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| − | <h1 style="color: white !important">Our project in five minutes</h1>
| + | <div class="content" style="color: white !important; font-size: 50px !important; line-height: 50px !important;text-align: center !important; font-family: 'Josefin Sans', sans-serif !important; padding-bottom: 30px;">Project Abstract</div> |
| − | <a href="https://2017.igem.org/Team:Heidelberg/Design" class="big-link"><h3 style="color: white !important; text-align: left !important">Background</h3></a>
| + | Darwinian evolution is an enormously powerful concept that drove biology towards astonishing complexity and beauty. This year, the iGEM team Heidelberg aims at harnessing this power to <a href="https://2017.igem.org/Team:Heidelberg/ Design" style=" color: #fbb74b !important"> accelerate</a> the engineering of biomolecules for human benefit. |
| − | Darwinian evolution is an enormously powerful concept that drove biology towards astonishing diversity, complexity and beauty. An efficient way to harness this power would highly accelerate the engineering of novel biomolecules for human benefit and fundamentally advance synthetic biology.<br> | + | <br><br> |
| − | Towards this vision, our team developed a comprehensive evolution toolbox comprising <a href="https://2017.igem.org/Team:Heidelberg /Parts" class="innerlink">standardized parts</a><a href="https://2017.igem.org/Team:Heidelberg/ Predcel" class=" innerlink">, protocols</a><a href="https: //2017.igem.org/Team:Heidelberg/Model" class="innerlink"> , interactive models</a> and <a href="https://2017.igem.org/Team:Heidelberg/Software" class="innerlink">AI-based software</a>, all assembled into a unique workflow tightly interconnecting in vivo and silico <a href="https://2017.igem.org/Team:Heidelberg/Design" class="innerlink">directed evolution</a>. <br><br> | + | To this end we build upon the <a href="https://2017.igem.org/Team:Heidelberg/Pace" style=" color: #fbb74b !important"> PACE</a> (phage-assisted continuous evolution) method, which couples the survival of quickly mutating phages encoding a gene of interest to directed selection within E. coli host. We present a standardized, comprehensive <a href="https://2017.igem.org/Team:Heidelberg/Toolbox" style="color: #fbb74b !important">evolution toolbox</a> that highly simplifies the complex PACE method and widely expands its utility towards various new application areas, including the biological production of <a href="https://2017.igem.org/Team:Heidelberg/ Organosilicons" style=" color: #fbb74b !important"> organosilicons</a>. <br>A central component of our <a href="https://2017.igem.org/Team:Heidelberg/Toolbox" style="color: # fbb74b !important"> toolbox</a> is an innovative workflow for directed in vivo evolution of novel enzymes, which we validated by redirecting the activity of a <a href="https://2017.igem.org/Team:Heidelberg/ Cytochrome_Engineering" style=" color: #fbb74b !important"> promiscuous cytochrome</a> towards a naturally unfavored reaction product. |
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| − | Our project grounds on the <a href="https://2017.igem.org/Team:Heidelberg/Pace" class=" innerlink">phage-assisted continuous evolution (PACE) method </a> (<i>Esvelt et al</i>, Nature, 2011), which couples the survival of quickly evolving M13 bacteriophages carrying a gene -of -interest to directed selection within E. coli hosts in a customized bioreactor. Despite its powerful concept, the application of PACE has thus far been hampered by its need of a continuous culture and flow setup demanding costly and highly sensitive hardware strongly limiting its application scope. <br> <a href="https://2017.igem.org/Team:Heidelberg/Toolbox" class="big-link"><h3 style="color: #fbb74b !important; text-align: left !important"> Our PREDCEL (phage-related discontinuous evolution ) toolbox </h3></a> | + | Complementary, to simplify the generation desired functionalities de novo, we created <a href="https://2017.igem.org/Team:Heidelberg/Software" style="color: # fbb74b !important">AiGEM</a>, our Artificial Intelligence for Genetic Evolution Mimicking software suite. The heart of our software is <a href="https://2017.igem.org/Team:Heidelberg/Software/DeeProtein" style=" color: #fbb74b !important">DeeProtein</a>, a deep neuronal network trained on ~10 million protein sequences and able to infer sequence-function relationships from raw sequence data with high accuracy. By interfacing DeeProtein with our genetic algorithm <a href="https://2017.igem.org/Team:Heidelberg/Software/GAIA" style=" color: #fbb74b !important">GAIA</a>, we – for the first time - established a fully closed, in silico evolution cycle driven by an intelligent network. We used <a href="https://2017.igem.org/Team:Heidelberg/ Software" style="color: #fbb74b !important"> AiGEM</a> to successfully modulate the catalytic efficiency of <a href="https://2017.igem.org/Team:Heidelberg/ Validation#lac" style="color: #fbb74b !important"> beta- lactamases</a>, thereby even producing variants which highly outperform the wild type enzyme. <br>To demonstrate AiGEM’s ability for generating functionality de novo, we created a novel, highly efficient <a href="https://2017.igem.org/Team:Heidelberg/ Validation#gusgal" style="color: #fbb74b !important"> beta-galactosidase</a> purely by in silico evolution of a beta- glucuronidase parental sequence. <br>< br> Finally, as part of our <a href="https://2017.igem.org/Team:Heidelberg/Human_Practices" style="color: # fbb74b !important">integrated human practices</a> project, we implemented <a href="https://2017.igem.org/Team:Heidelberg/Software/SafetyNet" style=" color: #fbb74b !important">SafetyNet</a>, a DeeProtein- based web application safeguarding directed evolution experiments. |
| − | To reduce complexity and increase flexibility of the <a href="https://2017.igem.org/Team:Heidelberg/Pace" class="innerlink">PACE </a> method, we created <a href="https://2017.igem.org/Team:Heidelberg/ Predcel" class=" innerlink">PREDCEL</a> (for phage-related discontinuous evolution), a simple, low-cost in vivo evolution protocol completely independent of any specialized equipment. In <a href="https: //2017.igem.org/Team:Heidelberg/Predcel" class="innerlink"> PREDCEL</a> , the phage gene pool is simply propagated batch-wise on highly mutagenic E. coli selection strains grown in standard flasks. <br> Our self-contained <a href="https://2017.igem.org/Team:Heidelberg/Predcel" class="innerlink">PREDCEL toolbox</a> comprises: (1) A golden-gate cloning standard for simple production of geneIII-deficient M13 phages carrying any gene-of-interest; (2) An accessory plasmid construction kit for selection of the phage gene pool via conditional geneIII complementation in E. coli; (3) An <a href="https://2017.igem.org/Team:Heidelberg/Optogenetics" class="innerlink">optogenetic selection stringency modulator</a> enabling light-induced supplementation of geneIII. Thereby, the selection pressure can be easily adapted to the fitness of the evolving phage gene pool during PREDCEL runs if needed; (4) A T7 polymerase-based platform for detailed PREDCEL evaluation as well as directed evolution of <a href="https://2017.igem.org/Team:Heidelberg/Protein_Interaction" class="innerlink">protein-protein interactions</a> and (5) The very heart of our toolbox: an innovative and generalizable workflow for <a href="https://2017.igem.org/Team:Heidelberg/Cytochrome_Engineering" class="innerlink">enzyme engineering</a> based on riboswitch-mediated coupling of product formation to enzyme variant selection. | + | <br><br> |
| − | An online <a href="https://2017.igem.org/Team:Heidelberg/Toolbox" class="innerlink">toolbox guide</a> and accompanying <a href="https://2017.igem.org/Team:Heidelberg/RFC" class="innerlink">RFC</a> simplifies the utilization of our PREDCEL toolbox for future iGEM teams and the synthetic biology community at large. | + | <div style="font-size: 25px !important; font-weight: 700 !important">Taken together, we provide a new foundational advance by introducing an innovative in vivo and in silico evolution interface as novel engineering paradigm to Synthetic Biology!</div> |
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| − | <a href="https://2017.igem.org/Team:Heidelberg/Model" class="big-link"><h3 style="color: #bb5651 !important; text-align: left !important">Toolbox Characterization and Integrated Modeling</h3></a>
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| − | To lay a thorough foundation for in vivo evolution with PREDCEL, we decided to apply bottom-up engineering and carefully characterize the individual components of our system in isolation first. Employing T7 polymerase as simple and modular platform, we established the required protocols for (i) cloning and generation of transgene-encoding phages, (ii) their propagation and selection on custom-made E. coli selection strains as well as (iii) accelerated in vivo mutation to expedite the evolutionary process. ODE-based <a href="https://2017.igem.org/Team:Heidelberg/Model" class="innerlink">modeling</a> and corresponding computer simulations were thereby used to quantitatively investigate and optimize the parameters of our experimental system, i.e. <a href="https://2017.igem.org/Team:Heidelberg/ Model/Phage_Titer" class=" innerlink">phage propagation times,</a><a href="https: //2017.igem.org/Team:Heidelberg/Model/Mutagenesis_Induction" class="innerlink"> mutagenesis-controlling inducer/inhibitor concentrations</a> and <a href="https://2017.igem.org/Team:Heidelberg/Model/Medium_Consumption" class="innerlink">medium consumption</a>. | + | |
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| − | Apart from such simple-to-control parameters, our models also suggested that the fitness of the initial (parental) phage gene pool regarding the function to be evolved strongly impacts the robustness as well as speed of PREDCEL-mediated evolution. We quickly realized that evolving truly novel functions on a given protein would be very challenging with any in vivo directed evolution method including PREDCEL and PACE, as they always depend on initial activity to build upon.
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| − | <a href="https://2017.igem.org/Team:Heidelberg/Software" class="big-link"><h3 style="color: #fee84c !important; text-align: left !important">Meet AiGEM, our Artificial Intelligence for Genetic Evolution Mimicking</h3> </a> | + | |
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| − | To address these profound limitations and enable true evolutionary jumps from no/minimal activity towards high activity, our team created <a href="https://2017.igem.org/Team:Heidelberg/Software" class="innerlink">AiGEM</a>, the Artificial Intelligence for Genetic Evolution Mimicking software . AiGEM significantly reduces the time and cost required for directed evolution of functional proteins by pre-optimizing the parental gene pool for a selected function in silico. The heart of our AiGEM software is <a href="https://2017.igem.org/Team:Heidelberg/Software/DeeProtein" class=" innerlink">DeeProtein</a>, a deep neuronal network trained on ~10 million protein sequences and able to infer sequence-function relationships from raw sequence data with high accuracy. To <a href="https://2017.igem.org/Team:Heidelberg/Software/DeeProtein" class="innerlink">validate DeeProtein’s predictive power</a>, we generated a set of <a href="https://2017.igem.org/Team:Heidelberg/Validation" class="innerlink">~30 beta-lactamase mutants</a> and found that the observed catalytic efficiencies (i.e. maximum antibiotic inhibitory concentrations) were reflected in the corresponding DeeProtein activity scores. Remarkably, DeeProtein even suggested a number of mutations, which resulted in beta-lactamase variants several fold more active than the wild type.<br><br> | + | |
| − | Then, by interfacing DeeProtein with our genetic algorithm <a href="https://2017.igem.org/Team:Heidelberg/Software/GAIA" class=" innerlink">GAIA</a>, we – for the first time - established a fully closed, in silico evolution cycle driven by an intelligent network and able to pre-optimize proteins for a selected activity. In other words, AiGEM can fast-forward directed evolution by means of intelligent computing. To demonstrate its predictive power, we used AiGEM for <a href="https://2017.igem.org/Team:Heidelberg/ Validation" class="innerlink">in silico evolution of novel beta-galactosidases</a> from human beta-glucuronidase parental sequences. | + | |
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| − | <a href="https://2017.igem.org/Team:Heidelberg/Predcel" class="big-link"><h3 style="color: #fbb74b !important; text-align: left !important">Our Applications</h3></a>
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| − | Having laid out and validated our concept of an interfaced in vivo and in silico directed evolution, we finally aimed at more deeply exploring its potential for applications in basic research and biochemical production.
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| − | <a href="https://2017.igem.org/Team:Heidelberg/Protein_Interaction" class="innerlink"><h4 style="color: white !important; text-align: left !important">Improving selected protein-protein interactions </h4></a> | + | |
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| − | Engineering selected protein-protein interaction is a major goal in synthetic biology and used, e.g. for the construction of split reporter-bases biosensors or improved antigens in context of vaccine development. Using split T7 polymerase as example, we aimed at studying whether we can improve protein-protein interactions (in this case the auto-reassembly of the two split fragments) in PACE and PREDCEL. Following only 3 days of evolution, we obtained numerous, recurrent split T7 mutants in the gene pool. Remarkably, some mutations we positioned right at the prospective interface of the two T7 fragments, hinting at the successful evolution of improved split T7 variants.
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| − | <a href="https://2017.igem.org/Team:Heidelberg/Cytochrome_Engineering" class="innerlink"><h4 style="color: white !important; text-align: left !important">Engineering novel enzymes for organosilicon production</h4></a> | + | |
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| − | Finally, we investigated the potential of our evolution toolbox for the development of novel enzymes for biochemical production. <a href="https://2017.igem.org/Team:Heidelberg/Organosilicons" class="innerlink">Organosilicons</a> are a molecular class of high relevance for industry and with great potential for drug development as we learned during discussions with experts in the field. Although biological systems do not employ carbo-silicon chemistry in nature, promiscuous enzymes such as cytochromes exist, which are in principle capable of catalyzing carbon-silicon bond formations albeit with low efficiency.
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| − | We developed a complete <a href="https://2017.igem.org/Team:Heidelberg/Cytochrome_Engineering" class="innerlink">PREDCEL workflow capable of optimizing cytochromes for a specific purpose</a> by redirecting their catalytic activity towards a desired, but naturally unfavored reaction product. The workflow employs riboswitches for detection of the enzymatic reaction product and corresponding variant selection, which we design fully computationally in our <a href="https://2017.igem.org/Team:Heidelberg/Software/MAWS" class="innerlink">MAWS (Making Aptamers Without Selex) 2.0 software</a>, an improved MAWS algorithm originally introduced by iGEM Team Heidelberg 2015. We show that the in silico predicted riboswitches are capable of <a href="https://2017.igem.org/Team:Heidelberg/Organosilicons" class="innerlink">specifically detecting selected, organosilicon compounds</a> produced in vitro using a modified cytochrome C. Finally, employing the caffeine-metabolizing <a href="https://2017.igem.org/Team:Heidelberg/Cytochrome_Engineering" class="innerlink">Cytochrome P450 1A2</a>, we provide evidence for the successful application of PREDCEL for the directed evolution of an enzyme towards increased production of a naturally unfavored product, theophylline.
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| − | <a href="https://2017.igem.org/Team:Heidelberg/Human_Practices" class="big-link"><h3 style="color: #6698be !important; text-align: left !important">Integrated Human Practices</h3></a> | + | |
| − | The most powerful technology can only have a positive impact on humanity, if it is widely accepted, safe, and applied in a responsible manner with the aim of making our world a better place to live. No single person or expert group is smart enough precisely foresee the impact – positive and negative - of a developing technology. Therefore, we consider integrated human practices of particular importance in context of foundational advance projects like ours, which aim at developing technologies with the potential to shape our future - and the future of our (future) kids.<br>
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| − | From the early beginning on, we thus openly discussed the aforementioned concepts and ideas with <a href="https://2017.igem.org/Team:Heidelberg/Interviews" class="innerlink">experts</a> from the different fields and <a href="https://2017.igem.org/Team:Heidelberg/Engagement" class="innerlink">reached out to the broad public</a> to listen to their hopes and concerns.
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| − | The <a href="https://2017.igem.org/Team:Heidelberg/HP/Gold_Integrated" class="innerlink">extensive feedback</a> we received pushed us to address three major, occurring issues: (i) Making our technology safe, (ii) stimulating its responsible use and (iii) apply it to address urgent human needs. (i) To safeguard in vivo evolution experiments, we created <a href="https://2017.igem.org/Team:Heidelberg/Software/SafetyNet" class=" innerlink">SafetyNet</a>, which is part of our <a href="https://2017.igem.org/Team:Heidelberg/Software" class="innerlink">AiGEM</a> (Artificial Intelligence for Genetic Evolution Mimicking) software. SafetyNet checks any user supplied input sequence for “sleeping” hazardous potential. Thereby, we can strongly decrease the risk of evolving hazardous proteins unintendedly. (ii) We integrated a questionnaire <a href="https://2017.igem.org/Team:Heidelberg/Toolbox" class="innerlink">(“Ready- to-PREDCEL?”)</a> into our evolution toolbox guide to stimulate the responsible use of our technology. (iii) In the wet lab, we chose projects with the highest ecological/medical potential, and focused in the application of our toolbox on the engineering of enzymes for ecofriendly synthesis of < a href="https://2017.igem.org/Team:Heidelberg/Organosilicons" class="innerlink"> organosilicons</a>, e.g. as novel pharmaceuticals. | + | |
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| − | <a href="https://2017.igem.org/Team:Heidelberg/Achievements" class="big-link"><h3 style="color: white !important; text-align: left !important">In Summary</h3></a> | + | |
| − | We developed and successfully validated a comprehensive evolution toolbox comprising our standardized PREDCEL parts and protocols, interactive models and accompanying, intelligent software, all integrated into unique workflows for accelerated production of novel proteins and bio-compounds. We believe that our work provides a new foundational advance by introducing an innovative in silico and in vivo directed evolution interface as novel <a href="https://2017.igem.org/Team:Heidelberg/Design" class="innerlink">engineering paradigm</a> to Synthetic Biology.
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| | </div> | | </div> |
| | </div> | | </div> |
