Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

Chermak, Edrisse; Donato, Renato De; Lensink, Marc F.; Petta, Andrea; Serra, Luigi; Scarano, Vittorio; Cavallo, Luigi; Oliva, Romina

doi:10.1371/journal.pone.0166460

Cited by 18 publications

(14 citation statements)

References 34 publications

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“…TCGA breast cancer RNA-Seq gene level expression data was downloaded from GDC data portal using TCGAbiolinks [4, 5]. Matched breast primary tumor-normal samples with available clinical, gene expression quantification data, 450K methylation and Copy Number Variation (CNV) information were extracted, resulting in total of 75 matched samples.…”

Section: Methods and Resultsmentioning

confidence: 99%

NOJAH: Not Just Another Heatmap for Genome-Wide Cluster Analysis

Rupji

Dwivedi

Kowalski

2018

Preprint

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23Since their inception, several tools have been developed for cluster analysis and 24 heatmap construction. The application of such tools to the number and types of genome-25 wide data available from next generation sequencing (NGS) technologies requires the 26 adaptation of statistical concepts, such as in defining a most variable gene set, and more 27 intricate cluster analyses method to address multiple omic data types. Additionally, the 28 growing number of publicly available datasets has created the desire to estimate the 29 statistical significance of a gene signature derived from one dataset to similarly group 30 samples based on another dataset. The currently available number of tools and their 31 combined use for generating heatmaps, along with the several adaptations of statistical 32 concepts for addressing the higher dimensionality of genome-wide NGS-derived data, 33 has created a further challenge in the ability to replicate heatmap results. We introduce 34 NOJAH (NOt Just Another Heatmap), an interactive tool that defines and implements a 35 workflow for genome-wide cluster analysis and heatmap construction by creating and 36 combining several tools into a single user interface. NOJAH includes several newly 37 developed scripts for techniques that though frequently applied are not sufficiently 38 documented to allow for replicability of results. These techniques include: defining a most 39 variable gene set (a.k.a., 'core genes'), estimating the statistical significance of a gene 40 signature to separate samples into clusters, and performing a result merging integrated 41 cluster analysis. With only a user uploaded dataset, NOJAH provides as output, among 42 other things, the minimum documentation required for replicating heatmap results. 43 Additionally, NOJAH contains five different existing R packages that are connected in the 44 interface by their functionality as part of a defined workflow for genome-wide cluster 45 analysis. The NOJAH application tool is available at 46

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Section: Methods and Resultsmentioning

confidence: 99%

NOJAH: Not Just Another Heatmap for Genome-Wide Cluster Analysis

Rupji

Dwivedi

Kowalski

2018

Preprint

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“…The number of maximum clusters, t, was set to 1/10 of the total models available per target/interface, to make the approach independent of the ensemble size. For further details see [20]. Clusters are ranked based on their population and are numbered consecutively, starting from 0.…”

Section: Consrank and Clust-consrankmentioning

confidence: 99%

“…Results relative to the most recently analyzed models are contemporarily reported in the maps in different colors, helping the user to visualize at a glance how much them resemble each other and how well each of them reflects the overall consensus. Later on, we developed Clust-CONSRANK, an algorithm introducing a contact-based clustering of the models as a preliminary step of the CONSRANK scoring process [20]. We conceived of it for the most challenging scoring cases, to the aim of differentiating their top 10 ranked models and possibly enrich them in correct solutions.…”

Section: Introductionmentioning

confidence: 99%

The CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses

et al. 2020

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Background Properly scoring protein-protein docking models to single out the correct ones is an open challenge, also object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), a community-wide blind docking experiment. We introduced in the field CONSRANK (CONSensus RANKing), the first pure consensus method. Also available as a web server, CONSRANK ranks docking models in an ensemble based on their ability to match the most frequent inter-residue contacts in it. We have been blindly testing CONSRANK in all the latest CAPRI rounds, where we showed it to perform competitively with the state-of-the-art energy and knowledge-based scoring functions. More recently, we developed Clust-CONSRANK, an algorithm introducing a contact-based clustering of the models as a preliminary step of the CONSRANK scoring process. In the latest CASP13-CAPRI joint experiment, we participated as scorers with a novel pipeline, combining both our scoring tools, CONSRANK and Clust-CONSRANK, with our interface analysis tool COCOMAPS. Selection of the 10 models for submission was guided by the strength of the emerging consensus, and their final ranking was assisted by results of the interface analysis. Results As a result of the above approach, we were by far the first scorer in the CASP13-CAPRI top-1 ranking, having high/medium quality models ranked at the top-1 position for the majority of targets (11 out of the total 19). We were also the first scorer in the top-10 ranking, on a par with another group, and the second scorer in the top-5 ranking. Further, we topped the ranking relative to the prediction of binding interfaces, among all the scorers and predictors. Using the CASP13-CAPRI targets as case studies, we illustrate here in detail the approach we adopted. Conclusions Introducing some flexibility in the final model selection and ranking, as well as differentiating the adopted scoring approach depending on the targets were the key assets for our highly successful performance, as compared to previous CAPRI rounds. The approach we propose is entirely based on methods made available to the community and could thus be reproduced by any user.

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“…They were clustered separately in search of recurrent solutions from different servers. The adopted clustering approach was an agglomerative (bottom-up), hierarchical one, recently described for the analysis and ranking of protein-protein docking models (48). As a distance measure we used the ligand root-mean-square deviation, calculated on the backbone of FN, after best superimposition of the receptor.…”

Section: Docking Simulationsmentioning

confidence: 99%

Mapping the minimum domain of the fibronectin binding site on transglutaminase 2 (TG2) and its importance in mediating signaling, adhesion, and migration in TG2‐expressing cells

et al. 2018

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The interaction between the enzyme transglutaminase 2 (TG2) and fibronectin (FN) is involved in the cell‐matrix interactions that regulate cell signaling, adhesion, and migration and play central roles in pathologic conditions, particularly fibrosis and cancer. A precise definition of the exact interaction domains on both proteins could provide a tool to design novel molecules with potential therapeutic applications. Although specific residues involved in the interaction within TG2 have been analyzed, little is known regarding the TG2 binding site on FN. This site has been mapped to a large internal 45‐kDa protein fragment coincident with the gelatin binding domain (GBD). With the goal of defining the minimal FN interacting domain for TG2, we produced several expression constructs encoding different portions or modules of the GBD and tested their binding and functional properties. The results demonstrate that the I8 module is necessary and sufficient for TG2‐binding in vitro, but does not have functional effects on TG2‐expressing cells. Modules I7 and I9 increase the strength of the binding and are required for cell adhesion. A 15‐kDa fragment encompassing modules I7–9 behaves as the whole 45‐kDa GBD and mediates signaling, adhesion, spreading, and migration of TG2+ cells. This study provides new insights into the mechanism for TG2 binding to FN.—Soluri, M. F., Boccafoschi, F., Cotella, D., Moro, L., Forestieri, G., Autiero, I., Cavallo, L., Oliva, R., Griffin, M., Wang, Z., Santoro, C., Sblattero, D. Mapping the minimum domain of the fibronectin binding site on transglutaminase 2 (TG2) and its importance in mediating signaling, adhesion, and migration in TG2‐expressing cells. FASEB J. 33, 2327–2342 (2019). http://www.fasebj.org

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Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

Cited by 18 publications

References 34 publications

NOJAH: Not Just Another Heatmap for Genome-Wide Cluster Analysis

NOJAH: Not Just Another Heatmap for Genome-Wide Cluster Analysis

The CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses

Mapping the minimum domain of the fibronectin binding site on transglutaminase 2 (TG2) and its importance in mediating signaling, adhesion, and migration in TG2‐expressing cells

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