Batch detection of CRISP proteins in fasta file Probably a naive question. I am inexperienced. 

I am interested in identifying potential CRISP (Cysteine-rich secretory proteins) in a certain tissue transcriptome (ca. 20k sequences in fasta). I have detected signalP and estimated % of cysteine in sequences. However CRISP rely on a certain pattern, and I am not aware of batch-detected algorithms.

Please would anyone know the best/simplest way of detecting CRISP candidates?

[EDIT]

I have been asked for the pattern. I summarise what the literature says:


Several of them have a CAP domain;
There should be a region called 'cysteine-rich domain' (CRD) upon the carboxyl terminal half of the protein containing 10 of roughly 16 conserved cysteines. 


I am particularly interested in venom-derived CRISPs, but I am not sure whether they cluster in a certain CRISP family.

References:
Roberts et al. (2007) Structure and function of epididymal protein cysteine‐rich secretory protein‐1.
Yamazaki &amp; Morita (2004) Structure and function of snake venom cysteine-rich secretory proteins.
Is your goal to detect the actual proteins from the sequences? If you'll settle for detection of transcripts from the genes encoding these proteins as opposed to detection of the proteins themselves, here is an approach.

Aligning your transcriptome to a reference genome (probably with STAR) will be the first step. You have lots of short reads in your FASTA, but in their current state they are no more informative than a box of puzzle pieces. Aligning your reads to the reference will allow you to extrapolate which genes that you short reads correspond with. From here you can quantify the features (genes) in your transcriptome.

Lucky for you, there are only 3 genes in the cysteine rich secretory protein family, CRISP1, CRISP2, and CRISP3. Alignment with a reference (and annotations) and quantification of genes that your reads align to will give you all you need. You'll just have to find the three genes in your final counts table.

I recommend STAR as an aligner with the --quantMode GeneCounts argument. It's very quick if you've got the resources and is geared towards RNA-seq reads.