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研究生:艾拉文
研究生(外文):Aravind Chandrasekaran
論文名稱:Protein dynamics and contact topology studies reveal characteristics of Protein-DNA binding and distribution of enzyme catalytic sites
論文名稱(外文):蛋白質的接觸拓樸和動力學研究揭露蛋白-DNA接合特徵及酵素活性中心的分佈偏好
指導教授:楊立威
指導教授(外文):Yang, Lee-Wei
學位類別:碩士
校院名稱:國立清華大學
系所名稱:生物資訊與結構生物研究所
學門:生命科學學門
學類:生物訊息學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:52
中文關鍵詞:彈性網絡模型構型變化DNA結合蛋白質DNA結合蛋白質結合位點預測酵素活性位置
外文關鍵詞:ENMConformational changeprotein-DNA bindingprotein-DNA binding siteEnzyme active site prediction
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Effects of protein dynamics on protein-DNA binding have not been analyzed thoroughly so far. Using GNM and ANM, we well predict conformational change between DNA-free/DNA-bound forms. However, we fail to find fluctuation-magnitude-based characteristics to predict DNA binding sites. GNM-based Domain-planes (D-planes) derived from the unbound proteins are used to determine significant DNA-binding orientations with the results that larger than 95% of the 110 DNA molecules being dissected through by these planes. In addition, we also report that enzyme active sites are close to the D-planes such that 90% of the studied 732 active sites are located within 50% rank from the D-planes. We compare and contrast the thermodynamic aspects of ligand-protein and DNA-protein binding. This study suggests potential applications for filtering out unlikely DNA-protein docking poses obtained from docking software.
Effects of protein dynamics on protein-DNA binding have not been analyzed thoroughly so far. Using GNM and ANM, we well predict conformational change between DNA-free/DNA-bound forms. However, we fail to find fluctuation-magnitude-based characteristics to predict DNA binding sites. GNM-based Domain-planes (D-planes) derived from the unbound proteins are used to determine significant DNA-binding orientations with the results that larger than 95% of the 110 DNA molecules being dissected through by these planes. In addition, we also report that enzyme active sites are close to the D-planes such that 90% of the studied 732 active sites are located within 50% rank from the D-planes. We compare and contrast the thermodynamic aspects of ligand-protein and DNA-protein binding. This study suggests potential applications for filtering out unlikely DNA-protein docking poses obtained from docking software.
Contents
1 Introduction 1
2 Methods 3
2.1 Dataset 3
2.1.1 Dataset of DNA-bound and DNA-free proteins 3
2.1.2 Dataset of enzymes along with their active sites 7
2.2 ENM 8
2.2.1 GNM 8
2.2.2 ANM 9
2.3 Protein Intrinsic Dynamics 10
2.3.1 Conformational changes (Displacement of CG nodes) 10
2.3.2 Squared Displacement of CG nodes 10
2.3.3 Squared Displacement of the distance between nodes i and j 11
2.3.4 Determining peaks, troughs in squared displacement profile 11
2.3.5 Performance measures 12
2.4 IDD: Intrinsic Dynamics Domains 12
2.4.1 Determining GNM-based IDDs 13
2.4.2 Determining D-planes 14
2.4.3 Tracing domain axis (D-axis) 16
2.5 Splitting Planes defined by the protein shape 16
2.6 Enrichment ratio 17
2.7 Random axis generation 17
2.7.1 Mean and variance of angle between two randomly oriented vectors 17
3 Results 19
3.1. Slow mode eigenvectors of GNM predict conformational change upon DNA binding better than ANM 19
3.2 Key mechanical sites, kinetically hot residues, flexible residues in a protein are not co-localized with DNA binding sites 19
3.3 Superimposed DNA orients in a random fashion with respect to DNA-free protein 25
3.4 Fragments of superimposed DNA (from DNA-bound form) are close to D-plane of free-form proteins 30
3.5 Enzyme active sites of proteins stay close to GNM-defined D-planes and PC1-defined S-planes 31
4. Discussion 34
4.1 Predicting conformational change upon DNA binding 34
4.2 Enzyme Active sites and superimposed DNA are found near regions of low configurational entropy 34
4.3 Rotational entropy based insights in protein-DNA binding 35
5. Conclusion 37
6. Appendix 39
References: 49


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