The state-of-the-art for Monte Carlo (MC) simulations of biomacromolecules is reviewed.
Available methodologies for sampling conformational equilibria and associations of …

A theoretical framework is developed for the calculation of rate constants by sampling
connected pathways composed of local minima and transition states that link them together …

We use geometrical considerations to provide a different perspective on the fact that a few
selected amino acids, the so-called “key residues,” act as nucleation centers for protein …

The discrete path sampling approach is applied to analyse the dynamics of several atomic
and molecular clusters. Permutational isomerization rates are first calculated for icosahedral …

We describe a new computational method, FRODA (framework rigidity optimized dynamic
algorithm), for exploring the internal mobility of proteins. The rigid regions in the protein are …

Supplement to “A framework for adaptive MCMC targeting multimodal distributions”. In
Supplementary Material A we present the proofs of our theoretical results of Section 3 and …

Bayesian models are powerful tools for studying complex data, allowing the analyst to
encode rich hierarchical dependencies and leverage prior information. Most importantly …

This perspective focuses on conceptual and computational aspects of the potential energy
landscape framework. It has two objectives: first to summarise some key developments of …

Accurately predicting protein–ligand binding affinities and binding modes is a major goal in
computational chemistry, but even the prediction of ligand binding modes in proteins poses …

In machine learning and statistics, probabilistic inference involving multimodal distributions
is quite difficult. This is especially true in high dimensional problems, where most existing …