Bio-engineering of biological sequences (identification and optimisation) relies on the effect of varying the composition of those sequences through mutations. In the case of proteins, each position in the sequence (residue) selected for mutation can be one of 20 possible amino acids. In order to optimise or improve a trait of a protein, the effect of those mutations need to be explored along the sequence in as many positions as possible. Testing variation for two residues would therefore lead a possible 20×20 = 400 variants, 6 residues: 64 million possibilities and so on exponentially. As a result, screening variants for improved trait become very quickly impractical.


Current methods allow the exploration of a very restricted sequence space or require the definition of a narrow sub-space in which to test variants (e.g. in the vicinity of an enzyme’s active site). These restrictions constrain the operator to ignore vast expanses of sequence space where the trait could be further improved and limit the improvement achievable to a local maximum as illustrated in Fig 1.

Fig 1. A fitness landscape and its navigation. The initial or wild-type activity denotes the starting point (initialisation) for a directed evolution study (red circle). Accumulation of mutations that increase activity is represented by four routes to different positions in the landscape. Route 1 successfully increases activity through a series of additive mutations, but becomes stuck in a local optimum. Due to the nature of rugged fitness landscapes some of the shorter paths to a maximum possible (global optimum) fitness (activity) can require movement into troughs before navigating a new higher peak (route 2). Alternatively, one can arrive at the global optimum using longer but typically less steep routes without deep valleys (equivalent over flat ground to neutral mutations – routes 3 and 4).

At the application end, this leads to the generation of poorly “optimised” enzymes with lower than desirable activity, stability and performance, biologicals with lower potency than theoretically possible or sub-optimal pharmacological characteristics and production strain for white biotechnology applications leading lower yield that can be expected.


The GeneORator is a ground-breaking synthetic biology approach to exploring wide sequence spaces while keeping the number of variants to be screened to a minimum. It enables the testing of many mutations in a sequence therefore unlocking access to variants that would not have been attainable through traditional methods. Through this methodology the team has mutated of a total of 269 amino acids in a single enzyme sequence, leading to selection of new variants with a significantly improved kcat. Compellingly, the most efficient mutations for improving the desired traits were found in areas of the structure away from the traditionally considered “hot spots”.

This approach is complemented by a suite of Manchester developed bioinformatics tools, allowing for careful design of the libraries and optimised codon usage depending on the organism of interest.


  • GeneOrator allows for exhaustive exploring of wide sequence spaces. This is not feasible through current technologies due to the exponential growth of sample numbers with diversity.
  • By reducing the number of samples to be tested it shortens the timeline and brings down the costs to delivery of an optimised product.
  • It is applicable to any biological sequences (nucleotide, amino-acids) and therefore to several market sectors e.g. biopharmaceuticals, biocatalysis.


  • Therapeutic biological engineering (potency, immunogenicity, etc.)
  • Biocatalyst development.
  • Synthetic biology.


We hold a UK priority patent application (priority date 12th June 2017); Software are copyright University of Manchester.


At this stage of development, our aim is to discuss this opportunity with potential partners in view of demonstrating the value of the approach in a commercial setting and seeking feedback on its capabilities


Dr Arnaud Garçon, IP Development and Partnering Manager; UMIP, The Incubator Building, 48 Grafton Street, Manchester M13 9XX;
Tel:+44 1616037757; Email: