Halogenase Sequence Motif Discovery for Broad Substrate Halogenation
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Solution Overview
Problem
The existing biocatalytic portfolio lacks halogenating enzymes with broad substrate specificity and tolerance, limiting the industrial application of halogenation in organic synthesis, which is crucial for pharmaceutical and agrochemical industries.
Innovation Solution
A novel consensus sequence motif (Fx•Px•Sx•G/FxxPxxSxG) is identified to definitively predict halogenase activity, enabling the discovery of enzymes with diverse substrate specificity through in silico methods, followed by experimental validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If research is limited to traditional electrophilic flavin-dependent halogenases from halogenated natural products, then the enzyme identification method is simple, but the substrate scope is narrow and activity is low
Solution Approach 1:
The patent replaces traditional mechanical/experimental enzyme identification methods with computational in silico methods. By using sequence motif search algorithms to identify halogenases based on conserved sequence patterns (FxPxSxG motif), the system achieves broad substrate scope discovery without requiring extensive laboratory screening, thus resolving the contradiction between versatility and complexity
Solution Approach 2:
The patent creates a universal identification framework that can detect halogenases across diverse organisms and substrate types through a single conserved sequence motif search method. This multi-functional approach applies to various halogenase subtypes (electrophilic, nucleophilic, radical mechanisms) and organisms (bacteria, fungi, plants, animals), enabling broad substrate scope discovery with one unified method
2Adaptability or versatility
If protein engineering is applied to expand substrate scope of existing halogenases, then substrate specificity is improved, but the process time and complexity increase
Solution Approach 1:
The patent performs preliminary identification and classification of halogenases through in silico sequence motif search before any experimental work or engineering begins. By pre-categorizing enzymes based on their conserved sequence motifs and predicted substrate specificities, the system eliminates the need for time-consuming trial-and-error protein engineering, directly obtaining enzymes with desired substrate scope
Solution Approach 2:
The patent creates computational models and sequence-based predictions that copy and replicate the functional characteristics of natural halogenases without requiring physical manipulation of the enzymes. Through sequence motif matching and bioinformatic analysis, the system identifies and predicts halogenase functions, substituting the time-consuming process of experimental protein engineering with rapid computational modeling
3Ease of manufacture
If chemical halogenation methods are used, then halogenation can be achieved, but harsh conditions and harmful by-products are generated
Solution Approach 1:
The patent introduces biocatalytic halogenases as intermediary agents that mediate the halogenation process under mild physiological conditions. These enzymes act as natural mediators that transfer halogen atoms to substrates through biologically compatible mechanisms, eliminating the need for harsh chemical reagents and generating minimal harmful by-products, thus resolving the contradiction between ease of manufacture and harmful factors
Data Source
AI summary
The present disclosure is based on the identification of a novel sequence motif present in halogenase type enzymes. From this, the disclosure provides methods for identifying and or detecting halogenases (halogenating enzymes) and novel halogenases identifiable using such methods. The disclosure also provides a novel cohort of enzymes and novel methods for achieving substrate halogenation.