AmorProt: Amino Acid Molecular Fingerprints Repurposing based Protein Fingerprint

• URL: http://arxiv.org/abs/2303.16209v1
• Date: Mon, 27 Mar 2023 23:57:47 GMT
• Title: AmorProt: Amino Acid Molecular Fingerprints Repurposing based Protein Fingerprint
• Authors: Myeonghun Lee and Kyoungmin Min
• Abstract summary: We propose the amino acid molecular fingerprints repurposing based protein (AmorProt) fingerprint. The performances of the tree based machine learning and artificial neural network models were compared. The results revealed that the current protein representation method can be applied to various fields related to proteins.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature based methods; (4) feature importance analysis; and (5) protein space analysis. Consequently, the significantly improved model performance and data set independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands.

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