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A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
2.1 と 1.8 2つの小さなタンパク質,HP-36 と s15 の2つの小さなタンパク質の平均C (α) RMSD構造の予測
M R Lee1, D Baker, P A Kollman
1Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California 94143-0446, USA.
Journal of the American Chemical Society
|July 18, 2001
まとめ
分子ダイナミクスシミュレーションは,最初からタンパク質構造の予測を精製した. この方法はモデルを正確にランク付けし,構造的精度を向上させ,配列からタンパク質構造の予測を進めました.
科学分野:
- 計算生物学とは,計算生物学である.
- 構造生物学 構造生物学とは
- バイオフィジックス 生物物理学
背景:
- 精密なタンパク質構造の予測は,生物学的機能を理解するために極めて重要です.
- ロゼッタのようなアブ・イニシオ・メソッドは,初期構造モデルを生成します.
- 高解像度の精度のために,これらのモデルの精細化が必要である.
研究 の 目的:
- Ab initioタンパク質構造の予測を精錬するために分子動力学 (MD) シミュレーションの有効性を評価する.
- MM/PBSAの無料エネルギー計算が,これらの洗練されたモデルをランク付けする能力を評価する.
- 配列からタンパク質の構造を予測する進歩を探求する.
主な方法:
- ロゼッタで予測されたビリンの頭部 (HP-36) とリボソームタンパク質S15.の構造を改良するためにMDシミュレーションを適用しました.
- クラスタ化されたシミュレーション軌道は,形状の家族に分類されます.
- 各ファミリーで計算されたMM/PBSA自由エネルギーとアルファ炭素RMSDの平均.
主要な成果:
- MM/PBSAの自由エネルギーが低いコンフォメーションファミリーは,よりよいC ((alpha) RMSD構造を生み出しました.
- 低平均C (α) RMSDs (1.8AはS15で,2.1AはHP-36コアで) を達成しました.
- MM/PBSAの自由エネルギーランクとC ((alpha) RMSDs (r ((s) = 0.77-0.83) の間で強い相関が観察されました.
結論:
- MDシミュレーションとMM/PBSA自由エネルギー計算を組み合わせたMDシミュレーションは,タンパク質構造の予測を精錬し,ランク付けするのに強力です.
- この統合的アプローチは,高解像度の構造的精錬を容易にする.
- この方法論は,配列データからタンパク質構造の予測の分野を前進させるのに有望であることを示しています.
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