Summary
Proteins embedded in membranes play key roles in maintaining cell integrity, homeostasis and communication. Emerging technologies (nanopore sequencing, synthetic cells, …) imitate biological systems and repurpose membrane protein for the transport and sensing of new analytes through synthetic membranes. These applications have fuelled the demand for (synthetic) membrane proteins with properties and functions not observed in nature. Structure-based computational protein design is revolutionizing many aspects of biotechnology but has almost exclusively focused on protein folding in water. The aim of PoreMADNeSS is to develop innovative strategies to enable the design of transmembrane β-barrels (TMBs), a class of membrane proteins with excellent properties to act as nanopore sensors. Using multidisciplinary approaches, we will address basic biophysical knowledge gaps that currently limit TMB design. The design of TMB folding in synthetic membranes gives access to a wealth of TMB sequences and structures not sampled by nature because of constraints associated with biogenesis and with the composition of biomembranes. We propose a combination of massive parallel de novo design and adaptive machine learning to explore this unknown TMB space, to gain crucial insight into the determinants of TMB folding and to develop robust design methods. As a proof-of-concept, PoreMADNeSS will focus on the design of steroid sensing nanopores. Our strategy is to design a cortisol binding site across the transmembrane channel, which would act as the reading head for single molecule fingerprinting. My lab was first to demonstrate the feasibility of TMB design and has established a design pipeline from computation to electrophysiology and biochemical characterization. This project has all the components to translate into transformative advances in nanopore sensing and sequencing by providing the nanopore R&D community with accurate and innovational computational design methodologies.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101117269 |
| Start date: | 01-04-2024 |
| End date: | 31-03-2029 |
| Total budget - Public funding: | 1 831 287,50 Euro - 1 499 250,00 Euro |
Cordis data
Original description
Proteins embedded in membranes play key roles in maintaining cell integrity, homeostasis and communication. Emerging technologies (nanopore sequencing, synthetic cells, …) imitate biological systems and repurpose membrane protein for the transport and sensing of new analytes through synthetic membranes. These applications have fuelled the demand for (synthetic) membrane proteins with properties and functions not observed in nature. Structure-based computational protein design is revolutionizing many aspects of biotechnology but has almost exclusively focused on protein folding in water. The aim of PoreMADNeSS is to develop innovative strategies to enable the design of transmembrane β-barrels (TMBs), a class of membrane proteins with excellent properties to act as nanopore sensors. Using multidisciplinary approaches, we will address basic biophysical knowledge gaps that currently limit TMB design. The design of TMB folding in synthetic membranes gives access to a wealth of TMB sequences and structures not sampled by nature because of constraints associated with biogenesis and with the composition of biomembranes. We propose a combination of massive parallel de novo design and adaptive machine learning to explore this unknown TMB space, to gain crucial insight into the determinants of TMB folding and to develop robust design methods. As a proof-of-concept, PoreMADNeSS will focus on the design of steroid sensing nanopores. Our strategy is to design a cortisol binding site across the transmembrane channel, which would act as the reading head for single molecule fingerprinting. My lab was first to demonstrate the feasibility of TMB design and has established a design pipeline from computation to electrophysiology and biochemical characterization. This project has all the components to translate into transformative advances in nanopore sensing and sequencing by providing the nanopore R&D community with accurate and innovational computational design methodologies.Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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