Anomal P: An approach for detecting anomalous protein conformations using deep autoencoders

Proteomics is nowadays one of the most important and relevant fields from computational biology, raising a lot of challenging and provocative questions. Gaining an understanding of protein dynamic and function as well as obtaining additional insights into the protein folding process is still of great interest in bioinformatics and medicine. This paper introduces a new approach AnomalP for detecting anomalous protein conformational transitions using deep autoencoders for encoding information about the structural similarity between proteins belonging to the same superfamily. Experiments are conducted on real protein data and the obtained results emphasize the potential of autoencoders to learn biological relevant patterns, such as proteins' structural characteristics and that they are useful for detecting conformations or proteins which are likely to be anomalous with respect to a superfamily. The study performed in this paper is aimed to provide better insights of proteins structural similarity, with the broader goal of learning to predict proteins conformational transitions.

Automated summary

This paper introduces a new approach AnomalP for detecting abnormal protein conformational transitions using deep autoencoders to encode information about the structural similarity between proteins belonging to the same superfamily. The study emphasizes the potential of autoencoders to learn biologically relevant patterns, such as protein structural characteristics, and their usefulness in detecting abnormal conformations or proteins related to a superfamily. The aim of the research is to provide better insights into protein structural similarity, with the broader goal of predicting protein conformational transitions.