12,830 research outputs found
Empirical Potential Function for Simplified Protein Models: Combining Contact and Local Sequence-Structure Descriptors
An effective potential function is critical for protein structure prediction
and folding simulation. Simplified protein models such as those requiring only
or backbone atoms are attractive because they enable efficient
search of the conformational space. We show residue specific reduced discrete
state models can represent the backbone conformations of proteins with small
RMSD values. However, no potential functions exist that are designed for such
simplified protein models. In this study, we develop optimal potential
functions by combining contact interaction descriptors and local
sequence-structure descriptors. The form of the potential function is a
weighted linear sum of all descriptors, and the optimal weight coefficients are
obtained through optimization using both native and decoy structures. The
performance of the potential function in test of discriminating native protein
structures from decoys is evaluated using several benchmark decoy sets. Our
potential function requiring only backbone atoms or atoms have
comparable or better performance than several residue-based potential functions
that require additional coordinates of side chain centers or coordinates of all
side chain atoms. By reducing the residue alphabets down to size 5 for local
structure-sequence relationship, the performance of the potential function can
be further improved. Our results also suggest that local sequence-structure
correlation may play important role in reducing the entropic cost of protein
folding.Comment: 20 pages, 5 figures, 4 tables. In press, Protein
Gene length as a regulator for ribosome recruitment and protein synthesis : theoretical insights
The authors would like to acknowledge the funding provided by the European Union Seventh Framework Programme [FP7/2007–2013] (NICHE; grant agreement 289384) (LDF). LDF also acknowledges the funding provided by the São Paulo Research Foundation (FAPESP - grant #2015/26989-4). AM was partially funded by the UK Biotechnology and Biological Research Council (BBSRC), through grant BB/N015711/1. LC would like to acknowledge Maria Carmen Romano, Jean Hausser, Marco Cosentino Lagomarsino, Jean-Charles Walter and Norbert Kern for early discussions on this work, and the CNRS for having granted him a “demi-délégation” (2017–18). We would like to dedicate this work in memory of Maxime Clusel and Vladimir Lorman.Peer reviewedPublisher PDFPublisher PD
Knowledge-based energy functions for computational studies of proteins
This chapter discusses theoretical framework and methods for developing
knowledge-based potential functions essential for protein structure prediction,
protein-protein interaction, and protein sequence design. We discuss in some
details about the Miyazawa-Jernigan contact statistical potential,
distance-dependent statistical potentials, as well as geometric statistical
potentials. We also describe a geometric model for developing both linear and
non-linear potential functions by optimization. Applications of knowledge-based
potential functions in protein-decoy discrimination, in protein-protein
interactions, and in protein design are then described. Several issues of
knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe
Protein folding using contact maps
We present the development of the idea to use dynamics in the space of
contact maps as a computational approach to the protein folding problem. We
first introduce two important technical ingredients, the reconstruction of a
three dimensional conformation from a contact map and the Monte Carlo dynamics
in contact map space. We then discuss two approximations to the free energy of
the contact maps and a method to derive energy parameters based on perceptron
learning. Finally we present results, first for predictions based on threading
and then for energy minimization of crambin and of a set of 6 immunoglobulins.
The main result is that we proved that the two simple approximations we studied
for the free energy are not suitable for protein folding. Perspectives are
discussed in the last section.Comment: 29 pages, 10 figure
Single chain properties of polyelectrolytes in poor solvent
Using molecular dynamics simulations we study the behavior of a dilute
solution of strongly charged polyelectrolytes in poor solvents, where we take
counterions explicitly into account. We focus on the chain conformational
properties under conditions where chain-chain interactions can be neglected,
but the counterion concentration remains finite. We investigate the
conformations with regard to the parameters chain length, Coulomb interaction
strength, and solvent quality, and explore in which regime the competition
between short range hydrophobic interactions and long range Coulomb
interactions leads to pearl-necklace like structures. We observe that large
number and size fluctuations in the pearls and strings lead to only small
direct signatures in experimental observables like the single chain form
factor. Furthermore we do not observe the predicted first order collapse of the
necklace into a globular structure when counterion condensation sets in. We
will also show that the pearl-necklace regime is rather small for strongly
charged polyelectrolytes at finite densities. Even small changes in the charge
fraction of the chain can have a large impact on the conformation due to the
delicate interplay between counterion distribution and chain conformation.Comment: 20 pages, 27 figures, needs jpc.sty (included), to appear in Jour.
Phys. Chem
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