Massive parallel de novo design of sensing nanopores

Introduction

Proteins embedded in membranes play key roles in maintaining cell integrity, homeostasis, and communication. Emerging technologies (nanopore sequencing, synthetic cells, etc.) imitate biological systems and repurpose membrane proteins for the transport and sensing of new analytes through synthetic membranes. These applications have fueled the demand for (synthetic) membrane proteins with properties and functions not observed in nature.

Current Challenges

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.

Research Objectives

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.

Methodology

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.

Proof-of-Concept

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.

Previous Work

My lab was the first to demonstrate the feasibility of TMB design and has established a design pipeline from computation to electrophysiology and biochemical characterization.

Expected Impact

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 innovative computational design methodologies.