3,595 research outputs found
Do theoretical physicists care about the protein-folding problem?
The prediction of the biologically active native conformation of a protein is
one of the fundamental challenges of structural biology. This problem remains
yet unsolved mainly due to three factors: the partial knowledge of the
effective free energy function that governs the folding process, the enormous
size of the conformational space of a protein and, finally, the relatively
small differences of energy between conformations, in particular, between the
native one and the ones that make up the unfolded state.
Herein, we recall the importance of taking into account, in a detailed
manner, the many interactions involved in the protein folding problem (such as
steric volume exclusion, Ramachandran forces, hydrogen bonds, weakly polar
interactions, coulombic energy or hydrophobic attraction) and we propose a
strategy to effectively construct a free energy function that, including the
effects of the solvent, could be numerically tractable. It must be pointed out
that, since the internal free energy function that is mainly described does not
include the constraints of the native conformation, it could only help to reach
the 'molten globule' state. We also discuss about the limits and the lacks from
which suffer the simple models that we, physicists, love so much.Comment: 27 pages, 4 figures, LaTeX file, aipproc package. To be published in
the book: "Meeting on Fundamental Physics 'Alberto Galindo'", Alvarez-Estrada
R. F. et al. (Ed.), Madrid: Aula Documental, 200
Unlocking novel therapies:cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning
Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties
Computational prediction of protein aggregation : advances in proteomics, conformation-specific algorithms and biotechnological applications
Protein aggregation is a widespread phenomenon that stems from the establishment of non-native intermolecular contacts resulting in protein precipitation. Despite its deleterious impact on fitness, protein aggregation is a generic property of polypeptide chains, indissociable from protein structure and function. Protein aggregation is behind the onset of neurodegenerative disorders and one of the serious obstacles in the production of protein-based therapeutics. The development of computational tools opened a new avenue to rationalize this phenomenon, enabling prediction of the aggregation propensity of individual proteins as well as proteome-wide analysis. These studies spotted aggregation as a major force driving protein evolution. Actual algorithms work on both protein sequences and structures, some of them accounting also for conformational fluctuations around the native state and the protein microenvironment. This toolbox allows to delineate conformation-specific routines to assist in the identification of aggregation-prone regions and to guide the optimization of more soluble and stable biotherapeutics. Here we review how the advent of predictive tools has change the way we think and address protein aggregation
Toward a Designable Extracellular Matrix: Molecular Dynamics Simulations of an Engineered Laminin-mimetic, Elastin-like Fusion Protein
Native extracellular matrices (ECMs), such as those of the human brain and
other neural tissues, exhibit networks of molecular interactions between
specific matrix proteins and other tissue components. Guided by these naturally
self-assembling supramolecular systems, we have designed a matrix-derived
protein chimera that contains a laminin globular-like (LG) domain fused to an
elastin-like polypeptide (ELP). All-atom, classical molecular dynamics
simulations of our designed laminin-elastin fusion protein reveal
temperature-dependent conformational changes, in terms of secondary structure
composition, solvent accessible surface area, hydrogen bonding, and surface
hydration. These properties illuminate the phase behavior of this fusion
protein, via the emergence of -sheet character in
physiologically-relevant temperature ranges.Comment: 53 pages, 7 figures in the main text; Supporting Information contains
1 table, 12 figures, 4 trajectory animations (videos
Amyloidogenic Regions and Interaction Surfaces Overlap in Globular Proteins Related to Conformational Diseases
Protein aggregation underlies a wide range of human disorders. The polypeptides involved in these pathologies might be intrinsically unstructured or display a defined 3D-structure. Little is known about how globular proteins aggregate into toxic assemblies under physiological conditions, where they display an initially folded conformation. Protein aggregation is, however, always initiated by the establishment of anomalous protein-protein interactions. Therefore, in the present work, we have explored the extent to which protein interaction surfaces and aggregation-prone regions overlap in globular proteins associated with conformational diseases. Computational analysis of the native complexes formed by these proteins shows that aggregation-prone regions do frequently overlap with protein interfaces. The spatial coincidence of interaction sites and aggregating regions suggests that the formation of functional complexes and the aggregation of their individual subunits might compete in the cell. Accordingly, single mutations affecting complex interface or stability usually result in the formation of toxic aggregates. It is suggested that the stabilization of existing interfaces in multimeric proteins or the formation of new complexes in monomeric polypeptides might become effective strategies to prevent disease-linked aggregation of globular proteins
Recurrent oligomers in proteins - an optimal scheme reconciling accurate and concise backbone representations in automated folding and design studies
A novel scheme is introduced to capture the spatial correlations of
consecutive amino acids in naturally occurring proteins. This knowledge-based
strategy is able to carry out optimally automated subdivisions of protein
fragments into classes of similarity. The goal is to provide the minimal set of
protein oligomers (termed ``oligons'' for brevity) that is able to represent
any other fragment. At variance with previous studies where recurrent local
motifs were classified, our concern is to provide simplified protein
representations that have been optimised for use in automated folding and/or
design attempts. In such contexts it is paramount to limit the number of
degrees of freedom per amino acid without incurring in loss of accuracy of
structural representations. The suggested method finds, by construction, the
optimal compromise between these needs. Several possible oligon lengths are
considered. It is shown that meaningful classifications cannot be done for
lengths greater than 6 or smaller than 4. Different contexts are considered
were oligons of length 5 or 6 are recommendable. With only a few dozen of
oligons of such length, virtually any protein can be reproduced within typical
experimental uncertainties. Structural data for the oligons is made publicly
available.Comment: 19 pages, 13 postscript figure
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
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