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Computational modelling of protein structures is therefore often employed to ease the prediction process. The ability to make informed predictions of this kind is of particular importance in the design of new drugs.ĭetermining a protein’s structure by experimentation, however, is an expensive, time consuming and difficult process. It is also possible to predict which molecules or drugs can bind to the protein and how they will bind. If the structure is known, then the protein’s function can be predicted based on its structural similarity to other known proteins.

This novel methodology can also be applied to homology detection which is fundamental to bioinformatics.Ī protein’s function is dictated by its three-dimensional structure.

Shuichiro Makigaki and Dr Takashi Ishida, from the Department of Computer Science at Tokyo Institute of Technology, are developing a new sequence alignment generation model that employs machine learning and dynamic programming to predict protein structures.

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