The starting point was a nucleotide sequence and a set of questions: which gene does it represent, what protein does it encode, and what can be learned about that protein from its sequence? The work moved from sequence identification into physical properties, secondary structure, domains, hydrophobicity, signal peptides, transmembrane regions, and subcellular localization. The query sequence was:

nTAAACGTTTCCCTTATCTCTCTCTCTCTCTCCACTCCAATGGGGACTCTCACCAGCGTCGCTTTTGCCGCTGCCGTTAACATTAGATTCCGATCATTTCACCGGGAAAACATTAAGACAACAATTACAACTCTGCCCAAATGGCAAAAGAGACTCTGTTTCTCTTCTACAGAGGATTCTCATAGATTTAGGATTGCAAAGTGTTTAGGCAATGACGAAAACAGTAACAGAGACGACAGTATCGGAGAAAACGGAGAAACCCACAAGTCTTCGGTTGTTAAAACGGCAACGTTTGAAGAAGAAGATGAAGAGACAAGTAAGAGTAGCTCTACTACTAGCAGCAGCAACGAGTTCGGTTCCGACAAGACTTCAATGCCATCAACTATTGATCCAACATATAGTAGCTTCCAAATAGATTCGTTTAAGCTAATGGAGCTTCTTGGACCAGAGAAAGTAGATCCTGCAGATGTCAAGTTAATAAAGGACAAACTTTTCGGTTATTCGACATTTTGGGTGACGAAAGAAGAGCCATTTGGGGACCTTGGGGAAGGAATTCTCTTTCTTGGGAACTTGAGAGGAAAGAAAGAAGATGTTTTTGCAAAACTTCAGAGAAAACTAGTTGAAGTTGCTAGTGATAAATATAATTTATTCATGATTGAGGAGCCTAACTCAGAAGGACCTGATCCTCGCGGTGGTGCTCGTGTTAGCTTTGGTTTGCTTCGTAAAGAAGTTTCGGAGCCTGGACCAACTACACTCTGGCAGTATGTGATTGCTTTAATATTGTTTCTTTTGACTATTGGTTCCTCTGTGGAGTTAGGAATTGCTTCTCAGATTAACCGCTTACCTCCTGAGGTGGTAAAGTATTTCACCGATCCAAATGCTGTTGAACCACCTGATATGGAGCTGCTGTATCCATTTGTAGATGCTGCATTGCCTTTAGCTTATGGTGTATTGGGAATTCTTTTGTTTCATGAATTAGGGCACTTTCTTGCTGCAGTTCCAAAGAAAGTTAAGCTTAGCATTCCATACTTCATTCCAAACATTACACTCGGCAGCTTTGGGGCAATCACACAGTTTAAGTCGATTCTTCCTGACCGGAGTACAAAAGTTGACATTTCGCTAGCTGGTCCATTTGCTGGAGCTGCACTCTCAGTTTCCATGTTTGCTGTTGGTCTGTTCCTATCTACAGAACCAGATGCAGCTAACGACCTGGTCCAGGTTCCCAGCATGTTATTTCAAGGTTCATTACTTCTTGGACTCATCAGCAGAGCAACTCTGGGATATGCAGCTTTGCATGCTGCAACAGTTTCAATCCACCCACTTGTAATTGCTGGATGGTGTGGTTTAACAACAACAGCATTTAATATGCTTCCAGTGGGATGTTTGGATGGAGGAAGAGCTGTACAGGGAGCATTCGGGAAAAATGCATTGGTTACATTTGGTTTGTCAACCTATGTAATGCTTGGACTCAGAGTGCTCGGTGGCCCTTTGGCACTTCCTTGGGGACTCTACGTATTAATCTGCCAGAGAACACCTGAAAAACCGTGTCTAAACGACGTGACCGAGGTTGGAACGTGGAGGAAGGCCCTTGTAGGGATTGCACTAATCCTGGTGGTTTTGACACTCCTGCCTGTATGGGACGAACTCGCAGAAGAAGTAGGCATAGGGCTTGTAACCACATTTTGATGTCATCTTCCGTCTTTGAGCAGACGTTGTAGTTAGGGCCAATCCGAGACACATTTTTTTGTTTTGGCAATATTTAGATTGCCAGATATAATGAGCCATGTATGTACACTAGTGAAAAAGTATTTATATGTAATTACTTAAATTCATTCTTCAATTATTACACTAATTACTTTTT

The first step was to find the coding region and its protein product. I used NCBI BLAST and chose blastx, which translates the nucleotide query in all six reading frames and searches protein databases. The search page and the top results are shown below.

NCBI BLAST page with blastx selected Top BLAST hits for the nucleotide query BLAST alignment details BLAST alignment details continued

The best match was NP_198372.1, annotated as a peptidase M50 family protein from Arabidopsis thaliana. I retrieved its FASTA sequence for the downstream analyses:

MGTLTSVAFAAAVNIRFRSFHRENIKTTITTLPKWQKRLCFSSTEDSHRFRIAKCLGNDENSNRDDSIGE
NGETHKSSVVKTATFEEEDEETSKSSSTTSSSNEFGSDKTSMPSTIDPTYSSFQIDSFKLMELLGPEKVD
PADVKLIKDKLFGYSTFWVTKEEPFGDLGEGILFLGNLRGKKEDVFAKLQRKLVEVASDKYNLFMIEEPN
SEGPDPRGGARVSFGLLRKEVSEPGPTTLWQYVIALILFLLTIGSSVELGIASQINRLPPEVVKYFTDPN
AVEPPDMELLYPFVDAALPLAYGVLGILLFHELGHFLAAVPKKVKLSIPYFIPNITLGSFGAITQFKSIL
PDRSTKVDISLAGPFAGAALSVSMFAVGLFLSTEPDAANDLVQVPSMLFQGSLLLGLISRATLGYAALHA
ATVSIHPLVIAGWCGLTTTAFNMLPVGCLDGGRAVQGAFGKNALVTFGLSTYVMLGLRVLGGPLALPWGL
YVLICQRTPEKPCLNDVTEVGTWRKALVGIALILVVLTLLPVWDELAEEVGIGLVTTF

Physical properties and composition

ProtParam was used to calculate the basic properties of the protein. The output gave a length of 548 amino acids, a molecular weight of 59,500.72, and a theoretical pI of 5.32.

ProtParam output for the protein sequence

The amino acid composition was:

<table> <thead> <tr> <th>Amino acid</th> <th>Count</th> <th>Percentage</th> </tr> </thead> <tbody> <tr> <td>Ala (A)</td> <td>40</td> <td>7.3%</td> </tr> <tr> <td>Arg (R)</td> <td>19</td> <td>3.5%</td> </tr> <tr> <td>Asn (N)</td> <td>17</td> <td>3.1%</td> </tr> <tr> <td>Asp (D)</td> <td>25</td> <td>4.6%</td> </tr> <tr> <td>Cys (C)</td> <td>6</td> <td>1.1%</td> </tr> <tr> <td>Gln (Q)</td> <td>10</td> <td>1.8%</td> </tr> <tr> <td>Glu (E)</td> <td>34</td> <td>6.2%</td> </tr> <tr> <td>Gly (G)</td> <td>46</td> <td>8.4%</td> </tr> <tr> <td>His (H)</td> <td>7</td> <td>1.3%</td> </tr> <tr> <td>Ile (I)</td> <td>29</td> <td>5.3%</td> </tr> <tr> <td>Leu (L)</td> <td>71</td> <td>13.0%</td> </tr> <tr> <td>Lys (K)</td> <td>29</td> <td>5.3%</td> </tr> <tr> <td>Met (M)</td> <td>9</td> <td>1.6%</td> </tr> <tr> <td>Phe (F)</td> <td>32</td> <td>5.8%</td> </tr> <tr> <td>Pro (P)</td> <td>32</td> <td>5.8%</td> </tr> <tr> <td>Ser (S)</td> <td>43</td> <td>7.8%</td> </tr> <tr> <td>Thr (T)</td> <td>39</td> <td>7.1%</td> </tr> <tr> <td>Trp (W)</td> <td>7</td> <td>1.3%</td> </tr> <tr> <td>Tyr (Y)</td> <td>11</td> <td>2.0%</td> </tr> <tr> <td>Val (V)</td> <td>42</td> <td>7.7%</td> </tr> <tr> <td>Pyl (O)</td> <td>0</td> <td>0.0%</td> </tr> <tr> <td>Sec (U)</td> <td>0</td> <td>0.0%</td> </tr> <tr> <td>B</td> <td>0</td> <td>0.0%</td> </tr> <tr> <td>Z</td> <td>0</td> <td>0.0%</td> </tr> <tr> <td>X</td> <td>0</td> <td>0.0%</td> </tr> </tbody> </table>

Hydrophobicity was examined with ProtScale. In the plot, positive peaks indicate hydrophobic regions, while negative troughs indicate hydrophilic regions.

ProtScale hydrophobicity plot

Domain annotation was checked with SMART. The protein length was confirmed as 548 aa, and the domain view is shown below.

SMART domain annotation SMART result confirming 548 amino acids

Transmembrane segments were checked with TMpred and TMHMM.

TMpred and TMHMM transmembrane prediction

Subcellular localization was predicted with TargetP.

TargetP subcellular localization prediction

Secondary structure

Secondary structure was predicted with NPSA GOR4. The output gave the following distribution:

NPSA GOR4 secondary structure prediction Secondary structure output

<table> <thead> <tr> <th>Secondary structure</th> <th>Residues</th> <th>Percentage</th> </tr> </thead> <tbody> <tr> <td>Alpha helix (Hh)</td> <td>168</td> <td>30.66%</td> </tr> <tr> <td>3-turn helix (Gg)</td> <td>0</td> <td>0.00%</td> </tr> <tr> <td>Pi helix (Ii)</td> <td>0</td> <td>0.00%</td> </tr> <tr> <td>Beta sheet (Bb)</td> <td>0</td> <td>0.00%</td> </tr> <tr> <td>Extended strand (Ee)</td> <td>93</td> <td>16.97%</td> </tr> <tr> <td>Beta turn (Tt)</td> <td>0</td> <td>0.00%</td> </tr> <tr> <td>Bend region (Ss)</td> <td>0</td> <td>0.00%</td> </tr> <tr> <td>Random coil (Cc)</td> <td>287</td> <td>52.37%</td> </tr> <tr> <td>Ambiguous states (?)</td> <td>0</td> <td>0.00%</td> </tr> <tr> <td>Other states</td> <td>0</td> <td>0.00%</td> </tr> </tbody> </table>

Another predictor, NNPredict, was available but is not demonstrated here.

Tertiary structure

Tertiary structure prediction is the final goal of protein structure work. Because a protein's higher-order structure, especially its three-dimensional shape, determines its function, understanding that structure is central to understanding the protein itself. The general approaches include homology modeling, fold recognition, ab initio prediction, and CASP.

I first tried SWISS-MODEL.

SWISS-MODEL interface

The model and report were generated, but the modeling match was only 24.39%, so I discarded it. I then used Phyre2.

Phyre2 model summary

Phyre2 returned a 99% match, which made the model usable.

The sequence therefore corresponds to a peptidase M50 family protein from Arabidopsis thaliana.