標題: 蛋白質-配體結合模式預測與其結合區域定性研究
A study for predicting protein-ligand binding modes and characterizing protein-ligand binding sites in structure-based drug design
作者: 陳彥甫
Chen, Yen-Fu
楊進木
Yang, Jinn-Moon
生物資訊及系統生物研究所
關鍵字: SiMMap;GEMDOCK;虛擬藥物篩選;篩選後分析;site-moiety map;SiMMap;GEMDOCK;virtual screening;post-screening analysis;site-moiety map
公開日期: 2009
摘要: 隨著蛋白質結晶結構的快速增加,以結構為基礎之藥物設計與虛擬藥物篩選(virtual screening)在先導藥物開發過程日漸重要。目前一系列的分子對接(protein-ligand docking)以及虛擬藥物篩選方法已經被應用到先導藥物發展中,並且已獲得數個成功的藥物開發案例。即使如此,目前由巨量的虛擬藥物篩選資料中找出真正具有活性的先導藥物仍然是一個困難的挑戰。其問題肇因於目前對蛋白質¬-配體之間的結合機制了解仍然有所不足,使得已發展的蛋白質-配體結合計分方程式不夠周全。 針對上述議題,我們已提出了以藥物孔洞為基礎的計分方程式(pharmacophore-based scoring function)與虛擬藥物篩選共通計分方法應用準則(consensus scoring criteria)之研究。其中,共通計分方法是透過結合數個計分方程式的共同處,相較於單一計分方法可以有更好的虛擬藥物篩選準確性。然而虛擬藥物篩選的計分方程式通常無法辨識蛋白質-配體間的關鍵結合特性[例如: 藥效基團熱點(pharmacophore hotspot)],而這些關鍵特性卻通常是觸發或抑制目標蛋白質對其調控的生物反應必要條件。雖然應用藥物孔洞方法與相關計分方程式可以找出關鍵結合特性,但是這些方法需要一系列已知的活性配體,這些資料必須由實驗取得,使應用性受到限制。因此,對於虛擬藥物篩選過程發展更好的篩選後分析(post-screening analysis)與關鍵特性之發現方法,將對於藥物發展具有重要價值。 在本研究中,我們已經發展出site-moiety map (簡稱SiMMap)方法,並且將其延伸應用到辨識與定性垂直同源蛋白質(ortholog)的共通結合環境 (orthologous SiMMap)研究之中。SiMMap透過統計對目標蛋白質與一群對其預測或共結晶之配體所產生的交互作用,推測位於目標蛋白質結合區域內之錨點(anchor),並用以描述分布在結合區域中的配體官能基偏好(moiety preference)以及物化特性集合。每一個錨點具有三個基本構成要件:1)由具一致交互作用之殘基構成的結合袋點(binding pocket);2)複數個虛擬配體構成的官能基組成;3)結合袋點與官能基之交互作用關係(包含靜電力、氫鍵及凡德瓦力交互作用)。實驗證據已顯示錨點通常是蛋白質-配體結合區域中的熱點。同時site-moiety map也可提供將官能基團(靜電力、氫鍵及凡德瓦力特性之官能基)之組合最佳化的建議,有助於設計潛在先導藥物。實驗結果也證實當小分子化合物與site-moiety map描述的錨點特性高度相符時,通常有高度潛力成為目標蛋白質的抑制劑或促進劑。SiMMap已提供全球服務,網址為http://simfam.life.nctu.edu.tw/。我們相信我們對於藥物孔洞為基之計分方程式、虛擬藥物篩選之共通計分方法應用準則的成果、以及site-moiety map之研究,將對藥物發現與了解蛋白質-配體機制有所幫助。
As the number of protein structures increases rapidly, structure-based drug design and virtual screening approaches are becoming important and helpful in lead discovery. A number of docking and virtual screening (VS) methods have been utilized to identify lead compounds, and some success stories have been reported. However, identifying lead compounds by exploiting thousands of docked protein-compound complexes is still a challenging task. The major weakness of virtual screenings is likely due to incomplete understandings of ligand binding mechanisms and the subsequently imprecise scoring algorithms. To address these issues, we have proposed a pharmcophore-based scoring function approach and a consensus strategies among different scoring methods in VS. The consensus scores would improve the performance and, on average, the performance of the combined method performs better than the average of the individual scoring functions. Nevertheless, the approaches generally cannot identify the key features (e.g., pharmacophore spots) that are essential to trigger or block the biological responses of the target protein. Although pharmacophore techniques have been applied to derive the key features, these methods require a set of known active ligands that were acquired experimentally. Therefore, the more powerful techniques for post-screening analysis to identify the key features through docked compounds and to characterize the binding site provide a great potential value for drug design. Recently, we have developed the site-moiety map (SiMMap) method and extended to characterize the consensus binding environments (i.e., anchors) of orthologous targets (orthSiMMap). SiMMap statistically derived anchors from the interaction profiles between query target protein and its docked (or co-crystallized) compounds, and then described the relationship between the moiety preferences and physico-chemical properties of the binding site. Each anchor includes three basic elements: a binding pocket with conserved interacting residues, the moiety composition of query compounds, and pocket-moiety interaction type (electrostatic, hydrogen-bonding, or van der Waals). Experimental results showed that an anchor is often a hot spot and the site-moiety map can be helpful to assemble potential leads by optimal steric, hydrogen-bonding, and electronic moieties. When a compound highly agrees with anchors of site-moiety map, this compound often activates or inhibits the target protein. The SiMMap web server is available at http://simfam.life.nctu.edu.tw/. We believe that our evolutionary approach with pharmacophore-based scoring functions, consensus scoring criteria for virtual screening, and the method of site-moiety map are useful for drug discovery and understanding biological mechanisms.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079351802
http://hdl.handle.net/11536/40649
Appears in Collections:Thesis


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