Evolution of shape-defined macromolecules into functional systems

Descripción del proyecto

Optimizar la función catalítica mediante la regulación de secuencias por aprendizaje automático

Las enzimas son catalizadores biológicos que aceleran las reacciones químicas dentro de los organismos vivos, lo que permite que los sustratos se conviertan en productos de forma eficiente con un menor gasto energético. Cada enzima es muy específica para su sustrato y ha evolucionado para funcionar en condiciones naturales. El equipo del proyecto SHAPE, financiado por el CEI, pretende desarrollar enzimas abióticas con capacidades catalíticas comparables a las naturales, lo que ampliará su uso en las transformaciones químicas. El trabajo se centrará en variar la secuencia de los monómeros y la estereoquímica para alterar la estructura secundaria y terciaria en un método de ingeniería controlada. Las técnicas computacionales avanzadas ayudarán a analizar e interpretar la relación entre secuencia y función catalítica, lo que permitirá optimizar el rendimiento catalítico.

Objetivo

Functionalities of enzymes are encoded in amino acid sequences and directed by their SHAPEs with complementary binding pockets for specific substrates. Natural enzymes are remarkable catalysts, however, they are typically optimized by evolution to operate under the constraints of the physiological environment of a living system, which strongly limits the scope of their applications in organic synthesis. Here, I propose to develop abiotic enzymes to selectively catalyze chemical transformations in non-physiological environments. The main objective of the project is to use monomer sequence control to fine-tune the SHAPE of abiotic macromolecules to obtain the desired catalytic functionality. This goal will be realised via four work packages:
(I) Primary structure control to input information into macromolecules – development of synthetic methods yielding high molar mass, sequence-defined polymers, to deliver abiotic proteins at high scales and numbers.
(II) SHAPE control by single chain folding and topology – secondary and tertiary structure evolution by varying the monomer sequence and stereochemistry to tune intramolecular interactions, leading to controlled engineering of globularly folded polymers.
(III) Introducing catalytic activity into abiotic polymers – enhancing selectivity and efficiency of catalytic reactions by advancing an outer sphere that surrounds the metal cofactor.
(IV) Sequence-function studies using machine learning – delivery of models able to interpret multivariate data that will guide the development of complex catalytic systems to find and predict dependencies inaccessible by conventional methods.
Our approach proposes an unexplored method for obtaining abiotic, sequence-defined polymers operating in a non-biological environment whose functions can rival those of natural macromolecules. The study will reveal valuable information on sequence-dependent properties of polymers, to open a field of abiotic enzymes for organic transformations.