Single-molecule FRET and crosslinking studies in structural biology enabled by noncanonical amino acids

2015 ◽  
Vol 32 ◽  
pp. 66-73 ◽  
Author(s):  
Swati Tyagi ◽  
Edward A Lemke
2019 ◽  
Author(s):  
Bijoy J. Desai ◽  
Ruben L. Gonzalez

Stunning advances in the structural biology of multicomponent biomolecular complexes (MBCs) have ushered in an era of intense, structure-guided mechanistic and functional studies of these complexes. Nonetheless, existing methods to site-specifically conjugate MBCs with biochemical and biophysical labels are notoriously impracticable and/or significantly perturb MBC assembly and function. To overcome these limitations, we have developed a general, multiplexed method in which we genomically encode non-canonical amino acids (ncAAs) into multiple, structure-informed, individual sites within a target MBC; select for ncAA-containing MBC variants that assemble and function like the wildtype MBC; and site-specifically conjugate biochemical or biophysical labels to these ncAAs. As a proof-of-principle, we have used this method to generate unique single-molecule fluorescence resonance energy transfer (smFRET) signals reporting on ribosome structural dynamics that have thus far remained inaccessible to smFRET studies of translation.


Author(s):  
Johannes Thomsen ◽  
Magnus B. Sletfjerding ◽  
Stefano Stella ◽  
Bijoya Paul ◽  
Simon Bo Jensen ◽  
...  

AbstractSingle molecule Förster Resonance energy transfer (smFRET) is a mature and adaptable method for studying the structure of biomolecules and integrating their dynamics into structural biology. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and fully automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histogram of biomolecule behavior, is a user-adjustable quality threshold. Integrating all standard features of smFRET analysis, DeepFRET will consequently output common kinetic information metrics for biomolecules. We validated the utility of DeepFRET by performing quantitative analysis on simulated, ground truth, data and real smFRET data. The accuracy of classification by DeepFRET outperformed human operators and current commonly used hard threshold and reached >95% precision accuracy only requiring a fraction of the time (<1% as compared to human operators) on ground truth data. Its flawless and rapid operation on real data demonstrates its wide applicability. This level of classification was achieved without any preprocessing or parameter setting by human operators, demonstrating DeepFRET’s capacity to objectively quantify biomolecular dynamics. The provided a standalone executable based on open source code capitalises on the widespread adaptation of machine learning and may contribute to the effort of benchmarking smFRET for structural biology insights.


eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Eitan Lerner ◽  
Anders Barth ◽  
Jelle Hendrix ◽  
Benjamin Ambrose ◽  
Victoria Birkedal ◽  
...  

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.


2014 ◽  
Vol 106 (2) ◽  
pp. 659a
Author(s):  
Drew Dolino ◽  
David Cooper ◽  
Swarna Ramaswamy ◽  
Christy Landes ◽  
Vasanthi Jayaraman

2015 ◽  
Vol 108 (2) ◽  
pp. 14a
Author(s):  
Marko Sustarsic ◽  
Timothy Craggs ◽  
Johannes Hohlbein ◽  
Andrew Cuthbert ◽  
Nicholas Taylor ◽  
...  

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