive simulation to generate different starting velocities . to study the flexibility of the rel1 active site , we extracted 
 6,500 frames from the simulations , evenly spaced 100 ps apart .
all 
 waters , counterions , atp molecules , and magnesium ions were removed . 

vmd s rmsd trajectory tool was 
 used to align the extracted frames . in order
to determine how differing 
 alignment methodologies would impact the povme analysis , we used several 
 different protocols .
the extracted frames were concatenated and aligned 
 by 1 ) the atoms of the bound atp ligand ; 2 ) the atoms of the active - site 
 residues ( e.g. , any residue within 5  of the crystallographic 
 ligand ) ; 3 ) the alpha - carbon atoms ( c ) of the active - site 
 residues ; and 4 ) the c of the entire protein .
each 
 of these four aligned trajectories was saved as a separate multiframe 
 pdb file ( text s1 ) .
separate povme 
 analyses were performed for each aligned trajectory . 
 in each case , we characterized the combined atp / transient pockets 
 using an inclusion region defined by 10 carefully positioned spheres , 
 chosen by visualizing the system in vmd .
points that were not contiguous with those contained within 
 a small sphere centered at the opening of the atp - binding pocket were 
 discarded .
povme 1.0 and povme 2.0 , we further considered the 
 rel1 trajectory aligned by all c. additional analyses 
 of this trajectory were performed using povme 1.0 and povme 2.0 with 
 the new convex - hull feature disabled .
as pocket volume and shape play critical roles in determining small - molecule 
 binding , they are often the focus of computer - docking campaigns , qsar 
 studies , and molecular - dynamics analyses .
we previously created a 
 novel algorithm for characterizing macromolecular pockets called povme 
 ( pocket volume measurer ) that has been widely adopted .
we here present a much - improved version of the algorithm , 
 povme 2.0 .
first , 
 it is an order of magnitude faster because it relies on the numpy and scipy python modules to perform 
 matrix - based calculations at nearly the speed of compiled c programs .
additionally , the user can instruct povme 2.0 to take advantage 
 of multiple processors to further improve the speed of the calculation .
second , povme 2.0 comes with an optional graphical user interface 
 ( gui ) to facilitate usability ( figure 1 ) .
the 
 gui requires that tkinter , a python binding 
 to the tk gui toolkit , be installed . 

fortunately , tkinter is included in the standard windows and os x 
 python distributions , as well as many linux distributions .
third , 
 povme 2.0 includes a new convex - hull - clipping option that 
 improves the accuracy of the volume calculation .
portions of the binding 
 pocket that fall outside the convex hull of nearby receptor atoms 
 are discarded ; consequently , only portions of the pocket that are 
 truly interior to the protein surface are considered .
fourth , 
 unlike the original version , povme 2.0 can analyze entire 
 trajectories in addition to single protein conformations . with povme 
 1.0 , users were required to save each trajectory frame as a separate 
 pdb file in order to study changes in pocket volume and shape over 
 the course of a md trajectory .
in contrast , povme 2.0 can read multiframe 
 trajectory files without requiring that each frame be saved separately . 
 when analyzing md trajectories , povme outputs both frame - by - frame 
 and whole - trajectory analyses . for frame - by - frame analysis ,
povme 
 saves the individual pocket shapes in the pdb format . for whole - trajectory 
 analysis
, povme creates a volumetric density map showing the frequency 
 with which different regions of the protein are included in the pocket 
 over the course of the trajectory . to demonstrate the utility of this new povme implementation
, 
 we used it to analyze an md simulation of rna editing ligase 1 ( rel1 ) 
 from the parasite trypanosoma brucei , the etiological 
 agent of african sleeping sickness .
rel1 is a critical component of 
 the t. brucei editosome , which edits transcriptional 
 rna prior to translation .
this extensive rna - editing process is essential 
 for trypanosomatid survival , and rel1 has been shown to be a viable 
 drug target . indeed ,
previous studies of related crystal structures have 
 hinted at the existence of a transient subpocket connected to the 
 distal portion of the primary atp - binding site that may provide unique 
 opportunities for drug discovery .
compounds 
 that bind to the rel1 primary site may also target other atp - binding 
 proteins with structurally similar pockets ; however , compounds that 
 bind to the unique transient pocket may prove more target specific . to better characterize the dynamics of the rel1 pockets
, we examined 
 6,500 combined atp - transient pockets extracted from 650 ns of md simulations . 

we first aligned the trajectory to ensure that the binding pocket 
 was consistently in the same location .
as with other pocket - analysis 
 programs , simulation - trajectory 
 alignment impacts the calculation of the average volumetric density 
 maps . to demonstrate this sensitivity , we performed four separate 
 povme analyses , aligning the rel1 trajectory by 1 ) all atp - ligand 
 atoms ; 2 ) all the atoms of the active - site residues ; 3 ) the alpha - carbon 
 ( c ) atoms of the active - site residues ; and 4 ) the 
 c atoms of the entire protein .
volumetric density 
 maps were calculated for each of these aligned trajectories and were 
 visualized superimposed on the receptor structure using vmd .
when 
 displayed as an isosurface , these density maps show the fraction of 
 frames with measured pockets that included the displayed volume .
for the purposes of comparison , we judged the utility of each alignment 
 protocol by how consistently the associated povme analysis captured 
 the atp - binding - pocket region over the course of the entire trajectory . 

as our simulations included a bound atp ligand , the atp - binding subpocket 
 should always be open ( i.e. , the region of the volumetric map corresponding 
 to atp in our simulations should have a high density , in excess of 
 95% ) .
when the trajectory was aligned by all active - site c atoms , the povme - identified pocket consistently included 
 the atp - binding 
 region ( figure 3b ) .
we also found that aligning 
 by all active - site atoms or even the atoms of the bound ligand itself 
 led to similar povme results ( figure s1 ) .
in contrast , the pocket analysis was less than optimal when the 
 trajectory was aligned by the c atoms of the whole 
 receptor ( figure 3c ) , likely because substantial 
 protein motions distant from the active site led to poor binding - pocket 
 alignment .
consequently , the transient pocket was identified as open 
 only half as often when the trajectory was aligned by all c atoms vs active - site c atoms .
crystallographic 
 water molecules indicate the location of a secondary binding pocket 
 that is transiently accessible from the atp - binding pocket .
at 
 least 95% of the time when the trajectory was aligned by the active - site 
 c atoms .
c ) the same region when the trajectory 
 was aligned by the c atoms of the whole protein . 

at least 25% of the time when the active - site - c alignment 
 was again used . while the best protocol 
 to use is likely system dependent , based 
 on these rel1 results we concur with others in recommending that trajectories 
 be aligned by active - site c atoms . this alignment 1 ) consistently identified the atp - binding 
 pocket ; 2 ) does not rely on the presence of a bound ligand and so 
 can be applied to apo systems as well ; and 3 ) requires 
 fewer atoms . when the binding pocket is partly composed of flexible 
 loops , aligning by pocket c atoms that belong to 
 stable secondary - structure elements may be more appropriate .
when the active - site - c alignment was used , povme 
 analysis revealed the full dynamics of the transient rel1 pocket , 
 as indicated by the density maps in figures 3b and 3d at isovalues of 95% and 25% , respectively . 
 as expected given that we simulated the holo protein , 
 the primary atp - binding pocket was persistently open throughout the 
 entire simulation ( figure 3d , 95% isovalue ) . 

the intermittent transient pocket was open at least 25% of the time 
 ( figure 3d ) , suggesting a persistence sufficient 
 to support our hypothesis of druggability . to verify that the results 
 of povme 2.0 are comparable to those of the previous version , we similarly 
 analyzed a rel1 trajectory using povme 1.0 .
when the convex - hull algorithm 
 was disabled , both povme 1.0 and 2.0 gave nearly identical volume 
 measurements ( figure 4 graph , in black ) .
when 
 the new convex - hull feature was enabled , povme - calculated volumes 
 were lower , as expected ( figure 4 graph , in 
 gray ) . to verify that the volumes calculated both with and without 
 the convex - hull feature
a two - tailed t - test suggested that the correlation 
 was statistically significant ( pearson correlation coefficient : 0.19 ; p - value : 0.0 ) .
the graph shows 
 benchmark rel1 pocket 
 volumes as a function of simulation time .
povme 1.0 and 2.0 give nearly 
 the same volume measurements ( in black ) .
when the povme 2.0 convex - hull 
 option is enabled , the volumes are smaller ( in gray ) .
the bottom panel , 
 generated using the 1xdn crystal structure , illustrates the difference . 

when the convex - hull option is enabled , the region of the binding 
 pocket is more accurately captured ( solid gray ) than when it is deactivated 
 ( black wireframe ) .
although the convex - hull feature does add computational expense , 
 in some contexts it may lead to more accurate characterizations . if 
 the user s ultimate goal is to calculate pocket volumes in 
 absolute terms , clearly discarding volumes that lie outside the convex 
 hull leads to improved accuracy ( figure 4 , 
 bottom panel ) .
in contrast , if the goal is to determine how frequently 
 different pocket regions are open vs closed by calculating volumetric 
 density maps , the convex - hull feature , which discards only volumes 
 that fall outside the pocket , will have little impact .
finally , 
 if the goal is to compare changes in 
 the total pocket volume over the course of a trajectory , the convex - hull 
 feature should be used with caution .
arbitrary movements of surface 
 side chains near pocket openings can subtly affect the specific convex 
 hull calculated for each frame . if the povme inclusion region includes 
 many points that lie outside the convex hull , and if the system being 
 studied has highly flexible pocket - adjacent surface residues , using 
 the convex - hull feature can introduce substantial  noise  
 into the total - volume calculations .
this noise can make it difficult 
 to detect subtle changes in the total volume caused by actual pocket 
 dynamics , especially if the pocket itself is relatively stable . to 
 overcome this challenge , we recommend carefully defining the povme 
 inclusion region to encompass only the pocket itself .
if the convex - hull 
 feature is used at all , rely on it only as a secondary refinement 
 to better account for those rare frames when the pocket opening  pulls 
 back ,  causing a few povme points that are typically located 
 within the pocket to barely fall outside the pocket boundaries .
many approaches 
 have been developed 
 to identify and measure protein pockets ( see recent reviews ) .
we here limit our comparison to free software packages that natively 
 process ensembles of structures , whether derived from multiple crystal 
 structures or md simulations , without requiring custom scripting . 

software packages with this native support , including povme 2.0 , epos , mdpocket , pocketanalyzer , and trj_cavity , can 
 output ensemble - dependent features such as the frequency of transient 
 subpocket opening and the pocket size over multiple frames .
povme 2.0 pocket - defining regions can be generated automatically using 
 the new grid - based povme pocket i d helper script described above . 

pocketanalyzer implements 
 a variant of the ligsite algorithm , and trj_cavity employs a novel neighbor - search method .
fpocket 
 calls the qhull algorithm to perform 
 a voronoi tessellation of the receptor ; the coordinates of each voronoi 
 vertex , together with the associated atomic and vertex neighbors , 
 constitute an  -sphere  with a distinct radius . 

finally , epos relies on the pass algorithm , 
 which first covers the entire protein surface with spheres and then 
 removes spheres with low burial counts . as a second step , the algorithm 
 covers the remaining spheres from the first step with an additional 
 layer of spheres .
epos also maps the identified pockets to a set of pocket - lining atoms 
 ( plas ) , avoiding the need to align the trajectory .
, 
 we repeated the trajectory - based analysis of the rel1 atp - binding 
 pocket using several programs ( active - site - c alignment ) . 
 for each program , a grid resolution of 1.0  was used where appropriate . 

the remaining parameters were set to their default values . given 
 the set of povme inclusion and exclusion spheres chosen , povme 2.0 
 without the convex - hull algorithm generally calculated pocket volumes 
 that were larger than those determined by other codes .
in contrast , 
 when the convex - hull algorithm was used to ignore regions of space 
 outside the confines of the pocket , the povme - calculated volumes decreased 
 substantially ( see table 2 and figure s2 ) .
the average pocket volume ( in 
  ) , plus or minus the standard deviation , measured 
 over the course of a rel1 trajectory using several pocket - analysis 
 programs .
note that the povme 1.0 results were , for all intents and 
 purposes , identical to the povme 2.0 results with the convex - hull 
 feature disabled .
additionally , epos volume measurements 
 are not included because that program does not output volume - per - frame 
 data .
a 
 pca calculation accounted for approximately 6 min of the pocketanalyzer run time ( marked with an asterisk ) .
as povme 2.0 is designed 
 to use multiple processors , the run times for parallel povme 2.0 calculations 
 are also shown .
if accuracy 
 is judged by consistency with other programs , using 
 povme 2.0 together with the convex - hull algorithm leads to substantial 
 improvements when the user wishes to measure pocket volume in absolute 
 terms .
nevertheless , the povme - calculated volumes are still somewhat 
 larger than those calculated by other algorithms , even with the convex - hull 
 algorithm enabled .
this discrepancy does not necessarily mean that 
 povme volumes are overestimated , as determining the accuracy of these 
 varied methods is not straightforward .
binding pockets are not uniquely 
 defined ; they often have an opening toward the solvent , and no commonly 
 agreed upon criteria exists for delineating the exact boundary between 
 the pocket and solvent - filled spaces .
the tunable parameters 
 of each algorithm may also contribute to 
 the differences in calculated volumes .
although it is useful to allow 
 the user to adjust key parameters as required to best analyze a specific 
 pocket of interest , doing so often presents a problem when the parameters 
 are not intuitive . with the end user in mind ,
povme 2.0 requires only 
 a small number of parameters with straightforward physical interpretations . 

we believe this is a distinct advantage of our code . while useful , 
 parameters such as degree of buriedness and minimal cluster size ( pocketanalyzer ) ; pass input ( epos ) ; and allowed range of 
 -sphere radii and number of -spheres per pocket ( mdpocket ) 
 are less intuitive .
pocketanalyzer and trj_cavity volumes 
 are somewhat smaller than the povme volumes obtained when the convex - hull 
 feature is enabled ; in contrast , mdpocket volumes are substantially 
 smaller and have only minor fluctuations ( table 2 and figure s2 ) , possibly because mdpocket 
 uses -spheres rather than equidistant points . to determine 
 if the volumes calculated using these four methods were even correlated , 
 we first reduced the noise in the data using a simple moving average 
 with a 40-point sample window .
we then performed linear regressions 
 to explore the relationships between these averaged povme volumes 
 and the averaged volumes obtained using pocketanalyzer , trj_cavity , and mdpocket , respectively .
a two - tailed t - test was applied to the pearson s correlation coefficients 
 to judge significance .
the p - values were 0.46 , 0.00 , 
 and 0.00 for povme vs pocketanalyzer , trj_cavity , and mdpocket , respectively , suggesting that the correlations between 
 povme volumes and both trj_cavity and mdpocket volumes 
 are statistically significant .
future efforts will focus on 
 expanding povme analysis beyond simple 
 volume and shape calculations .
we intend to implement other pocket 
 metrics ( e.g. , pocket surface - area calculations ) and analysis tools 
 ( e.g. , volume - based pharmacophore modeling ) .
the trajectory - based pocket calculations 
 were also used to compare the execution times of each program ( table 2 ) .
all calculations were run on the same machine 
 ( 2 intel xeon x5690 3.47ghz processors , 6 cpu cores per processor , 
 2 threads per core = 24 threads total ) . on a single thread ,
povme 
 2.0 is several orders of magnitude faster than its predecessor . when 
 the convex - hull feature is disabled , povme 2.0 is also faster than 
 epos and pocketanalyzer , comparable to trj_cavity , and only slightly slower than mdpocket .
we are 
 exploring alternative methods for optimizing the optional convex - hull 
 feature .
fortunately , the current povme implementation is highly parallelized ; 
 when 24 threads are used rather than one , povme 2.0 is substantially 
 faster than all other packages tested , even with the convex - hull feature 
 enabled .
povme 2.0 , trj_cavity , mdpocket , pocketanalyzer , and 
 epos all 
 output simple pdb file(s ) that are easily visualized with other software 
 packages .
povme 2.0 and mdpocket also output volumetric density maps 
 in the dx format to make exploration of potentially druggable transient 
 subpockets with varying opening frequencies simpler to interpret . 

finally , epos and pocketanalyzer both have 
 built - in features to cluster pocket shapes .
similarly , povme output 
 is easily clustered using utilities like the volume - overlap tool in 
 schrdinger s maestro suite .
povme 2.0 accepts a multiframe pdb file as input . as the pdb 
 format is general purpose and
universally accepted , povme is independent 
 of any specific simulation package or force field .
nevertheless , pdb 
 trajectories can become quite large , so being able to read binary 
 formats specific to selected simulation software packages would also 
 be useful . indeed , trj_cavity can be compiled to 
 read binary trajectories saved in the gromacs format .
future implementations 
 of povme may include support for additional formats , though we do 
 wish to avoid limiting our software to a specific simulation engine . 

we note that povme trajectories do not include water molecules , substantially 
 cutting down on the file size even when the pdb format is used .
additionally , 
 freely available software can easily convert binary formats to pdb . 
 a helpful tutorial is included in the supporting 
 information that describes how to perform this conversion using 
 vmd ( text s1 ) .
povme 2.0 is a much improved 
 version of our popular algorithm for 
 characterizing the volumes and shapes of macromolecular ( e.g. , protein ) 
 binding pockets .
version 2.0 implements a number of enhancements , 
 including speed improvements due to numpy / scipy integration and the optional use of multiple processors ; 
 better accuracy due to an optional convex - hull implementation ; additional 
 volumetric - analysis tools ( i.e. , volumetric density maps ) ; and a graphical 
 user interface that improves usability . although pocket - shape 
 and volumetric analyses are not novel , factors 
 such as the high computational cost of most algorithms have discouraged 
 widespread adoption .
povme 2.0 significantly reduces the amount of 
 time required , allowing users to more rapidly analyze large ensembles 
 of pocket shapes derived from multiple experimental structures or 
 simulation methods , such as md .
the added volumetric - density - map analysis 
 feature provides a pocket - centric view of receptor flexibility with 
 potentially useful drug - discovery applications .
indeed , others have 
 shown that docking into structurally distinct binding pockets can 
 lead to enhanced hit rates and chemical diversity . to demonstrate how povme 
 2.0 can provide pharmacologically relevant 
 information about pocket flexibility , we used it to analyze the dynamics 
 of an essential , atp - binding component of the t. brucei editosome , rel1 . given that atp - binding pockets 
 are ubiquitous , small - molecule inhibitors that bind exclusively to 
 the primary rel1 pocket may also bind to the atp pockets of critical 
 human enzymes , leading to undesirable side effects .
consequently , 
 we considered a unique secondary binding pocket that is transiently 
 accessible from the primary rel1 atp - binding pocket .
povme suggests 
 this transient pocket assumes an open conformation roughly 25% of 
 the time .
identifying less promiscuous rel1 inhibitors that exploit 
 this unique pocket is an important component of our ongoing efforts 
 to target this crucial enzyme .
 analysis of macromolecular / small - molecule 
 binding pockets can provide 
 important insights into molecular recognition and receptor dynamics . 
 since its release in 2011 , the povme ( pocket volume measurer ) algorithm 
 has been widely adopted as a simple - to - use tool for measuring and 
 characterizing pocket volumes and shapes . 
 we here present povme 2.0 , 
 which is an order of magnitude faster , has improved accuracy , includes 
 a graphical user interface , and can produce volumetric density maps 
 for improved pocket analysis . to demonstrate the utility of the algorithm , 
 we use it to analyze the binding pocket of rna editing ligase 1 from 
 the unicellular parasite trypanosoma brucei , the 
 etiological agent of african sleeping sickness . 
 the povme analysis 
 characterizes the full dynamics of a potentially druggable transient 
 binding pocket and so may guide future antitrypanosomal drug - discovery 
 efforts . 
 we are hopeful that this new version will be a useful tool 
 for the computational- and medicinal - chemist community . 
Introduction
Materials and Methods
Results and Discussion
Conclusion