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Updated: Jul 9, 2026
07:48
Tracking Drug-induced Changes in Receptor Post-internalization Trafficking by Colocalizational Analysis
Published on: July 3, 2015
LTHREADER: prediction of extracellular ligand-receptor interactions in cytokines using localized threading
Vinay Pulim1, Jadwiga Bienkowska, Bonnie Berger
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA.
Protein Science : a Publication of the Protein Society
|December 22, 2007
Summary
We developed LTHREADER, a novel computational method for predicting ligand-receptor interactions. This tool improves accuracy in identifying disease-related protein interactions, aiding drug design and disease treatment.
Area of Science:
- Computational biology
- Bioinformatics
- Structural biology
Background:
- Extracellular ligand-receptor interactions are crucial for drug design and disease treatment.
- Experimental high-throughput techniques for detecting these interactions face limitations.
- Computational prediction methods are needed to overcome experimental challenges.
Purpose of the Study:
- To introduce LTHREADER, a novel threading algorithm for accurate prediction of ligand-receptor interactions.
- To improve the identification of interacting residues within protein families.
- To enhance computational methods for predicting protein-protein interactions relevant to diseases.
Main Methods:
- LTHREADER employs a threading algorithm for local sequence-structure interface alignments.
- It integrates secondary structure and solvent accessibility predictions with residue contact maps.
- A decision tree classifier trained on experimental data combines statistical scores, energy functions, and mutation data for prediction.
Main Results:
- LTHREADER demonstrated superior accuracy in local sequence-structure interface alignments compared to state-of-the-art methods like RAPTOR.
- For the 4-helical long-chain cytokine family, LTHREADER achieved 75% sensitivity and 86% specificity, a 40% gain in sensitivity over RAPTOR.
- In the TNF-like family, LTHREADER achieved 70% sensitivity and 55% specificity, with a 70% gain in sensitivity.
- A localized PSI-BLAST approach showed 25%-50% improvement in sensitivity when only one structure was available.
Conclusions:
- LTHREADER provides a significant advancement in predicting ligand-receptor interactions, outperforming existing threading methods.
- The method accurately predicts interactions in diverse cytokine families, relevant to cancer and inflammatory diseases.
- LTHREADER's ability to integrate multiple data sources enhances its predictive power for protein-protein binding sites.
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