text: user3510: I'm participating in a bioinformatics machine-learning seminar at my university. The main task is predicting binary classification of protein-protein interactions using sequence data as input. 
  One of the subtasks is familiarization with the dataset and presenting the dataset. Now I'm wondering which additional information I can get out of the sequence data.
  I want to start out with binary classification for the protein interaction with the labels “Interact” and “Non-Interact. The dataset that I got provided consists of two fasta files. One containing the sequence header with the species and the corresponding amino-acid sequences ~300 entries. The other containing the exact same species header and the label “Interact” or "Non-interact”. Species are completely mixed.
  Is there something interesting that I can additionally include in my analysis? I'm used to exploratory data analysis though this is different due to the nature of the protein sequences.
  
  user1606: There are two types of features which can be extracted from protein sequences.  It is not possible to known which, if any, of these features would be useful in classification tasks.  It may not be possible to build a classifier at all, or it may be very straight forward.  To know this, a feature selection technique must be used, such as e.g. forward feature selection.
  Sequence based features
  Sequence based features are derived from sequence-only information.
  The most common used features are the PseAAC (pseudo amino acid composition) style features, including:
  
  Average Position of each amino acid - 20 features
  Average Distance between each amino acid - 20 features
  Motifs - 20 features, 20*20 features, and/or 20*20*20 features (depending on motif length)
  OAAC (Overall Amino Acid Composition) - 20 features
  SAAC (Split Amino Acid Composition) - 60+ features
  AAIndex Composition - 1 to 567+ features
  PCC (Physicochemical Composition) - ~13 features
  PSSM encoded as 1, 20, 200, 400 or 600 features (i.e. Psi-Blast output using NR or swissprot)
  Average predicted residue burial/exposure level
  Average predicted sequence entropy
  
  Structure based features (predicted)
  Using the sequence, you can either predict the 3d structure using homology, ab initio, or directly use a homologous structure.  You can then extract predicted 3d features, such as:
  
  3d size (i.e. max of x,y,z)
  Calculated ASA/SASA
  3d contacts (type, frequency, etc)
  any other local/global environmental information
  cath/scop classification
  dssp/stride composition
  
  
original_text: 3510: <p>I'm participating in a bioinformatics machine-learning seminar at my university. The main task is predicting binary classification of protein-protein interactions using sequence data as input. </p>
  
  <p>One of the subtasks is familiarization with the dataset and presenting the dataset. Now I'm wondering which additional information I can get out of the sequence data.</p>
  
  <p>I want to start out with binary classification for the protein interaction with the labels “Interact” and “Non-Interact. The dataset that I got provided consists of two fasta files. One containing the sequence header with the species and the corresponding amino-acid sequences ~300 entries. The other containing the exact same species header and the label “Interact” or "Non-interact”. Species are completely mixed.</p>
  
  <p>Is there something interesting that I can additionally include in my analysis? I'm used to exploratory data analysis though this is different due to the nature of the protein sequences.</p>
   1606: <p>There are two types of features which can be extracted from protein sequences.  It is not possible to known which, if any, of these features would be useful in classification tasks.  It may not be possible to build a classifier at all, or it may be very straight forward.  To know this, a feature selection technique must be used, such as e.g. forward feature selection.</p>
  
  <p><strong>Sequence based features</strong></p>
  
  <p>Sequence based features are derived from sequence-only information.</p>
  
  <p>The most common used features are the PseAAC (pseudo amino acid composition) style features, including:</p>
  
  <ul>
  <li>Average Position of each amino acid - 20 features</li>
  <li>Average Distance between each amino acid - 20 features</li>
  <li>Motifs - 20 features, 20*20 features, and/or 20*20*20 features (depending on motif length)</li>
  <li>OAAC (Overall Amino Acid Composition) - 20 features</li>
  <li>SAAC (Split Amino Acid Composition) - 60+ features</li>
  <li>AAIndex Composition - 1 to 567+ features</li>
  <li>PCC (Physicochemical Composition) - ~13 features</li>
  <li>PSSM encoded as 1, 20, 200, 400 or 600 features (i.e. Psi-Blast output using NR or swissprot)</li>
  <li>Average predicted residue burial/exposure level</li>
  <li>Average predicted sequence entropy</li>
  </ul>
  
  <p><strong>Structure based features (predicted)</strong></p>
  
  <p>Using the sequence, you can either predict the 3d structure using homology, ab initio, or directly use a homologous structure.  You can then extract predicted 3d features, such as:</p>
  
  <ul>
  <li>3d size (i.e. max of x,y,z)</li>
  <li>Calculated ASA/SASA</li>
  <li>3d contacts (type, frequency, etc)</li>
  <li>any other local/global environmental information</li>
  <li>cath/scop classification</li>
  <li>dssp/stride composition</li>
  </ul>
  