scispace - formally typeset
M

Maya Topf

Researcher at Birkbeck, University of London

Publications -  148
Citations -  7247

Maya Topf is an academic researcher from Birkbeck, University of London. The author has contributed to research in topics: Protein structure prediction & Gene. The author has an hindex of 46, co-authored 134 publications receiving 6090 citations. Previous affiliations of Maya Topf include Heinrich Pette Institute & Australian National University.

Papers
More filters
Journal ArticleDOI

Critical assessment of methods of protein structure prediction (CASP)-Round XIII

TL;DR: The most recent Critical Assessment of Structure Prediction (CASP13) as discussed by the authors assesses the state of the art in modeling protein structure from amino acid sequence, and the results showed dramatic improvements in three-dimensional structure accuracy.
Journal ArticleDOI

Protein structure fitting and refinement guided by cryo-EM density.

TL;DR: A heuristic optimization that relies on a Monte Carlo search, a conjugate-gradients minimization, and simulated annealing molecular dynamics is applied to a series of subdivisions of the structure into progressively smaller rigid bodies.
Journal ArticleDOI

A structural perspective on protein-protein interactions.

TL;DR: Structures of macromolecular complexes are necessary for a mechanistic description of biochemical and cellular processes and can be solved by experimental methods, as well as by computational protein structure prediction, docking and bioinformatics.
Journal ArticleDOI

Integrating diverse data for structure determination of macromolecular assemblies.

TL;DR: An approach to integrate structural information gathered at multiple levels of the biological hierarchy--from atoms to cells--into a common framework is proposed, illustrated by determining the configuration of the 456 proteins in the nuclear pore complex from baker's yeast.
Journal ArticleDOI

Mosaic RAS/MAPK variants cause sporadic vascular malformations which respond to targeted therapy

TL;DR: These findings uncover a major cause of sporadic VMs of different clinical types and thereby offer the potential of personalized medical treatment by repurposing existing licensed cancer therapies.