擅长:python、mysql、java
<p>汗腺性(每种AA)可在以下方面找到:</p>
<p><code>Bio.SeqUtils.ProtParamData.kd</code></p>
<pre><code>kd = {'A': 1.8, 'R':-4.5, 'N':-3.5, 'D':-3.5, 'C': 2.5,
'Q':-3.5, 'E':-3.5, 'G':-0.4, 'H':-3.2, 'I': 4.5,
'L': 3.8, 'K':-3.9, 'M': 1.9, 'F': 2.8, 'P':-1.6,
'S':-0.8, 'T':-0.7, 'W':-0.9, 'Y':-1.3, 'V': 4.2 }
</code></pre>
<p>我想你得加上所有的氨基酸值,然后得到总体的憎恶性。在</p>
<p><a href="http://biopython.org/DIST/docs/api/Bio.SeqUtils.ProtParamData-pysrc.html" rel="nofollow noreferrer">http://biopython.org/DIST/docs/api/Bio.SeqUtils.ProtParamData-pysrc.html</a></p>
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<p>您可以使用GROMACs(<a href="http://manual.gromacs.org/programs/gmx-sasa.html" rel="nofollow noreferrer">http://manual.gromacs.org/programs/gmx-sasa.html</a>)来获取其他两个参数。查看C源代码(<a href="https://github.com/gromacs/gromacs/blob/master/src/gromacs/trajectoryanalysis/modules/sasa.cpp" rel="nofollow noreferrer">https://github.com/gromacs/gromacs/blob/master/src/gromacs/trajectoryanalysis/modules/sasa.cpp</a>),这些参数远不是显而易见的计算。在</p>
<p>我会用一个<code>subprocess.Popen()</code>来包装<code>gmx_sasa</code>,然后得到结果。在</p>