8,665 research outputs found
Computational protein design with backbone plasticity
The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as βscaffoldsβ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increase search space but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process
Capturing the essence of folding and functions of biomolecules using Coarse-Grained Models
The distances over which biological molecules and their complexes can
function range from a few nanometres, in the case of folded structures, to
millimetres, for example during chromosome organization. Describing phenomena
that cover such diverse length, and also time scales, requires models that
capture the underlying physics for the particular length scale of interest.
Theoretical ideas, in particular, concepts from polymer physics, have guided
the development of coarse-grained models to study folding of DNA, RNA, and
proteins. More recently, such models and their variants have been applied to
the functions of biological nanomachines. Simulations using coarse-grained
models are now poised to address a wide range of problems in biology.Comment: 37 pages, 8 figure
A correspondence between solution-state dynamics of an individual protein and the sequence and conformational diversity of its family.
Conformational ensembles are increasingly recognized as a useful representation to describe fundamental relationships between protein structure, dynamics and function. Here we present an ensemble of ubiquitin in solution that is created by sampling conformational space without experimental information using "Backrub" motions inspired by alternative conformations observed in sub-Angstrom resolution crystal structures. Backrub-generated structures are then selected to produce an ensemble that optimizes agreement with nuclear magnetic resonance (NMR) Residual Dipolar Couplings (RDCs). Using this ensemble, we probe two proposed relationships between properties of protein ensembles: (i) a link between native-state dynamics and the conformational heterogeneity observed in crystal structures, and (ii) a relation between dynamics of an individual protein and the conformational variability explored by its natural family. We show that the Backrub motional mechanism can simultaneously explore protein native-state dynamics measured by RDCs, encompass the conformational variability present in ubiquitin complex structures and facilitate sampling of conformational and sequence variability matching those occurring in the ubiquitin protein family. Our results thus support an overall relation between protein dynamics and conformational changes enabling sequence changes in evolution. More practically, the presented method can be applied to improve protein design predictions by accounting for intrinsic native-state dynamics
Four small puzzles that Rosetta doesn't solve
A complete macromolecule modeling package must be able to solve the simplest
structure prediction problems. Despite recent successes in high resolution
structure modeling and design, the Rosetta software suite fares poorly on
deceptively small protein and RNA puzzles, some as small as four residues. To
illustrate these problems, this manuscript presents extensive Rosetta results
for four well-defined test cases: the 20-residue mini-protein Trp cage, an even
smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease
inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies,
several lines of evidence indicate that conformational sampling is not the
major bottleneck in modeling these small systems. Instead, approximations and
omissions in the Rosetta all-atom energy function currently preclude
discriminating experimentally observed conformations from de novo models at
atomic resolution. These molecular "puzzles" should serve as useful model
systems for developers wishing to make foundational improvements to this
powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special
Collectio
Exploiting Homology Information in Nontemplate Based Prediction of Protein Structures
In this paper we describe a novel strategy for exploring the conformational space of proteins and show that this leads to better models for proteins the structure of which is not amenable to template based methods. Our strategy is based on the assumption that the energy global minimum of homologous proteins must correspond to similar conformations, while the precise profiles of their energy landscape, and consequently the positions of the local minima, are likely to be different. In line with this hypothesis, we apply a replica exchange Monte Carlo simulation protocol that, rather than using different parameters for each parallel simulation, uses the sequences of homologous proteins. We show that our results are competitive with respect to alternative methods, including those producing the best model for each of the analyzed targets in the CASP10 (10th Critical Assessment of techniques for protein Structure Prediction) experiment free modeling category
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