Multiobjective Evolver

Developing biotech solutions, including new biomolecules and/or pathways, is typically an iterative process. Solutions are initialized and iteratively improved, gradually converging to an optimum. Such optimum could be highest binding affinity to a cancer antigen, highest degradation rate of plastic waste, or highest number of biofuel molecules produced.

Since biotech solutions often pursue more than one objective, the optimum may be indicated by more than one parameter, e.g. binding affinity of a nanobody to a brain tumor antigen and nanobody permeability of the blood-brain-barrier. Such parameters may be subject to a trade-off.

Based on in vitro and in vivo technologies, an iterative optimization process consumes substantial time and budget. Each iteration loop requires lab or field experiments to determine the goodness of the best solution. With progressing in silico technology, the time and budget requirements for iterative optimization drops significantly, as tests are fully computational. An iterative physical process of several weeks and months can be accelerated or even replaced by an iterative virtual process, running in a matter of hours, and eventually completed after a few days.

The iterative in silico optimization capability of our platform is virtually mimicking a process of population-based natural evolution, yielding an optimal combination of pathway and supporting molecules, with highest expected success in practice. Such evolutionary optimization (EO) approach has proven robust to uncertainty and noise (common in biological systems) and successful for many medical and industrial applications since the 1980s. It has been particularly successful for optimizing bioprocesses where mechanistic models are unavailable or incomplete.

What are examples of iterative optimization in silico?


We are providing here examples for enzymes, small molecules and double-stranded RNA (dsRNA):

Synthetic fermentation pathway for ethanol production
Enzyme example: PDC & ADH

Enzymes such as pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH) determine ethanol productivity and robustness in industrial fermenters. EO has evolved bacterial PDC and ADH and their pathway through iterative in silico rounds of mutation and selection to enhance thermostability.

  1. EO has integrated a stabilized PDC…
    • …into the established fermentation pathway;
    • …at a high-flux glycolysis, high-stress, chemically extreme node of fermentation;
    • …at 103 –fold improved half-life at 75°C (from losing > 90% activity above 45°C);
    • …subject to high ethanol, low pH, reactive metabolites and TPP cofactor imbalance.
  2. EO has integrated a matching ADH…
    • …achieving 50-fold improved half-life at 60°C;
    • …increasing final alcohol titers by factor 2-10;
    • …preventing by-product formation;
    • …reducing toxic aldehydes faster than conventional ADH.

Signaling pathway for cell proliferation and differentiation
Small molecules example: TKI

Mutated or overexpressed tyrosine kinases may facilitate uncontrolled cell growth in cancer cells. EO has enabled iterative de novo in silico design of tyrosine kinase inhibitors (TKI), evolving scaffolds and substituents to improve potency and selectivity of newly designed small molecules competing against ATP.

  1. EO has evolved small molecule candidates by mutating, recombining and selecting chemical structures that bind kinase active sites with high affinity and good drug likeness;
  2. EO has overcome challenges such as the large chemical space to be searched, and multiple objectives (affinity, selectivity, ADMET);
  3. EO has mastered constraints such as ADME properties, binding-site localization, H-bond requirements, toxicity filters and substructure rules;
  4. EO has optimized concurrent targets like affinity, selectivity and ADMET.

RNA interference (RNAi) pathway for plant virus defense
dsRNA example: SIGS

Cocoa trees in West Africa are threatened by the cocoa swollen shoot virus (CSSV). CSSV is a badnavirus, injected by mealy bugs into the cocoa trees’ phloem. The virus is integrating its RNA into the cocoa tree genome. To avoid complete removal of infected trees, a spray-induced gene silencing (SIGS) treatment is being developed. Iterative EO is facilitating design of matching solution components:

  1. Externally applied dsRNA targeting CSSV genes for replication, movement and coat protein to be silenced. The dsRNA is processed into siRNA inside the cocoa tree;
  2. Heat-, sun-, water- and enzyme resistant lipid nanocarrier encapsulating the siRNA, sprayed onto cocoa tree leaves in combination with other fungicides and insecticides;
  3. Stable spray solution (pH, ionic strength) with optimized droplet size, coverage, decay.
How do we compare solutions in silico?


Our proprietary in silico platform allows tracking multiple target criteria for optimality. This is done in every iteration of our multiobjective evolver, permitting a multiobjective comparison…

  • …of new molecules and pathways designed de novo or ex stuctura from a template or scaffold;
  • …against competing molecules and pathways, possibly developed by trial and error in the lab;
  • …and against syndicated benchmarks.

Our quantitative criteria for evaluation and comparison are computed entirely in silico. We thereby allow prioritization and optimization before going into the lab. We consider criteria with…

  • …same force field, solvent model and temperature;
  • …same MD protocol, including equilibrium, production length and analysis windows;
  • …same physical validation basis, including binding energy, stability and specificity.

We thereby prove that solutions evolved and optimized in silico exceed known syndicated benchmarks and are more promising than other solutions at hand, based on suitable and consistent criteria.

How do we differentiate?


As a focused in silico solution and service provider, we are offering dedicated collaborations, exclusive partnerships and joint ventures. We differentiate from other providers with such offerings in terms of application breadth, quantitative scrutiny and solution scope. These three features may be critical success factors for your biosolution development and/or productization project…

  1. Breadth of Application
    We are serving multiple industrial segments, including life science, chemicals, energy, utilities, food, textiles and construction material. The versatility and scalability of our in silico technology is anchored in the joint biological and chemical principles of the diverse industry applications.

    Resulting benefits:
    • Our in silico platform allows cross-industry benchmarking and validation, re-purposing of bioproducts, and extended data utilization and machine learning.
    • Our in silico platform allows efficient configuration to new and completed applications, without the burden of complexity-driving and time-consuming customization.
    • Our in silico platform promotes out-of-the-box thinking and guides cross-discipline innovation on a shared scientific basis.
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  2. Quantitative Scrutiny
    Data acquisition in vivo or in vitro, through lab experiments, typically comes at a high price. In silico technologies can compute insightful parameters and indicators with lower time and resource spending. Yet, type and accuracy of these parameters strongly depends on the underlying in silico technology. Some AI approaches generate only heuristic data. Stochastic approaches yield probabilities. Not all scores meet decision makers’ expectations.

    Our approach:
    • We are offering advanced metrics supporting design decisions at various levels. These metrics are scientifically grounded, explainable, understandable, justifiable and comparable.
    • For these metrics, we are offering proprietary and/or syndicated benchmarks, internal experience and external reference values.
    • We are monitoring consistency between metrics and benchmarks, securing plausibility of the overall design parameters, and engineering trust in the solution, conclusion and insights.
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  3. Scope of Solution
    Computational development of biosolutions does not end with proven molecule manufacturability, affinity and function. In silico technology takes biosolution development on a journey much further and broader,  leading from early target projection via molecule design and pathway engineering to a computational validation, trial and/or approval simulation and finally to IP protection.

    We differentiate by supporting a fairly large portion of this in silico capability spectrum:
    • Project Targets: Mine biological data sets, screen patent databases and identify targets. Prioritize targets based on scientific insight, technological possibilities and commercial opportunity.
    • Design Molecules: Generate hit and lead molecules for a given purpose. Determine their natural structure(s), ascertain their function(s) (QSAR) and preview properties (ADME) in their target environment.
    • Engineer Pathways: Determine how molecules behave in their biological network. Consider molecule features (e.g. solubility, permeability, stability, toxicity) and interaction (e.g. activation, inhibition, catalysis) along their metabolic or regulatory, human or industrial pathway. Adjust the pathway by modifying molecule(s), ambient conditions etc.
    • Simulate Trials (currently not implemented on our platform): Model cells, tissue, organs, patients and cohort permutations in a virtual clinical trial environment. Predict outcomes and adjust original model parameters to secure clinical and approval success.
    • Protect IP: Secure the ultimate commercial success of your innovation by utilizing in silico technology for patent formulation. This may even be extended to peparing regulatory registration, certification or approval, hazard classification, etc. 

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