Computational prediction methods that predict subcellular localization from the amino acid sequence represent an attractive alternative to experimental methods.
Some methods that predict the sorting signals limit their predictions to N-terminal signal peptides and their cleavage sites (21, 22).
Therefore, methods that predict structural contacts may be valuable for the purpose of structure prediction in membrane proteins, whose available structures are limited.
Objective image-quality research aims to develop methods that predict the subjective quality experience.
Thus, computational methods that predict three-dimensional RNA structures are in high demand.
Currently, only methods that predict content popularity after publication based on the aggregate behavior have been proposed.
Computational methods that predict membrane protein structure from sequence can potentially aid structure determination for such difficult target proteins.
Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics.
However, a correct alignment is essential, especially for in silico methods that predict binding partners on the basis of sequence and structure.
It is desirable to explore methods that predict binders of flexible lengths from sequence-derived physicochemical properties and trained from diverse sets of non-binders.
