How flexibility can enhance catalysis
Conformational changes are observed in many enzymes, but their role in catalysis is highly controversial. Here we present a theoretical model that illustrates how rigid catalysts can be fundamentally limited and how a conformational change induced by substrate binding can overcome this limitation, ultimately enabling barrier-free catalysis. The model is deliberately minimal, but the principle it illustrates is general and consistent with unique features of proteins as well as with previous informal proposals to explain the superiority of enzymes over other classes of catalysts. Implementing the discriminative switch suggested by the model could help overcome limitations currently encountered in the design of artificial catalysts.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Generating Cyclic Conformers with Flow Matching in Cremer-Pople Coordinates
Cyclic molecules are ubiquitous across applications in chemistry and biology. Their restricted conformational flexibility provides structural pre-organization that is key to their function in drug discovery and catalysis…
Drug DiscoveryStoechiometric and dynamical autocatalysis for diluted chemical reaction networks
Autocatalysis underlies the ability of chemical and biochemical systems to replicate. Recently, Blokhuis et al. gave a stoechiometric definition of autocatalysis for reaction networks, stating the existence of a combinat…
Defining Autocatalysis in Chemical Reaction Networks
Autocatalysis is a deceptively simple concept, referring to the situation that a chemical species $X$ catalyzes its own formation. From the perspective of chemical kinetics, autocatalysts show a regime of super-linear gr…
AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential
Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based eq…
Computational EfficiencyCatalysis distillation neural network for the few shot open catalyst challenge
The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts. Nonetheless, the challenges of electrocataly…
Computational chemistryFew-Shot LearningGraph Neural NetworkLanguage Modelling