Protein folds vs. protein folding: Differing questions, different challenges

Chen, Shijie; Hassan, Mubashir; Jernigan, Robert L.; Jia, Kejue; Kloczkowski, Andrzej; Kotelnikov, Sergei; Kozakov, Dima; Liang, Jie; Liwo, Adam; Matysiak, Silvina; Meller, Jarek; Micheletti, Cristian; Mitchell, Julie C.; Mondal, Sayantan; Nussinov, Ruth; Padhorny, Dzmitry; Skolnick, Jeffrey; Sosnick, Tobin R.; Stan, George; Vakser, Ilya A.; Zou, Xiaoqin; Rose, George D.

doi:10.1073/pnas.2214423119

Cited by 50 publications

(43 citation statements)

References 29 publications

(35 reference statements)

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“…After engineering of SIBER 171, we also observed significant expression yield increase in engineered SIBER 822. This is consistent with the discussions of correlations between protein folding and protein activity, where the thermodynamics of the sequences may play a role in proper and accurate folding (26). Beyond stability, further .…”

Section: Discussionsupporting

confidence: 88%

Expression and engineering of unexplored PET degrading enzymes fromMicrobispora, Nonomuraea, Micromonosporagenus

Tiong

Koo²,

et al. 2023

Preprint

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Low recycling rates have resulted in the alarming rate of accumulation of a widely used plastic material, polyethylene terephthalate (PET). With the build-up of plastics in our environment, there is an urgent need to source for more sustainable solutions to process them. Biological methods such as enzyme-catalyzed PET recycling or bioprocessing are seen as a potential solution to this problem. Actinobacteria, known for producing enzymes involved in the degradation of complex organic molecules, are of particular interest due to their potential to produce PET degrading enzymes. The highly thermostable enzyme, leaf-branch compost cutinase (LCC) found in Actinobacteria is one such example. This work expands on the discovery and characterization of new PET degrading enzymes from Microbispora, Nonomuraea, and Micromonospora genus. Within this genus, we analyzed enzymes from the polyesterase-lipase-cutinase family, which have ~60% similarity to LCC, where one of the enzymes was found to be capable of breaking down PET and BHET at 45-50 °C. Moreover, we were able to enhance the enzyme's depolymerization rate through further engineering, resulting in a two-fold increase in activity.

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Section: Discussionsupporting

confidence: 88%

Expression and engineering of unexplored PET degrading enzymes fromMicrobispora, Nonomuraea, Micromonosporagenus

Tiong

Koo²,

et al. 2023

Preprint

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“…AlphaFold2 may have also learned some physical principles of protein folding (Roney & Ovchinnikov, 2022), though not enough to reproduce experimentally observed folding pathways (Outeiral et al, 2022). This is not surprising since pattern recognition-rather than biophysics-underlies deep learning algorithms (Chen et al, 2023;Rose, 2021).…”

Section: Sequence-based Predictions Of Structural Heterogeneity In Pr...mentioning

confidence: 99%

Distinguishing features of fold‐switching proteins

2023

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Though many folded proteins assume one stable structure that performs one function, a small‐but‐increasing number remodel their secondary and tertiary structures and change their functions in response to cellular stimuli. These fold‐switching proteins regulate biological processes and are associated with autoimmune dysfunction, severe acute respiratory syndrome coronavirus‐2 infection, and more. Despite their biological importance, it is difficult to computationally predict fold switching. With the aim of advancing computational prediction and experimental characterization of fold switchers, this review discusses several features that distinguish fold‐switching proteins from their single‐fold and intrinsically disordered counterparts. First, the isolated structures of fold switchers are less stable and more heterogeneous than single folders but more stable and less heterogeneous than intrinsically disordered proteins (IDPs). Second, the sequences of single fold, fold switching, and intrinsically disordered proteins can evolve at distinct rates. Third, proteins from these three classes are best predicted using different computational techniques. Finally, late‐breaking results suggest that single folders, fold switchers, and IDPs have distinct patterns of residue–residue coevolution. The review closes by discussing high‐throughput and medium‐throughput experimental approaches that might be used to identify new fold‐switching proteins.

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“…[60,61] Deep learning breakthroughs in protein structure prediction [6,62] bear promise to bridge the gap in data availability, by making thousands of predicted protein structures available for downstream tasks. [63][64][65][66] Despite this, the quality of machine-learning-based structure predictions is known to depend on several factors, such as the protein length and its flexibility, as well as the presence of similar structures in the training set. [67,68] (Macro)molecules are dynamical entities and are always interconverting between a variety of conformations with varying energies, [69] with key implications in drug-target interaction.…”

Section: Data For Structure-based Drug Discoverymentioning

confidence: 99%

Structure‐Based Drug Discovery with Deep Learning**

et al. 2023

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Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand‐based approaches, structure‐based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding‐mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep‐learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure‐based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure‐based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

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Protein folds vs. protein folding: Differing questions, different challenges

Cited by 50 publications

References 29 publications

Expression and engineering of unexplored PET degrading enzymes fromMicrobispora, Nonomuraea, Micromonosporagenus

Expression and engineering of unexplored PET degrading enzymes fromMicrobispora, Nonomuraea, Micromonosporagenus

Distinguishing features of fold‐switching proteins

Structure‐Based Drug Discovery with Deep Learning**

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