protein  protein interactions are essential for many biological processes in the cell . in recent years , there has been a major progress in high - throughput identifications of interacting proteins ( 1 ) . despite recent advances in the structural genomics project ( 2 )
, the number of experimentally solved structures of protein  protein complexes remains very low ( 3 ) .
therefore , computational methods are essential for 3d characterization of protein  protein interactions .   protein  protein docking aims to predict the 3d structure of a protein complex given the structures of the individual proteins that assemble it .
protein flexibility ( 4 ) and the absence of robust scoring functions present a major challenge in the docking field .
the flexibility of proteins includes both backbone and side - chain movements , and dealing with it significantly extends the search space for the optimal complex structure ( 5,6 ) .
in addition , state of the art scoring functions are not tolerant enough to the inaccuracies in the predicted structures ( 7 ) .
first , soft rigid docking is performed ( 811 ) , allowing a certain amount of steric clashes .
then , thousands of solution candidates are refined and re - scored in a refinement stage ( 5,6 ) . in this stage , each solution candidate is improved by modeling the protein flexibility and optimizing the rigid - body orientation .
most of the refinement methods use an optimization of the side - chain conformations and/or rigid - body orientation in order to minimize a certain scoring function .
one of the main differences between the refinement methods is in the approach used for side - chain optimization : monte - carlo ( mc ) ( 12,13 ) , graph theory - based methods ( 14,15 ) , linear programming ( lp ) ( 16 ) , etc .
the optimization of the rigid - body orientation usually involves a randomized perturbation followed by energy minimization .
the minimization is performed using methods such as steepest descent ( 17 ) , newton  raphson ( 18,19 ) , quasi - newton ( 12,16 ) and more .
there are several web servers that deal with different aspects of the docking challenge : zdock ( 20 ) and patchdock ( 21 ) perform rigid - body docking .
smoothdock ( 24 ) is a newer version of cluspro that also refines the representatives of the largest clusters .
rosettadock ( 12,25 ) web server allows local search in the vicinity of a single given input solution candidate .
the gramm - x ( 26 ) and hex ( 27 ) web servers perform rigid - body docking followed by an optimization of the rigid - body orientation .
we present the firedock web server that provides a user - friendly interface for running this protocol online .
the method simultaneously targets the problem of flexibility and scoring of solutions produced by fast rigid - body docking algorithms .
the output provides a list of refined complexes , sorted by a binding energy function , and a 3d visualization for observing and comparing between the refined complexes .
additionally , we suggest a platform for performing full docking protocol : rigid - body docking by the patchdock web server ( 21 ) and a redirection of the results for refinement and scoring by firedock web server . this protocol was thoroughly tested ( 16 ) on protein  protein docking benchmarks ( 28,29 ) .
the method refines each candidate and ranks all the candidates according to the binding energy .
the firedock ( 16 ) method includes three main steps : 
 side - chain optimization : the side - chain flexibility of the receptor and the ligand is modeled by a rotamer library .
the optimal combination of rotamers for the interface residues is found by solving an integer lp problem ( 3032 ) .
this lp minimizes a partial energy function consisting repulsive van der waals and rotamer probabilities terms.rigid-body minimization : this minimization stage is performed by a mc technique that attempts to optimize an approximate binding energy by refining the orientation of the ligand structure .
the binding energy consists of softened repulsive and attractive van der waals terms . in each cycle of the mc method ,
a local minimization is performed by the quasi - newton algorithm ( 33,34 ) . by default
, 50 mc cycles are performed.scoring and ranking : this final ranking stage attempts to identify the near - native refined solutions .
the ranking is performed according to a binding energy function that includes a variety of energy terms : desolvation energy ( atomic contact energy , ace ) , van der waals interactions , partial electrostatics , hydrogen and disulfide bonds , -stacking and aliphatic interactions , rotamer 's ; probabilities and more .
side - chain optimization : the side - chain flexibility of the receptor and the ligand is modeled by a rotamer library .
the optimal combination of rotamers for the interface residues is found by solving an integer lp problem ( 3032 ) .
this lp minimizes a partial energy function consisting repulsive van der waals and rotamer probabilities terms .
rigid - body minimization : this minimization stage is performed by a mc technique that attempts to optimize an approximate binding energy by refining the orientation of the ligand structure .
the binding energy consists of softened repulsive and attractive van der waals terms . in each cycle of the mc method ,
a local minimization is performed by the quasi - newton algorithm ( 33,34 ) . by default ,
scoring and ranking : this final ranking stage attempts to identify the near - native refined solutions .
the ranking is performed according to a binding energy function that includes a variety of energy terms : desolvation energy ( atomic contact energy , ace ) , van der waals interactions , partial electrostatics , hydrogen and disulfide bonds , -stacking and aliphatic interactions , rotamer 's ; probabilities and more .
the firedock method was extensively tested ( 16 ) on docking candidates generated by the patchdock method ( 10,21 ) for cases from benchmark 1.0 and 2.0 ( 28,29 ) .
firedock succeeded in ranking a near - native solution in the top 15 predictions for 83% of the 30 enzyme 
inhibitor test cases and for 78% of the 18 semi - unbound antibody  antigen test cases .
the firedock web server is available at http://bioinfo3d.cs.tau.ac.il / firedock/. there are two input options for the firedock web server . in the first option , the user uploads or specifies codes of two protein data bank ( pdb ) ( 35 ) files ( receptor and ligand ) and provides a list of up to 1000 transformations ( the required format is detailed in the help page of the web server ) .
each transformation , when applied on the ligand , produces a candidate docking solution . in the second option
, the user can upload an input pdb file , with each docking solution represented by a model .
the candidate solutions for firedock can be generated by rigid - body docking methods , such as patchdock ( 10,21 ) , methods that use fast fourier transform ( fft ) such as zdock ( 20,36 ) , gramm - x ( 26 ) , hex ( 27 ) , etc .
the patchdock server ( http://bioinfo3d.cs.tau.ac.il/patchdock/ ) includes an option for automatic redirection of the solution candidates to firedock .
this parameter determines the number of best scoring candidates for which a pdb file with the refined structure will be generated .
the server allows generating up to 100 pdb files . a link to the output web - page
is sent to the email of the user as soon as the refinement process is finished .
the server includes optional advanced parameters for adjusting the refinement and scoring for a specific biological system .
the user can specify the type of the complex ( default , antibody  antigen or enzyme  inhibitor ) , which is used for adjusting the weights of the scoring function .
furthermore , the user can specify if the proteins are in their bound or unbound conformation and if certain side - chains are known to be flexible or fixed .
full refinement mode allows all the interface residues to be flexible and uses an extended rotamer library .
we recommend using the restricted mode at first , for coarse refinement , and to use the full mode on the final best candidates .
this parameter influences the range of rigid - body movements around each original solution candidate .
we recommend using 0.8 for coarse refinement (  restricted mode ) and 0.85 for a final refinement (  full mode ) of the best candidates .
the output of the server is a table of all the input solutions , where each row corresponds to a single input complex ( figure 1 ) .
different complexes can be viewed simultaneously for comparison and the 3d structures can be downloaded as pdb files . the table can be sorted by different energy terms , such as the attractive and repulsive van der walls forces , the ace and the contribution of the hydrogen bonds ( hb ) to the global binding energy .
an extended table with full specifications of the values of each energy term can be downloaded . 

there are two input options for the firedock web server . in the first option , the user uploads or specifies codes of two protein data bank ( pdb ) ( 35 ) files ( receptor and ligand ) and provides a list of up to 1000 transformations ( the required format is detailed in the help page of the web server ) .
each transformation , when applied on the ligand , produces a candidate docking solution . in the second option
, the user can upload an input pdb file , with each docking solution represented by a model .
the candidate solutions for firedock can be generated by rigid - body docking methods , such as patchdock ( 10,21 ) , methods that use fast fourier transform ( fft ) such as zdock ( 20,36 ) , gramm - x ( 26 ) , hex ( 27 ) , etc .
the patchdock server ( http://bioinfo3d.cs.tau.ac.il/patchdock/ ) includes an option for automatic redirection of the solution candidates to firedock .
this parameter determines the number of best scoring candidates for which a pdb file with the refined structure will be generated .
the server allows generating up to 100 pdb files . a link to the output web - page
is sent to the email of the user as soon as the refinement process is finished .
the server includes optional advanced parameters for adjusting the refinement and scoring for a specific biological system .
the user can specify the type of the complex ( default , antibody  antigen or enzyme  inhibitor ) , which is used for adjusting the weights of the scoring function .
furthermore , the user can specify if the proteins are in their bound or unbound conformation and if certain side - chains are known to be flexible or fixed .
the  full refinement mode allows all the interface residues to be flexible and uses an extended rotamer library .
we recommend using the restricted mode at first , for coarse refinement , and to use the full mode on the final best candidates .
this parameter influences the range of rigid - body movements around each original solution candidate .
we recommend using 0.8 for coarse refinement (  restricted mode ) and 0.85 for a final refinement (  full mode ) of the best candidates .
the output of the server is a table of all the input solutions , where each row corresponds to a single input complex ( figure 1 ) .
different complexes can be viewed simultaneously for comparison and the 3d structures can be downloaded as pdb files . the table can be sorted by different energy terms , such as the attractive and repulsive van der walls forces , the ace and the contribution of the hydrogen bonds ( hb ) to the global binding energy .
an extended table with full specifications of the values of each energy term can be downloaded . 

the firedock web server presented here is the first web server for addressing the problem of protein flexibility in docking and allows a high - throughput refinement of docking candidates . due to the high efficiency of the algorithm
, we are able to present a web server that allows online refinement of large number of candidates within reasonable running times .
the web server interface is easy to understand and simple , requiring no previous knowledge in docking algorithms .
in addition , we provide an option for redirection of the solution candidates from the patchdock web server to the refinement process of firedock .
this allows the users to perform a full docking protocol of global search ( patchdock ) and local refinement ( firedock ) in a fully automated manner .
the algorithm of firedock was proven to be successful in the task of refinement and selection of near - native solution out of thousand candidates ( 16 ) .
we believe that the server will enable biologists to use this state - of - the - art docking algorithm in order to predict the structure of new protein  protein complexes .
 structural details of protein  protein interactions are invaluable for understanding and deciphering biological mechanisms . 
 computational docking methods aim to predict the structure of a protein  protein complex given the structures of its single components . 
 protein flexibility and the absence of robust scoring functions pose a great challenge in the docking field . due to these difficulties 
 most of the docking methods involve a two - tier approach : coarse global search for feasible orientations that treats proteins as rigid bodies , followed by an accurate refinement stage that aims to introduce flexibility into the process . 
 the firedock web server , presented here , is the first web server for flexible refinement and scoring of protein  
 protein docking solutions . 
 it includes optimization of side - chain conformations and rigid - body orientation and allows a high - throughput refinement . 
 the server provides a user - friendly interface and a 3d visualization of the results . 
 a docking protocol consisting of a global search by patchdock and a refinement by firedock was extensively tested . 
 the protocol was successful in refining and scoring docking solution candidates for cases taken from docking benchmarks . 
 we provide an option for using this protocol by automatic redirection of patchdock candidate solutions to the firedock web server for refinement . 
 the firedock web server is available at http://bioinfo3d.cs.tau.ac.il / firedock/. 
INTRODUCTION
THE FIREDOCK METHOD
FIREDOCK WEB SERVER
Input
Output
CONCLUSIONS