Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
highlights:2025:itercr [2025/10/23 08:57] – Didier Barradas Bautistahighlights:2025:itercr [2025/10/23 10:48] (current) – Didier Barradas Bautista
Line 10: Line 10:
  
 <WRAP center> <WRAP center>
-{{:wiki:highlights:2025:PPI_IterCR_v1.png?400|}}+{{:wiki:highlights:2025:PPI_IterCR_v1.png?600|}}
 </WRAP> </WRAP>
  
-<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 \\
Line 32: Line 32:
  
 ===== Research Breakthrough ===== ===== Research Breakthrough =====
-<WRAP hi> +<WRAP>
-<fs 20>+
 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”
 </WRAP>  </WRAP> 
      
-<WRAP center> +
-{{:wiki:highlights:2025:membrane3d.png?1000}} +
-</WRAP>+
  
 <WRAP notice> <WRAP notice>
 <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]]. 
 <WRAP info> <WRAP info>
 +<WRAP center>
 +{{:wiki:highlights:2025:Graphical_abstract.gif|}}
 +</WRAP>
 +
 +
 +
 +</WRAP> /* end column */
 <fs 18>**🎯 KVL's Contribution**</fs> <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: KVL's visualization scientist **Didier Barradas-Bautista** contributed significantly to the development and validation of the Iter-CONSRANK algorithm through:
  
-Design and implementation of contact-based clustering to enhance scoring accuracy +  * Design and implementation of contact-based clustering to enhance scoring accuracy 
-Integration of COCOMAPS for interface contact analysis +  * Generation of the 3K-BM5up benchmark dataset using multiple docking tools 
-Generation of the 3K-BM5up benchmark dataset using multiple docking tools +  * Optimization of iteration parameters and performance evaluation across difficulty categories 
-Optimization of iteration parameters and performance evaluation across difficulty categories +  * Comparative benchmarking against 157 scoring functions, demonstrating top performance 
-Comparative benchmarking against 157 scoring functions, demonstrating top performance +  * Public release of the Iter-CONSRANK software for community use 
-Public release of the Iter-CONSRANK software for community use +  * Active participation in CAPRI scoring rounds, validating the method in blind tests
-Active participation in CAPRI scoring rounds, validating the method in blind tests+
  
-</WRAP> /* end column */ 
 </WRAP> /* end group */ </WRAP> /* end group */