Proteins are involved in almost all functions in a living cell, and functions of proteins are realized by their tertiary structures. Obtaining a global perspective of the variety and distribution of protein structures lays a foundation for our understanding of the building principle of protein structures. In light of the rapid accumulation of low-resolution structure data from electron tomography and cryo-electron microscopy, here we map and classify three-dimensional (3D) surface shapes of proteins into a similarity space. Surface shapes of proteins were represented with 3D Zernike descriptors, mathematical moment-based invariants, which have previously been demonstrated effective for biomolecular structure similarity search. In addition to single chains of proteins, we have also analyzed the shape space occupied by protein complexes. From the mapping, we have obtained various new insights into the relationship between shapes, main-chain folds, and complex formation. The unique view obtained from shape mapping opens up new ways to understand design principles, functions, and evolution of proteins. Proteins are the major molecules involved in almost all cellular processes. In this work, we present a novel mapping of protein shapes that represents the variety and the similarities of 3D shapes of proteins and their assemblies. This mapping provides various novel insights into protein shapes including determinant factors of protein 3D shapes, which enhance our understanding of the design principles of protein shapes. The mapping will also be a valuable resource for artificial protein design as well as references for classifying medium- to low-resolution protein structure images of determined by cryo-electron microscopy and tomography. Proteins are the primary workers in a living cell, involved in transportation, catalysis, signaling, energy production, and many other processes. Classification of protein structures provides fundamental information for our understanding of the principles that govern and determine protein structures, which is one of the essential goals of structural biology and protein bioinformatics. Understanding the repertoire of protein structures is also of practical importance for artificial protein design, which has broad applications in therapeutics such as designing inhibitors  and smalConventionally, protein structures have been classified based on their main-chain conformations and evolutionary history –6. Such Some recent studies mapped protein structures into a low-dimensional space to reveal high-level organization of the variety of protein structures. Kim and his colleagues computed structural similarity with DALI, a residue-contact map-based structure comparison method , and mapE. coli. Despite their low sequence identity and structure similarity, both of them share a characteristic pore to encircle DNA double strand. This function similarity can be easily captured by shape descriptors, but not captured by conventional main-chain conformation approach.Here, we present a global mapping of 3D surface shapes of single proteins and complexes. In contrast to the previous works –20 that Protein surface shapes were represented with 3D Zernike Descriptors (3DZD), mathematical moment-based invariants of 3D functions . 3DZD haWe first discuss the protein surface shape space for single chain proteins, followed by the analysis of shapes of protein complexes.Many entries of the Protein Data Bank (PDB)  contain The overall distribution  shows th2 test). Further, There are other noticeable trends in the mapping. Two-domain structures  are dominant on the positive end of the third (orange) principal axis  whereas 2 test). It was also observed that the positive end of the first and the second axes accumulate proteins with relatively large and deep pockets. The bias of proteins with a top 10% largest pocket being on the positive side of the two axes was statistically significant . Therefore, the first principal axis, which showed a gradual shift from spherical to elongated shapes, also represents the transition from monomers to complex-forming proteins.A single-chain may either exist as a monomer or form a complex with other proteins in a cell. Is there any shape difference between these two classes of proteins? In Listeria monocytogenes phage PSA endolysin (1xov) that binds to cell walls of host bacteria.The dataset includes 318 elongated proteins (with an eccentricity over 0.8) which PISA indicates monomers as their biological unit, not agreeing with the general trend. However, most of them (82.4%) turned out to be a part of a full structure, and if not, they interact with other proteins or nucleotides for their biological function. Examples include a ribosomal protein L22 (PDB ID: 1bxeA), which interacts with ribosomal RNAs and The protein fold spaces presented previously by the other groups –20 showeNext, we discuss the shape space of protein complexes . The datThe differences between the shape spaces of the single-chain proteins and complexes become apparent when they are superimposed . To compWe further examined the symmetry of complex structures . Almost There are structures with large pockets or penetrating holes, which can be identified by comparing the volume of the structure itself and that of its convex hull (convex envelop that covers the volume). In the current analysis, buried cavities were not included because inner surface of buried cavities was not considered when the protein surface was generated. Also, tunnels in channel proteins were not effectively considered because such tunnels were too narrow to survive as cavities when the surface was constructed, and there are only three complete channel structures in the dataset in the first place.In In this study, we have constructed a mapping of the protein structure space for the first time by considering the overall surface shape of both single-chain and complex proteins. The shape space visualized in this work would give an impression that the protein shape space is continuous, but this is not specific to the protein surface shape representation. Indeed, earlier works that mapped protein structures considering main-chain conformations also show continuous structure distributions –20; and The observed variety of protein shapes in this work will also be useful for designing protein representations used in a cell-scale physical simulation of biomolecules . Rather Last but not least, this work has strong implications for protein design. Our study indicates that a protein shape can be realized with utterly different backbone conformations that even belong to different fold classes as shown in In the coming age of medium- to low-resolution biomolecular structures, protein design needs a novel way of viewing biomolecular shapes. We expect that this work makes a unique and significant contribution by providing a foundation of understanding the protein shape universe.α atoms were clustered with a 9 Å cutoff. We further removed 82 chains were further removed from the list because their sequences had more than 25% sequence identity to other chains. This process yielded a dataset of 6,841 non-redundant protein structures.The representative set of single-chain protein structures was selected from a PISCES culled list with a resolution cutoff of 2.2 Å, an R factor cutoff of 0.2, and a pairwise sequence identity cutoff of 25% . From 7,From this dataset, we prepared another dataset by pruning structures that include less than 95% of residues relative to the whole chain length. The protein lengths were obtained from UniProt . There aFrom PDB, we identified structures that exist as a complex as defined in PISA and downloaded the first biological unit (BU). The same resolution, R factor, and length cutoffs as in the single chain dataset were applied. A complex is considered as redundant if there is another complex with the same number of chains and corresponding chains between them have over 25% sequence identity. Among redundant complex entries, we chose the one with the highest resolution and the lowest R factor. This procedure yielded 5,326 complexes. Symmetry information for complexes was obtained from PDB if the BU of the complex considered has the same composition as in PDB. Out of the 5,326 complexes, 2,876 of them acquired symmetry information.3 (N was set to 200). Protein size is not explicitly considered in 3DZD calculation. But in our previous study [f(x), for which a series is computed in terms of the Zernike-Canterakis basis [l<m<l, 0≤l≤n, and (n−l) even. Rnl(r) are radial functions defined by Canterakis, constructed so that f(x) are defined as the coefficients of the expansion in this orthonormal basis, i.e. by the formulal+1)-dimensional vectors Fnl are defined as norms of the vectors Ωnl. Thusn is called the order of the descriptor. The rotational invariance of 3D Zernike descriptors means e.g. that calculating Fnl for a protein and its rotated version would yield the same result. We used 20 as the order because it gave reasonable results in our previous works on protein 3D shape comparison [n of 20 represents a 3D structure as a vector of 121 invariants [dE between their 3DZDs, Xi and Yi represent the ith invariant for protein X and Y, respectively.We used 3DZD, mathematical rotation-invariant moment-based descriptors, to represent the surface shape of single-chain proteins and complexes. For a protein structure, a surface was constructed using the MSMS program  and thenus study , we haveus study  instead.is basis  that is mparison ,38–40. Avariants . The simTo illustrate the characteristics of 3DZDs, we compare it against two other structure similarity measures, the Procrustes distance  and TM-SIn http://kiharalab.org/3d-surfer/batch.php) [The 3DZD files of the single-chain and the complex datasets are made available at tch.php) ,38.We used principal component analysis (PCA) to project 3DZDs of 121 value vectors of protein structures into 3D. Three eigenvectors were chosen for the mapping because the scree plots  showed ta, b, and c are the length of longest, the second longest, and the third longest semi-principal axes of the ellipsoid, respectively. Elongated structures have an eccentricity close to 1, while spherical structures have an eccentricity close to 0.In order to quantify how elongated a structure is, we have defined the term eccentricity, which is calculated from the minimum volume enclosing ellipsoid (MVEE) of a structure. Given all atoms in a structure, protein MVEE is the ellipsoid with minimum volume that encloses all atoms. From MVEE, the eccentricity is defined as The volume of proteins was computed using MSROLL with a probe radius set to 0. For 42 cases in the single-chain dataset and 82 cases in the complex dataset where the MSROLL failed, we used the ProteinVolume program  instead.3. We analyzed the location of proteins with a large pocket whose size is within the top 10%  in the single-chain protein surface space , edges (E), faces (F), loops (L), shells (S), and genus (g) of a manifold: V + F–E–(L–F) = 2 (S–g). To obtain these values of a protein surface, we used triangular meshes computed by EDTSurf [Donut-shaped structures were identified by first screening structures with genus > 0 and then with the conditions of 0.9≤ EDTSurf . L is eqS1 FigTop, the histogram of the 3DZD distances of proteins from different combinations of fold classes. Fold class information was obtained from the CATH database. The y-axis shows the fraction of pairs that falls into each distance bins. Two peaks are observed for pairs that involve the few secondary structure (ss) class. There are only 28 chains in the few ss class. Those chains have roughly two kinds of shapes, either elongated, or relatively spherical. The peak at a relatively small distance corresponds to pairs within each category, while the peak at a relatively large distance corresponds to pairs across two categories. Bottom, the 3DZD distance distribution of up to a bin of 4.0–5.0. The y-axis is now the actual number of protein pairs.(PDF)Click here for additional data file.S2 FigThe blue line is for the single-chain protein dataset, while the orange line is for complex dataset.(PDF)Click here for additional data file.S3 Fig(A), Comparison of the Euclidian distance of 3DZD and the Procrustes distance for all the pairs of 20 ellipsoids with increasing eccentricity values from 0.0 to 0.92. On each ellipsoid 2500 points were sampled uniformly on the spherical coordinates. The two angles, θ (0 to π) and φ (0 to 2π) were evenly divided into 50 intervals and a point was placed on the ellipsoid surface for each combination of θ and φ. (B), The 3DZD and the Procrustes distances were compared for 1,278 single-chain protein pairs that have the same number of vertices in the surface triangle mesh representation. For computing the Procrustes distance for a protein pair, the closest surface point pairs from the two proteins were matched using the coherent point draft algorithm. (C), an example of protein pairs that have a large 3DZD distance and a small Procrustes distance. 2bwrA (CATH code: N/A) and 3ke3A . The 3DZD distance: 13.88; the Procrustes distance: 0.19. (D), another such example of protein pairs. 4gnrA  and 3ga7A (CATH: 3.40.50.1820). The 3DZD distance: 13.13; the Procrustes distance: 0.18.(PDF)Click here for additional data file.S4 Fig(A), each point represents a protein pair. (B), the same data are represented with the density information. (C), an example of protein pairs that has a small 3DZD Euclidian distance but from different fold classes, the α class and the β class. Left, PDB ID: 1c3cA; CATH code: 1.10.276.10. Right, 4jp0A, 2.80.10.50. The Euclidian distance of 3DZD was 2.4, while the TM-score was 0.265. (D), another example of protein pairs with a small 3DZD Euclidian distance but from different fold classes, the β class and the αβ class. Left, 3a6rA; 2.30.110.10. Right, 3h87A, 3.40.50.1010. The Euclidian distance of 3DZD was 2.4, while the TM-score was 0.254.(PDF)Click here for additional data file.S5 FigThe figure shows top 10 eigenvalues of the covariance matrix sorted in the descending order. The insert shows all 121 eigenvalues. Eigenvalues of single-chain, high-coverage single-chain and complex datasets are colored in red, orange and blue, respectively. The sharp drop up to the third eigenvalue indicates that adding fourth and more eigenvalues do not add substantially more information.(PDF)Click here for additional data file.S1 Data(ZIP)Click here for additional data file.S1 MovieSame as the representations used in (MOV)Click here for additional data file.S2 MovieSame as the representations adopted in (MOV)Click here for additional data file.S1 Pymol Filehttps://pymol.org/2/. Readers can download the Pymol software and open the session file in Pymol to rotate the shape space in 3D. Same as Pymol is a 3D biomolecular structure viewer and freely available at (PSE)Click here for additional data file.S2 Pymol FileThe coloring of the points follows the same pattern as (PSE)Click here for additional data file.S3 Pymol FileThe coloring of the points follows the same pattern as (PSE)Click here for additional data file.S4 Pymol FileThe color code is the same as the one used in (PSE)Click here for additional data file.S5 Pymol FileRed points represent single-chain proteins. Blue points represent complexes. This file corresponds to (PSE)Click here for additional data file.S6 Pymol FileThe color code is the same as the one used in (PSE)Click here for additional data file.


Magnaporthe oryzae is a destructive disease of rice and responsible for causing extensive damage to the crop. Pi54, a dominant blast resistance gene cloned from rice line Tetep, imparts a broad spectrum resistance against various M. oryzae isolates. Many of its alleles have been explored from wild Oryza species and landraces whose sequences are available in the public domain. Its cognate effector gene AvrPi54 has also been cloned from M. oryzae. Complying with the Flor’s gene-for-gene system, Pi54 protein interacts with AvrPi54 protein following fungal invasion leading to the resistance responses in rice cell that prevents the disease development. In the present study Pi54 alleles from 72 rice lines were used to understand the interaction of Pi54 (R) proteins with AvrPi54 (Avr) protein. The physiochemical properties of these proteins varied due to the nucleotide level polymorphism. The ab initio tertiary structures of these R- and Avr- proteins were generated and subjected to the in silico interaction. In this interaction, the residues in the LRR region of R- proteins were shown to interact with the Avr protein. These R proteins were found to have variable strengths of binding due to the differential spatial arrangements of their amino acid residues. Additionally, molecular dynamic simulations were performed for the protein pairs that showed stronger interaction than Pi54tetep  protein. We found these proteins were forming h-bond during simulation which indicated an effective binding. The root mean square deviation values and potential energy values were stable during simulation which validated the docking results. From the interaction studies and the molecular dynamics simulations, we concluded that the AvrPi54 protein interacts directly with the resistant Pi54 proteins through the LRR region of Pi54 proteins. Some of the Pi54 proteins from the landraces namely Casebatta, Tadukan, Varun dhan, Govind, Acharmita, HPR-2083, Budda, Jatto, MTU-4870, Dobeja-1, CN-1789, Indira sona, Kulanji pille and Motebangarkaddi cultivars show stronger binding with the AvrPi54 protein, thus these alleles can be effectively used for the rice blast resistance breeding program in future.Rice blast, caused by the ascomycete fungus  Magnaporthe oryzae, causes a big loss to the net production of rice Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: NoReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The papers presents the results of the nucleotide polymoephism in the Pi54 alleles which affect the Pi54 and AvrPi54 interaction. The title of the papers should be more specific since many of studies have been done on the R-Avr protein interaction including in the author papers published in 2014,which revealed that the Pi54of interacted with AVR-Pi54 through its non-LRR region, but Pi54 and Pi54rh interacted through CC-non-LRR-LRR regions and non-LRR-LRR regions, and the susceptible Pi54tp protein did not show interaction with AVR-Pi54, respectively. So this papers judged from the title only provides a study is similar to the former’.The major problem of the papers is that the conclusions in the papers should be verified by experiment, the molecular simulation is not real happened after all. Furthermoer, there are many problems with sentence structure and clause constrution.So try to set the problem discussed above in more detail!Reviewer #2: The authors analysed the Pi54 alleles from 72 rice lines by using bioinformatics methods to understand the interaction of Pi54 (R) proteins with AvrPi54 (Avr) protein. And they found that the physiochemical properties of these proteins varied due to the nucleotide level polymorphism. Generally, the authors present a well conducted study. The manuscript is concise and the results are mainly convincing.Some minor points:1. Figure 1, it can be hard to distinguish the 13 different amino acids. It may be useful to draw some lines.2. Figure 2, add the letters to indicate which one is 2A or 2B.3. It could be better for this manuscript if the authors could do some Y2H to check the interactions between avrpi54 and Pi54 alleles from resistant and susceptible lines. These experimental results could provide stronger evidence to explain the different phenotype**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  9 Sep 2019Reviewer 1 comments The papers presents the results of the nucleotide polymorphisms in the Pi54 alleles which affect the Pi54 and AvrPi54 interaction. The title of the papers should be more specific since many of studies have been done on the R-Avr protein interaction including in the author papers published in 2014, which revealed that the Pi54of interacted with AVR-Pi54 through its non-LRR region, but Pi54 and Pi54rh interacted through CC-non-LRR-LRR regions and non-LRR-LRR regions, and the susceptible Pi54tp protein did not show interaction with AVR-Pi54, respectively. So this papers judged from the title only provides a study is similar to the former’.The major problem of the papers is that the conclusions in the papers should be verified by experiment, the molecular simulation is not real happened after all. Furthermore, there are many problems with sentence structure and clause construction.So try to set the problem discussed above in more detail!)Our reply Title of the manuscript is changed as per the suggestion of the reviewer that is “Variation in the LRR region of Pi54 protein alters its interaction with the AvrPi54 protein revealed by in silico analysis”.The experimental proof for the interaction between Avr-Pi54 and Pi54 proteins was already done by Ray et al, 2016 . Here, the main objectives of this manuscript was to analyse sequence variation at the LRR regions of those alleles of Pi54 gene isolated from both phenotypically resistant and susceptible rice lines and analysed sequence variation at the LRR region and look at their interaction with AvrPi54 gene. The hypothesis was that the proteins of Pi54 alleles isolated from resistant rice lines should show in silico interaction with the AvrPi54 proteins and the susceptible one should not show any interaction which is akin to the Flor`s gene-for-gene hypothesis (Flor 1972). The interaction analysis revealed that Pi54 protein from resistance lines, showed significant in silico interactions with AvrPi54 protein, while Pi54 from susceptible lines did not interact with its counterpart. Hence, it proves our hypothesis.We made corrections in the conclusion section of the manuscript in lines 527 and 529. We also corrected many sentences throughout the manuscript, e.g. in lines 56, 71, 77, 83, etc. These changes can be seen in the track change version of the manuscript.In addition in the revised version of the MS, molecular dynamics simulation analysis was carried out to see whether the interactions between Avr-Pi54 and Pi54 proteins are stable at the thermodynamics level or not, and found that the interaction was stable at the different thermodynamic parameters.Reviewer 2 comments: The authors analysed the Pi54 alleles from 72 rice lines by using bioinformatics methods to understand the interaction of Pi54 (R) proteins with AvrPi54 (Avr) protein. And they found that the physiochemical properties of these proteins varied due to the nucleotide level polymorphism. Generally, the authors present a well conducted study. The manuscript is concise and the results are mainly convincing.Some minor points:1. Figure 1, it can be hard to distinguish the 13 different amino acids. It may be useful to draw some lines.Our replyThe figure 1 has been removed from the main text and instead of this figure a comparative composition of amino acids of the resistant and susceptible proteins with Pi54tetep protein is given, but annexed to supplementary data as figure S1 titled as “Composition of different amino acids in Pi54 proteins of the alleles cloned from resistant and susceptible rice line (A) compared with wild type Pi54tetep protein”.2. Figure 2, add the letters to indicate which one is 2A or 2B.Our replyAs suggested the changes have been made. 3. It could be better for this manuscript if the authors could do some Y2H to check the interactions between avrpi54 and Pi54 alleles from resistant and susceptible lines. These experimental results could provide stronger evidence to explain the different phenotype.Our replyThe Y2H assay has been already performed between the AvrPi54 and Pi54tetep protein to confirm their interaction by Ray et al, 2016, “Analysis of Magnaporthe oryzae genome reveals a fungal effector, which is able to induce resistance response in transgenic rice line containing resistance gene, Pi54” published in Frontiers in Plant Science journal and this publication has been cited in this manuscript. In this article, authors were identified the AvrPi54 gene and validated the gene using the in vitro as well as the in planta experiments. The significant interaction between the candidates of AvrPi54 and the Pi54 protein was initially begun with the bioinformatics approaches and led the analysis to pinpoint the AvrPi54 gene. Thus we hypothesized that to check whether the phenotypic results of rice blast disease screening experiment, on Pi54 resistant rice line and susceptible rice line would be matched with the results of in silico interaction between AvrPi54 and variants of Pi54 proteins. For that we conducted this study and disrupted interactions were found when variants of Pi54 derived from susceptible rice lines interacted with the AvrPi54 protein, while a strong interaction with AvrPi54 was found in case of Pi54 variants obtained from resistant rice lines. Thus, any of the Pi54 allele could be initially determined for its function through the in silico interaction analysis without doing phenotyping experiment. That is the main objective of this study. The interaction between Pi54-AvrPi54 proteins has been taken as a basis to extend our knowledge to discover the strength of interaction between various allelic Pi54 proteins with the AvrPi54 protein.AttachmentReply to reviewers comments.docxSubmitted filename: Click here for additional data file. 7 Oct 2019Variation in the LRR region of Pi54 protein alters its interaction with the AvrPi54 protein revealed by in silico analysisPONE-D-19-16056R1Dear Dr. Sharma,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Zonghua Wang, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 22 Oct 2019PONE-D-19-16056R1 in silico analysis  Variation in the LRR region of Pi54 protein alters its interaction with the AvrPi54 protein revealed by Dear Dr. Sharma:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. 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The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at http://lilab.jysw.suda.edu.cn/research/PremPDI/.Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol http://lilab.jysw.suda.edu.cn/research/PremPDI/.Developing methods for accurate prediction of effects of amino acid substitutions on protein-DNA interactions is important for a wide range of biomedical applications such as understanding disease-causing mechanism of missense mutations and guiding protein engineering. Very few methods have been developed for predicting the effects of mutations on protein-DNA binding affinity. Here we report a new computational method, PRedicts the Effects of single Mutations on Protein-DNA Interactions (PremPDI). The core of the PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms that makes the PremPDI algorithm efficient and being fast enough to handle large number of cases. The performance of the PremPDI protocol was tested against experimentally determined binding free energy changes of 219 mutations from 49 protein-DNA complexes and yields very good correlation coefficient. The PremPDI webserver is available to the community at  There has been a rapid development of genome-wide techniques in the last decade along with significant lowering of the cost of gene sequencing, which generated widely available genomic data. However, the interpretation of genomic data and prediction of the association of genetic variations with diseases and phenotypes still require significant improvement . CrucialWith recent rapid advances in computational biology, many approaches have been developed to offer a phenotypic classification of mutations into damaging and neutral categories –10, to chttp://lilab.jysw.suda.edu.cn/research/PremPDI/) which is based on molecular mechanics force fields and fast side-chain optimization algorithms. PremPDI can evaluate the effects of sequence variants and disease mutations  on protein-DNA interactions; calculate the quantitative change in binding affinity upon single mutation; assess deleterious effects and produce models of mutant complexes. PremPDI is validated using different types of cross-validation and is compared with two other methods using a variety of training and test sets. PremPDI can be applied to many tasks, including finding potential driver missense mutations in cancer, investigating the effects of sequence variations on protein fitness in evolution and protein design.To address this need we present a new computational method and webserver, PremPDI (ProNIT database  includes-1 Å-2) applied on the backbone atoms of all residues. Minimization was done only for protein-DNA complexes, and protein or nucleic acid structures of binding partners were retained assuming the rigid-body binding. The energy minimization was carried out with NAMD program version 2.12 [Crystal or NMR structures of wild-type protein-DNA complexes were obtained from the Protein Data Bank (PDB) , and bioion 2.12  using thion 2.12 . A 12 Å ion 2.12 . The curG value (change in binding affinity upon mutation) affecting protein-DNA interactions   in water for wild-type and mutant complexes respectively. These terms are calculated from solving the Poisson-Boltzmann equation with PBEQ module [ΔΔQ module  of CHARMQ module . For theQ module . program . program .Efold is a pairwise statistical potential for protein folding which was obtained from an optimization procedure that maximizes thermodynamic stability for all proteins simultaneously [http://www.genome.jp/aaindex/).Δaneously . It is oLmutis the length of mutated protein chain.The p-value and contribution of each term to the PremPDI model are shown in Gsolv has relatively strong correlation with Lmut with R of 0.74, and the rest of the correlations are either small or are not significantly different from zero. The VIFs of all features are less than three representing relatively low multicollinearity. We removed highly correlated features from our energy function that results in decrease of prediction accuracy. For instance, removal In addition, we performed multicollinearity analysis to investigate the linear association across each feature. Pearson correlation matrixes and variance inflation factors (VIF) for the energy features in PremPDI are shown in PremPDI takes about five minutes to perform calculations for a single mutation in a protein-DNA complex with 300 residues and 30 nucleotides running on a single processor core, and it requires additional two-to-three minutes for each additional mutation per complex.-1 for both cross validations . Namely, we trained the parameters on experimental ΔΔal mol-1 . In addial mol-1 .We also analyzed the variation of the weighting coefficient for each feature in “CV1”, “CV2” and “CV3” cross-validation respectively. The results are shown in G, 1, 1.5, 2.0 and 2.5 kcal mol-1, were tested for defining mutations with deleterious (highly destabilizing) effects  and precision-recall analyses. Sensitivity or true positive rate was defined as TPR = TP/(TP + FN) and specificity or true negative rate was defined as TNR = 1-FPR = TN/(FP+TN). Additionally, in order to account for imbalances in the labeled dataset, the quality of the predictions was described by Matthews correlation coefficient (MCC), a performance measure which is known to be more robust on unbalanced datasets:Predicting the quantitative values of binding affinity changes is quite challenging. A much easier task, attempted by many studies, is to classify mutations based on their effects into deleterious or neutral. Several thresholds of experimentally determined ΔΔects see . The num-1 has the highest AUC-ROC of 0.91 and MCC of 0.61 in distinguishing deleterious and neutral mutations  and predictions show that PremPDI presents a similar performance with mCSM-NA method and performs better than SAMPDI in predicting quantitative values of ΔΔG  results and the model built on the independent ‘P.O.M’ or ‘Prempdi-P.D.S.I’ dataset (named as PremPDI(Ind)) to compare with the other methods respectively. Pearson correlation coefficients and RMSE between experimental measurements , predicted binding affinity change induced by single mutation. Positive and negative signs correspond to destabilizing and stabilizing mutations predicted to decrease and increase binding affinity respectively.ΔΔG , PremPDI defines a residue to be located on the protein-DNA interface if residue’s solvent accessibility in the complex is lower than in the corresponding unbound partners.G is higher or equal to 1.10 kcal mol-1. This threshold corresponds to a minimum value of ER to compensate retrieval sensitivity and specificity.Deleterious (yes/no), PremPDI classifies a mutation as deleterious if ΔΔCoordinates of the minimized mutant structure are provided for download.Protein binding sites in protein-DNA complexes homologous to the query are identified using Inferred Biomolecular Interactions Server at NCBI (IBIS) server . It alloResults can be viewed directly on the browser  or downlS1 Fig(DOCX)Click here for additional data file.S2 Fig(a) The definition and the number of deleterious, neutral and stabilizing mutations for four thresholds. (b) ROC curves. (c) shows the ROC curves corresponding to FPR less than 10%. (d) Precision-recall curves. (e) shows the precision-recall curves corresponding to precision over 50%. (f) The AUC values of ROC curves and Precision-recall curves, and Matthews correlation (MCC) for four thresholds. The best performance is shown in bold font.(DOCX)Click here for additional data file.S3 FigRed point corresponds to the minimization of the value of error (DOCX)Click here for additional data file.S4 FigNine mutations do not have SAMPDI scores in the P.D.S.I test set, so they were excluded in the comparison.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableAll calculations were performed by PremPDI energy function. “Prempdi-dbAMEPNI” includes 126 mutations and the mutations from dbAMEPNI database were not included in it. R: Pearson correlation coefficient between experimental and predicted ΔΔG values, and RMSE: root-mean squared error.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TableCorrelation coefficients that are greater than 0.5 are underlined. Only correlation coefficients that are statistically significantly different from zero  are shown.(DOCX)Click here for additional data file.S7 TableThe weighting coefficients from the final PremPDI model were also shown for the comparison.(DOCX)Click here for additional data file.


CDAP ; an online package for comparing protein complex detection methods. CDAP can quickly rank the performance of methods based on previously defined as well as newly introduced criteria in various settings (4 PPI datasets and 3 gold standards). It has the capability of integrating various methods and apply several filterings on the results. CDAP can be easily extended to include new datasets, gold standards, and methods. Furthermore, the user can compare the results of a custom method with the results of existing methods. Thus, the authors of future papers can use CDAP for comparing their method with the previous ones. A case study is done on YGR198W, a well-known protein, and the detected clusters are compared to the known complexes of this protein.Methods for detecting protein complexes from protein-protein interaction networks are of the most critical computational approaches. Numerous methods have been proposed in this area. Therefore, it is necessary to evaluate them. Various metrics have been proposed in order to compare these methods. Nevertheless, it is essential to define new metrics that evaluate methods both qualitatively and quantitatively. In addition, there is no tool for the comprehensive comparison of such methods. In this paper, a new criterion is introduced that can fully evaluate protein complex detection algorithms. We introduce  In other words, a complex is a group of proteins that gather together at a specific time to perform a biological activity collectively. Individual proteins can participate in different protein complexes. Since the proteins carry out many of the biological processes in the form of complexes, study, and analysis of protein complexes is of significant importance. We know that cells of the simplest living creatures consist of thousands of proteins, so millions of different cases for constituent proteins of a complex are possible. Although there are precise experimental methods to verify the existence of a complex, being time-consuming, high cost and a huge number of candidate complexes that should be considered, have made experimental methods practically inefficient2. In these situations, it seems that computational methods can be quite effective in limiting probable cases of a complex3.Proteins are known as the smallest biological units and many biological activities are carried out by them. These small factories mainly do their own biological activities in groups which are known as complexes4. By using these methods, very large networks of interactions between proteins can be created which are a suitable bed for complex detection methods. These networks are commonly known as PPI networks and can be modeled by weighted graphs; so that each vertex represents a protein and each interaction between two proteins is represented by a weighted edge between the corresponding nodes. Different laboratory methods that detect interactions between proteins have different kinds of errors. Thus, each interaction that is detected using these methods has a different degree of reliability. The difference in the amount of reliability can be modeled by the weight of the associated edge in the mentioned graph5.Methods for detecting protein complexes from protein-protein interaction networks (PPINs) are of the most critical computational methods which have been widely used recently. Several methods have been proposed for the discovery of protein-protein interactions in recent years6. In other words, since the interactions within the proteins of a complex with each other  are usually much greater than the interactions of these proteins with other proteins outside the complex , complexes can be usually considered as dense subgraphs in PPI network8. Note that not only should the density be considered in the comparison of the number of internal interactions with external interactions, but also the total weight of internal interactions should be higher than the total weight of external interactions. Many algorithms have been proposed to detect protein complexes using this idea, among which the most important ones are the followings:9 forms clusters by a greedy method for finding maximal cliques with at least k vertices.CFinder10 uses an iterative greedy method to detect protein complexes. In each iteration, it selects a vertex as a core and extends it through the neighboring vertices with the aim of increasing the density of the growing cluster.ClusterONE11 uses the idea of partitioning the network into subgraphs with the aim of maximizing a cost function. The obtained subgraphs are introduced as candidates for protein complexes.RNSC12 removes a part of network hubs from the network. Then, by using a greedy evolutionary method such as ClusterONE, computes the initial clusters. Then, it returns some of the removed hubs to the network and applies a filtering step at the end.IMHRC4 utilizes random walk theory and Markov chains rule by iterating two stages of inflation and expansion, which are implemented using matrix multiplication and summation.MCL13 forms each cluster by selecting its core and adding the closest and most probable vertices via the random walk. The decisions for adding vertices to clusters are taken based on the maximization of flow in clusters.RRW14 ranks the proteins based on their importance. The essential proteins have high interaction and evolutionary similarity with the others. After ranking proteins, the complexes are detected using the spoke model.ProRank15 has the similar steps to ProRank. But it has several variations. ProRank requires a similarity matrix as an input which indicated the similarities between proteins, while ProRank+ do not need such input. Furthermore, ProRank+ can detect complexes with overlap and has some post-processing steps for refining complexes.ProRank+16 assesses the reliability of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient.PEWCCComplexes can be considered mainly as dense subgraphs of PPINsSince there are numerous methods for detecting protein complexes, it is essential to evaluate and compare their results. The inherent properties of complexes, such as the overlapping feature, have led to defining various evaluation metrics for comparing and ranking these algorithms. Although different evaluation criteria have been introduced in recent years, none of them are able to evaluate the results of the algorithms comprehensively and cover their shortcomings. Therefore, introducing a new criterion for analysis methods both quantitatively and qualitatively is requisite. Furthermore, an efficient online package for an extensive comparison of protein complex prediction algorithms was not available.In this paper, we present a survey of previously defined criteria and scrutinize their drawbacks. Also, we define a new evaluation metric for ranking protein complex detection algorithms.We introduce “CDAP” an online package for analysis and comparison of such algorithms. “CDAP” ranks algorithms on various PPI datasets  based on three well-known gold standard . It is able to rank methods according to each of the evaluation metrics. The functionality of the package can be easily extended to incorporate new datasets, gold standards and compare novel methods with previously developed methods. Moreover, it visualizes the performance of each method based on evaluation metrics, which can help the user to get a better visual intuition of the performance of methods. Additionally, it has several custom options for integrating and filtering the results of methods.http://www.eslahchilab.ir/softwares/cdap. In order to compare the results of protein complex detection algorithms, the user should set up the desired setting in 5 steps.The settings about the PPIN dataset must be specified. The list of PPIN datasets contains Collins, Gavin, Krogan-core, and Krogn-Extended.The gold standard should be selected. The gold standards are Mips, SGD, and CYC2008.The algorithms shall be chosen from the list of {Cfinder, ClusterOne, RNSC, IMHRC, MCL, RRW, ProRank, ProRank+, and PEWCC}. A part of this list is shown in Fig. The user can choose the type of integration, which is optional.SN, PPV and ACC), (MMR), , ,  and (AUMF). There are also some filtering options in this step which are optional.At the end, the evaluation criteria should be selected. The sets of evaluation criteria include  from the list shown in which are supposed to.et al.10. In fact, Nepusz et al. have calculated the best setting for every algorithm (except IMHRC) on each dataset and MIPS and SGD gold standards. The default values of IMHRC parameters on each dataset with MIPS and SGD gold standards are recommended by Maddi et al.12. Note that these default parameters are not for CYC2008 since it is less studied. ProRank, is the only method in this package that needs an extra biological information of protein similarities.When the user complete these steps, the server applies algorithms on PPIN and evaluate the outputs with the gold standard based on evaluation criteria. Once a method is chosen, the default parameter values are shown in the related boxes; however, the user can change them arbitrarily. It should be noted that the default values of parameters are the values that have been recommended by their authors or by Nepusz SN, PPV and ACC) or (AUMF) sets of criteria, no plot is shown, since setting a threshold for computing these criteria is meaningless. Thus, their absolute values are reported in a table.Detected clusters by the selected algorithm are downloadable from the output section of the server. The computed criteria for each of the algorithms are tabulated and also displayed via multi plots. For each criterion, the comparisons are presented in a tab that contains a multiplot and a table. The multiplot shows the computed criteria of all selected algorithms indexed by various thresholds used in the computation of criteria. The areas under these curves are projected in a table. An example of outputs is shown in Fig. θ for computing criteria based on it. To be more accurate, a cluster will be considered to be similar to a gold standard complex, if their overlap is greater than this threshold.In addition to the listed algorithms, the user may submit the detected clusters obtained by any method. It should be noted that when the results of a  new method(s) are uploaded, the selected dataset should be the one that is used the process of obtaining the results. Moreover, this server has the ability to run the mentioned algorithms on other PPIN datasets. So the user can upload custom datasets. It is also possible for the user to upload new gold standards for conducting comparison based on different complexes. The valid formats for uploading custom algorithms, PPINs and gold standards are described in Supplementary Table β methods.Moreover, the package has some option for filtering the detected clusters. The user can specify the minimum and maximum size of detected protein clusters. On the top of that, a very practical option of this package is that user can enter the STRING ID of a protein in order to receive all clusters detected by each method that contains the specified protein, an example of such applicable option is presented in the following. Another type of filtering can be done based on reliability, such that the results of methods will be filtered with the clusters that are detected by at least k is the number of selected methods). Each part contains the nodes that are representatives of its clusters. There will be an edge between two nodes of two different parts if their corresponding clusters have more than k/2, respectively. Once the union or intersection is selected, the computed criteria for the integrated results are presented in the Results tab as shown in Fig. The package has the capability to integrate the methods’ results. It can yield the intersection or union of the clusters detected by various methods. To do this, it constructs a k-partite graph  patches at the plasma membraneKnowing the complexes that included this protein, helps us to have better insight into its functionality. Four complexes have been reported for this protein in Yeast Resource Center (YRC) and Database of Interacting Proteins (DIP) which are listed in Supplementary Table Among all methods, just Cfinder, RNSC, and MCL returned clusters that contain YGR198W. Each of these methods on each setting found at most one cluster containing YGR198W, but with different size. As it is shown in Table θ. The dependency of criteria to the value of θ has several drawbacks.θ, the meaning of “true” and “false” for detected clusters is altered.By changing the values of threshold θ is crucial, challenging and hard.Setting an appropriate value for θ, the amounts of the difference of edge weights to θ are not considered. If two methods have the same bipartite graph that all weights are greater than θ and in the second graph, all edge weights are duplicated. However these two methods do not have equal performance, the calculated values of criteria for these two methods are equal; since in the calculation of threshold-based criteria, the quantities of weights are ignored.For a specified value of In our survey of previously defined criteria, it has alluded that some of the criteria values are dependent to threshold There is a critical point that should be noticed in utilizing threshold dependent criteria that is by changing the value of the threshold, a ranking of method performances will be altered. Therefore, for a specified evaluation metric and the specified value of the threshold, method A may be better than method B; while with a different value of the threshold and the same criterion, method B is better than method A.12 evaluations and validations are based on a specific value of the threshold, which is not an appropriate assessment. For example, RNSC is reported in12 as a method that does not perform well, while this conclusion is based on a specific value of the threshold. Using CDAP, one can realize that RNSC is of the best methods based on the area under curves of the criteria that are listed in Table In previous papers such asF – measure criterion, the area under the curve of the graph which plots the F – measure vs. threshold on x- and y-axes is calculated. It is worth mentioning that CDAP can compute the value of threshold dependent criteria for a particular threshold in case of user demand. Furthermore, CDAP computes two new values, namely “AUPR” and “AUPR+”. “AUPR” is the area under curve of Precision vs. Recall and “AUPR+” is the area under curve of Precision+ vs. Recall+.In addition to the definition of AUMF which is not dependent on the threshold, we revise previous criteria by considering the area under curve concept. For instance, for ACC, PPV, SN, Precision, Recall, F – measure, PrecisionN, RecallN, and MMR, each of which has some drawbacks and cannot fully reflect the quality of an algorithm. We analyzed these evaluation metrics in this paper and expressed their flaws by presenting some examples. It is reasonable that a protein complex detecting algorithm performs well whenever it returns the clusters the same as the complexes in the gold standard. So, the evaluation metrics should be able to measure the quality and quantity of one-to-one and spanning relationship between the estimated clusters and complexes in the gold standard. In this paper, we introduced a new evaluation metric MMR + Fmeasure+. The newly introduced metric can better express the quality and quantity of the one-to-one and spanning relationship between the clusters and complexes. Nevertheless, its value depends on the value of the threshold. Thus, we defined a new criterion that is invariant with respect to values of threshold, which is the Area Under  curve (AUMF). This criterion has the feature of assessing methods both qualitatively and quantitatively in addition to covering the drawbacks of previous criteria.Methods for detecting protein complexes from protein-protein interaction networks (PPIs) are one of the most critical computational methods which have been widely used recently. Several methods have been proposed for the discovery of protein-protein interactions in recent years, namely Cfinder, MCL, RNSC, RRW, ClusterOne, IMHRC, ProRank, ProRank+, and PEWCC. Evaluating these methods is of high importance. The inherent properties of complexes, such as the overlapping feature, have led to defining various evaluation metrics and criteria for comparing and ranking these algorithms. Many evaluation metrics have been proposed previously such as http://www.eslahchilab.ir/softwares/cdap. CDAP lets the user select the PPI dataset, gold standard, protein complex detection method and set of criteria for evaluation.We have developed CDAP, an online package for the comparison and visualization of the performance of protein complex detection methods. It is available at θ. In CDAP the calculated value for such criteria is the area under curves of criteria indexed by values of θ.Moreover, we revised previously defined criteria that are dependent on the threshold of 22 and CMC7 methods in our package, but there was some problem in the compatibility of platforms that we are going to remove them. We are trying our best to add these methods and other newly proposed methods to our package.CDAP also has the ability to compare the results of any custom method when its outputs are uploaded in a valid format on the website. Furthermore, one can upload new datasets or gold standards for training and evaluating algorithms. We tried to include APOne of the most applicable queries that can be done by CDAP is the option of filtering detected clusters by setting minimum or maximum size of their proteins or by specifying a protein as a query, which in this case the CDAP find all clusters detected by methods that contain the specified protein. Another type of filtering is based on the reliability such that filtered results contain only the clusters that are detected by several methods. A useful capability of the package is the integration of methods’ results via intersection and union.Due to the rapid trend of producing and analyzing data, proposing novel algorithms and defining new criteria, anyone can extend CDAP simply by sending new datasets, gold standards, methods or criteria to the contact mail. The guidelines for sending files is described in Supplementary Table To sum up, CDAP can be a very useful package to compare methods exhaustively and comprehensively and can facilitate the validation process for authors of future papers and help them in comparison of their proposed method to the previous ones.5, Gavin1, Krogan-core23 and Krogan-extended23. These datasets were used to learn methods in the package. All settings and parameters in every dataset were set based on what the original papers have proposed. We removed self-interactions and isolated proteins from all datasets. The weights of PPIs in the Gavin dataset are Socio-affinity indices, which show the log-odds of number of times that pairs of proteins are observed together as preys, or bait and a prey24. Selection of PPIs from the Collins was based on purification enrichment score which contains the top 9074 interactions. The weights of all PPIs in Krogan-core are greater than 0.273, while the weights of all PPIs in Krogan-extended are greater than 0.101.The datasets of PPINs used in this package are: Collins25. SGD includes Gene Ontology (GO) annotations which provide useful biological information for producing reference complexes.Gene Ontology-based protein complex annotations from SGD (Saccharomyces Genome Database)26 (the Munich Information Center for Protein Sequences). The MIPS catalog has a hierarchical structure so the complexes may be composed of several subcomplexes10.The catalog of protein complexes from MIPS27.The benchmark protein dataset CYC2008 that contains manually curated heteromeric protein complexesThe results were compared with three gold standards for Saccharomyces cerevisiae, namely SGD, MIPS and CYC2008:Pi) in the first part are representatives of gold standard complexes and the square nodes (Cj) in the second part are the representatives of detected clusters. Figure ni or mj) shows the number of proteins in that node (cluster or complex). The weight tij on edge  reports the number of common proteins between complex Pi and cluster Cj.A common approach for comparing and evaluating protein complex detection algorithms is to match their outputs with reference complexes in gold standard sets. We call the true complexes of the gold standard as “reference complex” and detected complexes by methods as “detected clusters”. A remarkable point is the existing overlap among protein groups. These overlaps between reference complexes, as well as the existence of similar overlap between the protein clusters detected by the above algorithms, have led to different comparison and evaluation methods. It should be noted that a reference protein complex may correspond to several clusters detected by the algorithms. Conversely, a detected cluster may correspond to more than one reference protein complex. It is worth mentioning that the relation between detected clusters and gold standard complexes is often not complete and is sometimes partial. Thus, some reference complexes may have no relation with any detected clusters and vice versa. These connections can be represented by a bipartite graph, in which the circle nodes , positive predictive value (PPV), and accuracy (ACC) introduced by Brohee and van Helden28. ACC is the geometric mean of two criteria: SN and PPV. Suppose that there are n complexes in a gold standard as the member of the reference complex and m clusters are detected by an algorithm, tij represents the number of proteins that the reference complex i and the detected cluster j share in common and ni denotes the number of proteins that are in the reference complex i. SN and PPV are defined as follows:The first category consists of sensitivity . Although, PPV tends to 1 by repeating similar clusters, while the results of algorithms is not great. This issue can be seen in Figs  Additionally, if there are a lot of similar groups between the detected clusters, the value of this criterion is almost equal to value of PPV is defined in such a way that the accumulation of proteins in a group (the existence of a giant component) leads to reduction of PPV by incresing the denominator. Notice to PPV values in Fig. PPV ignores false negatives, i.e. if there are so many complexes that are not matched by clusters well, this issue do not decrease PPV value, but it declines SN value. Figures ACC) is used to balance the values of the two criteria of SN and PPV.Therefore, both of these criteria has some drawbacks. So the accuracy criterion  often acquire high values of PPV and SN. And False negatives does not affect PPV, but they decline SN. Therefore, it is essential to analyze methods with another criterion.In sum, false positives does not affect PreN, RecN, F – measureNet al.14 defined a set of criteria which is almost analogous to the previous set :PPV, Sn, ACC). The threshold-dependency of is an extra disadvantage of this set of criteria.Zaki MMRMMR  criterion was introduced in10 to overcome some of mentioned drawbacks of previous criteria. In this criterion, a complete bipartite weighted graph is constructed based on threshold θ similar to the mentioned graph whereNA, which is known as neighborhood affinity score, shows how the reference complex Pi has been able to match the detected cluster Cj3.In this definition, MMR value is calculated by normalizing the total weight of the maximal matching edges on the number of reference complexes, so its value is in range MMR. Because adding similar clusters to a set of detected clusters do not change the maximum matching. Thus, both the numerator and denominator do not change. Dependency of this criterion to the value of θ is its another disadvantage.By applying a weighted bipartite graph matching algorithm on this graph, a one-to-one mapping between the first and second sections is obtained. The Quantitative metricsPrecision, Recall and F – measureF – measure, Precision and Recall. F – measure is the harmonic mean of Precision and Recall criteria. These criteria are defined as follows:et al. used slightly different versions of Precision and Recall as follows14:This category of criteria includes Precc and Recc have almost similar meaning to Precision and Recall, we do not incorporate Precc and Recc in CDAP.Since Both of these category of criteria attempt to examine the results of protein complex detection algorithms quantitatively. In fact, in these set of criteria, the main attention is to the number of detected clusters and less attention is paid to the internal quality of these clusters. Thus, the matching size more than a specified threshold cannot influence the quality measured by these criteria. This category has several drawbacks:θ threshold increase the error rate when the size of the groups get bigger. For example, if Recall does not change but Precision increases which lead to an increase in F – measure. The reason for this drawbacks is that several clusters can be validated by one complexThe existence of exorbitant overlapping complexes does not have a significant effect on quality reduction. In other words, the low values for θ.The values of the mentioned criteria is dependent on the value of In order to address this issue, we introduce a new set of criteria.MMR shortcomings, ACC and Recall have been used in2. As explained above, ACC has a fundamental defect due to the presence of overlap between groups. In addition, ACC measures the groups qualitatively similar to MMR. Thus, there is no need for ACC criterion while using MMR. Moreover, usage of Recall criterion solely and without Precision criterion does not make sense. Therefore, the use of F – measure instead of the Recall criterion is more logical. One can add quantitative evaluation to the main evaluation process by using F – measure; however, all the deficiencies have not been resolved yet due to the mentioned defects for the second category. We used MMR criterion alongside the modified F – measure to have a more accurate evaluation system. The modified F – measure, Precision, and Recall are represented by F – measure+, Recall+, and Precision+, respectively.As mentioned in the previous section, each set of criteria has some drawbacks. One way to have a more expressive set of criteria is to gather various criteria. In order to cover θ. Since both MMR and F – measure+ are in range In this definition, Obviously, the best algorithm for identifying protein complexes is an algorithm that has a one-to-one and spanning relationship between the detected clusters and the complexes within the gold standard. Therefore, this metric is suitable for evaluation, which can measure the quantity as well as the quality of this relation, while the old metrics do not have this property. The criterion introduced in this section explicitly examines the quality and quantity of such a relationship. It can be inferred that this criterion takes its maximum value 2 when the algorithm returns the clusters the same as the gold standard complexes. The values of this criterion for all methods applied on Collins dataset and compared with Mips gold standard is shown in Fig. θ. Thus, for having a metric that is invariant with respect to θ, we consider the Area Under  curve (AUMF) as a criterion for evaluating the results of the algorithms. The values of AUMF for 8 methods with default parameter values on each pair of dataset and gold standard are reported in Supplementary Table This criterion still depends on the value of Supplementary FileLaTeX Supplementary File


Protein-Protein Interaction Sitesbase (PPInS), a high-performance database of protein-protein interacting interfaces, is presented. The atomic level information of the molecular interaction happening amongst various protein chains in protein-protein complexes (as reported in the Protein Data Bank [PDB]) together with their evolutionary information in Structural Classification of Proteins (SCOPe release 2.06), is made available in PPInS. Total 32468 PDB files representing X-ray crystallized multimeric protein-protein complexes with structural resolution better than 2.5 Å had been shortlisted to demarcate the protein-protein interaction interfaces (PPIIs). A total of 111857 PPIIs with ~32.24 million atomic contact pairs (ACPs) were generated and made available on a web server for on-site analysis and downloading purpose. All these PPIIs and protein-protein interacting patches (PPIPs) involved in them, were also analyzed in terms of a number of residues contributing in patch formation, their hydrophobic nature, amount of surface area they contributed in binding, and their homo and heterodimeric nature, to describe the diversity of information covered in PPInS. It was observed that 42.37% of total PPIPs were made up of 6–20 interacting residues, 53.08% PPIPs had interface area ≤1000 Å2 in PPII formation, 82.64% PPIPs were reported with hydrophobicity score of ≤10, and 73.26% PPIPs were homologous to each other with the sequence similarity score ranging from 75–100%. A subset “Non-Redundant Database (NRDB)” of the PPInS containing 2265 PPIIs, with over 1.8 million ACPs corresponding to the 1931 protein-protein complexes (PDBs), was also designed by removing structural redundancies at the level of SCOP superfamily (SCOP release 1.75). The web interface of the PPInS (http://www.cup.edu.in:99/ppins/home.php) offers an easy-to-navigate, intuitive and user-friendly environment, and can be accessed by providing PDB ID, SCOP superfamily ID, and protein sequence. Another approach has been to combine the experimentally gathered interaction data (generated by the application of high-throughput techniques) to identify the protein-protein complexes and databases like GRID4; DIP5; IntAct6; BIND7; MINT8; HPRD9; and STRING10 store such information. These databases have also been combined to evolve a more participative approach, such as HitPredict11, with a multitude of protocols to quantitatively score the PPI. Some groups have also looked at the residue-based interactions, by employing a “distance dependent atom contact” definition, to look at the “set of interacting residues”. Databases like HotRegion12, JAIL13, SNAPPI-DB14, etc. have considered “interacting interface residue set” as a more apt parameter for characterization of PPIs.Proteins are the biomolecular substance which are responsible for a large number of cellular processes like catalysis of biochemical reactions, transportation of molecules, synthesis and repair of DNA molecules, etc. in living organisms. Considering their inability to perform in isolation, proteins interact with other molecules like protein, DNA, lipid, etc., and forms the supramolecular entities and carries out most of these biological functioning in living beings. Among all type of complexes that proteins form, protein-protein complexes have attracted the attention of a wide range of scientific community to decipher their underlying principles, role in the various biological phenomenon, and applicability in therapeutic strategies. Consequently, a substantial amount of experimental data, attributed to the technological advancement, pertaining to the protein-protein interactions (PPIs) has been made available. Several attempts have been made by people to organize this PPI data. As a result, databases like 3D Interlogs15, Ofran and Rost16, and Murakami and Jones17 were designed by analysis the dataset of known proteins-protein complexes from structural perspectives. The approach given by Aytuna et al.18 was emphasized on the structure and sequence conservation based features of PPI sites retrieved from known protein complexes. Sequence profile of proteins was assessed by Zhou and Shan19 and Shen et al.20 for their ability to function as protein-protein interaction descriptor. “Weighted sparse representation” based classification and the concept of global encoding was studied in a protein sequence-oriented approach of Huang et al.21. Evolutionary information of known PPI sites was examined in conjunction with machine learning approaches like neural network22 and random forest23. Newer concepts in machine learning approaches viz. “relevance vector machine”24, “discriminative vector machine”25, and “rotation forest based classifier”26 were also introduced to get new perspective from the protein evolutionary information. In addition to these, hot spot residues-based27, knowledge-based28, and ensemble approaches30 were also devised by examining the information derived from the known protein-protein interactions.Development of such databases has huge applications in designing computational approaches for PPI sites prediction. Information derived by analysing existing PPIs in terms of protein sequences, structural information, evolutionary conservation, binding-energy, etc., provides scientific facts that need be considered in formulating novel computational strategies. For example, patch analysis based approaches of Jones and ThorntonIn this article, we are presenting the Protein-Protein Interaction Sitesbase (PPInS), a database with high-performance and one of the largest collection, to the best of our knowledge, of protein-protein interaction interfaces (PPIIs). Each PPII is linked to its cognate SCOP superfamily pair. PPInS is an advancement over all existing databases as it not only provides a clear demarcation of PPIIs but also gives information like number and type of residues , SCOP superfamily of interacting patches, and the three-dimensional structural representation of complex under the study. It also covers a vast diversity of protein-protein interaction patches (PPIPs) in terms of a number of residues involved in PPIP formation, their hydrophobicity level, homo and heterodimeric nature of interacting PPIPs, and the amount of surface of the PPIPs devoted to PPII formation. A smaller subset of PPInS is also designed by removing all structural redundancies at the level of SCOP superfamily (SCOP release 1.75).31), around 81 K PDBs for which the information of structural classification was reported in SCOPe32 , were processed. Only X-ray crystallized multimeric protein-protein complexes with structural resolution better than 2.5 Å were retained for this work. Further, to ensure that only experimentally validated protein structures were used, PDBs representing homology models, other than X-ray crystallography-based structures, nucleic acids and multi-model structures were discarded. At the end, we were left with 32468 PDBs , and Kulharia et al.35. Another type of distance criteria for demarcating the PPI has been to use a sphere of fixed radius 37 centered on the interacting atoms. Here we have used the atom specific distance criteria wherein the threshold value to determine the presence or absence of interaction is calculated by taking into account the type of atoms. The collection of ACPs between a pair of interacting protein chains was referred as “Protein-Protein Interaction Interface” (PPII). For the notational purpose, atoms from each protein chain involved in PPII formation were collectively termed as a protein-protein interaction patch (PPIP). With these definitions, a total of 111857 PPIIs, with around 32 million ACPs in them, were generated from the 32468 PDBs and given the name of Protein-Protein Interaction Sitesbase (PPInS).Two atoms belonging to two different protein chains of a PDB were considered to be in contact and demarcated as an atomic contact pair (ACP) if the intervening distance between them was less than the sum of their van der Waals radii plus 1 Å as tolerance factor Fig. . Toleran38 version 1.75 was used which had structural classification data for 38221 PDBs. Using the same filtering approach as mentioned in designing of PPInS, 13797 PDBs were shortlisted for the construction of NRDB  was designed using php scripts and Apache web server. The complete overview of PPInS implementation is given in Fig. Web server for PPInS  in a downloadable format to the user. For on-site analysis of PPIIs, information like cognate SCOP superfamily of the interacting protein chains in the PPIIs, number of amino acids in interacting chains and their role in PPII , are fetched from the local database and displayed to the user. A feature to compare the query protein sequence of a given PPII against the database of protein sequences of PPInS, using BLAST40 algorithm. We have kept the threshold for sequence similarity as ≥90% to ensure only close homologs are clustered in one group. For each cluster, a cluster head (or cluster representative) is designated amongst the cluster members by CD-HIT in such a manner that the cognate cluster members have sequence similarity of ≥90% to the cluster head. In this way, a total of 13190 clusters were produced. The protein sequence entered by user is aligned with cluster heads of these 13190 clusters and cluster heads with ≥90% sequence similarity are presented to the user. Such cluster heads, along with their cluster members, are seen participating as PPIPs in PPII formation, therefore, these clusters heads are provided to user in the forms of PPII for downloading purpose. Two case studies describing the usage of PPInS is also provided here.PPInS also provide for an additional utility, to identify close homologs for a given protein sequence that participates in formations of PPIIs. To improve the speed for the homolog detection, all protein chains from the known three-dimensional complexes included in PPInS are clustered on the basis of their sequence similarity using CD-HITviz. A_B_150 L.int, A_C_150 L.int, B_C_150L.int, B_D_150L.int, and C_D_150L.int, representing the PPIIs between chains “A&B”, “A&C”, “B&C”, “B&D” and “C&D”, respectively. The user has an option of downloading these PPIIs files selectively or collectively in zip format by clicking on the “Download” button. The user can also access more information on PPII by clicking on “More Info” button given next to each PPII, which includes:Let us consider the PDB ID “150 L”  as an input. This file has a pentanary complex which can be deconvoluted as five binary protein-protein complexes The SCOP superfamily identity of both the protein chains involved in PPII is displayed to the user. For example, in the current instance of PDB “150L”, each of its protein chains participating in its five PPIIs belong to SCOP superfamily ID 53955. It is also possible for a protein chain to have multiple SCOP superfamily identities i.e. when one domain of protein belongs to one SCOP superfamily while a different SCOP superfamily is reported for another domain.The amino acid sequences of the chains involved in a PPII is also displayed to the user. The role of each amino acid in the formation of PPII is depicted by a coloring scheme. The amino acids are marked red, blue and pink representing the “interacting”, “non-interacting” and “missing or unknown amino acids”, respectively.The user has an option to identify proteins similar in sequence  to a given protein. This feature is a method for the user to look for the homologous protein-protein complex interface. Each protein chain is compared for sequence similarity, using BLAST, against the comprehensive database of interacting protein chains from which the PPInS has been derived. The results are displayed in a format of <chain one>_<chain two>_<PDB code>. For example “A_C_150L” specifies that “A” chain of the PPII between protein chains A and C of 150L is similar to the queried protein chain. The extent of sequence similarity for these two protein chains , is also mentioned as a part of the BLAST result. The PPIIs A_C_150L and C_A_150L both are same, and only the former exists in the PPInS.The three-dimensional view of protein structure using the JSmol online viewer.On inputting SCOP superfamily “63707” to the PPInS, a list of 10 PPIIs will be displayed with an option to download them. Each of these 10 PPIIs has at least one interacting protein chain which belongs to SCOP superfamily “63707”. All the functions that are discussed for the retrieved PPIIs in case study 1 are also applicable here too.Desulfovibrio africanus. This was not surprising as homodimer generally tend to have larger interacting interface due to symmetrical structural arrangement around interaction zone. The analysis of PPInS revealed that around 19.39% PPIPs were very short ones and comprised of 1–5 amino acids, 42.37% PPIPs consisted of 6–20 interacting residues, 25.91% PPIPs were reported with 21–40 interacting residues, while only 7.91% PPIPs were made up of 41–60 interacting residues . It was observed that 53.08% of PPIPs had less than 1000 Å2 SASA, 27.53% PPIPs had 1001–2000 Å2, while 12.03% PPIPs were reported with 2001–3000 Å2 SASA  to 3.49 (for aspartic acid) was used for this purpose. A PPIP with the less hydrophobic score on this scale implies its high hydrophobicity. Observations made from this analysis underlined the hydrophobic nature of PPI sites as it was observed that 82.64% PPIPs were reported with hydrophobicity score of less than 10 and 14.2% PPIPs were reported with hydrophobicity score in the range of 11–20  or when a single mutation is catastrophic in itself for the structural integrity of the complex does the protein-protein interaction partners stop interacting. This is analogous to the trend observed in Fig. 2 their protein surface in PPII formation, 82.64% PPIPs were reported with hydrophobicity score ≤10, and 73.26% PPIPs were homologous to each other with the sequence similarity score ranging from 75–100%.Proteins are the most important molecular machines whose functional involves a network of dynamic interaction with different molecular surfaces. Understanding these interaction networks help in getting a clearer picture of the cellular processes. A number of experimental and theoretical approaches have been implemented by various research groups for the study of the PPIs. These have resulted in the generation of the very wide amount of data. In this paper, a novel approach to organizing the interaction data is presented in the form of PPInS. Such databases are very useful for the researchers for mining the available information in a logical, systematic and comprehensive fashion. PPInS looks at the protein-protein interaction at the atomic level interaction of the protein surfaces and categorizes the interactions based on the SCOP superfamily (a measure of evolutionary conservation of structure). PPInS has been designed to solely from the experimentally determined structures. To keep the confidence level in the complex structures and to minimize the experimental errors we have restricted the PPInS to include only those structures that have resolution better than 2.5 Å. The interacting partners of protein-protein complexes were demarcated by incorporating the concept of interatomic distances and van der Waals radii of atoms from the interacting proteins of the known protein-protein complex. The PPIIs demarcated in this work has been used in examining the PPIPs with respect to hydrophobicity, solvent accessible surface area, and the number of amino acids contributing in the interacting patch. The homo and heterodimeric nature of the PPIPs in PPIIs was also determined. From which it was observed that 42.37% of total PPIPs were made up of 6–20 interacting residues, 53.08% PPIPs had contributed ≤1000 ÅPPInS offers the maximum output in terms of the sheer size and the vast coverage of protein-protein complexes categorized on the evolutionary structural conservation of the interacting proteins. The additional information provided in the context of interacting proteins like graphical representation of the interacting, non-interacting and missing amino acids, SCOP superfamily of interacting proteins, total number of amino acids in interacting proteins, functionality to search for similar protein sequences in PPInS, and three-dimensional structural representation of the protein-protein complex under observation, makes the PPInS more informative.et al.42 and Taherzadah et al.43, structure based approaches of Moal et al.44 and Xue et al.45, energy based approaches of Lise et al.46, evolutionary conservation based methods of Kotlyar et al.47, Li et al.48, Huang et al.49, position-specific scoring matrix based approaches for identification of self-interacting proteins by An et al.50, and machine learning based approach of Guo et al.51. The non-redundant database of PPIIs (NRDB) is created to facilitate the developments of such prediction tools. The NRDB is peculiar in having very stringent redundancy control which makes it suited for assessing the various PPI sites parameters (as mentioned above) on a larger scale to draw patterns about the PPI sites and interacting partners.An unbiased and “proportionally representative” comprehensive dataset is critical for the development of novel PPI prediction tools. In the recent past, vast gamut of methods have been developed for predicting the interacting interface residues, the affinity of protein-protein interaction and conformational changes associated with the binding process. These methods use sequence, protein structure information or more fundamental descriptors for physicochemical characteristics. Such information has been used to generate knowledge-defined prediction methods (based on semantics and syntactic of protein sequence or structure) or for training the machine learning methods based on protein sequences recently proposed by You