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highlights:2025:itercr [2025/10/23 08:47] – Didier Barradas Bautistahighlights:2025:itercr [2025/10/23 10:48] (current) – Didier Barradas Bautista
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-<fs 32>🧬 Increasing the Fraction of Correct Solutions in Ensembles of Protein-Protein Docking Models by an Iterative Consensus Algorithm +<fs 32> Increasing the Fraction of Correct Solutions in Ensembles of Protein-Protein Docking Models by an Iterative Consensus Algorithm 
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-{{:wiki:highlights:2025:PPI_IterCR_v1.png?400|}}+{{:wiki:highlights:2025:PPI_IterCR_v1.png?600|}}
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-<WRAP center round important box 100% large> {{:favicon.ico?nolink&0x30}} <fs:26px>** KVL Staff on Project**</fs>+<WRAP center round box 100% large> {{:favicon.ico?nolink&0x30}} <fs:26px>** KVL Staff on Project**</fs>
  
 {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Didier Barradas Bautista \\ {{:icon:twbs:link:w-25:h-auto:person.svg?nolink&}} Didier Barradas Bautista \\
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 ===== Research Breakthrough ===== ===== Research Breakthrough =====
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 Protein–protein docking generates thousands of possible interaction models—but only a few are correct. In our latest collaborative work, we introduce Iter-CONSRANK, an iterative scoring algorithm that filters out incorrect models and enriches the ensemble with correct ones. Protein–protein docking generates thousands of possible interaction models—but only a few are correct. In our latest collaborative work, we introduce Iter-CONSRANK, an iterative scoring algorithm that filters out incorrect models and enriches the ensemble with correct ones.
-📊 Tested on two challenging datasets, Iter-CONSRANK increased the fraction of correct models by up to 8× for medium-difficulty targets and outperformed over 150 scoring functions in ranking accuracy. +  * 📊 Tested on two challenging datasets, Iter-CONSRANK increased the fraction of correct models by up to 8× for medium-difficulty targets and outperformed over 150 scoring functions in ranking accuracy. 
-🎯 The method is available for use in pre-processing docking ensembles or as an independent scoring tool. +  * 🎯 The method is available for use in pre-processing docking ensembles or as an independent scoring tool. 
-🔬 This work was led in collaboration with Luigi Cavallo and our partners at the University of Naples “Parthenope”.</fs>+  * 🔬 This work was led in collaboration with Luigi Cavallo and our partners at the University of Naples “Parthenope”
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 <fs 18>**🔬 Research Visualization**</fs> <fs 18>**🔬 Research Visualization**</fs>
-The stunning graphical abstract above showcases the innovative approach used in this groundbreaking research. The paper entitled "Impact of active layer morphology on salt permeability in RO composite membranes: 3D modelling from TEM geometry and effective membrane thickness" can be accessed [[https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.70314|here]]. Please note that all figures used in this article are directly from the original published paper. +The graphical abstract below illustrates the iterative filtering strategy used to enrich correct protein–protein docking models. The paper entitled "Increasing the Fraction of Correct Solutions in Ensembles of Protein-Protein Docking Models by an Iterative Consensus Algorithm" is available for download and review. [[https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.70314|here]]. 
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-<fs 18>**🎯 KVL's Contribution**</fs> +<WRAP center> 
-KVL's visualization expert Dr. Ronell Sicat provided crucial support through: +{{:wiki:highlights:2025:Graphical_abstract.gif|}} 
-  * Advanced pre-processing of TEM scans +</WRAP> 
-  * Development and testing of segmentation methods + 
-  * Expert post-processing of segmentation data +
-  * Creation of simulation-ready 3D geometries KVL also provided free access to Avizo software for data visualization, segmentation, and analysis and a powerful workstation with a pen-and-tablet interface that enabled faster manual segmentation of the TEM data. KVL's contribution is acknowledged in the paper.+
  
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 +<fs 18>**🎯 KVL's Contribution**</fs>
 +KVL's visualization scientist **Didier Barradas-Bautista** contributed significantly to the development and validation of the Iter-CONSRANK algorithm through:
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 +  * Design and implementation of contact-based clustering to enhance scoring accuracy
 +  * Generation of the 3K-BM5up benchmark dataset using multiple docking tools
 +  * Optimization of iteration parameters and performance evaluation across difficulty categories
 +  * Comparative benchmarking against 157 scoring functions, demonstrating top performance
 +  * Public release of the Iter-CONSRANK software for community use
 +  * Active participation in CAPRI scoring rounds, validating the method in blind tests
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-📎 Read the full paper: [Link Placeholder]+