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Predicting local structural properties from antibody sequence
Beňo, Roman ; Příhoda, David (advisor) ; Hoksza, David (referee)
Predicting local structural properties of antibodies at residual level is vital for detecting the presence of post-translational modifications (PTMs), which often induce structural change in the antibody, negatively impact its shelf-life and possibly lead to the loss of the therapeutic potential. In this work, we predict relative solvent accessibility (RSA) of individual residues. This property is, alongside with the type of amino acid in question, the key indicator for presence of methionine oxidations and other types of PTMs. Due to the conservation of the antibody structure, we identified that different classes of prediction methods yield almost interchangeable results - total mean absolute error (MAE) of 5.64 RSA percentage units measured for the best performing machine learning pipeline compared to the 5.96 measured for the best performing statistical pipeline. The significant prediction quality improvement observed within comparison to the random prediction method with MAE of 35.996 may be as well attributed to the sequence conservancy. In CDR regions, RSA values are harder to predict. Although the range of methods and procedures employed throughout this work is by far not able to yield complex structure predictions, it might constitute a modular, high-throughput tool to support one's choices when...
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