597 research outputs found
Crucial stages of protein folding through a solvable model: predicting target sites for enzyme-inhibiting drugs
An exactly solvable model based on the topology of a protein native state is
applied to identify bottlenecks and key-sites for the folding of HIV-1
Protease. The predicted sites are found to correlate well with clinical data on
resistance to FDA-approved drugs. It has been observed that the effects of drug
therapy are to induce multiple mutations on the protease. The sites where such
mutations occur correlate well with those involved in folding bottlenecks
identified through the deterministic procedure proposed in this study. The high
statistical significance of the observed correlations suggests that the
approach may be promisingly used in conjunction with traditional techniques to
identify candidate locations for drug attacks.Comment: 12 pages, 5 figure
Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration
We review a selection of methods for performing enhanced sampling in molecular dynamics simulations. We consider methods based on collective variable biasing and on tempering, and offer both historical and contemporary perspectives. In collective-variable biasing, we first discuss methods stemming from thermodynamic integration that use mean force biasing, including the adaptive biasing force algorithm and temperature acceleration. We then turn to methods that use bias potentials, including umbrella sampling and metadynamics. We next consider parallel tempering and replica-exchange methods. We conclude with a brief presentation of some combination methods. \ua9 2013 by the author; licensee MDPI, Basel, Switzerland
Inverse Statistical Physics of Protein Sequences: A Key Issues Review
In the course of evolution, proteins undergo important changes in their amino
acid sequences, while their three-dimensional folded structure and their
biological function remain remarkably conserved. Thanks to modern sequencing
techniques, sequence data accumulate at unprecedented pace. This provides large
sets of so-called homologous, i.e.~evolutionarily related protein sequences, to
which methods of inverse statistical physics can be applied. Using sequence
data as the basis for the inference of Boltzmann distributions from samples of
microscopic configurations or observables, it is possible to extract
information about evolutionary constraints and thus protein function and
structure. Here we give an overview over some biologically important questions,
and how statistical-mechanics inspired modeling approaches can help to answer
them. Finally, we discuss some open questions, which we expect to be addressed
over the next years.Comment: 18 pages, 7 figure
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