<i>De novo</i> designed transmembrane domains tune engineered receptor functions

Elazar, Assaf; Chandler, Nicholas J; Davey, Ashleigh S; Weinstein, Jonathan; Nguyen, Julie V.; Trenker, Raphael; Cross, Ryan; Jenkins, Misty R.; Call, Melissa; Call, Matthew E.; Fleishman, Sarel J.

doi:10.1101/2020.07.26.221598

Cited by 3 publications

(2 citation statements)

References 68 publications

(136 reference statements)

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“…As a general rule, design studies, particularly ones devoted to backbone design, often reveal significant deviations between experimental structures and design conceptions. When the designs are small (<100 amino acids), they may be subjected to atomistic forward-folding ab initio simulations to verify that the sequences favor the designed conformation over others [1][2][3]41]. In large proteins, however, accurate forward-folding simulations were until recently impossible.…”

Section: Discussionmentioning

confidence: 99%

Assessing and enhancing foldability in designed proteins

Listov

Lipsh‐Sokolik

Rosset

et al. 2021

Preprint

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Recent advances in protein-design methodology have led to a dramatic increase in its reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major cause of design failure is inaccuracy and misfolding relative to the design model. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs; second, we demonstrate that the AlphaFold2 ab initio structure prediction method flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme, thereby improving its catalytic efficiency by 330 fold. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.

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Section: Discussionmentioning

confidence: 99%

Assessing and enhancing foldability in designed proteins

Listov

Lipsh‐Sokolik

Rosset

et al. 2021

Preprint

Self Cite

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“…While finer-grained control holds utility in numerous situations, the manual assignment of residue-wise condition demands domain expertise and might limit the exploration space of sampling. The global control, in contrast, resembling a top-down approach to control the designed topology, offers advantages in scenarios where both topology-level control and residue-level variation are desirable [35], [36]. We believe that the combined utilization of global and residue-wise conditions could represent a potent approach for achieving controllable de novo design.…”

Section: Discussionmentioning

confidence: 99%

TopoDiff: Improving Protein Backbone Generation with Topology-aware Latent Encoding

Zhang,

Ma,

Gong

2023

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Thede novodesign of protein structures is an intriguing research topic in the field of protein engineering. Recent breakthroughs in diffusion-based generative models have demonstrated substantial promise in tackling this task, notably in the generation of diverse and realistic protein structures. While existing models predominantly focus on unconditional generation or fine-grained conditioning at the residue level, the holistic, top-down approaches to control the overall topological arrangements are still insufficiently explored. In response, we introduce TopoDiff, a diffusion-based framework augmented by a global-structure encoding module, which is capable of unsupervisedly learning a compact latent representation of natural protein topologies with interpretable characteristics and simultaneously harnessing this learned information for controllable protein structure generation. We also propose a novel metric specifically designed to assess the coverage of sampled proteins with respect to the natural protein space. In comparative analyses with existing models, our generative model not only demonstrates comparable performance on established metrics but also exhibits better coverage across the recognized topology landscape. In summary, TopoDiff emerges as a novel solution towards enhancing the controllability and comprehensiveness ofde novoprotein structure generation, presenting new possibilities for innovative applications in protein engineering and beyond.

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