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Enhanced Characterization Performance of Propionylation PTM utilizing multiple feature fusion

Published: 11 August 2022 Publication History

Abstract

Propionylation post-transnational modification (PTM) is a relatively new and salient modification occurs in the side chain of lysine amino acids under different conditions in both eukaryotes and prokaryotes living cells. It has significant roles in the metabolic processes, regulation of bio-molecules, biological activities including various adverse health problems. Characterization of propionylation PTMs in laboratory using mass-spectrometer requires expertise involvement, moreover, the process is sluggish and cost ineffective. Development of computational models can replace the laboratory methods expanding its global reach to any level of users. We developed an efficient model combining assorted features like physicochemical, amino acid frequency and evolutionary based features by feeding into different classifiers. We managed to get accuracy, sensitivity, specificity and MCC of 95.12 ± 0.8%, 92.41 ± 1.7%, 97.82 ± 1.2% and 90.42 ± 1.6% respectively which outperformed the previously developed state-of-the-art models.

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        cover image ACM Other conferences
        ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements
        March 2022
        543 pages
        ISBN:9781450397346
        DOI:10.1145/3542954
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 11 August 2022

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        Author Tags

        1. LightGBM
        2. PLMD
        3. PTM
        4. Propionylation
        5. RF
        6. SVM

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