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<div class="section" id="d-script-deep-learning-ppi-prediction"> |
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<h1>D-SCRIPT: Deep Learning PPI Prediction<a class="headerlink" href="#d-script-deep-learning-ppi-prediction" title="Permalink to this headline">¶</a></h1> |
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<li><p><a class="reference external" href="http://dscript.csail.mit.edu">D-SCRIPT Home Page</a></p></li> |
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<p>D-SCRIPT is a deep learning method for predicting a physical interaction between two proteins given just their sequences. |
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It generalizes well to new species and is robust to limitations in training data size. |
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Its design reflects the intuition that for two proteins to physically interact, a subset of amino acids from each protein should be in contact with the other. |
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The intermediate stages of D-SCRIPT directly implement this intuition, with the penultimate stage in D-SCRIPT being a rough estimate of the inter-protein |
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contact map of the protein dimer. This structurally-motivated design enhances the interpretability of the results and, since structure is more conserved |
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evolutionarily than sequence, improves generalizability across species.</p> |
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<p>If you use D-SCRIPT, please cite “Sequence-based prediction of protein-protein interactions: a structure-aware interpetable deep learning model” |
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by <a class="reference external" href="http://samsledje.github.io/">Sam Sledzieski</a>, <a class="reference external" href="http://people.csail.mit.edu/rsingh/">Rohit Singh</a>, <a class="reference external" href="http://www.cs.tufts.edu/~cowen/">Lenore Cowen</a>, and <a class="reference external" href="http://people.csail.mit.edu/bab/">Bonnie Berger</a> [link TBD].</p> |
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1> |
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