Visualizing manually annotated functional domains in a protein alignment like in Benchling

Question

[nina.maryn]

Hello all, I have a visualization question and I'm kind of drowning in potential options.

I have the following file for my protein of interest, which I created by manually annotating the file with functional information in Benchling and downloading the gp version. I want to use this information to create an annotated protein alignment with the same protein from other species, to show how various functional domains differ at the amino acid level compared to this species' protein. This can be done in benchling, but I want more control over adding a removing sequences, wrapping text to show the whole protein alignment (it's way to long to show it all linearly) and to isolate different domains to zoom in on. I've been doing this by hand in powerpoint, and there must be a better way. Any open source software (preferably R or Python) that can help with this? Thanks!

LOCUS       AT_Protein_Annotated         462 aa            linear       14-JUL-2022
DEFINITION  .
FEATURES             Location/Qualifiers
     misc_feature    85..97
                     /label="Transmembrane Domain"
                     /color="#f8d3a9"
     misc_feature    119..360
                     /label="VDE"
                     /color="#b1ff67"
     misc_feature    214..221
                     /label="B1"
                     /color="#d6b295"
     misc_feature    222..232
                     /label="L1"
                     /color="#c7b0e3"
     misc_feature    227..227
                     /label="pH"
                     /color="#f58a5e"
     misc_feature    229..233
                     /label="Dimerization"
                     /color="#faac61"
     misc_feature    230..230
                     /label="pH"
                     /color="#f58a5e"
     misc_feature    233..239
                     /label="B2"
                     /color="#b4abac"
     misc_feature    243..253
                     /label="B3"
                     /color="#b4abac"
     misc_feature    247..253
                     /label="Dimerization"
                     /color="#faac61"
     misc_feature    251..251
                     /label="pH"
                     /color="#f58a5e"
     misc_feature    254..258
                     /label="L3"
                     /color="#c7b0e3"
     misc_feature    259..263
                     /label="Dimerization"
                     /color="#faac61"
     misc_feature    259..270
                     /label="B4"
                     /color="#b4abac"
     misc_feature    266..266
                     /label="Active Site"
                     /color="#9eafd2"
     misc_feature    277..280
                     /label="B5"
                     /color="#b4abac"
     misc_feature    281..287
                     /label="L4"
                     /color="#c7b0e3"
     misc_feature    288..288
                     /label="Active site"
                     /color="#9eafd2"
     misc_feature    288..298
                     /label="B6"
                     /color="#b4abac"
     misc_feature    290..290
                     /label="Active site"
                     /color="#9eafd2"
     misc_feature    292..292
                     /label="Active Site"
                     /color="#9eafd2"
     misc_feature    306..315
                     /label="B7"
                     /color="#b4abac"
     misc_feature    316..317
                     /label="L5"
                     /color="#c7b0e3"
     misc_feature    318..328
                     /label="B8"
                     /color="#b4abac"
     misc_feature    327..327
                     /label="Active Site"
                     /color="#9eafd2"
     misc_feature    335..347
                     /label="A1"
                     /color="#b7e6d7"
     misc_feature    352..354
                     /label="A2"
                     /color="#b7e6d7"
     misc_feature    356..357
                     /label="B9"
                     /color="#b4abac"
     misc_feature    384..407
                     /label="Coiled-coil(1)"
                     /color="#b7e6d7"
     misc_feature    416..436
                     /label="Coiled-coil(2)"
                     /color="#b7e6d7"
ORIGIN
        1 MAVATHCFTS PCHDRIRFFS SDDGIGRLGI TRKRINGTFL LKILPPIQSA DLRTTGGRSS
       61 RPLSAFRSGF SKGIFDIVPL PSKNELKELT APLLLKLVGV LACAFLIVPS ADAVDALKTC
  121 ACLLKGCRIE LAKCIANPAC AANVACLQTC NNRPDETECQ IKCGDLFENS VVDEFNECAV
  181 SRKKCVPRKS DLGEFPAPDP SVLVQNFNIS DFNGKWYITS GLNPTFDAFD CQLHEFHTEG
  241 DNKLVGNISW RIKTLDSGFF TRSAVQKFVQ DPNQPGVLYN HDNEYLHYQD DWYILSSKIE
  301 NKPEDYIFVY YRGRNDAWDG YGGAVVYTRS SVLPNSIIPE LEKAAKSIGR DFSTFIRTDN
  361 TCGPEPALVE RIEKTVEEGE RIIVKEVEEI EEEVEKEVEK VGRTEMTLFQ RLAEGFNELK
  421 QDEENFVREL SKEEMEFLDE IKMEASEVEK LFGKALPIRK VR

//

[Jeremy]

I'm not sure about R or Python. I think you can use MEGA, although I haven't tried it myself. I've used SnapGene in the past, but you would need the full version to perform sequence alignments, which would require a license.