Moving beyond static snapshots: Protein dynamics and the Protein Data Bank

Miller, Mitchell D.; Phillips, G.N.

doi:10.1016/j.jbc.2021.100749

Cited by 49 publications

(52 citation statements)

References 140 publications

(126 reference statements)

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“…After removal of these misfolded models, PCA analysis of the remaining models yields two separate clusters suggestive of more than one intermediate between the open and closed structures ( Fig 2H , Fig B in S1 Text ). This set of models is consistent with a wide variety of studies on this canonical example of protein flexibility that indicate that the ATP binding lid and AMP binding lid are capable of opening and closing independently [ 11 , 12 ].…”

Section: Resultssupporting

confidence: 84%

“…PCA for the AK crystal structures and the remaining AF2 models yields an interesting plot of principal components 1 and 2 (PC1, PC2) with one grouping of structures highly divergent from the rest of the models and the two crystal structures ( . This set of models is consistent with a wide variety of studies on this canonical example of protein flexibility that indicate that the ATP binding lid and AMP binding lid are capable of opening and closing independently [11,12].…”

Section: Canonical Examples Of Conformational Flexibilitysupporting

confidence: 85%

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SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

2022

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The unprecedented performance of Deepmind’s Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold’s ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.

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Section: Resultssupporting

confidence: 84%

Section: Canonical Examples Of Conformational Flexibilitysupporting

confidence: 85%

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

2022

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“…Conformational dynamics are indispensable to enzyme function throughout the catalytic cycle ( 39 , 40 , 41 , 42 , 43 , 44 , 45 ), with conformational selection and shifts in the population of protein sub-states predicted to occur along the reaction path ( 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ). As shown herein, ligand binding to mADA shifts the enzyme’s conformational landscape toward a more rigidified ensemble that is anticipated to enhance the alignment of functional groups and to shorten their distances in preparation for catalysis.…”

Section: Discussionmentioning

confidence: 99%

Temperature-dependent hydrogen deuterium exchange shows impact of analog binding on adenosine deaminase flexibility but not embedded thermal networks

Gao

Zhang²,

Barrow³

et al. 2022

Journal of Biological Chemistry

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“…We further tested if ProtGPT2 sequences show similar dynamic properties as natural sequences. Proteins are dynamic entities; without their inherent flexibility, they would not be capable of interacting with other biomolecules and performing their functions in the cell 48 . To evaluate whether ProtGPT2 sequences show flexibility patterns in the same range as natural proteins, we randomly selected 12 sequences per dataset and ran three replicas of molecular dynamics (MD) of 100 ns each, totaling 108 trajectories and an aggregate time of 10.8 microseconds (Methods).…”

Section: Protgpt2 Generates Ordered Structuresmentioning

confidence: 99%

ProtGPT2 is a deep unsupervised language model for protein design

2022

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Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available.

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Moving beyond static snapshots: Protein dynamics and the Protein Data Bank

Cited by 49 publications

References 140 publications

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

Temperature-dependent hydrogen deuterium exchange shows impact of analog binding on adenosine deaminase flexibility but not embedded thermal networks

ProtGPT2 is a deep unsupervised language model for protein design

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