Abstract
ProCos (Protein Composition Server, script version), one of the machine learning techniques, was used to classify nitrilases as aliphatic and aromatic nitrilases. Some important feature vectors were used to train the algorithm, which included pseudo-amino acid composition (PAAC) and five-factor solution score (5FSS). This clearly differentiated into two groups of nitrilases, i.e., aliphatic and aromatic, achieving maximum sensitivity of 100.00%, specificity of 90.00%, accuracy of 95.00% and Mathew Correlation Coefficient (MCC) of about 0.90 for the pseudo-amino acid composition. On the other hand, five-factor solution score achieved a sensitivity of 96.00%, specificity of 84.00%, accuracy of 90.00% and Mathew Correlation Coefficient (MCC) of about 0.81. The total count of aliphatic amino acids, Ala (A), Gly (G), Leu (L), Ile (I), Val (V), Met (M) and Pro (P), was found to be higher, i.e., 42.7 in case of aliphatic nitrilases, whereas it was 40.1 in aromatic nitrilases. On the other hand, aromatic amino acids, Tyr (Y), Trp (W), His (H) and Phe (F) number, were found to be higher, i.e., 12.7 in aromatic nitrilases as compared to aliphatic nitrilases which was 10.7. This approach will help in predicting a nitrilase as aromatic or aliphatic nitrilase based on its amino acid sequence. Access to the scripts can be done logging onto GitHub using keyword ‘Nitrilase’ or ‘https://github.com/rover2380/Nitrilase.git’.
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The authors are thankful to the Department of Biotechnology, New Delhi for the continuous support to the Bioinformatics Centre, Himachal Pradesh University, Summer Hill, Shimla, India.
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Sharma, N., Verma, R., Savitri et al. Classifying nitrilases as aliphatic and aromatic using machine learning technique. 3 Biotech 8, 68 (2018). https://doi.org/10.1007/s13205-018-1102-9
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DOI: https://doi.org/10.1007/s13205-018-1102-9