6,787 research outputs found
Distance-based Protein Folding Powered by Deep Learning
Contact-assisted protein folding has made very good progress, but two
challenges remain. One is accurate contact prediction for proteins lack of many
sequence homologs and the other is that time-consuming folding simulation is
often needed to predict good 3D models from predicted contacts. We show that
protein distance matrix can be predicted well by deep learning and then
directly used to construct 3D models without folding simulation at all. Using
distance geometry to construct 3D models from our predicted distance matrices,
we successfully folded 21 of the 37 CASP12 hard targets with a median family
size of 58 effective sequence homologs within 4 hours on a Linux computer of 20
CPUs. In contrast, contacts predicted by direct coupling analysis (DCA) cannot
fold any of them in the absence of folding simulation and the best CASP12 group
folded 11 of them by integrating predicted contacts into complex,
fragment-based folding simulation. The rigorous experimental validation on 15
CASP13 targets show that among the 3 hardest targets of new fold our
distance-based folding servers successfully folded 2 large ones with <150
sequence homologs while the other servers failed on all three, and that our ab
initio folding server also predicted the best, high-quality 3D model for a
large homology modeling target. Further experimental validation in CAMEO shows
that our ab initio folding server predicted correct fold for a membrane protein
of new fold with 200 residues and 229 sequence homologs while all the other
servers failed. These results imply that deep learning offers an efficient and
accurate solution for ab initio folding on a personal computer
AIDA: ab initio domain assembly server.
AIDA: ab initio domain assembly server, available at http://ffas.burnham.org/AIDA/ is a tool that can identify domains in multi-domain proteins and then predict their 3D structures and relative spatial arrangements. The server is free and open to all users, and there is an option for a user to provide an e-mail to get the link to result page. Domains are evolutionary conserved and often functionally independent units in proteins. Most proteins, especially eukaryotic ones, consist of multiple domains while at the same time, most experimentally determined protein structures contain only one or two domains. As a result, often structures of individual domains in multi-domain proteins can be accurately predicted, but the mutual arrangement of different domains remains unknown. To address this issue we have developed AIDA program, which combines steps of identifying individual domains, predicting (separately) their structures and assembling them into multiple domain complexes using an ab initio folding potential to describe domain-domain interactions. AIDA server not only supports the assembly of a large number of continuous domains, but also allows the assembly of domains inserted into other domains. Users can also provide distance restraints to guide the AIDA energy minimization
Protein Structure Prediction: The Next Generation
Over the last 10-15 years a general understanding of the chemical reaction of
protein folding has emerged from statistical mechanics. The lessons learned
from protein folding kinetics based on energy landscape ideas have benefited
protein structure prediction, in particular the development of coarse grained
models. We survey results from blind structure prediction. We explore how
second generation prediction energy functions can be developed by introducing
information from an ensemble of previously simulated structures. This procedure
relies on the assumption of a funnelled energy landscape keeping with the
principle of minimal frustration. First generation simulated structures provide
an improved input for associative memory energy functions in comparison to the
experimental protein structures chosen on the basis of sequence alignment
Universality and diversity of folding mechanics for three-helix bundle proteins
In this study we evaluate, at full atomic detail, the folding processes of
two small helical proteins, the B domain of protein A and the Villin headpiece.
Folding kinetics are studied by performing a large number of ab initio Monte
Carlo folding simulations using a single transferable all-atom potential. Using
these trajectories, we examine the relaxation behavior, secondary structure
formation, and transition-state ensembles (TSEs) of the two proteins and
compare our results with experimental data and previous computational studies.
To obtain a detailed structural information on the folding dynamics viewed as
an ensemble process, we perform a clustering analysis procedure based on graph
theory. Moreover, rigorous pfold analysis is used to obtain representative
samples of the TSEs and a good quantitative agreement between experimental and
simulated Fi-values is obtained for protein A. Fi-values for Villin are also
obtained and left as predictions to be tested by future experiments. Our
analysis shows that two-helix hairpin is a common partially stable structural
motif that gets formed prior to entering the TSE in the studied proteins. These
results together with our earlier study of Engrailed Homeodomain and recent
experimental studies provide a comprehensive, atomic-level picture of folding
mechanics of three-helix bundle proteins.Comment: PNAS, in press, revised versio
Introduction to protein folding for physicists
The prediction of the three-dimensional native structure of proteins from the
knowledge of their amino acid sequence, known as the protein folding problem,
is one of the most important yet unsolved issues of modern science. Since the
conformational behaviour of flexible molecules is nothing more than a complex
physical problem, increasingly more physicists are moving into the study of
protein systems, bringing with them powerful mathematical and computational
tools, as well as the sharp intuition and deep images inherent to the physics
discipline. This work attempts to facilitate the first steps of such a
transition. In order to achieve this goal, we provide an exhaustive account of
the reasons underlying the protein folding problem enormous relevance and
summarize the present-day status of the methods aimed to solving it. We also
provide an introduction to the particular structure of these biological
heteropolymers, and we physically define the problem stating the assumptions
behind this (commonly implicit) definition. Finally, we review the 'special
flavor' of statistical mechanics that is typically used to study the
astronomically large phase spaces of macromolecules. Throughout the whole work,
much material that is found scattered in the literature has been put together
here to improve comprehension and to serve as a handy reference.Comment: 53 pages, 18 figures, the figures are at a low resolution due to
arXiv restrictions, for high-res figures, go to http://www.pabloechenique.co
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Recently exciting progress has been made on protein contact prediction, but
the predicted contacts for proteins without many sequence homologs is still of
low quality and not very useful for de novo structure prediction. This paper
presents a new deep learning method that predicts contacts by integrating both
evolutionary coupling (EC) and sequence conservation information through an
ultra-deep neural network formed by two deep residual networks. This deep
neural network allows us to model very complex sequence-contact relationship as
well as long-range inter-contact correlation. Our method greatly outperforms
existing contact prediction methods and leads to much more accurate
contact-assisted protein folding. Tested on three datasets of 579 proteins, the
average top L long-range prediction accuracy obtained our method, the
representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21
and 0.30, respectively; the average top L/10 long-range accuracy of our method,
CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding
using our predicted contacts as restraints can yield correct folds (i.e.,
TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and
CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively.
Further, our contact-assisted models have much better quality than
template-based models. Using our predicted contacts as restraints, we can (ab
initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast,
when the training proteins of our method are used as templates, homology
modeling can only do so for 10 of them. One interesting finding is that even if
we do not train our prediction models with any membrane proteins, our method
works very well on membrane protein prediction. Finally, in recent blind CAMEO
benchmark our method successfully folded 5 test proteins with a novel fold
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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