LTHREADER: prediction of extracellular ligand-receptor interactions in cytokines using localized threading.
Identification of extracellular ligand-receptor interactions is important for drug design and the treatment of diseases. Difficulties in detecting these interactions using high-throughput experimental techniques motivate the development of computational prediction methods. We propose a novel threading algorithm, LTHREADER, which generates accurate local sequence-structure interface alignments and integrates various statistical scores and experimental binding data to predict interactions within ligand-receptor families. LTHREADER uses a profile of secondary structure and solvent accessibility predictions with residue contact maps to guide and constrain alignments. Using a decision tree classifier and low-throughput experimental data for training, it combines information inferred from statistical interaction potentials, energy functions, correlated mutations, and conserved residue pairs to predict interactions. We apply our method to cytokines, which play a central role in the development of many diseases including cancer and inflammatory and autoimmune disorders. We tested our approach on two representative families from different structural classes (all-alpha and all-beta proteins) of cytokines. In comparison with the state-of-the-art threader RAPTOR, LTHREADER generates on average 20% more accurate alignments of interacting residues. Furthermore, in cross-validation tests, LTHREADER correctly predicts experimentally confirmed interactions for a common binding mode within the 4-helical long-chain cytokine family with 75% sensitivity and 86% specificity with 40% gain in sensitivity compared to RAPTOR. For the TNF-like family our method achieves 70% sensitivity with 55% specificity with 70% gain in sensitivity. LTHREADER combines information from multiple complex templates when such data are available. When only one solved structure is available, a localized PSI-BLAST approach also outperforms standard threading methods with 25%-50% improvements in sensitivity.
Pulim
Vinay
V
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA.
Bienkowska
Jadwiga
J
Berger
Bonnie
B
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D016207:Cytokines / Q000737:chemistry* / Q000378:metabolism*; D008024:Ligands; D008969:Molecular Sequence Data; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D012984:Software
D016207:Cytokines; D008024:Ligands