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updated documentation
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‎example1/1-CreateDataset.html‎

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</div>
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<div class="inner_cell">
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<h1 id="Create-Dataset">Create Dataset<a class="anchor-link" href="#Create-Dataset">&#182;</a></h1><p>This notebook extracts from the Protein Data Bank information about the secondary structure of proteins. The ultimate goal is to assign a fold classification for a set of representative proteins.</p>
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<h1 id="Create-Dataset">Create Dataset<a class="anchor-link" href="#Create-Dataset">&#182;</a></h1><p>This notebook extracts from the Protein Data Bank information about the secondary structure of proteins. The ultimate goal is to assign a fold classification from a protein sequence.</p>
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<h2 id="Classify-Sequences-by-Secondary-Structure-Content">Classify Sequences by Secondary Structure Content<a class="anchor-link" href="#Classify-Sequences-by-Secondary-Structure-Content">&#182;</a></h2><p>Next we classify each protein chain into one of four classes. We use a threshold of 25% to define a predominant class.</p>
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<ul>
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<li>alpha: predominantly alpha (&gt;=25%)</li>
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<li>beta: predominantly beta (&gt;=25%)</li>
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<li>alpha+beta: significant alpha (&gt;=25%) and beta (&gt;=25%)</li>
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<li>alpha: predominantly alpha (&gt; 25%)</li>
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<li>beta: predominantly beta (&gt; 25%)</li>
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<li>alpha+beta: significant alpha (&gt; 25%) and beta (&gt; 25%)</li>
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<li>other: cases that do not fit into the 3 classes above </li>
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</ul>
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<p>Protein chains in the <em>other</em> class will be ignored in the subsequent analysis.</p>
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<p>Protein chains in the <strong>other</strong> class will be ignored in the subsequent analysis.</p>
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<div class="prompt input_prompt">In&nbsp;[7]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">protein_fold_class</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">minThreshold</span><span class="p">,</span> <span class="n">maxThreshold</span><span class="p">):</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">protein_fold_class</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">min_threshold</span><span class="p">,</span> <span class="n">max_threshold</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Returns fold classification:</span>
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<span class="sd"> &quot;alpha&quot;, &quot;beta&quot;, &quot;alpha+beta&quot;, and &quot;other&quot; based upon the </span>
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<span class="sd"> fraction of alpha and beta.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&gt;</span> <span class="n">maxThreshold</span> <span class="ow">and</span> <span class="n">data</span><span class="o">.</span><span class="n">beta</span> <span class="o">&lt;</span> <span class="n">minThreshold</span><span class="p">:</span>
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<span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&gt;</span> <span class="n">max_threshold</span> <span class="ow">and</span> <span class="n">data</span><span class="o">.</span><span class="n">beta</span> <span class="o">&lt;</span> <span class="n">min_threshold</span><span class="p">:</span>
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<span class="k">return</span> <span class="s2">&quot;alpha&quot;</span>
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<span class="k">elif</span> <span class="n">data</span><span class="o">.</span><span class="n">beta</span> <span class="o">&gt;</span> <span class="n">maxThreshold</span> <span class="ow">and</span> <span class="n">data</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&lt;</span> <span class="n">minThreshold</span><span class="p">:</span>
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<span class="k">elif</span> <span class="n">data</span><span class="o">.</span><span class="n">beta</span> <span class="o">&gt;</span> <span class="n">max_threshold</span> <span class="ow">and</span> <span class="n">data</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&lt;</span> <span class="n">min_threshold</span><span class="p">:</span>
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<span class="k">return</span> <span class="s2">&quot;beta&quot;</span>
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<span class="k">elif</span> <span class="n">data</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&gt;</span> <span class="n">maxThreshold</span> <span class="ow">and</span> <span class="n">data</span><span class="o">.</span><span class="n">beta</span> <span class="o">&gt;</span> <span class="n">maxThreshold</span><span class="p">:</span>
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<span class="k">elif</span> <span class="n">data</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&gt;</span> <span class="n">max_threshold</span> <span class="ow">and</span> <span class="n">data</span><span class="o">.</span><span class="n">beta</span> <span class="o">&gt;</span> <span class="n">max_threshold</span><span class="p">:</span>
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<span class="k">return</span> <span class="s2">&quot;alpha+beta&quot;</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">return</span> <span class="s2">&quot;other&quot;</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># assign protein fold class</span>
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<span class="n">df</span><span class="p">[</span><span class="n">value_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">protein_fold_class</span><span class="p">,</span> <span class="n">minThreshold</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">maxThreshold</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="n">df</span><span class="p">[</span><span class="n">value_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">protein_fold_class</span><span class="p">,</span> <span class="n">min_threshold</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">max_threshold</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="c1"># exclude protein chains without a dominant classification from further analysis.</span>
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<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">value_col</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;other&#39;</span><span class="p">]</span>

‎example1/1-CreateDataset.ipynb‎

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"metadata": {},
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"source": [
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"# Create Dataset\n",
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"This notebook extracts from the Protein Data Bank information about the secondary structure of proteins. The ultimate goal is to assign a fold classification for a set of representative proteins."
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"This notebook extracts from the Protein Data Bank information about the secondary structure of proteins. The ultimate goal is to assign a fold classification from a protein sequence."
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]
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},
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{
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"## Classify Sequences by Secondary Structure Content\n",
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"Next we classify each protein chain into one of four classes. We use a threshold of 25% to define a predominant class.\n",
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"\n",
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"* alpha: predominantly alpha (>=25%)\n",
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"* beta: predominantly beta (>=25%)\n",
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"* alpha+beta: significant alpha (>=25%) and beta (>=25%)\n",
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"* alpha: predominantly alpha (> 25%)\n",
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"* beta: predominantly beta (> 25%)\n",
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"* alpha+beta: significant alpha (> 25%) and beta (> 25%)\n",
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"* other: cases that do not fit into the 3 classes above \n",
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"\n",
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"Protein chains in the *other* class will be ignored in the subsequent analysis."
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"Protein chains in the **other** class will be ignored in the subsequent analysis."
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def protein_fold_class(data, minThreshold, maxThreshold):\n",
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"def protein_fold_class(data, min_threshold, max_threshold):\n",
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" \"\"\"\n",
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" Returns fold classification:\n",
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" \"alpha\", \"beta\", \"alpha+beta\", and \"other\" based upon the \n",
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" fraction of alpha and beta.\n",
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" \"\"\"\n",
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" if data.alpha > maxThreshold and data.beta < minThreshold: \n",
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" if data.alpha > max_threshold and data.beta < min_threshold: \n",
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" return \"alpha\" \n",
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" elif data.beta > maxThreshold and data.alpha < minThreshold: \n",
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" elif data.beta > max_threshold and data.alpha < min_threshold: \n",
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" return \"beta\" \n",
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" elif data.alpha > maxThreshold and data.beta > maxThreshold: \n",
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" elif data.alpha > max_threshold and data.beta > max_threshold: \n",
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" return \"alpha+beta\" \n",
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],
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"source": [
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"# assign protein fold class\n",
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"df[value_col] = df.apply(protein_fold_class, minThreshold=0.05, maxThreshold=0.25, axis=1)\n",
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"df[value_col] = df.apply(protein_fold_class, min_threshold=0.05, max_threshold=0.25, axis=1)\n",
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"\n",
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"# exclude protein chains without a dominant classification from further analysis.\n",
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"df = df[df[value_col] != 'other']\n",

‎example1/3-FitModel.html‎

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<script type="text/javascript">
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var element = $('#4f72f8e2-c8e8-4175-be53-0a1f3cf27e75');
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var element = $('#32da9708-9524-4db9-bd1f-de45152cc9e5');
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</script>
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<script type="application/vnd.jupyter.widget-view+json">
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{"model_id": "31791fc7b90c40088bc8e281f4434ed4", "version_major": 2, "version_minor": 0}

‎example1/4-Predict.html‎

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<script type="text/javascript">
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var element = $('#229d132e-5bde-46bd-9981-6990339e47ff');
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var element = $('#065d7337-9dad-445e-b0e4-25a6a0c63588');
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</script>
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<script type="application/vnd.jupyter.widget-view+json">
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{"model_id": "450d2400f1134413be6083bf5021d15e", "version_major": 2, "version_minor": 0}

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