Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

The vast expansion of protein sequence databases provides an opportunity for new protein
design approaches which seek to learn the sequence-function relationship directly from …

Potts Hamiltonian models of protein sequence co-variation are statistical models constructed
from the pair correlations observed in a multiple sequence alignment (MSA) of a protein …

Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far
outpaces the research on the structure and functional interactions of these RNA gene …

Proteins often accumulate neutral mutations that do not affect current functions but can
profoundly influence future mutational possibilities and functions. Understanding such …

During their evolution, proteins explore sequence space via an interplay between random
mutations and phenotypic selection. Here, we build upon recent progress in reconstructing …

Inactive conformations of protein kinase catalytic domains where the DFG motif has a “DFG-
out” orientation and the activation loop is folded present a druggable binding pocket that is …

Predicting the effects of mutations on protein function is an important issue in evolutionary
biology and biomedical applications. Computational approaches, ranging from graphical …

Directed laboratory evolution applies iterative rounds of mutation and selection to explore
the protein fitness landscape and provides rich information regarding the underlying …

To optimize proteins for particular traits holds great promise for industrial and
pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict …