Uso de machine learning para a predição das energias de interação de peptídeos antimicrobianos (WANG, LI, WANG, 2016) frente a Glicoproteína Spike de Sars-Cov-2.
Baseado em teoria dos grafos (DANISHUDDIN, KHAN, 2016),
descritores moleculares topológicos foram implementados por meio
da biblioteca RDKit (https://pypi.org/project/rdkit-pypi/).
O descritores são: BalabanJ (BALABAN, 1982), Hall Kier Alpha
(HALL, KIER, 1991) e Kappa (HALL, KIER, 1991).
Além destes descritores, foi implementado o descritor Sequence
Order Coupling Number (CHOU, 2000) acompanhado
das matrizes de distância entre os aminoácidos propostas
por Schneider e Wrede (SCHNEIDER, WREDE, 1994) e Grantham
(GRANTHAM, 1974).
Utilizando da biblioteca Sci-kit learn, foram implementados 5 modelos de regressão. São eles:
- Random Florest Regressor
- Support Vector Regression
- Linear Support Vector Regression
- Nu Support Vector Regression
- Linear Regression
pip install rdkit-pypi
python manipulate_files.py
BALABAN, Alexandru T. Highly discriminating distance-based topological index. Chemical Physics Letters, v. 89, n. 5, p. 399–404, 1982.
CHOU, K. C. Prediction of Protein Subcellular Locations by Incorporating Quasi-Sequence-Order Effect. Biochemical and Biophysical Research Communications, v. 278, n. 2, p. 477–483, 19 nov. 2000.
DANISHUDDIN; KHAN, A. U. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discovery Today, v. 21, n. 8, p. 1291–1302, 2016.
GRANTHAM, R. Amino Acid Difference Formula to Help Explain Protein Evolution. Science, v. 185, n. 4154, p. 862–864, 6 set. 1974.
HALL, L. H.; KIER, L. B. The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Modeling. p. 367–422, 5 jan. 2007.
SCHNEIDER, G.; WREDE, P. The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site. Biophysical Journal, v. 66, n. 2 Pt 1, p. 335, 1994.
WANG, G.; LI, X.; WANG, Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Research, v. 44, n. D1, p. D1087–D1093, 4 jan. 2016.