Page 274 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

Chapter 9 Applications of deep learning in biomedical engineering 265

Figure 9.11 Protein Structure Prediction. From https://commons.wikimedia.org/wiki/File:Family_with_sequence_similarity_
231_member_B_(FAM231B)_human_protein_secondary_structure.png.

41. Protein secondary structure prediction

Protein SS prediction focuses on extracting SSs of proteins
grounded the information of amino acids. The SS defines the re-
lationships of hydrogen bonds joining backbone amine and
carboxyl groups [33].

41.1 Protein tertiary structure prediction
Protein tertiary structure (3D) represents the three-
dimensional shape of protein, which contains polypeptide chain
along with SSs and protein domains [34].

41.2 Protein quality assessment
Protein quality assessment (QA) is the substantial mechanism
in protein structure prediction and analysis. It helps to identify
the quality of the model generated by protein 3D prediction [2].

41.3 Protein loop modeling and disorder
prediction
It focuses on predicting structural arrangements of loop re-
gions in protein. Loops are having highly irregular sequences
due to unaligned regions. Such regions of a structural model
can be predicted by nonetemplate-based loop modeling and
template-based loop modeling [35].

42. Protein Interaction Prediction
Genetic activities in the human body are regulated by the
appropriate structure of proteins, capable to perform different