# Extract features from fasta sequences and train the classifier

I am new to the bioinformatics field. I have positive and negative protein sequences for acetylation PTM. Now, I want to train a classifier, say SVM. What will be the next step? How can I convert these sequences into usable features? Any information or links would help.

>P31327_55|1|testing
KAQTAHIVLEDGTKMKGYSFGHPSSVA
>P31327_57|1|testing
QTAHIVLEDGTKMKGYSFGHPSSVAGE
>P31327_119|1|testing
APDTTALDELGLSKYLESNGIKVSGLL
>P31327_157|1|testing
LATKSLGQWLQEEKVPAIYGVDTRMLT

• It may be worth checking what other folk do for motif classification, for example see elm.eu.org/elms/candidates.html#MOD_acetylation which lists the different motifs (different enzymes recognise different motifs). Although ELM uses metadata such as protein localisation to filter the motifs for much better results. Mar 5 at 16:06
• Q to others. a ML features (measurable property) is a different thing than the sequence feature present in a GenBank file (i.e. an annotation on the sequence, e.g. acetylation sites). This the second time I have seen this tag usage, is this something worth discussing in Meta? Mar 5 at 16:08