I'm not sure what you mean by it being a mess? That looks like a pretty good alignment to me, and it most certainly has aligned all the sequences. You are nearly always going to have gaps (see my MSA below, which are all paralogs from the same genome so they couldn't really be more closely related, and yet, there are gaps). That comes with its own set of problems mind you, as you need to decide how you're going to deal with them.
You also don't necessarily have to do a profile alignment. Those sequences don't look massively divergent to me, so straight forward sequence alignment would probably give you more or less the same result.
My goal is to visualize the number of BLAST hits per amino acid in my protein of interest.
This doesn't really make sense. You should really be searching for the number of BLAST hits with a particular domain, if you want to infer homology.
If you want a per-position visualisation of the conservation, you could look at the shannon entropy for each column and plot that. I wrote a script to do just that a little while ago: https://github.com/jrjhealey/bioinfo-tools/blob/master/Shannon.py
Just beware it's not super well tested yet. Feed an MSA in with as many sequences as you want to analyse, but you'll have to have identified the sequences and done the alignment first.
For example, given this MSA:
16 149
PAU_02775 MSTTPEQIAV EYPIPTYRFV VSLGDEQIPF NSVSGLDISH DVIEYKDGTG
PLT_01696 MSTTPEQIAV EYPIPTYRFV VSIGDEQIPF NSVSGLDISH DVIEYKDGTG
PAK_02606 MSTTPEQIAV EYPIPTYRFV VSIGDEQVPF NSVSGLDISH DVIEYKDGTG
PLT_01736 MSTTPEQIAV EYPIPTYRFV VSIGDEKVPF NSVSGLDISH DVIEYKDGTG
PAK_01896 MTTTT----V DYPIPAYRFV VSVGDEQIPF NNVSGLDITY DVIEYKDGTG
PAU_02074 MATTT----V DYPIPAYRFV VSVGDEQIPF NSVSGLDITY DVIEYKDGTG
PLT_02424 MSVTTEQIAV DYPIPTYRFV VSVGDEQIPF NNVSGLDITY DVIEYKDGTG
PLT_01716 MTITPEQIAV DYPIPAYRFV VSVGDEKIPF NNVSGLDVHY DVIEYKDGTG
PLT_01758 MAITPEQIAV EYPIPTYRFV VSVGDEQIPF NNVSGLDVHY DVIEYKDGIG
PAK_03203 MSTSTSQIAV EYPIPVYRFI VSIGDDQIPF NSVSGLDINY DTIEYRDGVG
PAU_03392 MSTSTSQIAV EYPIPVYRFI VSVGDEKIPF NSVSGLDISY DTIEYRDGVG
PAK_02014 MSITQEQIAA EYPIPSYRFM VSIGDVQVPF NSVSGLDRKY EVIEYKDGIG
PAU_02206 MSITQEQIAA EYPIPSYRFM VSIGDVQVPF NSVSGLDRKY EVIEYKDGIG
PAK_01787 MSTTADQIAV QYPIPTYRFV VTIGDEQMCF QSVSGLDISY DTIEYRDGVG
PAU_01961 MSTTADQIAV QYPIPTYRFV VTIGDEQMCF QSVSGLDISY DTIEYRDGVG
PLT_02568 MSTTVDQIAV QYPIPTYRFV VTVGDEQMSF QSVSGLDISY DTIEYRDGIG
NYYKMPGQRQ AINISLRKGV FSGDTKLFDW INSIQLNQVE KKDISISLTN
NYYKMPGQRQ AINISLRKGV FSGDTKLFDW INSIQLNQVE KKDISISLTN
NYYKMPGQRQ AINISLRKGV FSGDTKLFDW INSIQLNQVE KKDISISLTN
NYYKMPGQRQ AINITLRKGV FSGDTKLFDW LNSIQLNQVE KKDISISLTN
NYYKMPGQRQ LINITLRKGV FPGDTKLFDW LNSIQLNQVE KKDVSISLTN
NYYKMPGQRQ LINITLRKGV FPGDTKLFDW LNSIQLNQVE KKDVSISLTN
NHYKMPGQRQ LINITLRKGV FPGDTKLFDW LNSIQLNQVE KKDVSISLTN
NYYKMPGQRQ SINITLRKGV FPGDTKLFDW INSIQLNQVE KKDIAISLTN
NYYKMPGQRQ SINITLRKGV FPGDTKLFDW INSIQLNQVE KKDIAISLTN
NWFKMPGQSQ LVNITLRKGV FPGKTELFDW INSIQLNQVE KKDITISLTN
NWFKMPGQSQ STNITLRKGV FPGKTELFDW INSIQLNQVE KKDITISLTN
NYYKMPGQIQ RVDITLRKGI FSGKNDLFNW INSIELNRVE KKDITISLTN
NYYKMPGQIQ RVDITLRKGI FSGKNDLFNW INSIELNRVE KKDITISLTN
NWLQMPGQRQ RPTITLKRGI FKGQSKLYDW INSISLNQIE KKDISISLTD
NWLQMPGQRQ RPTITLKRGI FKGQSKLYDW INSISLNQIE KKDISISLTD
NWLQMPGQRQ RPSITLKRGI FKGQSKLYDW INSISLNQIE KKDISISLTD
EAGTEILMTW SVANAFPTSL TSPSFDATSN EVAVQEITLT ADRVTIQAA
EAGTEILMTW SVANAFPTSL ISPSFDATSN EVAVQEITLT ADRVTIQAA
EAGTEILMTW SVANAFPTSL TSPSFDATSN EVAVQEITLT ADRVTIQAA
EAGTEILMTW SVANAFPTSL TAPAFDATSN EVAVQEISLT ADRVTIQAA
ETGTEILMSW SVANAFPTSL TSPSFDATSN DIAVQEIKLT ADRVTIQAA
EVGTEILMTW SVANAFPTSL TSPSFDATSN DIAVQEIKLT ADRVTIQAA
EAGTEILMSW SVANAFPTSL TSPSFDATSN DIAVQEIKLT ADRVMIQAA
ETGSQILMTW NVANAFPTSF TSPSFDAASN DIAIQEIALV ADRVTIQAP
EAGTEILMTW NVANAFPTSF TSPSFDATSN EIAVQEIALT ADRVTIQAA
DAGTELLMTW NVSNAFPTSL TSPSFDATSN DIAVQEITLT ADRVIMQAV
DAGTELLMTW NVSNAFPTSL TSPSFDATSN DIAVQEITLM ADRVIMQAV
DTGSEVLMSW VVSNAFPSSL TAPSFDASSN EIAVQEISLV ADRVTIQVP
DTGSKVLMSW VVSNAFPSSL TAPSFDASSN EIAVQEISLV ADRVTIQVP
ETGSNLLITW NIANAFPEKL TAPSFDATSN EVAVQEMSLK ADRVTVEFH
ETGSNLLITW NIANAFPEKL TAPSFDATSN EVAVQEISLK ADRVTVEFH
ETGSNLLITW NIANAFPEKL TAPSFDATSN EVAVQEISLK ADRVTVEFH
You'd get this plot:
I would like to have a graph with my protein on the x-axis and a plot
like the regions of high conservation for the multiple alignments,
except with the spikes corresponding to a high number of BLAST hits.
This would allow me to identify regions of my protein that have higher
sequence similarity to bacteria than others.
I'm not sure your logic is quite right here though. Blast won't give you hits depending on a particular position. It's a local aligner, so it'll just return you hits where at least some part of your query matches at least some part of another.
What you could do is take the logic in the script above, and just use a different metric. For example, perhaps you could count the proportion of sequences which have the most common amino acid at a given position within your MSA. That would be fairly crude though.
As you say in the comments,
My final goal is to produce a plot visualizing regions of high bacterial sequence similarity to my human protein of interest.
your original alignment will show you this intrinsically, if only you include the sequences of all the BLAST hits in the first place. Thus your work flow will be:
- Blast sequence of interest.
- Download all/as many hits as you want (bear in mind the E-value/bitscore and the number of hits you get. It might only be a few dozen, in which case you can use the lot, but if not, just take all the hits below a certain cut-off.)
- Align all the sequences.
- Look at the column scores for the whole MSA. You can use whatever metric of conservation you like really. Might be as simple as proportion of sequences with the most common residue, or something more complex like Shannon entropy (though as you can see in the graph above Shannon entropy can be kinda noisy) etc.
