ligand 
 affinity screening by nuclear magnetic resonance ( nmr ) spectroscopy 
 is a versatile method routinely used to support drug discovery and 
 functional proteomics research .
the real power of nmr ligand affinity 
 screens arises from its ability to directly detect protein 
the 
 most common nmr screening approaches ( i.e. , line - broadening , std - nmr , and waterlogsy ) are focused on detecting changes in the 1d h spectrum of a ligand upon binding to a protein .
hundreds 
 of compounds can be analyzed in a day by coupling these benefits to 
 an automated sample changer , software to optimize probe tuning and 
 other parameters , and rapid nmr data collection of with a cryogenic 
 probe .
compounds are usually evaluated 
 in mixtures to improve the efficiency 
 of nmr ligand affinity screens .
the use of mixtures provides two significant 
 benefits : ( 1 ) a larger number of compounds can be screened in a shorter 
 amount of time and ( 2 ) the amount of protein required for the entire 
 screen is reduced ; however , mixtures suffer from drawbacks .
as the 
 size of a mixture increases , so does the probability that a mixture 
 contains more than one compound competing for the same binding site , 
 thus weakening the nmr observable binding event for each compound .
also , compounds in the mixture may react or interact 
 with each other , leading to chemical changes , lowered solubility , 
 or aggregation .
finally , the nmr signals from 
 the multiple compounds may overlap , leading to ambiguity in analyzing 
 the binding results and the necessity for rescreening with individual 
 compounds .
this last problem can be mitigated by creating mixtures 
 with minimal to no peak overlaps , but the task is onerous and impractical 
 for typical screening libraries composed of hundreds or thousands 
 of compounds .
a previous investigation has shown that mixtures with 
 minimal peak overlap could be efficiently created by using a simulated 
 annealing algorithm ; however , robust 
 software that implements the algorithm into effective practice is 
 not available . here
we describe nmrmix , a freely available , 
 open - source software 
 solution that utilizes a simulated annealing algorithm to generate 
 mixtures with minimal peak overlap .
nmrmix was written in python 2.7 
 ( https://www.python.org ) and utilizes the qt 5 framework 
 ( http://www.qt.io ) with pyqt5 bindings ( https://www.riverbankcomputing.com ) to build a graphical user interface ( gui ) .
the input to nmrmix 
 consists of a list of peaks from 1d h nmr spectra for 
 each compound , a target value for the number of compounds in each 
 mixture , and a user - defined parameter that specifies overlap ranges 
 for the peaks . in optimizing mixtures nmrmix utilizes a scoring function 
 that considers the proportion of peaks in a compound that are overlapped 
 as well as the intensity of the peaks .
the graphical interface supports 
 access to customizable parameters , downloads of peak list data , interactive 
 views of simulated spectra for each mixture , and graphs of statistics . 

nmrmix outputs regions of interest ( rois ) in a simple , text - based tabular format that can be used to automate 
 the analysis of nmr ligand affinity screening data .
nmrmix can be 
 built on most operating systems ( linux , mac osx , and windows ) , and 
 it is available preinstalled in the nmrfam virtual machine ( http://www.nmrfam.wisc.edu/software ) .
generating 
 optimized nmr mixtures in nmrmix requires information on the compounds 
 to be mixed as well as a peak list containing the h chemical 
 shifts for each compound .
the compound information 
 can be added manually within the nmrmix interface , or it can be imported 
 from a csv ( comma - separated values ) file that contains the information 
 for all of the compounds in the library .
the only required compound 
 information for optimization is the name of the compound , a unique 
 identifier , and the source of the peak list .
additional characteristics , 
 such as smiles strings or stock solution solvent , can also be included 
 to provide enhancements within nmrmix .
the h nmr peak 
 lists can be imported from either local or online sources .
nmrmix 
 can input peak list data in several file formats : bruker topspin , 
 agilent vnmrj , mestrelab mnova , acd / nmr , nmr - star , hmdb chemical shift 
 values , or a manually created peak list file . by providing the relevant 
 compound identifiers , nmrmix can download data from the biological 
 magnetic resonance data bank ( bmrb ) standard compound database or the human metabolome database ( hmdb ) .
nmrmix provides a helpful interface for 
 visually inspecting the imported data and for setting user - specified 
 parameters .
for example , overall statistics about the compound library , 
 including chemical shift histograms and aromaticity , can be examined . 

the user can set particular spectral regions to be ignored when creating 
 mixtures ; these ignore regions may contain nmr signals 
 from solvents , buffers , or internal standards that will be present 
 across multiple samples .
peaks that fall within specified ignore regions are not considered in scoring overlaps , and 
 the peak list statistics in nmrmix are updated to indicate which compounds 
 have peaks in ignore regions . to evaluate nmrmix
, 
 we selected 872 entries from the bmrb standards 
 database with associated h peak lists .
the characteristics 
 of these compounds were added to a csv file in the format required 
 by nmrmix ( available in the supporting information ) . upon import into nmrmix ,
another 59 compounds were removed because 
 they lacked h chemical shifts for one or more hydrogens . 

the resulting virtual library of 736 compounds was used to create 
 simulated mixtures . for the purpose of evaluating optimization by 
 nmrmix , no ignore regions were set . to determine whether an overlap occurs 
 in a mixture , each h nmr signal in each compound
is assigned 
 a spectral range defined by a tolerance  ( in ppm ) added to 
 and subtracted from its chemical shift value c ( c   in ppm ) ( figure 1a ) .
this user - defined overlap range effectively 
 represents the spectral region belonging to each peak of a compound . 

an overlap in a mixture occurs when the overlap range of a peak for 
 one compound overlaps with the overlap range of a peak from a different 
 compound ( figure 1b ) . 

the overlap range can be set independently for each peak in a compound , 
 or a single global value for all peaks can be used .
( a ) simulated 1d h nmr spectrum of a mixture containing 
 phenol ( red peaks ) and 4-chlorocatechol ( blue peaks ) .
the colored 
 lines centered above each peak , which represent overlap ranges of 
 0.025 ppm , define the overlap regions .
( b ) this same mixture as viewed 
 in nmrmix : peaks from different compounds whose ranges overlap are 
 inverted ; and , elsewhere , overlapping peak ranges from a given compound 
 form a region of interest ( roi ) for that compound .
the goal of generating mixtures with no overlaps 
 is frequently 
 not achievable , owing to the number of compounds in each mixture and 
 the distribution of h nmr chemical shifts in the compound 
 library .
overlaps that do occur should not represent a significant 
 proportion of the peaks belonging to any one compound in the mixture 
 to minimize the ambiguity in identifying compounds in a mixture .
therefore , 
 instead of using the total number of overlaps to optimize the mixtures , 
 nmrmix uses a scoring function based on the proportion of peaks in 
 each compound that are overlappedwhere sc represents 
 the overlap score for the compound , k represents 
 the score scaling function , no represents 
 the number of peaks in the compound that are overlapped , and nt represents the total number of peaks in the 
 compound . in this approach ,
the penalty associated with an overlap 
 is lower in a compound with a large number of peaks when compared 
 with a compound that has only a few or just one peak .
nmrmix 
 also has the ability to use peak intensities as a factor 
 in evaluating the optimization of mixtures . in
most nmr ligand - detect 
 screening approaches ( e.g. , line - broadening , std , or waterlogsy ) , 
 identifying a binding event depends on monitoring changes in the line 
 width , intensity , or position of peaks belonging to a compound .
the 
 presence of nearby strong signals can hinder the detection of such 
 changes in weak signals . to minimize this problem
, nmrmix offers an 
 optional scoring function ( sc * ) for use 
 in optimizing mixtureswhere sc * represents 
 the modified overlap score for the compound , k represents 
 the score scaling function , io represents 
 the sum of all of the intensities of the overlapped peaks in the compound , 
 and it represents the sum of all of the 
 intensities of all of the peaks in the compound .
once the peak lists have been loaded , 
 the overlap range defined , and the ignore regions set , nmrmix creates an initial randomized set of mixtures prior 
 to optimization . by default ,
the maximum size of each mixture is five 
 compounds ; however , this value can be changed to any mixture size 
 between 2 and 20 compounds .
nmrmix will automatically create the necessary 
 number of mixtures such that each mixture contains the maximum number 
 of compounds ( e.g. , 736 compounds with a maximum mixture size of 5 
 will generate 148 mixtures ) .
the optimization of mixtures in 
 nmrmix occurs through simulated annealing . in brief , a small number 
 of compounds from different mixtures are swapped during each step 
 of the annealing process .
the number of compounds being swapped ( the 
 mixing rate ) is a user - defined parameter that can be specified prior 
 to optimization . after each annealing step , the new arrangement of 
 compounds in the mixtures is evaluated based on a total overlap score , 
 which is either accepted or rejected .
this process continues until 
 the maximum number of annealing steps ( set by the user ) is reached 
 or stops before if none of the mixtures contains overlaps .
the 
 total overlap score ( st ) used to 
 evaluate each arrangement of compounds in the mixtures is simply the 
 sum of all of the overlap scores for each compound ( sc or sc * ) 
 in their respective mixturesduring each step of the 
 annealing process , 
 if the total overlap score for the new arrangement of mixtures is 
 less than or equal to the total overlap score of the current arrangement 
 of mixtures , the new arrangement is automatically accepted ; however , 
 to minimize the possibility that the total overlap score for the mixtures 
 becomes trapped in a local minimum due to a particular arrangement 
 of compounds in the mixtures , each step of the simulated annealing 
 process in which the total overlap score increases is evaluated according 
 to a modified boltzmann probabilitywhere p is the probability 
 of acceptance , st is the difference 
 between the total overlap score of the new set of mixtures and that 
 of the current set of mixtures , kt is 
 the temperature scaling factor ( default 25  000 ) , ks is the score scaling factor ( default 10  000 ) , m is the mixing rate , and t is the step 
 temperature . to facilitate the calculation of a probability of acceptance , 
 each annealing step
value that decreases on each successive step . at the beginning of 
 the annealing process , when the temperature is higher , the probability 
 for accepting a more overlapped arrangement of compounds is greater 
 ( figure 2 ) .
even a 
 new arrangement at a temperature of 10  000 that increases the 
 total overlap score by 20  000 ( equivalent to two completely 
 overlapped compounds ) still has an 8.2% chance of acceptance .
each 
 subsequent step of the annealing process lowers the temperature , thus 
 lowering the base acceptance probability . in most cases ,
once the 
 temperature reaches approximately 100 ( using default parameters ) , 
 the probability of acceptance becomes zero .
temperature dependence 
 of the boltzmann acceptance probability 
 as a function of st , the difference between the total overlap score of the new set of 
 mixtures and that of the current set of mixtures , with st values : ( blue ) 500 ; 1000 ( green ) ; 
 2500 ( yellow ) ; 5000 ( orange ) ; 7500 ( red ) ; 10  000 ( violet ) ; 
 15  000 ( gray ) ; and 20  000 ( maroon ) .
the calculations 
 of acceptance probabilities used the default values for the temperature 
 scaling factor ( 25  000 ) , score scaling factor ( 10  000 ) , 
 and mixing rate ( 2 ) . prior to optimization , users are able to define several parameters 
 of the optimization process , including : maximum mixture size ( default 
 5 ) , starting temperature ( default 10  000 ) , final temperature 
 ( default 25 ) , maximum number of steps ( default 100  000 ) , mixing 
 rate ( default 2 ) , and number of optimization iterations ( default 5 ) . 
 unless otherwise stated , the default parameters were used for the 
 cases described here .
the nmrmix 
 gui provides 
 several features that facilitate the viewing of results : peak list 
 statistics , mixture optimization analysis , an interactive mixture 
 table , and a simulated mixture spectrum viewer .
the primary view of 
 nmrmix is the interactive mixture table ( figure 3a ) , which shows the current state of the 
 mixtures .
each row of the mixture table represents one mixture that 
 is labeled by a unique mixture identifier , the overlap score for the 
 mixture , the identity and overlap score of each compound in the mixture , 
 and the primary solvent ( if any ) . overlap scores for both the mixture 
 and the individual compounds are color - coded to visually indicate 
 the degree of overlap . when any scoring parameter ( score scale , overlap 
 range , or intensity scoring ) is changed by using nmrmix s gui 
 functions , an automatic update of the table is triggered , resulting 
 in updates to the scores and colors consistent with the new scoring 
 scheme .
additionally , clicking on the compound information in the 
 mixture table produces a pop - up window showing more information about 
 the individual compound ( i.e. , its structure , simulated spectrum , 
 peak list , etc . ) .
finally , each row of the mixture table includes 
 a button to view a simulated spectrum of the mixture ( figure 3b ) with the peaks from each 
 compound in the mixture displayed in a different color .
the simulated 
 mixture spectrum is interactive : it allows scrolling and zooming and 
 provides an effective way to visually evaluate each mixture .
any mixtures 
 containing overlapped peaks will , by default , display those peaks 
 as negative peaks , which makes identifying the location of the overlaps 
 a simple process .
the simulated mixture spectrum also displays colored 
 rois for the nonoverlapped regions of the spectrum .
these rois are 
 created directly from the overlap range of each nonoverlapped peak . 
 if two or more rois from the same compound overlap , the rois are merged 
 into a single roi that spans the range of all of the merged rois . 

these rois indicate the areas of the spectrum where peak identities 
 should be unambiguous ( figure 1b ) .
( a ) representation of the mixing table view of nmrmix : each row 
 of the table represents a mixture and displays the compounds within 
 each mixture and the overlap scores for each individual compound as 
 well as the entire mixture .
these mixtures were created using the 
 default parameters , intensity scoring , and only 2500 annealing steps . 

this mixture contains cytidine-5-monophosphate ( red ) , 3,4-dihydroxy - l - phenylalanine ( blue ) , trans-2-hydroxycinnamic 
 acid ( green ) , adrenosterone ( yellow ) , and acetosyringone ( purple ) . 

as indicated in the mixing table view , mixture 1012 should have six 
 overlapped peaks ; these are visible in nmrmix view as negative peaks . 
 also shown
in addition to viewing 
 results in nmrmix , all graphical outputs , 
 such as graphs and spectra , can be saved locally on a computer as 
 an image .
information regarding the optimized mixtures can also be 
 stored in the convenient and portable csv ( spreadsheet ) format .
the 
 saved information includes : the arrangement of compounds in each mixture , 
 the ignore regions used , the compound library , the 
 parameters , a list of all of the peaks , and the rois . stored results 

the h peak 
 lists for the 736 compounds in the bmrb virtual library contained 
 a total of 14  611 peaks .
the distribution of the chemical shifts 
 for these peaks showed that the majority were aliphatic ( figure 4a ) .
a majority of 
 the compounds had 20 peaks per compound ( figure 4b ) , indicative of their small 
 size .
not only were most of the peaks aliphatic , nearly half of the 
 compounds had only aliphatic peaks ( figure 4c ) .
these statistics are not surprising because 
 the bmrb virtual library consists of mostly small metabolites .
such 
 a high density of aliphatic peaks may reasonably be expected to provide 
 a challenge toward creating mixtures with nonoverlapped peaks ; however , 
 a previous analysis indicated that library size and peak distribution 
 do not appear to significantly affect the results of optimization 
 by simulated annealing , and this expectation 
 was confirmed by the results of nmrmix shown below .
nmrmix - generated representations 
 of the peak list statistics for 
 the virtual compound library imported from bmrb .
( a ) histogram of 
 the chemical shifts showing that the majority of the peaks occur in 
 the low - frequency aliphatic region .
( b ) histogram representing the 
 number of peaks for each compound ; the majority of the compounds have 
 fewer than 20 peaks .
( c ) percentage of aromatic peaks ( 4.7 
 ppm ) per compound showing that nearly half of the compounds have < 10% 
 aromatic character .
in addition to providing 
 statistics on the compound library peak 
 list , nmrmix also makes it easy to evaluate the effects that solvents , 
 buffers , and internal standards may have on the design of mixtures 
 for ligand screening .
setting an ignore region in 
 nmrmix for the residual water signal ( 4.64.9 ppm ) that likely 
 occurs in every aqueous nmr sample removed 209 peaks from 90 compounds . 

nmrmix results indicated that two of these compounds , formaldehyde 
 and formamide , would be completely ignored ( i.e. , have no peaks outside 
 the ignored region ) and thus would not be used in any mixture .
this 
 feature of nmrmix can help inform which solvents , buffers , or internal 
 standards would be best for the compound library being screened . for 
 example , a comparison of four internal standards ( sodium formate , 
 dmso , t - butanol , and benzoic acid ) with the bmrb 
 virtual compound library showed that sodium formate would likely make 
 the best internal standard based solely on peak overlaps .
the sodium 
 formate ignore region ( 8.4358.450 ppm ) removed 
 only 2 peaks from 2 compounds , whereas the dmso ignore region ( 2.6602.680 ppm ) removed 50 peaks from 33 compounds , the t - butanol ignore region ( 1.2351.245 
 ppm ) removed 33 peaks from 30 compounds , and the benzoic acid ignore regions ( 7.4567.491 , 7.5277.562 , 
 and 7.8547.875 ppm ) removed 139 peaks from 72 compounds . the 
 only internal standard that eliminated all peaks from a compound was 
 dmso , which caused two of the compounds to be eliminated from the 
 mixtures .
one of the most 
 important parameters in nmrmix is the overlap range ( the overlap range 
 is twice the peak tolerance , measured in ppm units ) .
this parameter 
 is user - defined and can have a significant influence on the optimization 
 results .
it is not surprising that as the size of the overlap range 
 increases the total overlap percentage ( total number of overlapped 
 peaks in library / total number of peaks in library ) in the initial 
 randomized mixtures prior to optimization also increases from 13.63 
  0.70% at 0.005 ppm to 49.77  1.06% at 0.010 ppm ( figure 5a ) .
mixture optimization 
 serves to reduce the overlaps , although they still rise with larger 
 overlap ranges ( figure 5b ) . with an overlap range of 0.005 ppm
, nmrmix was able to create 
 perfect mixtures with no overlaps . at 0.100 ppm ,
( a ) percentage of overlap 
 ranges of various size present in randomly 
 generated mixtures prior to optimization .
( b ) percentage of overlap 
 ranges of various size present following optimization through stimulated 
 annealing with default parameters and 100  000 annealing steps : 
 following scoring with the designated overlap range ( blue ) and after 
 rescoring with an overlap range of 0.025 ppm ( green ) .
( c ) effect of 
 mixture size on properties of random mixtures : total overlap percentage 
 with standard scoring ( blue ) ; total overlap percentage with intensity 
 scoring ( green ) ; total score percentage with standard scoring ( yellow ) ; 
 and total score percentage with intensity scoring ( orange ) .
( d ) effect 
 of mixture size on properties of mixtures following optimization with 
 default parameters and 100  000 annealing steps : total overlap 
 percentage with standard scoring ( blue ) ; total overlap percentage 
 with intensity scoring ( green ) ; the total score percentage ( yellow ) ; 
 and total score percentage with intensity scoring ( orange ) .
it is important to set the overlap 
 range to a value consistent 
 with the nmr data to be collected .
important factors to consider are 
 the line widths of the peaks and the broadening that may occur upon 
 protein binding .
overlap ranges that are too small will result in 
 the creation of mixtures in which signals from individual compounds 
 can not be separately resolved in the experimental data .
overlap ranges 
 that are too large will exaggerate overlaps and skew the composition 
 of the mixtures created to contain more real peak overlaps ( figure 5b ) . the compounds 
 in the library have peak widths near the noise level between 0.005 
 and 0.015 ppm at 600 mhz , and thus we typically use an overlap range 
 of 0.025 ppm in creating mixtures .
we allow a small buffer in the 
 overlap range beyond the peak width to allow for small chemical shift 
 changes that may occur from changes in solution conditions or from 
 interactions between components in the mixture . in general
, 
 it is more efficient to use the largest practical number 
 of compounds per mixture ( mixture size ) because it reduces the time 
 required to screen a set of compounds and reduces the amount of protein 
 needed ; however , the potential reduction in time comes at the cost 
 of increased peak density and thus a greater number of peak overlaps . 

this is abundantly clear when initial random mixtures of different 
 sizes are compared ( figure 5c ) . at a mixture size of only 3 , the percent total overlap 
 averages 14.7  1.1% ; however , a mixture size of 20 has a starting 
 total overlap percentage of 74.3  0.2% .
after nmrmix optimizes 
 the mixture size for three and four compounds , a perfect mixture with 
 no overlaps can be obtained in both cases . optimizing a maximum mixture 
 size of 20 compounds
improves the overlap to only 57.2  0.2% 
 ( figure 5d ) . while 
 keeping the mixture size small is more beneficial , nmrmix can still 
 be used to optimize for the largest mixture size with minimal overlap .
sometimes external factors may require mixtures to be larger than 
 ideal . in these cases , an additional priority for mixture
optimization 
 is to ensure that the overlapped peaks are not those with the higher 
 intensity .
the nmrmix intensity scoring option can be used to create 
 intensity - optimized mixtures .
the benefit of overlap scoring can be 
 seen in the case of a mixture containing selenomethionine and n--acetyl - l - lysine . of the total of 51 peaks 
 from the two compounds ,
, this corresponds to a total mixture score of 1323 ; however , 
 when intensity scoring is used , the total mixture score jumps to 7318 . 

the simulated spectrum shows that two of the three overlapped peaks 
 are the most intense peaks for each compound ( figure 6 ) .
given that the intensity of the second 
 largest peak for each compound is one - fourth the size of the largest 
 peak , this mixture arrangement would be less than ideal in a situation 
 where signal sensitivity is an issue .
simulated 1d h nmr spectrum 
 representing a mixture 
 containing selenomethionine ( red peaks ) and n--acetyl - l - lysine ( blue peaks ) .
an overlap between two selenomethionine 
 peaks and one n--acetyl - l - lysine 
 peak is indicated by the red arrow .
because intensity scoring prioritizes the minimization of 
 overlapped 
 peaks with high peak intensities over the number of overlapped peaks , 
 it is not surprising to find that optimizations using the intensity 
 score may result in an increased total overlap percentage ( figure 5d ) ; however , despite 
 the increase in the number of overlaps , the total overlap score percentage 
 ( total overlap score for the library / total possible overlap score 
 for the library ) using intensity scoring is equal to or lower than 
 the total overlap score percentage using standard scoring ( figure 5d ) , which indicates 
 that overlaps that do occur are likely not the most intense peaks 
 for the compounds . while this focus on intense peaks is useful for 
 larger mixtures , standard scoring is still recommended for mixture 
 sizes of three , four , or five compounds . at these smaller mixture 
 sizes ,
the likelihood of generating mixtures without any overlaps 
 is fairly high , and scoring based on the number of overlapped peaks 
 is more efficient than intensity scoring .
generating optimized 
 mixtures using simulated annealing requires 
 the efficient sampling of different mixture states .
because the changes 
 between each mixture state are small , a large number of annealing 
 steps is required to efficiently mix the compounds . for the bmrb
virtual 
 library of 736 compounds with a maximum mixture size of 5 , 1000 annealing 
 steps led to a large reduction in the total overlap percentage ( 13.3 
  0.7% ) over that of the initial random mixtures ( 27.9  
 1.5% ) ( figure 7 ) ; however , 
 the improvements became smaller as more annealing steps were used . 
 at 100  000 steps ,
the total overlap percentage was 0.21  
 0.04% ( 30 overlapped peaks for this compound library ) . at 200  000 
 steps , the total overlap decreased to 0.06  0.02% ( 9 overlapped 
 peaks ) .
finally , at 1  000  000 steps , nmrmix generated 
 a mixture state without any overlaps . on a standard laptop
, optimization 
 of this compound library with 200  000 annealing steps and a 
 maximum mixture size of 5 compounds can be completed in < 15 min , 
 while 1  000  000 steps for the same mixture size requires 
 1.25 h. even when optimizing with 1  000  000 
 steps and a mixture size of 20 compounds , nmrmix takes only 15 h. 
 because mixtures are often used for multiple screens , increasing the 
 number of annealing steps to achieve the most optimized results may 
 be worth the greater investment in computational time .
effect of the number 
 of annealing steps used for optimizing mixtures 
 with default parameters and a maximum mixture size of five compounds : 
 total overlap percentage with standard scoring ( blue ) ; total overlap 
 percentage with intensity scoring ( green ) ; total score percentage 
 with standard scoring ( yellow ) ; and total score percentage with intensity 
 scoring ( orange ) .
nmrmix 
 is a powerful , freely available , open - source tool for generating 
 mixtures of small molecules with minimal nmr peak overlap .
the optimization 
 of mixtures is accomplished by using a simulated annealing algorithm 
 previously described .
the user - friendly 
 gui facilitates easy mixture optimization and data analysis , and nmrmix 
 only requires information about the compound library and a source 
 for reference 1d h peak lists to get started .
additionally , 
 nmrmix introduces the concept of intensity scoring , which penalizes 
 overlaps that occur on the most intense peaks instead of treating 
 overlaps of all peaks equally .
after optimization , the resulting mixture 
 table and roi list can be exported to an easily readable csv format . 

the availability of the roi list in an easily readable format can 
 also facilitate automation of data analysis for nmr - based ligand screening . 

the ranges of the roi list can be easily extracted through scripting 
 and imported into various nmr analysis tools as integration regions 
 to automatically quantitate and compare spectra and identify hits . 

future versions of nmrmix could include other non - nmr criteria , such 
 as reactivity , solubility , aggregation , or structural similarity , 
 into the score for optimizing mixtures .
additionally , nmrmix could 
 be adapted toward optimizing mixtures for other nuclei used for nmr 
 ligand affinity screens such as f - based screens .
 nmr ligand affinity screening is 
 a powerful technique that is routinely 
 used in drug discovery or functional genomics to directly detect protein  
 ligand 
 binding events . 
 binding events can be identified by monitoring differences 
 in the 1d 1h nmr spectrum of a compound with and without 
 protein . 
 although a single nmr spectrum can be collected within a 
 short period ( 210 min per sample ) , one - by - one screening of 
 a protein against a library of hundreds or thousands of compounds 
 requires a large amount of spectrometer time and a large quantity 
 of protein . 
 therefore , compounds are usually evaluated in mixtures 
 ranging in size from 3 to 20 compounds to improve the efficiency of 
 these screens in both time and material . 
 ideally , the nmr signals 
 from individual compounds in the mixture should not overlap so that 
 spectral changes can be associated with a particular compound . 
 we 
 have developed a software tool , nmrmix , to assist in creating ideal 
 mixtures from a large panel of compounds with known chemical shifts . 
 
 input to nmrmix consists of an 1h nmr peak list for each 
 compound , a user - defined overlap threshold , and additional user - defined 
 parameters if default settings are not used . 
 nmrmix utilizes a simulated 
 annealing algorithm to optimize the composition of the mixtures to 
 minimize spectral peak overlaps so that each compound in the mixture 
 is represented by a maximum number of nonoverlapping chemical shifts . 
 a built - in graphical user interface simplifies data import and visual 
 evaluation of the results . 
Introduction
Materials and Methods
Results and Discussion
Conclusions