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mCSM-NA is a novel, updated, scalable method capable of quantitatevely predicting the effects of mutations in protein coding regions on nucleic acid binding affinities. It is a machine learning approach that uses graph-based structural signatures to train and test predictive models. We have significantly enhanced the original method by including a pharmacophore modelling and information of nucleic acid properties into our graph-based signatures, considering the reverse mutation and by using a refined, more reliable dataset, based on a new release of the ProNIT.
To run a single prediction:
Your results (1) for a single mutation will be displayed once computations are completed. The results will display the predicted
change in affinity upon mutation (ΔΔG in kcal/mol). A negative value (and red writing) corresponds
to a mutation predicted as reducing affinity; while a positive sign (and blue writing) corresponds to a mutation
predicted as increasing affinity. Complementary information also displayed include:
Your results for a list of mutations will be displayed in a table format with the following information:
In case you experience any trouble using mCSM-NA or have any suggestions or comments, please do not hesitate in contacting us either via e-mail (1) or through the online form (2) .