1,577 research outputs found
Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures
The protein-folding problem has been extensively studied during the last
fifty years. The understanding of the dynamics of global shape of a protein and the influence
on its biological function can help us to discover new and more effective
drugs to deal with diseases of pharmacological relevance. Different computational approaches
have been developed by different researchers in order to foresee the threedimensional
arrangement of atoms of proteins from their sequences. However, the
computational complexity of this problem makes mandatory the search for new models,
novel algorithmic strategies and hardware platforms that provide solutions in a
reasonable time frame. We present in this revision work the past and last tendencies
regarding protein folding simulations from both perspectives; hardware and software.
Of particular interest to us are both the use of inexact solutions to this computationally hard problem as
well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciĂłnSĂ©neca (Agencia Regional de Ciencia y TecnologĂa, RegiĂłn de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.IngenierĂa, Industria y ConstrucciĂł
Protein accumulation in the endoplasmic reticulum as a non-equilibrium phase transition
Several neurological disorders are associated with the aggregation of
aberrant proteins, often localized in intracellular organelles such as the
endoplasmic reticulum. Here we study protein aggregation kinetics by mean-field
reactions and three dimensional Monte carlo simulations of diffusion-limited
aggregation of linear polymers in a confined space, representing the
endoplasmic reticulum. By tuning the rates of protein production and
degradation, we show that the system undergoes a non-equilibrium phase
transition from a physiological phase with little or no polymer accumulation to
a pathological phase characterized by persistent polymerization. A combination
of external factors accumulating during the lifetime of a patient can thus
slightly modify the phase transition control parameters, tipping the balance
from a long symptomless lag phase to an accelerated pathological development.
The model can be successfully used to interpret experimental data on
amyloid-\b{eta} clearance from the central nervous system
Design of Sequences with Good Folding Properties in Coarse-Grained Protein Models
Background: Designing amino acid sequences that are stable in a given target
structure amounts to maximizing a conditional probability. A straightforward
approach to accomplish this is a nested Monte Carlo where the conformation
space is explored over and over again for different fixed sequences, which
requires excessive computational demand. Several approximate attempts to remedy
this situation, based on energy minimization for fixed structure or high-
expansions, have been proposed. These methods are fast but often not accurate
since folding occurs at low .
Results: We develop a multisequence Monte Carlo procedure, where both
sequence and conformation space are simultaneously probed with efficient
prescriptions for pruning sequence space. The method is explored on
hydrophobic/polar models. We first discuss short lattice chains, in order to
compare with exact data and with other methods. The method is then successfully
applied to lattice chains with up to 50 monomers, and to off-lattice 20-mers.
Conclusions: The multisequence Monte Carlo method offers a new approach to
sequence design in coarse-grained models. It is much more efficient than
previous Monte Carlo methods, and is, as it stands, applicable to a fairly wide
range of two-letter models.Comment: 23 pages, 7 figure
RNA and protein 3D structure modeling: similarities and differences
In analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been very few such methods for RNA. This review discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed “protein-like” modeling methods for RNA. We highlight analogies between many successful methods for modeling of these two types of biological macromolecules and argue that RNA 3D structure can be modeled using “protein-like” methodology. We also highlight the areas where the differences between RNA and proteins require the development of RNA-specific solutions
Simple models of protein folding and of non--conventional drug design
While all the information required for the folding of a protein is contained
in its amino acid sequence, one has not yet learned how to extract this
information to predict the three--dimensional, biologically active, native
conformation of a protein whose sequence is known. Using insight obtained from
simple model simulations of the folding of proteins, in particular of the fact
that this phenomenon is essentially controlled by conserved (native) contacts
among (few) strongly interacting ("hot"), as a rule hydrophobic, amino acids,
which also stabilize local elementary structures (LES, hidden, incipient
secondary structures like --helices and --sheets) formed early
in the folding process and leading to the postcritical folding nucleus (i.e.,
the minimum set of native contacts which bring the system pass beyond the
highest free--energy barrier found in the whole folding process) it is possible
to work out a succesful strategy for reading the native structure of designed
proteins from the knowledge of only their amino acid sequence and of the
contact energies among the amino acids. Because LES have undergone millions of
years of evolution to selectively dock to their complementary structures, small
peptides made out of the same amino acids as the LES are expected to
selectively attach to the newly expressed (unfolded) protein and inhibit its
folding, or to the native (fluctuating) native conformation and denaturate it.
These peptides, or their mimetic molecules, can thus be used as effective
non--conventional drugs to those already existing (and directed at neutralizing
the active site of enzymes), displaying the advantage of not suffering from the
uprise of resistance
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How Water's Properties Are Encoded in Its Molecular Structure and Energies.
How are water's material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth's living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies-water's solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions-hydroxide and protons-diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water's molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and interact with each other through van der Waals forces. Unlike simpler liquids, water's orientation-dependent hydrogen bonding leads to open tetrahedral cage-like structuring that contributes to its remarkable volumetric and thermal properties
The prospects of quantum computing in computational molecular biology
Quantum computers can in principle solve certain problems exponentially more
quickly than their classical counterparts. We have not yet reached the advent
of useful quantum computation, but when we do, it will affect nearly all
scientific disciplines. In this review, we examine how current quantum
algorithms could revolutionize computational biology and bioinformatics. There
are potential benefits across the entire field, from the ability to process
vast amounts of information and run machine learning algorithms far more
efficiently, to algorithms for quantum simulation that are poised to improve
computational calculations in drug discovery, to quantum algorithms for
optimization that may advance fields from protein structure prediction to
network analysis. However, these exciting prospects are susceptible to "hype",
and it is also important to recognize the caveats and challenges in this new
technology. Our aim is to introduce the promise and limitations of emerging
quantum computing technologies in the areas of computational molecular biology
and bioinformatics.Comment: 23 pages, 3 figure
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