scholarly journals Artificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy

2021 ◽  
Author(s):  
Dong Si ◽  
Andrew Nakamura ◽  
Runbang Tang ◽  
Haowen Guan ◽  
Jie Hou ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Artificial Intelligence ◽  
Electron Microscopy ◽  
De Novo ◽  
Structure Modeling ◽  
Cryo Electron Microscopy

scholarly journals Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 82 ◽  
Author(s):  
Eman Alnabati ◽  
Daisuke Kihara
Keyword(s):  
Electron Microscopy ◽  
De Novo ◽  
Molecular Structures ◽  
Structure Modeling ◽  
Rapid Progress ◽  
Macromolecular Complexes ◽  
Modeling Methods ◽  
Cryo Electron Microscopy ◽  
Density Maps

Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.

scholarly journals Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
George Ueda ◽  
Aleksandar Antanasijevic ◽  
Jorge A Fallas ◽  
William Sheffler ◽  
Jeffrey Copps ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
De Novo ◽  
Viral Glycoprotein ◽  
Influenza Hemagglutinin ◽  
Self Assembling ◽  
Cryo Electron Microscopy ◽  
Viral Glycoproteins ◽  
Protein Nanoparticles ◽  
New Generation ◽  
Tailored Design

Multivalent presentation of viral glycoproteins can substantially increase the elicitation of antigen-specific antibodies. To enable a new generation of anti-viral vaccines, we designed self-assembling protein nanoparticles with geometries tailored to present the ectodomains of influenza, HIV, and RSV viral glycoprotein trimers. We first de novo designed trimers tailored for antigen fusion, featuring N-terminal helices positioned to match the C termini of the viral glycoproteins. Trimers that experimentally adopted their designed configurations were incorporated as components of tetrahedral, octahedral, and icosahedral nanoparticles, which were characterized by cryo-electron microscopy and assessed for their ability to present viral glycoproteins. Electron microscopy and antibody binding experiments demonstrated that the designed nanoparticles presented antigenically intact prefusion HIV-1 Env, influenza hemagglutinin, and RSV F trimers in the predicted geometries. This work demonstrates that antigen-displaying protein nanoparticles can be designed from scratch, and provides a systematic way to investigate the influence of antigen presentation geometry on the immune response to vaccination.

scholarly journals Near-Atomic Resolution Structure of a Highly Neutralizing Fab Bound to Canine Parvovirus

2016 ◽  
Vol 90 (21) ◽  
pp. 9733-9742 ◽  
Author(s):  
Lindsey J. Organtini ◽  
Hyunwook Lee ◽  
Sho Iketani ◽  
Kai Huang ◽  
Robert E. Ashley ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Receptor Binding ◽  
Conformational Changes ◽  
De Novo ◽  
Mutational Analysis ◽  
Antibody Fragment ◽  
Canine Parvovirus ◽  
Single Chain ◽  
Cryo Electron Microscopy ◽  
Resolution Structure

ABSTRACT Canine parvovirus (CPV) is a highly contagious pathogen that causes severe disease in dogs and wildlife. Previously, a panel of neutralizing monoclonal antibodies (MAb) raised against CPV was characterized. An antibody fragment (Fab) of MAb E was found to neutralize the virus at low molar ratios. Using recent advances in cryo-electron microscopy (cryo-EM), we determined the structure of CPV in complex with Fab E to 4.1 Å resolution, which allowed de novo building of the Fab structure. The footprint identified was significantly different from the footprint obtained previously from models fitted into lower-resolution maps. Using single-chain variable fragments, we tested antibody residues that control capsid binding. The near-atomic structure also revealed that Fab binding had caused capsid destabilization in regions containing key residues conferring receptor binding and tropism, which suggests a mechanism for efficient virus neutralization by antibody. Furthermore, a general technical approach to solving the structures of small molecules is demonstrated, as binding the Fab to the capsid allowed us to determine the 50-kDa Fab structure by cryo-EM. IMPORTANCE Using cryo-electron microscopy and new direct electron detector technology, we have solved the 4 Å resolution structure of a Fab molecule bound to a picornavirus capsid. The Fab induced conformational changes in regions of the virus capsid that control receptor binding. The antibody footprint is markedly different from the previous one identified by using a 12 Å structure. This work emphasizes the need for a high-resolution structure to guide mutational analysis and cautions against relying on older low-resolution structures even though they were interpreted with the best methodology available at the time.

scholarly journals Author response: De novo modeling of the F420-reducing [NiFe]-hydrogenase from a methanogenic archaeon by cryo-electron microscopy

2012 ◽  
Author(s):  
Deryck J Mills ◽  
Stella Vitt ◽  
Mike Strauss ◽  
Seigo Shima ◽  
Janet Vonck
Keyword(s):  
Electron Microscopy ◽  
De Novo ◽  
Author Response ◽  
Cryo Electron Microscopy

scholarly journals Architecture and subunit arrangement of native AMPA receptors elucidated by cryo-EM

Science ◽  
2019 ◽  
Vol 364 (6438) ◽  
pp. 355-362 ◽  
Author(s):  
Yan Zhao ◽  
Shanshuang Chen ◽  
Adam C. Swensen ◽  
Wei-Jun Qian ◽  
Eric Gouaux
Keyword(s):  
Molecular Structure ◽  
Electron Microscopy ◽  
Signal Transduction ◽  
Ligand Binding ◽  
Ampa Receptor ◽  
Ampa Receptors ◽  
Mammalian Brain ◽  
Receptor Complexes ◽  
Cryo Electron Microscopy ◽  
Chemical Synapses

Glutamate-gated AMPA receptors mediate the fast component of excitatory signal transduction at chemical synapses throughout all regions of the mammalian brain. AMPA receptors are tetrameric assemblies composed of four subunits, GluA1–GluA4. Despite decades of study, the subunit composition, subunit arrangement, and molecular structure of native AMPA receptors remain unknown. Here we elucidate the structures of 10 distinct native AMPA receptor complexes by single-particle cryo–electron microscopy (cryo-EM). We find that receptor subunits are arranged nonstochastically, with the GluA2 subunit preferentially occupying the B and D positions of the tetramer and with triheteromeric assemblies comprising a major population of native AMPA receptors. Cryo-EM maps define the structure for S2-M4 linkers between the ligand-binding and transmembrane domains, suggesting how neurotransmitter binding is coupled to ion channel gating.

scholarly journals De Novo Drug Design using Artificial Intelligence ASYNT-GAN

2020 ◽  
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Molecular Structure ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
De Novo ◽  
Protein Structures ◽  
Generative Adversarial Networks ◽  
Text Generation ◽  
De Novo Drug Design ◽  
3D Space ◽  
Adversarial Networks

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

scholarly journals De Novo Drug Design using Artificial Intelligence ASYNT-GAN

2020 ◽  
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Molecular Structure ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
De Novo ◽  
Protein Structures ◽  
Generative Adversarial Networks ◽  
Text Generation ◽  
De Novo Drug Design ◽  
3D Space ◽  
Adversarial Networks

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

Sign in / Sign up

Export Citation Format

Share Document