DOCKSCORE: a webserver for ranking protein-protein docked poses
© Malhotra et al.; licensee BioMed Central. 2015
Received: 22 July 2014
Accepted: 13 April 2015
Published: 24 April 2015
Proteins interact with a variety of other molecules such as nucleic acids, small molecules and other proteins inside the cell. Structure-determination of protein-protein complexes is challenging due to several reasons such as the large molecular weights of these macromolecular complexes, their dynamic nature, difficulty in purification and sample preparation. Computational docking permits an early understanding of the feasibility and mode of protein-protein interactions. However, docking algorithms propose a number of solutions and it is a challenging task to select the native or near native pose(s) from this pool. DockScore is an objective scoring scheme that can be used to rank protein-protein docked poses. It considers several interface parameters, namely, surface area, evolutionary conservation, hydrophobicity, short contacts and spatial clustering at the interface for scoring.
We have implemented DockScore in form of a webserver for its use by the scientific community. DockScore webserver can be employed, subsequent to docking, to perform scoring of the docked solutions, starting from multiple poses as inputs. The results, on scores and ranks for all the poses, can be downloaded as a csv file and graphical view of the interface of best ranking poses is possible.
The webserver for DockScore is made freely available for the scientific community at: http://caps.ncbs.res.in/dockscore/.
KeywordsDocking Interactome Protein interfaces Quaternary structure prediction
Proteins in the cell rarely act in isolation and in fact, are known to interact with other biomolecules like DNA, RNA, other proteins, small molecules etc. . Studying and understanding these interactions will provide insights into the physiological roles and regulation mechanism. These interaction sites can further be studied for the effect of mutations or for therapeutic purposes. There are excellent experimental methods available to study protein-protein interactions (like yeast two-hybrid, co-immunoprecipitation etc. [2,3]) and also to pinpoint the site of interactions using mutation studies, structure determination methods (such as X-ray, NMR) and label transfer . Protein-protein docking is the computational method to study protein-protein interactions, based on electrostatics, shape and geometric complementarities [5-8]. Docking of the interacting pairs of proteins provides insights into the specific atomic details of interactions. There are several docking programs available as downloadable softwares and as webservers (such as HADDOCK, ; ZDOCK, ; ClusPro, ; GRAMM-X webserver, ; FRODOCK,  and HADDOCK webserver ). These programs employ scoring functions which are based on ranking the poses based on the energy values. However, upon docking, there are number of proposed solutions and selection of biologically meaningful pose from this pool still remains a challenging task .
It is possible to limit the search space by guiding the docking around certain residues based on evolutionary or biochemical data. However, in the absence of such an information or even from a set of docking decoys, selection of the best docked pose becomes a difficult task. In these cases, one can analyze the interfaces which are proposed by the docking program. We had recently proposed a scoring scheme, named DockScore, to re-rank the docked poses and identify the native or near-native poses from the pool . DockScore is initiated with the identification of interface residues based on distance-based criteria and then considers several interface parameters namely surface area, conservation, hydrophobicity, spatial clustering and short contacts to perform the scoring.
We had assessed the performance of DockScore on 30 protein-protein complexes and CAPRI targets and compared the performance of our scoring scheme with two other methods namely dDFIRE  and FireDock  for our testing dataset. We have shown that DockScore was able to rank the native complex as a top-ranking pose in 26 out of the 30 complexes tested, whereas dDFIRE and FireDock were able to achieve this in 16 of the cases .
There are several scoring programs available as a downloadable package [19-21] in order to re-rank the docked poses, but the webserver implementation or availability for easy access is less common [17,22,23]. In this article, we report the availability of DockScore in the public domain as a webserver for the scientific community. This includes user-interactive tools webserver and convenient graphical display of interface regions of high-scoring poses. The webserver can also be used to perform scoring of protein-protein interactions or re-ranking the docked poses to identify the biologically meaningful pose(s) out of the pool. Users can upload a zipped file containing the pool of docked poses which need to be ranked. Each parameter of the scoring scheme can be turned on/off depending on the discretion of the user. In the output, we provide a list of all docked poses with all the scores marked in the list. User can also visualise the five top-most poses in Jmol with the interface residues from two protein chains colored differently. The file containing different scores and ranks of the docked poses can be downloaded in CSV format.
DockScore webserver parameters
The webserver presented here employs the scoring scheme called DockScore to perform the ranking of the docked poses. It utilizes the parameters of the interface formed upon interaction of the two given protein chains. These interface parameters are surface area, conserved residues, hydrophobicity, short contacts and spatial clustering. There is an additional parameter, which is based on the presence of positively charged residues at the interface. This can be employed selectively and especially when the interacting protein chains are DNA-binding (for e.g. transcription factors) or RNA-binding in nature. The presence of positively charged residues at the interface is penalized to minimize the overlap of protein-protein interaction site with that of DNA-binding region.
The interface residues are identified using the distance-based criteria, inter-chain Cβ-Cβ distance cut-off of 7 Å. The interface parameters that are employed for performing the ranking are explained below briefly. Weights for each of the parameter can be easily assigned, if a new training dataset is choosen by estimating the importance of each parameter (i.e. using only one parameter at a time and assessing the performance, please refer to DockScore publication ) Each of these parameters is assigned weights based on the training dataset.
Surface Area: It is computed using NACCESS ('NACCESS', computer program. (1993) by S. J. Hubbard, J. M. Thornton).
Conservation of residues: The individual protein chains are used as queries to perform PSI-BLAST  in order to collect homologues from the SWISSPROT database and multiple sequence alignment is built using CLUSTALW [25,26]. Conservation scores per residue are evaluated using our in-house program MOTIFS , where permitted amino acid exchanges and identities are scored high. The score cut-off of 60 is usually used for close homologues and 40 if the distantly related members are included in the alignment, to identify the conserved residues (Figure 1). The number of conserved residues at the interface is normalized by the total number of interface residues.
Inter-chain short contacts: Our in-house program CoilCheck  is employed to identify short contacts.
Spatial Clustering: The pairwise distances between the interface residues were computed between the two chains and the residues with a Cβ-Cβ distance cut-off of 14 Å were considered as spatially clustered residues.
Hydrophobic residues: We ranked those docked poses with high numbers of hydrophobic residues (A, V, L, I, M, F, W and Y) at the interface with a high score, as protein-protein interfaces are known to be rich in such residues [1,29,30].
Zipped file containing the docked poses in PDB format with coordinates of both the interacting chains
PDB coordinates and chain ID of both the protein chains used to perform docking
The computation is not initiated if the files are not in PDB format and coordinates for both the interacting chains are not provided by the user.
Scores for each of the parameters individually
Normalized weighted score
Z-score for the normalized weighted score
For each pose, a Z-score is calculated to assign a significance of normalized weighted score. We have tested this on our test dataset (30 cases) and note Z-score >1.5 is discriminatory to identify the native (or near-native) pose (Additional file 1).
In the output page, webserver displays a list as an output with docked poses ranked according to the normalized weighted score (Figure 2). The list can be sorted according to any of the parameter/score by clicking on that column. This list with the entire scores can also be downloaded from the webserver in the CSV format. The user can input his/her email ID and the result link will be posted at this address.
Subsequent to the scoring, the five top-most poses can be visualized using JSmol (JSmol: an open-source Java viewer for chemical structures in 3D. http://wiki.jmol.org/index.php/JSmol). The interface residues from the two interacting chains are highlighted in different colors (Figure 2).
Results and discussion
The server can be used for performing the scoring of protein-protein interactions. Figure 1 represents the screenshot of the server explaining all the parameters considered for scoring. The user has a choice to select parameters to be employed for scoring, or the user can rank the poses based on any parameter or normalized score of their choice upon scoring.
This website is in the public domain and is open to all users and there is no login requirement.
In the output page, webserver displays a list with docked poses ranked according to the normalized weighted score. The list can be sorted according to any of the parameter/score by clicking on that column. This list with the entire scores can also be downloaded from the webserver in the CSV format. The user can input his/her email ID and the result link will be posted at this address.
Ranked next to native
Ranked next to native
Scale-up in docked poses
We next examined the effect of sampling additional number of docked poses, rather than 99 poses, with two cases referred as ‘example’ and a ‘difficult example’ derived from the DockScore testing dataset.
In the ‘example’ (PDB code 1GHD), which was a success while testing DockScore, we sampled 1000 docked poses to see if DockScore is still able to rank the native pose as a top-ranking pose out of a pool of 1000 docked poses. We find that the performance of DockScore is not reduced due to enhanced sampling (Additional file 3). In the ‘difficult example’ (PDB code 1IZY), the native pose was not the top-ranking pose while performing the test runs. So, we sampled 1000 poses to see if DockScore ranks the native pose as top-ranking one and still the performance did not seem to improve (Additional file 4).
DockScore helps in distinguishing the native/near-native complexes from the pool of docked poses. It can be employed post-docking to rank the poses. Different interface parameters are considered to perform this scoring like interface surface area, conservation, hydrophobicity, spatial clustering and short contacts. We implemented this scoring scheme in the form of webserver for its use by the community. The web tool provides a list of all scores for the given docked poses provided as input. The top-ranking poses can also be visualized.
Availability and requirements
Project name: DockScore webserver
Project home page: http://caps.ncbs.res.in/dockscore/
Operating system(s): Platform independent
Other requirements: Java plug-in for the respective browser
License: Free for academic use
Any restrictions to use by non-academics: Free for academic purposes. For commercial use please contact the corresponding author
The software driving the webserver can be made available upon request for academic use.
S.M. is supported by Department of Biotechnology (India) fellowship. OKM is supported by Centre of Excellence project (BT/01/COE/09/01) funded by Department of Biotechnology, India. We thank NCBS (National Centre for Biological Sciences) for infrastructure and other facilities.
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