Protein subcellular localization prediction using artificial intelligence technology.
Proteins perform many important tasks in living organisms, such as catalysis of biochemical reactions, transport of nutrients, and recognition and transmission of signals. The plethora of aspects of the role of any particular protein is referred to as its "function." One aspect of protein function that has been the target of intensive research by computational biologists is its subcellular localization. Proteins must be localized in the same subcellular compartment to cooperate toward a common physiological function. Aberrant subcellular localization of proteins can result in several diseases, including kidney stones, cancer, and Alzheimer's disease. To date, sequence homology remains the most widely used method for inferring the function of a protein. However, the application of advanced artificial intelligence (AI)-based techniques in recent years has resulted in significant improvements in our ability to predict the subcellular localization of a protein. The prediction accuracy has risen steadily over the years, in large part due to the application of AI-based methods such as hidden Markov models (HMMs), neural networks (NNs), and support vector machines (SVMs), although the availability of larger experimental datasets has also played a role. Automatic methods that mine textual information from the biological literature and molecular biology databases have considerably sped up the process of annotation for proteins for which some information regarding function is available in the literature. State-of-the-art methods based on NNs and HMMs can predict the presence of N-terminal sorting signals extremely accurately. Ab initio methods that predict subcellular localization for any protein sequence using only the native amino acid sequence and features predicted from the native sequence have shown the most remarkable improvements. The prediction accuracy of these methods has increased by over 30% in the past decade. The accuracy of these methods is now on par with high-throughput methods for predicting localization, and they are beginning to play an important role in directing experimental research. In this chapter, we review some of the most important methods for the prediction of subcellular localization.
Nair
Rajesh
R
CUBIC Department of Biochemistry and Molecular Biophysics and Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA.
Rost
Burkhard
B
D020816:Amino Acid Motifs; D000818:Animals; D001185:Artificial Intelligence; D030562:Databases, Protein; D021382:Protein Sorting Signals; D021381:Protein Transport; D011506:Proteins / Q000235:genetics / Q000378:metabolism*; D013347:Subcellular Fractions / Q000737:chemistry*; D018875:Vocabulary, Controlled
D021382:Protein Sorting Signals; D011506:Proteins