Abstract
Addition of chemical structural information in enzymatic reactions has proven to be significant for accurate enzyme function prediction. However, such chemical data lack systematic feature mining and hardly exist in enzyme-related databases. Therefore, global mining of enzymatic reactions will offer a unique landscape for researchers to understand the basic functional mechanisms of natural bioprocesses and facilitate enzyme function annotation. Here, we established a new knowledge base called EnzyMine, through which we propose to elucidate enzymatic reaction features and then link them with sequence and structural annotations. EnzyMine represents an advanced database that extends enzyme knowledge by incorporating reaction chemical feature strategies, strengthening the connectivity between enzyme and metabolic reactions. Therefore, it has the potential to reveal many new metabolic pathways involved with given enzymes, as well as expand enzyme function annotation. Database URL: http://www.rxnfinder.org/enzymine/.
Highlights
Enzyme function annotation has excellent implications in metabolic engineering, synthetic biology and pathophysiology [1]
Along with the rapid expansion of protein sequences, predicting enzymatic reactions of unannotated sequences using computational methodology is widely becoming used [2, 3]. This function prediction contains enzyme feature extraction and classification optimization as two main procedures associated with machine learning and deep learning [4]
To conquer the deficiencies mentioned earlier, we proposed EnzyMine, a comprehensive enzyme feature and annotation database
Summary
Enzyme function annotation has excellent implications in metabolic engineering, synthetic biology and pathophysiology [1]. Along with the rapid expansion of protein sequences, predicting enzymatic reactions of unannotated sequences using computational methodology is widely becoming used [2, 3]. This function prediction contains enzyme feature extraction and classification optimization as two main procedures associated with machine learning and deep learning [4]. Feature extraction is no longer limited to sequence similarity but includes more conservative features independent of sequence length [3],.
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