分子交互作用網路及結構功能演化在醫藥應用之研究(I)

Abstract

後基因體時代的一大挑戰是瞭解基因、蛋白質與多種小分子物質之間的交互作用網路及 機制。由於實驗方法的進步，現在已經有了一個極好的起步提供給科學家研究此類議題。 包括蛋白質功能、蛋白質-蛋白質交互作用(protein-protein interactions, PPI)、功能區塊交互 作用(domain-domain interactions, DDI), 以及訊息傳遞網路等。其中，依演化親緣關係的跨 物種比較應是研究這類議題的有效方法。 我們業已發展出一套方法，名為」3D-domain interologs」，可在多物種間預測其各個蛋白 質的同源功能區塊(orthologous domains)間的可能交互作用關係，相似的方法也應用在蛋白 質-DNA 之間的作用上。3D-domain interologs 的基本假設如下：若已知某一物種細胞內的兩 個蛋白質的功能區塊a 與b 會發生交互作用，且在另一物種內可以找到a 與b 的同源功能 區塊a』與b』，則a』與b』之間也有可能會發生交互作用，且擁有相似的結合模式(binding model, 例如氫鍵作用力(hydrogen-bond interaction)和保存胺基酸(conserved residues))。我們先前的 研究已經顯示這是一套研究跨物種間PPIs 及DDIs 共演化(co-evolution)的有效方法。 在本計畫中我們會更深化 3D-domain interologs 以深入瞭解細胞生化網路的可能機制及 跨物種間關聯性，並將它應用在醫學用途。我們將達成下列目標：(i) 發展以知識為基礎的 測量函式(knowledge-based scoring functions)預測protein-protein 及protein-DNA 分子間的親 合力(binding affinity)；(ii) 找出會發生交互作用的功能區塊、其交互作用模式，以及跨物種 間共有的protein-protein/protein-DNA 交互作用基本模組 (consensus patterns)；(iii) 找出跨物 種間共通的生化網路(conserved networks/pathways)；(iv) 運用找到的共有交互作用基本模組 將計算protein-ligand 嵌合的函式最佳化，並且用於預測疾病的可能致病機制；以及 (v) 應 用這些發展出的理論及演算方法於癌症相關生化機制之研究以及各類疾病的藥物開發。 現在我們已經完成某些初步成果，例如可預測 protein-protein 交互作用的 3D-partner server 系統、分析多物種間同源交互作用(orthologous interactions)的3D-interologs database， 以及兩套預測protein-protein 及protein-DNA 分子間親合力的知識為基礎測量函式。我們先 以肺癌相關生化機制為研究對象，已經成功預測protein-protein 間(i.e. CDK2/CKS1 complexes across 56 species)以及 protein-DNA 間的交互作用(Myc/Max across 72 species)。我 們的長期目標在於結合已知的細胞內生化路徑知識以及我們預測出的protein-protein、 protein-ligand、 protein-DNA/RNA 交互作用關係，建構(跨物種間)分子間的交互作用網路。 這對於諸多醫學研究、疾病治療方法的開發以及藥物發展都是非常有幫助的。

A major challenge of postgenomic biology is to understand the networks of interacting genes, proteins and small molecules that produce biological functions. The large number of protein interactions, generated by large-scale experimental methods, provides opportunities and challenges in annotating protein functions, protein-protein interactions (PPI) and domain-domain interactions (DDI), and in modeling the cellular signaling and regulatory networks. An approach based on evolutionary cross-species comparisons is valuable for addressing these issues. We have utilized "3D-domain interologs" to discover potential protein-protein and protein-DNA interactions which are predicted as orthologous interactions among different species, in particular about inferred interacting domain pairs and binding models (e.g. hydrogen-bond interactions and conserved residues). The 3D-domain interologs is defined as "Domain a (in chain A) interacts with domain b (in chain B) in a known 3D complex, meaning that their inferring protein pair A' (containing domain a) and B' (containing domain b) in the same species would be likely to interact with each other if both protein pairs are homologous". We have demonstrated that 3D-domain interologs is useful to understand the interacting evolution (co-evolution) of PPIs and DDIs across multiple species. In this project, we will enhance and modify 3D-domain interologs to understand the mechanisms of biological networks across multiple species and its applications on biomedical applications. We will achieve the following specific aims: (i) Developing knowledge-based scoring functions for calculating the binding affinity of protein-protein and protein-DNA interactions; (ii) Determining interacting domains, binding models (e.g. hydrogen-bond interactions and conserved residues), and consensus patterns of protein-protein/protein-DNA interactions across multiple species; (iii) Identifying the conserved networks (pathways) of protein-protein/protein-DNA networks across multiple species; (iv) Using consensus patterns of protein-protein/protein-DNA interactions to understand the mechanisms of protein-ligand binding models and to realize the probable mechanism of pathogenicity; and (v) Applying these methods and scoring functions on new disease targets, cancer-related pathways and drug discovery. Currently, we have achieved some preliminary results, such as a 3D-partner server for protein-protein interaction prediction, a 3D-interologs database for examining orthologous interactions across multiple species, and two knowledge-based scoring systems for measuring the binding affinity of protein-protein and protein-DNA interactions. Our methods and scoring functions have successfully identified the protein-protein interactions (i.e. CDK2/CKS1 complexes across 56 species) and protein-DNA interactions (Myc/Max across 72 species) of cancer-related pathways of lung cancer. Our long-term objective is to combine the known cellular processes and our predicted protein-protein, protein-ligand, and protein-DNA/RNA interactions to construct molecular interaction networks, which are essential for many medical applications, therapeutic strategies and drug development.