9,242 research outputs found
The Energy Landscape, Folding Pathways and the Kinetics of a Knotted Protein
The folding pathway and rate coefficients of the folding of a knotted protein
are calculated for a potential energy function with minimal energetic
frustration. A kinetic transition network is constructed using the discrete
path sampling approach, and the resulting potential energy surface is
visualized by constructing disconnectivity graphs. Owing to topological
constraints, the low-lying portion of the landscape consists of three distinct
regions, corresponding to the native knotted state and to configurations where
either the N- or C-terminus is not yet folded into the knot. The fastest
folding pathways from denatured states exhibit early formation of the
N-terminus portion of the knot and a rate-determining step where the C-terminus
is incorporated. The low-lying minima with the N-terminus knotted and the
C-terminus free therefore constitute an off-pathway intermediate for this
model. The insertion of both the N- and C-termini into the knot occur late in
the folding process, creating large energy barriers that are the rate limiting
steps in the folding process. When compared to other protein folding proteins
of a similar length, this system folds over six orders of magnitude more
slowly.Comment: 19 page
LightDock: a new multi-scale approach to protein–protein docking
Computational prediction of protein–protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed.
We describe here a new multi-scale protein–protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases.B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and
Competitiveness. This work was supported by I+D+I Research Project grants BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy
and Competitiveness. This work is partially supported by the European Union H2020
program through HiPEAC (GA 687698), by the Spanish Government through Programa
Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and
Technology (TIN2015-65316-P) and the Departament d’Innovació, Universitats i
Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaciói Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
Structurally constrained protein evolution: results from a lattice simulation
We simulate the evolution of a protein-like sequence subject to point
mutations, imposing conservation of the ground state, thermodynamic stability
and fast folding. Our model is aimed at describing neutral evolution of natural
proteins. We use a cubic lattice model of the protein structure and test the
neutrality conditions by extensive Monte Carlo simulations. We observe that
sequence space is traversed by neutral networks, i.e. sets of sequences with
the same fold connected by point mutations. Typical pairs of sequences on a
neutral network are nearly as different as randomly chosen sequences. The
fraction of neutral neighbors has strong sequence to sequence variations, which
influence the rate of neutral evolution. In this paper we study the
thermodynamic stability of different protein sequences. We relate the high
variability of the fraction of neutral mutations to the complex energy
landscape within a neutral network, arguing that valleys in this landscape are
associated to high values of the neutral mutation rate. We find that when a
point mutation produces a sequence with a new ground state, this is likely to
have a low stability. Thus we tentatively conjecture that neutral networks of
different structures are typically well separated in sequence space. This
results indicates that changing significantly a protein structure through a
biologically acceptable chain of point mutations is a rare, although possible,
event.Comment: added reference, to appear on European Physical Journal
Frustration in Biomolecules
Biomolecules are the prime information processing elements of living matter.
Most of these inanimate systems are polymers that compute their structures and
dynamics using as input seemingly random character strings of their sequence,
following which they coalesce and perform integrated cellular functions. In
large computational systems with a finite interaction-codes, the appearance of
conflicting goals is inevitable. Simple conflicting forces can lead to quite
complex structures and behaviors, leading to the concept of "frustration" in
condensed matter. We present here some basic ideas about frustration in
biomolecules and how the frustration concept leads to a better appreciation of
many aspects of the architecture of biomolecules, and how structure connects to
function. These ideas are simultaneously both seductively simple and perilously
subtle to grasp completely. The energy landscape theory of protein folding
provides a framework for quantifying frustration in large systems and has been
implemented at many levels of description. We first review the notion of
frustration from the areas of abstract logic and its uses in simple condensed
matter systems. We discuss then how the frustration concept applies
specifically to heteropolymers, testing folding landscape theory in computer
simulations of protein models and in experimentally accessible systems.
Studying the aspects of frustration averaged over many proteins provides ways
to infer energy functions useful for reliable structure prediction. We discuss
how frustration affects folding, how a large part of the biological functions
of proteins are related to subtle local frustration effects and how frustration
influences the appearance of metastable states, the nature of binding
processes, catalysis and allosteric transitions. We hope to illustrate how
Frustration is a fundamental concept in relating function to structural
biology.Comment: 97 pages, 30 figure
Forward Flux Sampling for rare event simulations
Rare events are ubiquitous in many different fields, yet they are notoriously
difficult to simulate because few, if any, events are observed in a conventiona
l simulation run. Over the past several decades, specialised simulation methods
have been developed to overcome this problem. We review one recently-developed
class of such methods, known as Forward Flux Sampling. Forward Flux Sampling
uses a series of interfaces between the initial and final states to calculate
rate constants and generate transition paths, for rare events in equilibrium or
nonequilibrium systems with stochastic dynamics. This review draws together a
number of recent advances, summarizes several applications of the method and
highlights challenges that remain to be overcome.Comment: minor typos in the manuscript. J.Phys.:Condensed Matter (accepted for
publication
Mean Field Approach for a Statistical Mechanical Model of Proteins
We study the thermodynamical properties of a topology-based model proposed by
Galzitskaya and Finkelstein for the description of protein folding. We devise
and test three different mean-field approaches for the model, that simplify the
treatment without spoiling the description. The validity of the model and its
mean-field approximations is checked by applying them to the -hairpin
fragment of the immunoglobulin-binding protein (GB1) and making a comparison
with available experimental data and simulation results. Our results indicate
that this model is a rather simple and reasonably good tool for interpreting
folding experimental data, provided the parameters of the model are carefully
chosen. The mean-field approaches substantially recover all the relevant exact
results and represent reliable alternatives to the Monte Carlo simulations.Comment: RevTeX-4, 11 pages, 6 eps-figures, To Appear on J.Chem.Phy
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