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mchelem/fragments.ipynb

Created November 17, 2014 01:44
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Comparação de matrizes de substituição para alinhamento de pequenos fragmentos de proteínas
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fragments.ipynb
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"metadata": {
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"signature": "sha256:156aa60a4282cbac5967a00a4eee72f0100dba2ad3ca9d80d3a419f9f1b0d8e1"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compara\u00e7\u00e3o de matrizes de substitui\u00e7\u00e3o para alinhamento de pequenos fragmentos de prote\u00ednas\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defini\u00e7\u00e3o das matrizes de substitui\u00e7\u00e3o e custo de espa\u00e7amentos\n",
"\n",
"Para verificar quais matrizes de substitui\u00e7\u00e3o apresentam melhores resultados quando utilizadas com fragmentos curtos, testamos todas as combina\u00e7\u00f5es de matrizes e custos de espa\u00e7amentos permitidos pelo NCBI-BLAST."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"substitution_matrices = {\n",
" 'PAM30': \n",
" [(x,2) for x in range(5, 8)] + [(x, 1) for x in range(8, 11)],\n",
" 'PAM70': \n",
" [(x,2) for x in range(6, 9)] + [(x, 1) for x in range(9, 12)],\n",
" 'PAM250': \n",
" [(x,3) for x in range(11, 16)] + [(x,2) for x in range(13, 18)] + [(x, 1) for x in range(17, 22)],\n",
" 'BLOSUM45':\n",
" [(x,3) for x in range(10, 14)] + [(x,2) for x in range(12, 17)] + [(x, 1) for x in range(16, 20)],\n",
" 'BLOSUM50':\n",
" [(x,3) for x in range(9, 14)] + [(x,2) for x in range(12, 17)] + [(x, 1) for x in range(15, 20)],\n",
" 'BLOSUM62':\n",
" [(x,2) for x in range(6, 12)] + [(x, 1) for x in range(9, 14)],\n",
" 'BLOSUM80':\n",
" [(x,2) for x in range(6, 10)] + [(x, 1) for x in range(9, 12)] + [(13, 2), (25, 2)],\n",
" 'BLOSUM90':\n",
" [(x,2) for x in range(6, 10)] + [(x, 1) for x in range(9, 12)],\n",
"}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 123
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Aqui um exemplo dos custos permitidos quando se usa a matriz PAM30 (custo de cria\u00e7\u00e3o, custo de extens\u00e3o)\n",
"substitution_matrices['PAM30']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"[(5, 2), (6, 2), (7, 2), (8, 1), (9, 1), (10, 1)]"
]
}
],
"prompt_number": 42
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chamada da ferramenta blastp e obten\u00e7\u00e3o dos bit scores e e-values"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from Bio.Blast.Applications import NcbiblastxCommandline\n",
"from Bio.Blast import NCBIXML\n",
"import io\n",
"\n",
"def blastp(matrix, gapopen, gapextend, query=9):\n",
" results = []\n",
" blastx = NcbiblastxCommandline(\n",
" cmd=\"blastp\", \n",
" db=\"blastdb/pdbseqres\", \n",
" num_alignments=500, \n",
" evalue=100, \n",
" query=str(query) + '.fasta', \n",
" matrix=matrix, \n",
" gapopen=gapopen, \n",
" gapextend=gapextend,\n",
" outfmt=5,\n",
" )\n",
" stdout, stderr = blastx()\n",
"\n",
" if not stderr:\n",
" blast_record = NCBIXML.read(io.StringIO(stdout))\n",
" for alignment in blast_record.alignments:\n",
" pdb = alignment.hit_def[:4]\n",
" hsp = alignment.hsps[0]\n",
" results.append((matrix, gapopen, gapextend, query, pdb, hsp.align_length, hsp.score, hsp.bits, hsp.expect))\n",
"\n",
" else:\n",
" print(stderr)\n",
" return set(results)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Exemplo: obten\u00e7\u00e3o de alinhamentos usando a matriz de substitui\u00e7\u00e3o PAM30 e custos de espa\u00e7amento 10 e 1, \n",
"# para uma sequencia de 9 amino\u00e1cidos\n",
"blastp('PAM30', 10, 1, 9)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 76,
"text": [
"{('PAM30', 10, 1, 9, '1l6x', 9, 46.0, 23.2434, 7.06087),\n",
" ('PAM30', 10, 1, 9, '1oqo', 9, 46.0, 23.2434, 7.06087),\n",
" ('PAM30', 10, 1, 9, '1oqx', 9, 46.0, 23.2434, 7.06087),\n",
" ('PAM30', 10, 1, 9, '1zdc', 9, 46.0, 23.2434, 7.10177),\n",
" ('PAM30', 10, 1, 9, '1zdd', 9, 46.0, 23.2434, 7.10177),\n",
" ('PAM30', 10, 1, 9, '4jsn', 8, 47.0, 23.6892, 7.70653),\n",
" ('PAM30', 10, 1, 9, '4jsp', 8, 47.0, 23.6892, 7.70653),\n",
" ('PAM30', 10, 1, 9, '4jsv', 8, 47.0, 23.6892, 7.70653),\n",
" ('PAM30', 10, 1, 9, '4jsx', 8, 47.0, 23.6892, 7.70653),\n",
" ('PAM30', 10, 1, 9, '4jt5', 8, 47.0, 23.6892, 7.70653),\n",
" ('PAM30', 10, 1, 9, '4jt6', 8, 47.0, 23.6892, 7.70653),\n",
" ('PAM30', 10, 1, 9, '4nqs', 9, 46.0, 23.2434, 7.06087)}"
]
}
],
"prompt_number": 76
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Teste de todas as combina\u00e7\u00f5es de matrizes e custos de espa\u00e7amento dispon\u00edveis"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results = []\n",
"\n",
"for sequence_length in [5, 7, 9]:\n",
" for matrix in substitution_matrices:\n",
" for gap_cost in substitution_matrices[matrix]:\n",
" results += list(blastp(matrix, gap_cost[0], gap_cost[1], sequence_length))\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Adiciona dados em uma tabela para facilitar a an\u00e1lise\n",
"import pandas\n",
"df = pandas.DataFrame(results, columns=['matrix', 'gap_open', 'gap_extend', 'seq_len', 'pdb', 'alignment_length', 'score', 'bits', 'expect'])\n",
"# 10 primeiras linhas do resultado\n",
"df[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>matrix</th>\n",
" <th>gap_open</th>\n",
" <th>gap_extend</th>\n",
" <th>seq_len</th>\n",
" <th>pdb</th>\n",
" <th>alignment_length</th>\n",
" <th>score</th>\n",
" <th>bits</th>\n",
" <th>expect</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> PAM250</td>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 1oqx</td>\n",
" <td> 7</td>\n",
" <td> 47</td>\n",
" <td> 18.3743</td>\n",
" <td> 93.8457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> PAM250</td>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 4nqs</td>\n",
" <td> 7</td>\n",
" <td> 47</td>\n",
" <td> 18.3743</td>\n",
" <td> 93.8457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> PAM250</td>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 1zdc</td>\n",
" <td> 7</td>\n",
" <td> 47</td>\n",
" <td> 18.3743</td>\n",
" <td> 94.4993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> PAM250</td>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 1l6x</td>\n",
" <td> 7</td>\n",
" <td> 47</td>\n",
" <td> 18.3743</td>\n",
" <td> 93.8457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> PAM250</td>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 1oqo</td>\n",
" <td> 7</td>\n",
" <td> 47</td>\n",
" <td> 18.3743</td>\n",
" <td> 93.8457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> PAM250</td>\n",
" <td> 21</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 1zdd</td>\n",
" <td> 7</td>\n",
" <td> 47</td>\n",
" <td> 18.3743</td>\n",
" <td> 94.4993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> BLOSUM90</td>\n",
" <td> 8</td>\n",
" <td> 2</td>\n",
" <td> 7</td>\n",
" <td> 1l6x</td>\n",
" <td> 7</td>\n",
" <td> 36</td>\n",
" <td> 18.9175</td>\n",
" <td> 97.5351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> BLOSUM90</td>\n",
" <td> 8</td>\n",
" <td> 2</td>\n",
" <td> 7</td>\n",
" <td> 1zdd</td>\n",
" <td> 7</td>\n",
" <td> 36</td>\n",
" <td> 18.9175</td>\n",
" <td> 99.8136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> BLOSUM90</td>\n",
" <td> 8</td>\n",
" <td> 2</td>\n",
" <td> 7</td>\n",
" <td> 4nqs</td>\n",
" <td> 7</td>\n",
" <td> 36</td>\n",
" <td> 18.9175</td>\n",
" <td> 97.5351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> BLOSUM90</td>\n",
" <td> 8</td>\n",
" <td> 2</td>\n",
" <td> 7</td>\n",
" <td> 1oqx</td>\n",
" <td> 7</td>\n",
" <td> 36</td>\n",
" <td> 18.9175</td>\n",
" <td> 97.5351</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
" matrix gap_open gap_extend seq_len pdb alignment_length score \\\n",
"0 PAM250 21 1 7 1oqx 7 47 \n",
"1 PAM250 21 1 7 4nqs 7 47 \n",
"2 PAM250 21 1 7 1zdc 7 47 \n",
"3 PAM250 21 1 7 1l6x 7 47 \n",
"4 PAM250 21 1 7 1oqo 7 47 \n",
"5 PAM250 21 1 7 1zdd 7 47 \n",
"6 BLOSUM90 8 2 7 1l6x 7 36 \n",
"7 BLOSUM90 8 2 7 1zdd 7 36 \n",
"8 BLOSUM90 8 2 7 4nqs 7 36 \n",
"9 BLOSUM90 8 2 7 1oqx 7 36 \n",
"\n",
" bits expect \n",
"0 18.3743 93.8457 \n",
"1 18.3743 93.8457 \n",
"2 18.3743 94.4993 \n",
"3 18.3743 93.8457 \n",
"4 18.3743 93.8457 \n",
"5 18.3743 94.4993 \n",
"6 18.9175 97.5351 \n",
"7 18.9175 99.8136 \n",
"8 18.9175 97.5351 \n",
"9 18.9175 97.5351 "
]
}
],
"prompt_number": 69
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expect para todos os experimentos"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import pylab\n",
"pylab.rcParams['figure.figsize'] = (20.0, 8.0)\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"df[['matrix', 'expect']].boxplot(by='matrix')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 70,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8147f9f208>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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5BQAAAEAxyqmKs6wdAAAAqs+Zv/VTTlWcZe1AKcpxAADoP2f+1k85BS1MuUAj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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f816c22dcc0>"
]
}
],
"prompt_number": 70
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expect somente para fragmentos com 7 amino\u00e1cidos"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[df['seq_len'] == 7][['matrix', 'expect']].boxplot(by='matrix')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8147d39cc0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AAACKUU4BAAAAUIxyCgAAAIBi3HMKABgwefLk0hH2smHDhtIRAAA4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\nEZf5H9WyUgrZIQUNAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f814c0ee048>"
]
}
],
"prompt_number": 72
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expect somente para fragmentos com 9 amino\u00e1cidos"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[df['seq_len'] == 9][['matrix', 'expect']].boxplot(by='matrix')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8147a9b780>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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lAQCwr0aWDgAADD3NMzvT1dVVOgYAwAF1zz2NuffepiTJvHlN2batJUlyxhk9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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f816c218358>"
]
}
],
"prompt_number": 74
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bit score para todos os experimentos"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[['matrix', 'bits']].boxplot(by='matrix')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 75,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f81479efb38>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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fluMBAAAAUDglFAAAAACFG9fL8QAAgMHruXJx8sJzZcc4QM+SBWVH+LUp09J4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\n3f8Ao7ZCKKF3kXAAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f8147adbeb8>"
]
}
],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy\n",
"numpy.random.seed(42)\n",
"groups = df[['matrix', 'expect', 'gap_open']].groupby('matrix')\n",
"pylab.rcParams['figure.figsize'] = (20.0, 8.0)\n",
"colors = pandas.tools.plotting._get_standard_colors(len(groups), color_type='random')\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.set_color_cycle(colors)\n",
"ax.margins(0.05)\n",
"i = 0\n",
"for name, group in groups:\n",
" if name.startswith('PAM'):\n",
" ax.plot(group.gap_open, group.expect, marker='o', linestyle='', label=name, markersize=15)\n",
"ax.legend(loc='best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"<matplotlib.legend.Legend at 0x7f814c49f588>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AAAAAAOCG2N7lgM2bN2ve2jelW3xltflIFotkGLIXFErn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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f814c9c9c18>"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots()\n",
"ax.set_color_cycle(colors)\n",
"ax.margins(0.05)\n",
"i = 0\n",
"for name, group in groups:\n",
" if name.startswith('BLOSUM'):\n",
" ax.plot(group.gap_open, group.expect, marker='o', linestyle='', label=name, markersize=15)\n",
"ax.legend(loc='best')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"<matplotlib.legend.Legend at 0x7f814c412518>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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usJvraeLk5/n/Fh7FXAHAEb4yV5S0TYxiEHxW+zvvV5TuKvO4jJxURbS8rNXr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\niIiIiIiIiKgZYTGIiIiIiIiIiKgZ+X+OkReElpU2iQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f814c523470>"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Superimposi\u00e7\u00e3o de estruturas tridimensionais\n",
"\n",
"Durante o alinhamento, obtemos o c\u00f3digo PDB das estruturas. Podemos, portanto, comparar a estrutura original com as estruturas obtidas."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Lista dos 5 primeiros pdbs encontrados\n",
"pdbs = [x.upper() for x in df[df['seq_len'] == 7][['pdb']]['pdb']]\n",
"pdbs[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 138,
"text": [
"['1OQX', '4NQS', '1ZDC', '1L6X', '1OQO']"
]
}
],
"prompt_number": 138
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from Bio.PDB import *\n",
"\n",
"# Ler arquivos PDB\n",
"source = parser.get_structure('source', '1ZDD.pdb')\n",
"target = parser.get_structure('target', pdbs[0] + '.pdb')\n",
"\n",
"# Calcular RMS\n",
"superimposer = Superimposer()\n",
"source_atoms = list(source.get_atoms())\n",
"target_atoms = list(target.get_atoms())[:len(source_atoms)]\n",
"superimposer.set_atoms(source_atoms, target_atoms)\n",
"print(superimposer.rms)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"13.8418594153\n"
]
}
],
"prompt_number": 142
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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