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In addition to the incorporation of experimental data into the prediction methods, other information generated by computational methods can also be used as constraints.
The importance of a gating residue identified by computational methods can be confirmed experimentally by site-directed mutagenesis and kinetic experiments.
Consequently those known protein complexes or those predicted by computational methods can be conveniently used as partition of a PPI network.
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The main questions are: what are the possible Nash equilibria in such a situation, how are they characterised and by which computational methods can they be determined?
Also, high-confidence PPIs predicted by various computational methods can focus future experiments and reduce their false positives and false negatives (Kotlyar and Jurisica 2006; Kotlyar et al. 2011).
With these variables in mind, SSEM-Lasso, and by extension, similar computational methods, can be a tremendously useful tool for therapeutic discovery when implemented under the appropriately informed testing and training conditions.
Computational methods can assist by predicting peptide-MHC binding affinities that can later be experimentally validated.
Computational methods can address these issues by providing for objective, automatic measurements.
Computational methods can address this need by predicting epitopes for a particular MHC allotype.
Computational methods can assist in this search by generating experimentally verifiable predictions of ligand binding sites in proteins.
As such, few computational methods can predict supertertiary structure, even when all subdomains structures are known2,3.
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