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Prediction of interacting RNA structure can be considered as a kind of optimization problem in a sense that we seek to minimize the free energy of the joint structure or maximize a score such as an interaction probability under the possible topology of the interacting structure.
We provide a prediction of interacting TFs in 22 human tissues based on their DNA-binding affinity in promoter regions.
In the present work, we report a novel sequence-based method for the prediction of interacting protein pairs using ELM combined with local and global descriptors.
In this study, we report a novel sequence-based method for the prediction of interacting protein pairs using a matrix-based protein sequence descriptors combined with support vector machine (SVM) algorithm.
Hence, incorporating negative data in regression analysis through SemiSVR improves quantitative prediction of interacting peptides, and even a simple application of the SemiSVR method given a set of peptides per domain is useful.
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(D ) For complexes in the benchmark set, inter-EC pairs with EVcomplex score ≥0.8 give predictions of interacting residue pairs between the complex subunits to varying accuracy (8 Å TP distance cutoff).
This domain is soluble and folded with approximately 60% α-helicity, in agreement with our predictions, and capable of interacting with several known Sec10p binding partners.
This provides an important means for the prediction of the interacting sites on protein with the ligand molecules.
Our findings have shown that comparing the ranked lists of target genes results in plausible predictions of interacting TFs in human tissues.
We provide a comprehensive analysis of our predictions against secondary structures of interacting RNA molecules drawn from the literature.
On the basis of some recent investigations, this paper is addressed to the prediction of the equilibrium energies of interacting conservative resonators and to a better understanding of the principle of energy equipartition for large undamped systems.
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