{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 🧪 Análisis de Dinámica Molecular (MD) de PIA-KRASv2-Nb\n",
        "\n",
        "Este cuaderno de Google Colab está diseñado para analizar simulaciones de dinámica molecular de un nanobody candidato frente a KRAS.  \n",
        "El flujo de trabajo está dividido en varias celdas, cada una con una función específica:\n",
        "\n",
        "---\n",
        "\n",
        "## 📌 Flujo del análisis\n",
        "\n",
        "1. **Instalación de librerías (Celda 1)**  \n",
        "   - Instala automáticamente las dependencias necesarias: `MDTraj`, `Pandas`, `Matplotlib`.  \n",
        "   - Estas librerías permiten cargar, procesar y visualizar trayectorias de dinámica molecular.\n",
        "\n",
        "2. **Carga de archivos de simulación (Celda 2)**  \n",
        "   - Permite subir el archivo de **estructura** (`protein_system_CORRECTED.pdb`) y el de **trayectoria** (`trajectory.dcd`).  \n",
        "   - Estos archivos son generados por motores de simulación como AMBER, GROMACS o NAMD.\n",
        "\n",
        "3. **Análisis de RMSD (Celda 3)**  \n",
        "   - Calcula el **Desplazamiento Medio Cuadrático (RMSD)** para:\n",
        "     - El armazón del nanobody (Framework).  \n",
        "     - Los bucles de unión (CDRs).  \n",
        "   - Exporta los valores a un archivo `.csv` y genera un gráfico de estabilidad conformacional a lo largo del tiempo de simulación.\n",
        "\n",
        "4. **Análisis de red de contactos (Celda 4)**  \n",
        "   - Detecta y cuantifica el número de **pares de residuos en contacto (< 4 Å)** entre el nanobody y KRAS durante toda la trayectoria.  \n",
        "   - Produce un archivo `.csv` con la evolución de los contactos y un gráfico comparando con la predicción estática de AlphaFold.\n",
        "\n",
        "5. **Análisis estadístico de tendencias (Celda 5)**  \n",
        "   - Aplica una **regresión lineal** al número de contactos residuo-residuo a lo largo del tiempo.  \n",
        "   - Informa si existe una tendencia estadísticamente significativa (p-valor < 0.05) de aumento o disminución en la estabilidad de la interfaz.\n",
        "\n",
        "---\n",
        "\n",
        "## 📊 Resultados esperados\n",
        "- **Archivos `.csv` descargables** con los valores de RMSD y número de contactos.  \n",
        "- **Gráficos de estabilidad conformacional y contactos** listos para incluir en informes o publicaciones.  \n",
        "- **Estadísticos clave (pendiente, p-valor, R²)** para validar la robustez de la interacción.  \n",
        "\n",
        "---\n",
        "\n",
        "➡️ En conjunto, este cuaderno permite evaluar de forma cuantitativa si el complejo Nanobody–KRAS es estable durante la simulación de dinámica molecular y si presenta señales de robustez estructural que justifiquen su validación experimental.\n"
      ],
      "metadata": {
        "id": "w-SBwS2Brt_Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wtVSaLB7fqh",
        "outputId": "6594ec71-e663-465a-b626-66c838e1851f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Instalando las librerías necesarias para el análisis (MDTraj, Pandas)...\n",
            "¡Instalación completada!\n"
          ]
        }
      ],
      "source": [
        "# --- Celda 1: Instalar Librerías de Análisis ---\n",
        "print(\"Instalando las librerías necesarias para el análisis (MDTraj, Pandas)...\")\n",
        "!pip install mdtraj pandas matplotlib &> /dev/null\n",
        "print(\"¡Instalación completada!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "9Oqhy5PN9Piy",
        "outputId": "01ea8e53-92ea-4c41-a4d6-4b7fd6986a03",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Por favor, selecciona el archivo de ESTRUCTURA (protein_system_CORRECTED.pdb)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "        style=\"border:none\" />\n",
              "     <output id=\"result-622e2d7b-4d15-4bd2-b993-b67478f59299\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
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              "  return element;\n",
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              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
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              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving protein_system_CORRECTED (1).pdb to protein_system_CORRECTED (1).pdb\n",
            "\n",
            "Ahora, selecciona el archivo de TRAYECTORIA (trajectory.dcd)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c49f1277-d8d0-478d-a5d6-e4936611d87d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c49f1277-d8d0-478d-a5d6-e4936611d87d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
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              "}\n",
              "\n",
              "/**\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving trajectory.dcd to trajectory.dcd\n",
            "\n",
            "¡Archivos subidos con éxito!\n"
          ]
        }
      ],
      "source": [
        "# --- Celda 2: Subir Archivos de Simulación ---\n",
        "from google.colab import files\n",
        "\n",
        "print(\"Por favor, selecciona el archivo de ESTRUCTURA (protein_system_CORRECTED.pdb)...\")\n",
        "files.upload()\n",
        "\n",
        "print(\"\\nAhora, selecciona el archivo de TRAYECTORIA (trajectory.dcd)...\")\n",
        "files.upload()\n",
        "\n",
        "print(\"\\n¡Archivos subidos con éxito!\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Celda 3: Análisis de RMSD para la Simulación de 10 ns ---\n",
        "\n",
        "print(\"--- Iniciando análisis de RMSD de la trayectoria ---\")\n",
        "\n",
        "import mdtraj as md\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import files\n",
        "\n",
        "# ==============================================================================\n",
        "# >> PANEL DE CONTROL <<\n",
        "# ==============================================================================\n",
        "PDB_FILE = \"protein_system_CORRECTED.pdb\"\n",
        "TRAJECTORY_FILE = \"trajectory.dcd\"\n",
        "# Parámetros REALES de tu simulación (para construir el eje de tiempo correcto)\n",
        "TIMESTEP_FS = 2.0\n",
        "REPORT_INTERVAL_STEPS = 1000\n",
        "# ==============================================================================\n",
        "\n",
        "if not os.path.exists(PDB_FILE) or not os.path.exists(TRAJECTORY_FILE):\n",
        "    print(f\"\\n¡Error! No se encuentran los archivos.\")\n",
        "else:\n",
        "    print(f\"\\nCargando la estructura y la trayectoria...\")\n",
        "    traj = md.load(TRAJECTORY_FILE, top=PDB_FILE)\n",
        "    print(f\"Trayectoria cargada: {traj.n_frames} fotogramas.\")\n",
        "\n",
        "    # --- Construcción Manual y Robusta del Eje de Tiempo ---\n",
        "    TIME_PER_FRAME_PS = (TIMESTEP_FS * REPORT_INTERVAL_STEPS) / 1000.0\n",
        "    time_ns = (np.arange(traj.n_frames) * TIME_PER_FRAME_PS) / 1000.0\n",
        "    SIMULATION_TIME_NS = time_ns[-1]\n",
        "    print(f\"Duración real de la trayectoria analizada: {SIMULATION_TIME_NS:.1f} ns\")\n",
        "\n",
        "    # --- Diagnóstico automático de cadenas ---\n",
        "    protein_chains = [chain for chain in traj.topology.chains if any(res.is_protein for res in chain.residues)]\n",
        "    if len(protein_chains) < 2:\n",
        "        raise SystemExit(\"Error: No se encontraron al menos dos cadenas de proteína.\")\n",
        "    chain_lengths = [(chain.index, len([res for res in chain.residues if res.is_protein])) for chain in protein_chains]\n",
        "    chain_lengths.sort(key=lambda x: x[1], reverse=True)\n",
        "    nanobody_chain_id = chain_lengths[0][0]\n",
        "    kras_chain_id = chain_lengths[1][0]\n",
        "    print(f\"Nanobody identificado en Cadena: {nanobody_chain_id}, KRAS en Cadena: {kras_chain_id}\")\n",
        "\n",
        "    # --- Selecciones para RMSD ---\n",
        "    fw_indices_str = f'protein and chainid {nanobody_chain_id} and (resid 0 to 25 or resid 33 to 49 or resid 58 to 94 or resid 103 to 112)'\n",
        "    cdr_indices_str = f'protein and chainid {nanobody_chain_id} and (resid 26 to 32 or resid 50 to 57 or resid 95 to 102)'\n",
        "    nanobody_framework_atoms = traj.topology.select(fw_indices_str)\n",
        "    nanobody_cdr_atoms = traj.topology.select(cdr_indices_str)\n",
        "\n",
        "    # --- Cálculos de RMSD ---\n",
        "    print(\"\\nAlineando trayectoria y calculando RMSD...\")\n",
        "    reference_indices = traj.topology.select(f'protein and chainid {kras_chain_id} and backbone')\n",
        "    traj.superpose(traj, frame=0, atom_indices=reference_indices)\n",
        "\n",
        "    rmsd_framework = md.rmsd(traj, traj, frame=0, atom_indices=nanobody_framework_atoms) * 10\n",
        "    rmsd_cdrs = md.rmsd(traj, traj, frame=0, atom_indices=nanobody_cdr_atoms) * 10\n",
        "    print(\"Cálculos completados.\")\n",
        "\n",
        "    # --- Exportación a CSV (solo datos de RMSD) ---\n",
        "    OUTPUT_CSV_FILE = f\"rmsd_analysis_{SIMULATION_TIME_NS:.1f}ns.csv\"\n",
        "    print(f\"\\nCreando el archivo '{OUTPUT_CSV_FILE}'...\")\n",
        "    df = pd.DataFrame({\n",
        "        'Time_ns': time_ns,\n",
        "        'RMSD_Framework_A': rmsd_framework,\n",
        "        'RMSD_CDRs_A': rmsd_cdrs\n",
        "    })\n",
        "    df.to_csv(OUTPUT_CSV_FILE, index=False, float_format='%.4f')\n",
        "    files.download(OUTPUT_CSV_FILE)\n",
        "    print(f\"¡Exportación a CSV completada!\")\n",
        "\n",
        "    # --- Generación del Gráfico de RMSD ---\n",
        "    print(\"\\nGenerando gráfico de análisis de RMSD...\")\n",
        "    plt.style.use('seaborn-v0_8-whitegrid')\n",
        "    plt.figure(figsize=(16, 8))\n",
        "\n",
        "    plt.plot(df['Time_ns'], df['RMSD_Framework_A'], label='Armazón (Framework)', linewidth=2)\n",
        "    plt.plot(df['Time_ns'], df['RMSD_CDRs_A'], label='Bucles CDR', linewidth=2, alpha=0.9)\n",
        "\n",
        "    plt.title(f'Estabilidad Conformacional del Nanobody ({SIMULATION_TIME_NS:.1f} ns)', fontsize=18, fontweight='bold')\n",
        "    plt.xlabel('Tiempo de Simulación (ns)', fontsize=14)\n",
        "    plt.ylabel('RMSD (Å)', fontsize=14)\n",
        "    plt.legend(loc='upper left', fontsize=12)\n",
        "    plt.ylim(bottom=0)\n",
        "    plt.xlim(left=0, right=SIMULATION_TIME_NS)\n",
        "\n",
        "    plt.savefig(f'rmsd_analysis_plot_{SIMULATION_TIME_NS:.1f}ns.png', dpi=300, bbox_inches='tight')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "WhVPsg0-OPHj",
        "outputId": "33915237-211c-44c0-b1be-53a3e87a91bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Iniciando análisis de RMSD de la trayectoria ---\n",
            "\n",
            "Cargando la estructura y la trayectoria...\n",
            "Trayectoria cargada: 5000 fotogramas.\n",
            "Duración real de la trayectoria analizada: 10.0 ns\n",
            "Nanobody identificado en Cadena: 0, KRAS en Cadena: 1\n",
            "\n",
            "Alineando trayectoria y calculando RMSD...\n",
            "Cálculos completados.\n",
            "\n",
            "Creando el archivo 'rmsd_analysis_10.0ns.csv'...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_9b1d195d-654b-452c-903e-8ea0b66e5e7d\", \"rmsd_analysis_10.0ns.csv\", 105037)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "¡Exportación a CSV completada!\n",
            "\n",
            "Generando gráfico de análisis de RMSD...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# --- Celda 4 Análisis de red de contactos ---\n",
        "# --- Paso 1: Instalar e importar las librerías necesarias ---\n",
        "print(\"Instalando librerías necesarias (MDTraj, Pandas)...\")\n",
        "!pip install mdtraj pandas &> /dev/null\n",
        "import mdtraj as md\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import itertools\n",
        "from google.colab import files\n",
        "\n",
        "print(\"Librerías importadas.\")\n",
        "\n",
        "# --- Paso 2: Definir archivos y parámetros ---\n",
        "PDB_FILE = \"protein_system_CORRECTED.pdb\"\n",
        "TRAJECTORY_FILE = \"trajectory.dcd\"\n",
        "contact_cutoff_A = 4.0\n",
        "contact_cutoff_nm = contact_cutoff_A / 10.0\n",
        "\n",
        "# --- Paso 3: Cargar la trayectoria ---\n",
        "if not os.path.exists(PDB_FILE) or not os.path.exists(TRAJECTORY_FILE):\n",
        "    print(f\"\\n¡Error! No se encuentran los archivos.\")\n",
        "else:\n",
        "    print(\"\\nCargando la estructura y la trayectoria...\")\n",
        "    traj = md.load(TRAJECTORY_FILE, top=PDB_FILE)\n",
        "    topology = traj.topology\n",
        "    print(f\"Trayectoria cargada: {traj.n_frames} fotogramas.\")\n",
        "\n",
        "    # --- Paso 4: SELECCIÓN BASADA EN LA ESTRUCTURA REAL (CADENAS SEPARADAS) ---\n",
        "    print(\"\\nIdentificando los residuos del epítopo y del nanocuerpo por cadena...\")\n",
        "\n",
        "    # Basado en el diagnóstico, Nanobody es la Cadena 0 y KRAS la Cadena 1\n",
        "    nanobody_residues = [res.index for res in topology.chain(0).residues if res.is_protein]\n",
        "    kras_epitope_residues = [res.index for res in topology.chain(1).residues if res.is_protein]\n",
        "\n",
        "    if not kras_epitope_residues or not nanobody_residues:\n",
        "        print(\"\\n¡Error en la selección! No se pudieron encontrar residuos de proteína en las Cadenas 0 y 1.\")\n",
        "    else:\n",
        "        print(f\"Se analizarán {len(nanobody_residues)} residuos del Nanobody (Cadena 0).\")\n",
        "        print(f\"Se analizarán {len(kras_epitope_residues)} residuos del Epítopo de KRAS (Cadena 1).\")\n",
        "\n",
        "        # --- Paso 5: MÉTODO OPTIMIZADO - Calcular contactos ---\n",
        "        print(\"\\nCalculando el número de pares de residuos en contacto (método optimizado)...\")\n",
        "\n",
        "        residue_pairs_to_check = list(itertools.product(nanobody_residues, kras_epitope_residues))\n",
        "        distances, pairs = md.compute_contacts(traj, contacts=residue_pairs_to_check, scheme='closest-heavy')\n",
        "        contact_counts = np.sum(distances < contact_cutoff_nm, axis=1)\n",
        "        average_contacts = np.mean(contact_counts)\n",
        "        print(f\"Cálculo completado. Número medio de contactos: {average_contacts:.2f}\")\n",
        "\n",
        "        # --- Paso 6: Crear y descargar el archivo .csv ---\n",
        "        print(\"\\nCreando y descargando el archivo de datos CSV...\")\n",
        "\n",
        "        # Construimos el eje de tiempo correcto manualmente.\n",
        "        time_ps = np.arange(traj.n_frames) * 2.0\n",
        "        time_ns = time_ps / 1000.0\n",
        "        # CAMBIO 1: Calcular la duración real para usarla en los nombres de archivo\n",
        "        SIMULATION_TIME_NS = time_ns[-1]\n",
        "\n",
        "        df = pd.DataFrame({'Time_ns': time_ns, 'NumberOfResidueContacts': contact_counts})\n",
        "        # CAMBIO 2: Actualizar el nombre del archivo CSV\n",
        "        csv_filename = f'contact_analysis_residue_level_{SIMULATION_TIME_NS:.1f}ns_CORRECTED.csv'\n",
        "        df.to_csv(csv_filename, index=False)\n",
        "        files.download(csv_filename)\n",
        "        print(f\"Archivo '{csv_filename}' descargado.\")\n",
        "\n",
        "        # --- Paso 7: Generar el gráfico ---\n",
        "        print(\"\\nGenerando el gráfico de la red de contactos con el eje de tiempo corregido...\")\n",
        "        plt.style.use('seaborn-v0_8-whitegrid')\n",
        "        plt.figure(figsize=(16, 8))\n",
        "\n",
        "        plt.plot(time_ns, contact_counts, label=f'Contactos Dinámicos (Media = {average_contacts:.1f})', color='purple', linewidth=1.5)\n",
        "        plt.axhline(y=35, color='red', linestyle='--', linewidth=2, label='Predicción Estática de AlphaFold (35 pares)')\n",
        "\n",
        "        # CAMBIO 3: Actualizar el título y los límites del gráfico\n",
        "        plt.title(f'Evolución de la Red de Contactos en la Interfaz ({SIMULATION_TIME_NS:.1f} ns)', fontsize=18, fontweight='bold')\n",
        "        plt.xlabel('Tiempo de Simulación (ns)', fontsize=14)\n",
        "        plt.ylabel('Número de Pares de Residuos en Contacto (< 4 Å)', fontsize=14)\n",
        "        plt.legend(loc='lower left', fontsize=12) # Cambiado a 'upper left' por si la línea sube\n",
        "\n",
        "        plt.ylim(bottom=0)\n",
        "        plt.xlim(left=0, right=SIMULATION_TIME_NS)\n",
        "\n",
        "        plt.savefig(f'contact_network_plot_{SIMULATION_TIME_NS:.1f}ns_final.png', dpi=300, bbox_inches='tight')\n",
        "        plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ROOngEvRPFUH",
        "outputId": "87fc7461-b0a9-4e27-a796-13ff3d6b0bc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Instalando librerías necesarias (MDTraj, Pandas)...\n",
            "Librerías importadas.\n",
            "\n",
            "Cargando la estructura y la trayectoria...\n",
            "Trayectoria cargada: 5000 fotogramas.\n",
            "\n",
            "Identificando los residuos del epítopo y del nanocuerpo por cadena...\n",
            "Se analizarán 120 residuos del Nanobody (Cadena 0).\n",
            "Se analizarán 10 residuos del Epítopo de KRAS (Cadena 1).\n",
            "\n",
            "Calculando el número de pares de residuos en contacto (método optimizado)...\n",
            "Cálculo completado. Número medio de contactos: 29.17\n",
            "\n",
            "Creando y descargando el archivo de datos CSV...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_895bad14-4756-4cd5-884e-f11623a0a612\", \"contact_analysis_residue_level_10.0ns_CORRECTED.csv\", 43932)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archivo 'contact_analysis_residue_level_10.0ns_CORRECTED.csv' descargado.\n",
            "\n",
            "Generando el gráfico de la red de contactos con el eje de tiempo corregido...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Celda 5 Análisis Estadístico de Tendencia ---\n",
        "\n",
        "print(\"--- Iniciando análisis de regresión lineal ---\")\n",
        "\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import os\n",
        "\n",
        "CSV_FILE = 'contact_analysis_residue_level_FINAL.csv'\n",
        "\n",
        "if not os.path.exists(CSV_FILE):\n",
        "    print(f\"\\n¡Error! No se encuentra el archivo '{CSV_FILE}'.\")\n",
        "    print(\"Asegúrate de haber ejecutado primero la celda de análisis que genera el CSV.\")\n",
        "else:\n",
        "    try:\n",
        "        # Cargar los datos desde el archivo CSV\n",
        "        df = pd.read_csv(CSV_FILE)\n",
        "\n",
        "        # Extraer las columnas de tiempo y número de contactos\n",
        "        time = df['Time_ns']\n",
        "        contacts = df['NumberOfResidueContacts']\n",
        "\n",
        "        # Realizar la regresión lineal\n",
        "        slope, intercept, r_value, p_value, std_err = stats.linregress(time, contacts)\n",
        "\n",
        "        # --- Presentar los resultados de forma clara ---\n",
        "        print(\"\\n=======================================================\")\n",
        "        print(\"    Resultados del Análisis de Tendencia\")\n",
        "        print(\"=======================================================\")\n",
        "        print(f\"Pendiente (Slope) de la tendencia: {slope:.4f} contactos/ns\")\n",
        "        print(f\"P-valor (significancia estadística): {p_value:.6f}\")\n",
        "        print(f\"Coeficiente de correlación (R^2): {r_value**2:.4f}\")\n",
        "        print(\"=======================================================\")\n",
        "\n",
        "        # --- Interpretación de los resultados ---\n",
        "        print(\"\\nInterpretación:\")\n",
        "\n",
        "        if p_value < 0.05:\n",
        "            print(\"✅ El p-valor es menor que 0.05, lo que indica que la tendencia es ESTADÍSTICAMENTE SIGNIFICATIVA.\")\n",
        "            if slope > 0:\n",
        "                print(f\"   Tu observación es CORRECTA. Hay una tendencia clara y medible al AUMENTO en el número de contactos.\")\n",
        "                print(f\"   En promedio, el complejo gana aproximadamente {slope:.2f} pares de residuos en contacto por cada nanosegundo de simulación.\")\n",
        "            elif slope < 0:\n",
        "                print(f\"   Hay una tendencia clara y medible a la DISMINUCIÓN en el número de contactos.\")\n",
        "                print(f\"   En promedio, el complejo pierde aproximadamente {abs(slope):.2f} pares de residuos en contacto por cada nanosegundo.\")\n",
        "            else:\n",
        "                print(\"   La pendiente es prácticamente cero, por lo que no hay una tendencia significativa al alza o a la baja.\")\n",
        "        else:\n",
        "            print(\"❌ El p-valor es mayor que 0.05, lo que indica que la tendencia observada NO es estadísticamente significativa.\")\n",
        "            print(\"   Las fluctuaciones en el número de contactos se consideran ruido aleatorio alrededor de la media, sin una dirección clara al alza o a la baja.\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"\\nOcurrió un error durante el análisis: {e}\")"
      ],
      "metadata": {
        "id": "kw1SJEqXpxny"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}