studies focusing on protein interfaces have revealed that binding energies are not uniformly distributed along the protein interfaces . instead , there are certain critical residues called 
hot spots. these residues comprise only a small fraction of interfaces yet account for the majority of the binding energy ( 13 ) .
these residues are observed to be critical for function and stability of the protein association ( 1 ) .
thorn and bogan ( 4 ) deposited hot spots from alanine scanning mutagenesis experiments , in a database called asedb .
bid is an effort to organize protein interaction data compiled from the literature and presents amino acids at the protein  protein binding interfaces ( 5 ) . yet
computational methods can introduce alternative approaches to experimental techniques to detect and catalog hot spots ( 6 ) .
several groups have developed energy - based methods to predict hot spots ( 79 ) .
molecular dynamics studies can also be used to investigate the energetic contributions of interface residues ( 1012 ) .
while both energy and md - based methods are very efficient , they are at the same time costly and not applicable in large - scale hot spot prediction .
residues in protein interfaces ( 13 ) and functional sites ( 14 ) were observed to be mutating at a slower pace compared to the rest of the protein surface .
a very recent study based on sequence environment and evolutionary profile of residues predicts computational hot spots ( 15 ) .
correlation between hot spot residues and structurally conserved residues were found to be remarkable ( 1619 ) .
these hot spots are also found to be buried and tightly packed with other residues ( 18 ) resulting in densely packed clusters of networked hot spots , called 
hot regions. here , we present hotsprint , a database documenting computational hot spots in the protein interfaces combining conservation and solvent accessibility of residues in the protein interfaces .
hotsprint contains protein interfaces extracted from the structures in protein data bank ( pdb ) and is the first database , to our knowledge , which exploits sequence conservation to detect hot spots on a large scale .
total 49 512 interfaces are extracted from 34 817 pdb entries as of february 2006 . conserved
naccess is used to obtain the solvent accessibility of residues ( 21 ) . in summary ,
hotsprint marks residues that are highly conserved and tightly packed in protein interfaces as hot spots .
the interfaces , used for the identification of the computational hot spots in the hotsprint , are taken from the updated version of interface dataset generated by keskin et al .
interfaces were generated by the atomic distance criteria : if the distance between any atoms of two residues , one from each chain , is less than the summation of their van der waals radii plus a tolerance 0.5  , these residues are named as interface residues .
if the distance between non - interacting and interacting residues in the same chain is smaller than 6  , the non - interacting residue is named a 
nearby residues are important for the information about the architecture of the interface and provided in our database .
all 15 268 multi - chain pdb structures are used to extract two chain interfaces and then interfaces having less than 10 residues are eliminated .
the resulting dataset contains 49 512 two - chained interfaces that are denoted by six - letter nomenclature where the first four letters denote the pdb i d , and the last two letters are the chain identifier .
hotsprint database can be accessed through a web interface where users can search for computational hot spots in protein interfaces .
rate4site makes use of topology and branch lengths of the phylogenetic trees constructed from multiple sequence alignments ( msa ) of proteins and estimates conservation rates of amino acids based on the empirical bayesian rule .
msas of proteins constituting interfaces are taken from hssp ( homology - derived secondary structure of proteins ) ( 23 ) database as of 14 january 2006 .
all msas obtained from hssp are converted to fasta format to be used in rate4site step .
in addition , some residues are more frequently observed to be hot spots , so each of the 20 amino acids has a different propensity to be a hot spot .
further , hot spots prefer to reside in protein cavities ( 24 ) , therefore surface area accessibility of interface residues are incorporated into our hot spot scoring formula .
the computational hot spot score of ith residue in a chain is defined as pscorei = scorei x pk , where scorei is the conservation score from rate4site ( 25 ) , pk is the propensity of residue type k ( i.e , k = ala , val , etc . ) to be conserved in the interface ( details are given in the supplementary data ) . for an amino acid in a protein interface to be considered as a computational hot spot ,
we propose that following formulation should be satisfied : pscorei > t and asa > tasa and asacomplex < tasax where t , tasa and tasax are user - defined thresholds , the default values are set to 6.2 , and 49 and 12  , respectively .
asa is the asa change of the residue upon complexation , asa = asamonomer  asacomplex , asa of the residue in the monomer and complex form , respectively . in asa calculations ,
naccess ( 21 ) is used and buried asas of interface are calculated for each interface .
thus , this formulation combines amino acid conservation scores obtained from rate4site [ scaled with amino acid conservation propensities ( e.g. aromatic residues are observed to be hot spots independent of their sequence position ) ] and asa of the residue .
figure 1.the flowchart of the procedure to predict hot spots and deposit them in the hotsprint .
the flowchart of the procedure to predict hot spots and deposit them in the hotsprint .
we have evaluated prediction performance of our formulation by comparing the results with the experimental hot spot data extracted from asedb ( 4 ) .
f - measure. our formulation yields 76.83% , 60.1% , 86.56% , 63.06% and 65.69% for accuracy ( percentage of correctly predicted hot spot and non - hot spot residues over all interface residues ) , sensitivity ( ratio of correctly predicted hot spots to all hot spots residues on the interface ) , specificity ( ratio of correctly predicted non - hot spots to all non - hot spot interface residues ) , positive predictive value ( number of correctly predicted hot spots divided by number of interface residues predicted as hot spot ) and f - measure [ 2  sensitivity  ppv/(sensitivity + ppv ) where ppv is the positive predictive value ] , respectively .
ofran and rost recently developed a sequence environment and evolutionary profile - based method to predict computational hot spots ( 15 ) .
when we adopt the same convention , their positive predictive value ( referred as positive accuracy in their text ) of 60% , outperforms ours ( 46% ) .
however , our sensitivity ( 57% , coverage in their text ) is remarkably higher than theirs ( 15% ) .
hotsprint provides an easy query screen with three distinct query boxes : ( i ) hot spot search in protein interfaces for a given pdb i d , ( ii ) advanced search box and ( iii ) conservation and asa querying of the complete protein ( including non - interface residues ) .
the computational hot spots in the interfaces can be identified based on one of the three options mentioned in supplementary data .
one may either choose ( i ) the default hot spot criterion as defined in the methods section ( pscore + asa , conservation score rescaled with conservation propensity + contribution of asa ) , ( ii ) only conservation criterion ( score ) or ( iii ) conservation score rescaled with conservation propensity ( pscore ) in the query page .
the first query box allows the user to fetch associated interfaces of a given protein using its pdb identifier .
if there exists only a single interface associated with the input pdb identifier ( e.g. for pdb i d : 1axd ) , then information for that interface ( 1axdab ) is displayed .
however , there may be more than one interface extracted from that protein . in this case ,
interface identifiers of interfaces associated with that pdb are displayed ( e.g. for the pdb i d 1yp2 , four interfaces are available 1yp2ab , 1yp2ad , 1yp2bc and 1yp2cd ) .
when one selects one of the interface identifiers listed , information for that interface is presented .
figure 2 demonstrates the result page yielded after querying the interface 1yp2ab among the associated interfaces of 1yp2 . 

figure 2.interface information page for 1yp2ab interface . overall properties ( number of computational hot spots , number of conserved residues , average conservation score , buried asa and a link to interface information in the original dataset ) , individual residues and graphical representation of the interface are all displayed in this page . using the link to the original dataset
, users can get detailed information about interfaces : whether it is a biological or crystal interface , and interface amino acid composition .
the graphical representation part contains snapshots of the interface and its hot spots from four different perspectives and a jmol plugin is loaded in a new window when these images are clicked .
overall properties ( number of computational hot spots , number of conserved residues , average conservation score , buried asa and a link to interface information in the original dataset ) , individual residues and graphical representation of the interface are all displayed in this page . using the link to the original dataset
, users can get detailed information about interfaces : whether it is a biological or crystal interface , and interface amino acid composition .
the graphical representation part contains snapshots of the interface and its hot spots from four different perspectives and a jmol plugin is loaded in a new window when these images are clicked . the page presenting interface information
overall properties of the interface such as number of computational hot spots on the interface , number of conserved residues on the interface , average conservation score of interface residues and buried asa of the interface are presented .
the next section lists residues of the interface along with their position , name , conservation score , asa in monomer , asa in complex , type ( contacting interface residue , neighboring interface residue or none ) .
a residue is highlighted with a red background if it is a computational hot spot .
static snapshots of the interface from four different perspectives are shown using rasmol ( 26 ) at the bottom of the page ( figure 3 ) .
it is possible to include only contacting residues in the presented results using the check box at the bottom of the query box . 

figure 3.one of the four snapshots displayed in hotsprint generated by rasmol for interface 1yp2ab .
an interface is composed of two sides ( chain a and chain b of potato tuber adp - glucose phyrophosphorylase with pdb i d 1yp2 ) from two interacting proteins .
interface residues are shown as balls whereas the rest of the protein is shown as the trace .
the purple and red residues represent interface residues of the a and b chains of the interface , respectively .
the yellow and green residues are predicted hot spots on the chains a and b , respectively . one of the four snapshots displayed in hotsprint generated by rasmol for interface 1yp2ab . an interface is composed of two sides ( chain a and chain b of potato tuber adp - glucose phyrophosphorylase with pdb i d 1yp2 ) from two interacting proteins .
interface residues are shown as balls whereas the rest of the protein is shown as the trace .
the purple and red residues represent interface residues of the a and b chains of the interface , respectively .
the yellow and green residues are predicted hot spots on the chains a and b , respectively . the second query box allows advanced search with different options .
one can find structures satisfying given criteria among all the structures stored in the database .
interfaces with certain number of computational hot spots , number of conserved residues and average conservation score can be fetched .
furthermore , one may also be interested in finding interfaces with specified conserved propensities or buried accessible surface areas ( asa ) in a given range . for example , if interfaces with more than seven hot spots and which have 1000   asa  2000  are queried , a table listing the interface ids with respective properties is provided . at the bottom
resides the final query box that can be used to access residue information ( position , name , conservation score , monomer asa ) of the whole protein including both the interface and non - interface residues . the results for the given structure identifier will be output by the server . as a case study
, we compare the experimental hot spots of the numb ptb domain with hotsprint predictions .
figure 4 displays the ribbon diagram of the numb ptb domain that is in complex with numb - associated kinase ( nak)-c ( pdb i d : 1ddm ) ( 27 ) .
numb ptb domain is known to interact with a diverse set of peptides through a large hydrophobic cavity on its surface ( 28 ) .
the left figure presents the predicted hot spots by using pscore only , whereas the right panel illustrates the results when the pscore + asa is used .
red and yellow residues are the identified as hot spots by alanine scanning substitutions on the protein complex . considering only propensity scaled conservation scores of the residues ( left figure ) in the interface of 1ddmab , 8 of the 10 experimentally identified hot spots ( red residues )
are predicted computationally . including asa further filters some of the hot spot predictions ( 5 of the 10 hot spots
right figures present the results for the prediction of hot spots using pscore and pscore + asa , respectively .
right figures present the results for the prediction of hot spots using pscore and pscore + asa , respectively .
the interfaces , used for the identification of the computational hot spots in the hotsprint , are taken from the updated version of interface dataset generated by keskin et al .
interfaces were generated by the atomic distance criteria : if the distance between any atoms of two residues , one from each chain , is less than the summation of their van der waals radii plus a tolerance 0.5  , these residues are named as interface residues .
if the distance between non - interacting and interacting residues in the same chain is smaller than 6  , the non - interacting residue is named a 
nearby residues are important for the information about the architecture of the interface and provided in our database .
all 15 268 multi - chain pdb structures are used to extract two chain interfaces and then interfaces having less than 10 residues are eliminated .
the resulting dataset contains 49 512 two - chained interfaces that are denoted by six - letter nomenclature where the first four letters denote the pdb i d , and the last two letters are the chain identifier .
hotsprint database can be accessed through a web interface where users can search for computational hot spots in protein interfaces .
rate4site makes use of topology and branch lengths of the phylogenetic trees constructed from multiple sequence alignments ( msa ) of proteins and estimates conservation rates of amino acids based on the empirical bayesian rule .
msas of proteins constituting interfaces are taken from hssp ( homology - derived secondary structure of proteins ) ( 23 ) database as of 14 january 2006 .
all msas obtained from hssp are converted to fasta format to be used in rate4site step .
in addition , some residues are more frequently observed to be hot spots , so each of the 20 amino acids has a different propensity to be a hot spot .
further , hot spots prefer to reside in protein cavities ( 24 ) , therefore surface area accessibility of interface residues are incorporated into our hot spot scoring formula .
the computational hot spot score of ith residue in a chain is defined as pscorei = scorei x pk , where scorei is the conservation score from rate4site ( 25 ) , pk is the propensity of residue type k ( i.e , k = ala , val , etc . ) to be conserved in the interface ( details are given in the supplementary data ) . for an amino acid in a protein interface to be considered as a computational hot spot
, we propose that following formulation should be satisfied : pscorei > t and asa > tasa and asacomplex < tasax where t , tasa and tasax are user - defined thresholds , the default values are set to 6.2 , and 49 and 12  , respectively .
asa is the asa change of the residue upon complexation , asa = asamonomer  asacomplex , asa of the residue in the monomer and complex form , respectively . in asa calculations ,
naccess ( 21 ) is used and buried asas of interface are calculated for each interface .
thus , this formulation combines amino acid conservation scores obtained from rate4site [ scaled with amino acid conservation propensities ( e.g. aromatic residues are observed to be hot spots independent of their sequence position ) ] and asa of the residue .
figure 1.the flowchart of the procedure to predict hot spots and deposit them in the hotsprint .
the flowchart of the procedure to predict hot spots and deposit them in the hotsprint .
we have evaluated prediction performance of our formulation by comparing the results with the experimental hot spot data extracted from asedb ( 4 ) .
we assessed success of the formulations using the statistical analysis using  accuracy and  f - measure. our formulation yields 76.83% , 60.1% , 86.56% , 63.06% and 65.69% for accuracy ( percentage of correctly predicted hot spot and non - hot spot residues over all interface residues ) , sensitivity ( ratio of correctly predicted hot spots to all hot spots residues on the interface ) , specificity ( ratio of correctly predicted non - hot spots to all non - hot spot interface residues ) , positive predictive value ( number of correctly predicted hot spots divided by number of interface residues predicted as hot spot ) and f - measure [ 2  sensitivity  ppv/(sensitivity + ppv ) where ppv is the positive predictive value ] , respectively .
ofran and rost recently developed a sequence environment and evolutionary profile - based method to predict computational hot spots ( 15 ) .
when we adopt the same convention , their positive predictive value ( referred as positive accuracy in their text ) of 60% , outperforms ours ( 46% ) .
however , our sensitivity ( 57% , coverage in their text ) is remarkably higher than theirs ( 15% ) .
hotsprint provides an easy query screen with three distinct query boxes : ( i ) hot spot search in protein interfaces for a given pdb i d , ( ii ) advanced search box and ( iii ) conservation and asa querying of the complete protein ( including non - interface residues ) .
the computational hot spots in the interfaces can be identified based on one of the three options mentioned in supplementary data .
one may either choose ( i ) the default hot spot criterion as defined in the methods section ( pscore + asa , conservation score rescaled with conservation propensity + contribution of asa ) , ( ii ) only conservation criterion ( score ) or ( iii ) conservation score rescaled with conservation propensity ( pscore ) in the query page .
the first query box allows the user to fetch associated interfaces of a given protein using its pdb identifier .
the default thresholds in these expressions can also be modified by the user . if there exists only a single interface associated with the input pdb identifier ( e.g. for pdb i d : 1axd ) , then information for that interface ( 1axdab ) is displayed .
however , there may be more than one interface extracted from that protein . in this case ,
interface identifiers of interfaces associated with that pdb are displayed ( e.g. for the pdb i d 1yp2 , four interfaces are available 1yp2ab , 1yp2ad , 1yp2bc and 1yp2cd ) . when one selects one of the interface identifiers listed , information for that interface is presented .
figure 2 demonstrates the result page yielded after querying the interface 1yp2ab among the associated interfaces of 1yp2 . 

overall properties ( number of computational hot spots , number of conserved residues , average conservation score , buried asa and a link to interface information in the original dataset ) , individual residues and graphical representation of the interface are all displayed in this page . using the link to the original dataset
, users can get detailed information about interfaces : whether it is a biological or crystal interface , and interface amino acid composition .
the graphical representation part contains snapshots of the interface and its hot spots from four different perspectives and a jmol plugin is loaded in a new window when these images are clicked .
overall properties ( number of computational hot spots , number of conserved residues , average conservation score , buried asa and a link to interface information in the original dataset ) , individual residues and graphical representation of the interface are all displayed in this page . using the link to the original dataset
, users can get detailed information about interfaces : whether it is a biological or crystal interface , and interface amino acid composition .
the graphical representation part contains snapshots of the interface and its hot spots from four different perspectives and a jmol plugin is loaded in a new window when these images are clicked . the page presenting interface information
overall properties of the interface such as number of computational hot spots on the interface , number of conserved residues on the interface , average conservation score of interface residues and buried asa of the interface are presented .
the next section lists residues of the interface along with their position , name , conservation score , asa in monomer , asa in complex , type ( contacting interface residue , neighboring interface residue or none ) .
a residue is highlighted with a red background if it is a computational hot spot .
static snapshots of the interface from four different perspectives are shown using rasmol ( 26 ) at the bottom of the page ( figure 3 ) .
it is possible to include only contacting residues in the presented results using the check box at the bottom of the query box . 

figure 3.one of the four snapshots displayed in hotsprint generated by rasmol for interface 1yp2ab .
an interface is composed of two sides ( chain a and chain b of potato tuber adp - glucose phyrophosphorylase with pdb i d 1yp2 ) from two interacting proteins .
interface residues are shown as balls whereas the rest of the protein is shown as the trace .
the purple and red residues represent interface residues of the a and b chains of the interface , respectively .
the yellow and green residues are predicted hot spots on the chains a and b , respectively . one of the four snapshots displayed in hotsprint generated by rasmol for interface 1yp2ab .
an interface is composed of two sides ( chain a and chain b of potato tuber adp - glucose phyrophosphorylase with pdb i d 1yp2 ) from two interacting proteins .
interface residues are shown as balls whereas the rest of the protein is shown as the trace .
the purple and red residues represent interface residues of the a and b chains of the interface , respectively .
the yellow and green residues are predicted hot spots on the chains a and b , respectively . the second query box allows advanced search with different options .
one can find structures satisfying given criteria among all the structures stored in the database .
interfaces with certain number of computational hot spots , number of conserved residues and average conservation score can be fetched .
furthermore , one may also be interested in finding interfaces with specified conserved propensities or buried accessible surface areas ( asa ) in a given range .
for example , if interfaces with more than seven hot spots and which have 1000   asa  2000  are queried , a table listing the interface ids with respective properties is provided . at the bottom
resides the final query box that can be used to access residue information ( position , name , conservation score , monomer asa ) of the whole protein including both the interface and non - interface residues .
the results for the given structure identifier will be output by the server . as a case study , we compare the experimental hot spots of the numb ptb domain with hotsprint predictions .
figure 4 displays the ribbon diagram of the numb ptb domain that is in complex with numb - associated kinase ( nak)-c ( pdb i d : 1ddm ) ( 27 ) .
numb ptb domain is known to interact with a diverse set of peptides through a large hydrophobic cavity on its surface ( 28 ) .
the left figure presents the predicted hot spots by using pscore only , whereas the right panel illustrates the results when the pscore + asa is used .
red and yellow residues are the identified as hot spots by alanine scanning substitutions on the protein complex . considering only propensity scaled conservation scores of the residues ( left figure ) in the interface of 1ddmab ,
8 of the 10 experimentally identified hot spots ( red residues ) are predicted computationally . including asa further filters some of the hot spot predictions ( 5 of the 10 hot spots
right figures present the results for the prediction of hot spots using pscore and pscore + asa , respectively .
right figures present the results for the prediction of hot spots using pscore and pscore + asa , respectively .
in this article , a database of computational hot spots in protein interfaces ( hotsprint ) is introduced .
49 512 protein interfaces are extracted from the 34 817 structures in protein data bank ( pdb ) as of february 2006 .
we defined a hot spot as an interface residue that is conserved and buried in the complex form .
it is the first database , to our knowledge , which exploits sequence conservation to detect hot spots on a large scale .
we believe study and characterization of hot spots will help to unravel insights of protein associations and will constitute an important step in understanding recognition and binding processes .
the dataset can be downloaded as a single sql file from the website . a non - redundant subset of the database ( 40% homology with respect to blast ) is also provided for retrieval .
 we present a new database of computational hot spots in protein interfaces : hotsprint . 
 hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy . 
 hotsprint contains data for 35 776 protein interfaces among 49 512 protein interfaces extracted from the multi - chain structures in protein data bank ( pdb ) as of february 2006 . 
 the conserved residues in interfaces with certain buried accessible solvent area ( asa ) and complex asa thresholds are flagged as computational hot spots . 
 the predicted hot spots are observed to correlate with the experimental hot spots with an accuracy of 76% . 
 several machine - learning methods ( svm , decision trees and decision lists ) are also applied to predict hot spots , results reveal that our empirical approach performs better than the others . a web interface for the hotsprint database allows users to browse and query the hot spots in protein interfaces . 
 hotsprint is available at http://prism.ccbb.ku.edu.tr/hotsprint ; and it provides information for interface residues that are functionally and structurally important as well as the evolutionary history and solvent accessibility of residues in interfaces . 
INTRODUCTION
METHODOLOGY AND RESULTS
Interface datasets
Detection of computational hot spots in protein interfaces
Web interface and querying the HotSprint database
CONCLUSION
AVAILABILITY