BioGPS Descriptors for Rational Engineering of Enzyme Promiscuity and Structure Based Bioinformatic Analysis

A new bioinformatic methodology was developed founded on the Unsupervised Pattern Cognition Analysis of GRID-based BioGPS descriptors (Global Positioning System in Biological Space). The procedure relies entirely on three-dimensional structure analysis of enzymes and does not stem from sequence or structure alignment. The BioGPS descriptors account for chemical, geometrical and physical-chemical features of enzymes and are able to describe comprehensively the active site of enzymes in terms of “pre-organized environment” able to stabilize the transition state of a given reaction. The efficiency of this new bioinformatic strategy was demonstrated by the consistent clustering of four different Ser hydrolases classes, which are characterized by the same active site organization but able to catalyze different reactions. The method was validated by considering, as a case study, the engineering of amidase activity into the scaffold of a lipase. The BioGPS tool predicted correctly the properties of lipase variants, as demonstrated by the projection of mutants inside the BioGPS “roadmap”.本研究基于网格的生物空间全局定位系统（BioGPS，Global Positioning System in Biological Space）描述符的无监督模式认知分析，开发了一种全新的生物信息学方法。该方法完全依托酶的三维结构分析展开，并非基于序列或结构比对构建。BioGPS描述符涵盖了酶的化学、几何与物理化学特征，能够从"预组织环境"的维度全面刻画酶的活性位点——该环境可稳定特定反应的过渡态。通过对四类具有相同活性位点组织形式、却可催化不同反应的丝氨酸水解酶进行一致性聚类，验证了该新型生物信息学策略的有效性。本研究以"将酰胺酶活性工程化接入脂肪酶骨架"为案例开展方法验证。通过将突变体投影至BioGPS"路线图"中，证实该工具可准确预测脂肪酶变体的特性。