A5085794545- Protein Structure and Dynamics
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Enzyme Structure and Function
- Enzyme Catalysis and Immobilization
- Microbial Metabolic Engineering and Bioproduction
- Computational Drug Discovery Methods
- Pancreatic function and diabetes
- Gene Regulatory Network Analysis
- Mass Spectrometry Techniques and Applications
- Cellular transport and secretion
- Endoplasmic Reticulum Stress and Disease
- Healthcare Policy and Management
- Legal and Constitutional Studies
- Amino Acid Enzymes and Metabolism
- Genomics and Chromatin Dynamics
- American Constitutional Law and Politics
- Evolution and Genetic Dynamics
National Institutes of Health
2023-2025
National Center for Biotechnology Information
2023-2025
United States National Library of Medicine
2023
University of Michigan
2021
University of Arizona
2019-2020
Abstract Recent work suggests that AlphaFold (AF)–a deep learning-based model can accurately infer protein structure from sequence–may discern important features of folded energy landscapes, defined by the diversity and frequency different conformations in state. Here, we test limits its predictive power on fold-switching proteins, which assume two structures with regions distinct secondary and/or tertiary structure. We find (1) AF is a weak predictor fold switching (2) some successes result...
Although most globular proteins fold into a single stable structure, an increasing number have been shown to remodel their secondary and tertiary structures in response cellular stimuli. State-of-the-art algorithms predict that these fold-switching adopt only one missing functionally critical alternative folds. Why is unclear, but all of them infer protein structure from coevolved amino acid pairs. Here, we hypothesize coevolutionary signatures are being missed. Suspecting single-fold...
Though typically associated with a single folded state, some globular proteins remodel their secondary and/or tertiary structures in response to cellular stimuli. AlphaFold2
Recent work suggests that AlphaFold2 (AF2)-a deep learning-based model can accurately infer protein structure from sequence-may discern important features of folded energy landscapes, defined by the diversity and frequency different conformations in state. Here, we test limits its predictive power on fold-switching proteins, which assume two structures with regions distinct secondary and/or tertiary structure. Using several implementations AF2, including published enhanced sampling...
The design of artificial enzymes is an emerging field research. Although progress has been made, the catalytic proficiency many designed low compared to natural enzymes. Nevertheless, recently Hilvert et al. ( Nat. Chem. 2017, 9, 50-56) created a series five retro-aldolase via directed evolution, with final variant exhibiting rate comparable naturally occurring enzyme fructose 1,6 bisphosphate aldolase. We present study this system in atomistic detail that elucidates effects mutational...
Abstract Though typically associated with a single folded state, globular proteins are dynamic and often assume alternative or transient structures important for their functions 1,2 . Wayment-Steele, et al. steered ColabFold 3 to predict of several using method they call AF-cluster 4 They propose that “enables sample alternate states known metamorphic high confidence” by first clustering multiple sequence alignments (MSAs) in way “deconvolves” coevolutionary information specific different...
AlphaFold2 (AF2), a deep-learning-based model that predicts protein structures from their amino acid sequences, has recently been used to predict multiple conformations. In some cases, AF2 successfully predicted both dominant and alternative conformations of fold-switching proteins, which remodel secondary and/or tertiary in response cellular stimuli. Whether learned enough folding principles reliably outside its training set is unclear. Previous work suggests these by memorizing them during...
Protein engineering is a growing field with variety of experimental techniques available for altering protein function. However, creating an enzyme de novo still in its infancy, so far yielding enzymes modest catalytic efficiency. In this study, system artificial retro-aldolase found to have chemistry coupled dynamics was examined. The original design created computationally, and then subjected directed evolution improve the initial low We that re-engineering resulted rapid density...
ABSTRACT AlphaFold2 (AF2), a deep-learning based model that predicts protein structures from their amino acid sequences, has recently been used to predict multiple conformations. In some cases, AF2 successfully predicted both dominant and alternative conformations of fold-switching proteins, which remodel secondary tertiary in response cellular stimuli. Whether learned enough folding principles reliably outside its training set is unclear. Here, we address this question by assessing whether...
Although most globular proteins fold into a single stable structure 1 , an increasing number have been shown to remodel their secondary and tertiary structures in response cellular stimuli 2 . State-of-the-art algorithms 3-5 predict that these fold-switching assume only one 6,7 missing functionally critical alternative folds. Why is unclear, but all of them infer protein from coevolved amino acid pairs. Here, we hypothesize coevolutionary signatures are being missed. Suspecting...
Creating efficient and stable enzymes for catalysis in pharmaceutical industrial laboratories is an important research goal. Arnold et al. used directed evolution to engineer a natural tryptophan synthase create mutant that operable under laboratory conditions without the need allosteric effector. The use of allows researchers improve understanding structure-activity relationship. Here, we present transition path sampling study key chemical transformation catalytic cycle. We observed while...