text: user727: I'm a newcomer to the world of bioinformatics, and in need of help solving a problem.
  My goal is to take a list of human proteins, and identify segments (13-17aa in length) with a high degree of similarity to microbial sequences. Ideally, I would like to start with list of FASTA sequences, and have an easy way to generate an output of the corresponding high similarity segments of each protein.
  Are there existing tools or software that I should be aware of that will make my life easier?
  Thanks in advance.
  
  user298: Sounds like precisely the job BLAST was developed for. Now, which flavor will depend on what you want to do and what data you have available. Some options:
  
  PSI-BLAST: this is usually the best choice if you are trying to find protein homologs. It works by building a hidden markov model describing your query sequence and using that model to query a database of proteins. The advantage is that it is run in multiple iterations, giving you the chance to add or remove results (so you add the ones that are true positives and remove false ones), eventually building a pretty good model of your protein. This is far moer powerful than a simple homology-based approach since proteins work via protein domains and simple homology is not as important as specific conserved functional residues. 
  For this, go to the NCBI protein blast page and select PSI-BLAST:
  
  BLASTp: Simple protein-protein blast. It will identify homologous proteins based on sequence similarity. Whether or not that also implies functional homology is not that simple and will depend on each case you investigate. 
  As above, go to the NCBI protein blast page, but this time use the defaults.
  tBLASTn: this is a tool that takes protein sequences as input and compares them to a database of DNA which is dynamically translated in all 6 possible reading frames. Very good for finding homologous sequences when you don't have well annotated protein information for the target species. It has the benefit of being more sensitive and able to find more distant homologies than basic nucleotide BLASTn and the go-to approach when your target species is distant and not well annotated. 
  NCBI's tBLASTn page.
  
  All of these can be run online through the NCBI's BLAST page. If you want to investigate hundreds of proteins, I suggest you install blast locally. You can then either download the relevant target sequences from NCBI and rebuild the blast database locally (if so, I suggest you ask a new question about how to do that) or, use NCBI's remote blast client which lets you use a locally stored query file and will run blast on the NCBI's servers. 
  Now, these programs will return what are known as High Scoring Pairs (HSPs), the regions of your query sequence(s) that align well to the target. There are various options you can play with to improve the sensitivity or the specificity but a discussion of those would require far more details about what you're doing and would be best in a new question as well. 
  Once you have your HSPs, you can relatively easily parse them to select regions with a given range of sequence similarity values and of a specific length. Once again, that would be better discussed in a separate question once you have your results and can show an example. 
  
original_text: 727: <p>I'm a newcomer to the world of bioinformatics, and in need of help solving a problem.</p>
  
  <p>My goal is to take a list of human proteins, and identify segments (13-17aa in length) with a high degree of similarity to microbial sequences. Ideally, I would like to start with list of FASTA sequences, and have an easy way to generate an output of the corresponding high similarity segments of each protein.</p>
  
  <p>Are there existing tools or software that I should be aware of that will make my life easier?</p>
  
  <p>Thanks in advance.</p>
   298: <p>Sounds like precisely the job BLAST was developed for. Now, which flavor will depend on what you want to do and what data you have available. Some options:</p>
  
  <ol>
  <li><p><strong>PSI-BLAST</strong>: this is usually the best choice if you are trying to find protein homologs. It works by building a hidden markov model describing your query sequence and using that model to query a database of proteins. The advantage is that it is run in multiple iterations, giving you the chance to add or remove results (so you add the ones that are true positives and remove false ones), eventually building a pretty good model of your protein. This is far moer powerful than a simple homology-based approach since proteins work via protein domains and simple homology is not as important as specific conserved functional residues. </p>
  
  <p>For this, go to the NCBI <a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&amp;PAGE_TYPE=BlastSearch&amp;LINK_LOC=blasthome" rel="nofollow noreferrer">protein blast page</a> and select PSI-BLAST:</p>
  
  <p><a href="https://i.stack.imgur.com/q4yAK.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/q4yAK.png" alt="psi-blast option at ncbi"></a></p></li>
  <li><p><strong>BLASTp</strong>: Simple protein-protein blast. It will identify homologous proteins based on sequence similarity. Whether or not that also implies functional homology is not that simple and will depend on each case you investigate. </p>
  
  <p>As above, go to the NCBI <a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastp&amp;PAGE_TYPE=BlastSearch&amp;LINK_LOC=blasthome" rel="nofollow noreferrer">protein blast page</a>, but this time use the defaults.</p></li>
  <li><p><strong>tBLASTn</strong>: this is a tool that takes protein sequences as input and compares them to a database of DNA which is dynamically translated in all 6 possible reading frames. Very good for finding homologous sequences when you don't have well annotated protein information for the target species. It has the benefit of being more sensitive and able to find more distant homologies than basic nucleotide BLASTn and the go-to approach when your target species is distant and not well annotated. </p>
  
  <p><a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=tblastn&amp;PAGE_TYPE=BlastSearch&amp;BLAST_SPEC=&amp;LINK_LOC=blasttab&amp;LAST_PAGE=blastp" rel="nofollow noreferrer">NCBI's tBLASTn page</a>.</p></li>
  </ol>
  
  <p>All of these can be run online through the <a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi" rel="nofollow noreferrer">NCBI's BLAST page</a>. If you want to investigate hundreds of proteins, I suggest you install blast locally. You can then either download the relevant target sequences from NCBI and rebuild the blast database locally (if so, I suggest you ask a new question about how to do that) or, use NCBI's remote blast client which lets you use a locally stored query file and will run blast on the NCBI's servers. </p>
  
  <p>Now, these programs will return what are known as High Scoring Pairs (HSPs), the regions of your query sequence(s) that align well to the target. There are various options you can play with to improve the sensitivity or the specificity but a discussion of those would require far more details about what you're doing and would be best in a new question as well. </p>
  
  <p>Once you have your HSPs, you can relatively easily parse them to select regions with a given range of sequence similarity values and of a specific length. Once again, that would be better discussed in a separate question once you have your results and can show an example. </p>
  