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1.
Elife ; 102021 10 06.
Artigo em Inglês | MEDLINE | ID: mdl-34612205

RESUMO

Most eukaryotic cells retain a mitochondrial fatty acid synthesis (FASII) pathway whose acyl carrier protein (mACP) and 4-phosphopantetheine (Ppant) prosthetic group provide a soluble scaffold for acyl chain synthesis and biochemically couple FASII activity to mitochondrial electron transport chain (ETC) assembly and Fe-S cluster biogenesis. In contrast, the mitochondrion of Plasmodium falciparum malaria parasites lacks FASII enzymes yet curiously retains a divergent mACP lacking a Ppant group. We report that ligand-dependent knockdown of mACP is lethal to parasites, indicating an essential FASII-independent function. Decyl-ubiquinone rescues parasites temporarily from death, suggesting a dominant dysfunction of the mitochondrial ETC. Biochemical studies reveal that Plasmodium mACP binds and stabilizes the Isd11-Nfs1 complex required for Fe-S cluster biosynthesis, despite lacking the Ppant group required for this association in other eukaryotes, and knockdown of parasite mACP causes loss of Nfs1 and the Rieske Fe-S protein in ETC complex III. This work reveals that Plasmodium parasites have evolved to decouple mitochondrial Fe-S cluster biogenesis from FASII activity, and this adaptation is a shared metabolic feature of other apicomplexan pathogens, including Toxoplasma and Babesia. This discovery unveils an evolutionary driving force to retain interaction of mitochondrial Fe-S cluster biogenesis with ACP independent of its eponymous function in FASII.


Assuntos
Proteína de Transporte de Acila/genética , Ácidos Graxos/biossíntese , Ferro/metabolismo , Plasmodium falciparum/fisiologia , Proteínas de Protozoários/genética , Enxofre/metabolismo , Proteína de Transporte de Acila/metabolismo , Biogênese de Organelas , Plasmodium falciparum/genética , Proteínas de Protozoários/metabolismo
2.
Proteins ; 89(12): 1722-1733, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34331359

RESUMO

The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings and template information weighted by sequence similarity to the target. We also developed a refinement pipeline that recombines models generated by template-free and template utilizing versions of trRosetta guided by the DeepAccNet accuracy predictor. Both benchmark tests and CASP results show that the new pipeline is a considerable improvement over the original trRosetta, and it is faster and requires less computing resources, completing the entire modeling process in a median < 3 h in CASP14. Our human group improved results with this pipeline primarily by identifying additional homologous sequences for input into the network. We also used the DeepAccNet accuracy predictor to guide Rosetta high-resolution refinement for submissions in the regular and refinement categories; although performance was quite good on a CASP relative scale, the overall improvements were rather modest in part due to missing inter-domain or inter-chain contacts.

3.
Science ; 373(6557): 871-876, 2021 08 20.
Artigo em Inglês | MEDLINE | ID: mdl-34282049

RESUMO

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.


Assuntos
Aprendizado Profundo , Conformação Proteica , Dobramento de Proteína , Proteínas/química , Proteínas ADAM/química , Sequência de Aminoácidos , Simulação por Computador , Microscopia Crioeletrônica , Cristalografia por Raios X , Bases de Dados de Proteínas , Proteínas de Membrana/química , Modelos Moleculares , Complexos Multiproteicos/química , Redes Neurais de Computação , Subunidades Proteicas/química , Proteínas/fisiologia , Receptores Acoplados a Proteínas G/química , Esfingosina N-Aciltransferase/química
4.
Proteins ; 89(12): 1824-1833, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34324224

RESUMO

For CASP14, we developed deep learning-based methods for predicting homo-oligomeric and hetero-oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes predicted interchain contacts to guide iterative restrained minimization from random backbone structures. We supplemented this gradient-based fold-and-dock method with template-based and ab initio docking approaches using deep learning-based subunit predictions on 29 assembly targets. These methods produced oligomer models with summed Z-scores 5.5 units higher than the next best group, with the fold-and-dock method having the best relative performance. Over the eight targets for which this method was used, the best of the five submitted models had average oligomer TM-score of 0.71 (average oligomer TM-score of the next best group: 0.64), and explicit modeling of inter-subunit interactions improved modeling of six out of 40 individual domains (ΔGDT-TS > 2.0).

5.
Nat Commun ; 12(1): 1340, 2021 02 26.
Artigo em Inglês | MEDLINE | ID: mdl-33637700

RESUMO

We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Proteínas/química , Algoritmos , Caspases/química , Modelos Biológicos , Modelos Moleculares , Conformação Proteica , Software
6.
J Chem Theory Comput ; 17(3): 2000-2010, 2021 Mar 09.
Artigo em Inglês | MEDLINE | ID: mdl-33577321

RESUMO

Accurate and rapid calculation of protein-small molecule interaction free energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here, we describe a new approach in which small molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized, so native crystal lattice arrangements had the lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid-based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1112 complexes was improved by more than 10% over previously published methods, with solutions within <1 Å in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for guiding molecular force field development, and the improved Rosetta energy function should increase accuracy in a wide range of small molecule structure prediction and design studies.


Assuntos
Simulação de Acoplamento Molecular , Proteínas/química , Bibliotecas de Moléculas Pequenas/química , Algoritmos , Cristalografia por Raios X , Ligantes
7.
Front Bioeng Biotechnol ; 8: 558247, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33134287

RESUMO

Software to predict the change in protein stability upon point mutation is a valuable tool for a number of biotechnological and scientific problems. To facilitate the development of such software and provide easy access to the available experimental data, the ProTherm database was created. Biases in the methods and types of information collected has led to disparity in the types of mutations for which experimental data is available. For example, mutations to alanine are hugely overrepresented whereas those involving charged residues, especially from one charged residue to another, are underrepresented. ProTherm subsets created as benchmark sets that do not account for this often underrepresent tense certain mutational types. This issue introduces systematic biases into previously published protocols' ability to accurately predict the change in folding energy on these classes of mutations. To resolve this issue, we have generated a new benchmark set with these problems corrected. We have then used the benchmark set to test a number of improvements to the point mutation energetics tools in the Rosetta software suite.

8.
PLoS Comput Biol ; 16(9): e1008103, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32956350

RESUMO

Highly coordinated water molecules are frequently an integral part of protein-protein and protein-ligand interfaces. We introduce an updated energy model that efficiently captures the energetic effects of these ordered water molecules on the surfaces of proteins. A two-stage method is developed in which polar groups arranged in geometries suitable for water placement are first identified, then a modified Monte Carlo simulation allows highly coordinated waters to be placed on the surface of a protein while simultaneously sampling amino acid side chain orientations. This "semi-explicit" water model is implemented in Rosetta and is suitable for both structure prediction and protein design. We show that our new approach and energy model yield significant improvements in native structure recovery of protein-protein and protein-ligand docking discrimination tests.


Assuntos
Sítios de Ligação/fisiologia , Simulação de Acoplamento Molecular , Ligação Proteica/fisiologia , Proteínas , Água , Algoritmos , Aminoácidos/química , Aminoácidos/metabolismo , Ligação de Hidrogênio , Ligantes , Método de Monte Carlo , Proteínas/química , Proteínas/metabolismo , Água/química , Água/metabolismo
9.
Proc Natl Acad Sci U S A ; 117(3): 1496-1503, 2020 01 21.
Artigo em Inglês | MEDLINE | ID: mdl-31896580

RESUMO

The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.


Assuntos
Conformação Proteica , Análise de Sequência de Proteína/métodos , Software , Animais , Aprendizado Profundo , Humanos
10.
Proteins ; 87(12): 1276-1282, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31325340

RESUMO

Because proteins generally fold to their lowest free energy states, energy-guided refinement in principle should be able to systematically improve the quality of protein structure models generated using homologous structure or co-evolution derived information. However, because of the high dimensionality of the search space, there are far more ways to degrade the quality of a near native model than to improve it, and hence, refinement methods are very sensitive to energy function errors. In the 13th Critial Assessment of techniques for protein Structure Prediction (CASP13), we sought to carry out a thorough search for low energy states in the neighborhood of a starting model using restraints to avoid straying too far. The approach was reasonably successful in improving both regions largely incorrect in the starting models as well as core regions that started out closer to the correct structure. Models with GDT-HA over 70 were obtained for five targets and for one of those, an accuracy of 0.5 å backbone root-mean-square deviation (RMSD) was achieved. An important current challenge is to improve performance in refining oligomers and larger proteins, for which the search problem remains extremely difficult.


Assuntos
Biologia Computacional/métodos , Conformação Proteica , Dobramento de Proteína , Proteínas/química , Algoritmos , Modelos Moleculares , Reprodutibilidade dos Testes , Termodinâmica
11.
Nature ; 561(7724): 485-491, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30209393

RESUMO

The regular arrangements of ß-strands around a central axis in ß-barrels and of α-helices in coiled coils contrast with the irregular tertiary structures of most globular proteins, and have fascinated structural biologists since they were first discovered. Simple parametric models have been used to design a wide range of α-helical coiled-coil structures, but to date there has been no success with ß-barrels. Here we show that accurate de novo design of ß-barrels requires considerable symmetry-breaking to achieve continuous hydrogen-bond connectivity and eliminate backbone strain. We then build ensembles of ß-barrel backbone models with cavity shapes that match the fluorogenic compound DFHBI, and use a hierarchical grid-based search method to simultaneously optimize the rigid-body placement of DFHBI in these cavities and the identities of the surrounding amino acids to achieve high shape and chemical complementarity. The designs have high structural accuracy and bind and fluorescently activate DFHBI in vitro and in Escherichia coli, yeast and mammalian cells. This de novo design of small-molecule binding activity, using backbones custom-built to bind the ligand, should enable the design of increasingly sophisticated ligand-binding proteins, sensors and catalysts that are not limited by the backbone geometries available in known protein structures.


Assuntos
Compostos de Benzil/química , Fluorescência , Imidazolinas/química , Proteínas/química , Animais , Compostos de Benzil/análise , Células COS , Chlorocebus aethiops , Escherichia coli , Proteínas de Fluorescência Verde/genética , Proteínas de Fluorescência Verde/metabolismo , Ligação de Hidrogênio , Imidazolinas/análise , Ligantes , Ligação Proteica , Domínios Proteicos , Dobramento de Proteína , Estabilidade Proteica , Estrutura Secundária de Proteína , Reprodutibilidade dos Testes , Leveduras
12.
J Chem Inf Model ; 58(6): 1234-1243, 2018 06 25.
Artigo em Inglês | MEDLINE | ID: mdl-29786430

RESUMO

The second extracellular loops (ECL2s) of G-protein-coupled receptors (GPCRs) are often involved in GPCR functions, and their structures have important implications in drug discovery. However, structure prediction of ECL2 is difficult because of its long length and the structural diversity among different GPCRs. In this study, a new ECL2 conformational sampling method involving both template-based and ab initio sampling was developed. Inspired by the observation of similar ECL2 structures of closely related GPCRs, a template-based sampling method employing loop structure templates selected from the structure database was developed. A new metric for evaluating similarity of the target loop to templates was introduced for template selection. An ab initio loop sampling method was also developed to treat cases without highly similar templates. The ab initio method is based on the previously developed fragment assembly and loop closure method. A new sampling component that takes advantage of secondary structure prediction was added. In addition, a conserved disulfide bridge restraining ECL2 conformation was predicted and analytically incorporated into sampling, reducing the effective dimension of the conformational search space. The sampling method was combined with an existing energy function for comparison with previously reported loop structure prediction methods, and the benchmark test demonstrated outstanding performance.


Assuntos
Receptores Acoplados a Proteínas G/química , Animais , Bases de Dados de Proteínas , Dissulfetos/química , Humanos , Modelos Moleculares , Conformação Proteica , Estrutura Secundária de Proteína
13.
Proc Natl Acad Sci U S A ; 115(12): 3054-3059, 2018 03 20.
Artigo em Inglês | MEDLINE | ID: mdl-29507254

RESUMO

Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.


Assuntos
Simulação por Computador , Modelos Químicos , Biologia Computacional/métodos , Modelos Moleculares , Simulação de Dinâmica Molecular , Conformação Proteica , Dobramento de Proteína , Termodinâmica
14.
Proteins ; 86 Suppl 1: 283-291, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-28913931

RESUMO

Many naturally occurring protein systems function primarily as symmetric assemblies. Prediction of the quaternary structure of these assemblies is an important biological problem. This article describes automated tools we have developed for predicting the structures of symmetric protein assemblies in the Robetta structure prediction server. We assess the performance of this pipeline on a set of targets from the recent CASP12/CAPRI blind quaternary structure prediction experiment. Our approach successfully predicted 5 of 7 symmetric assemblies in this challenge, and was assessed as the best participating server group, and 1 of only 2 groups (human or server) with 2 predictions judged as high quality by the assessors. We also assess the method on a broader set of 22 natively symmetric CASP12 targets, where we show that oligomeric modeling can improve the accuracy of monomeric structure determination, particularly in highly intertwined oligomers.


Assuntos
Biologia Computacional/métodos , Bases de Dados de Proteínas , Modelos Moleculares , Conformação Proteica , Multimerização Proteica , Proteínas/química , Software , Humanos , Análise de Sequência de Proteína
15.
Proteins ; 86 Suppl 1: 113-121, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-28940798

RESUMO

We describe several notable aspects of our structure predictions using Rosetta in CASP12 in the free modeling (FM) and refinement (TR) categories. First, we had previously generated (and published) models for most large protein families lacking experimentally determined structures using Rosetta guided by co-evolution based contact predictions, and for several targets these models proved better starting points for comparative modeling than any known crystal structure-our model database thus starts to fulfill one of the goals of the original protein structure initiative. Second, while our "human" group simply submitted ROBETTA models for most targets, for six targets expert intervention improved predictions considerably; the largest improvement was for T0886 where we correctly parsed two discontinuous domains guided by predicted contact maps to accurately identify a structural homolog of the same fold. Third, Rosetta all atom refinement followed by MD simulations led to consistent but small improvements when starting models were close to the native structure, and larger but less consistent improvements when starting models were further away.


Assuntos
Biologia Computacional/métodos , Modelos Moleculares , Conformação Proteica , Dobramento de Proteína , Proteínas/química , Algoritmos , Cristalografia por Raios X , Humanos , Análise de Sequência de Proteína
16.
Protein Sci ; 26(12): 2426-2437, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-28980354

RESUMO

The steroid hormone 17α-hydroxylprogesterone (17-OHP) is a biomarker for congenital adrenal hyperplasia and hence there is considerable interest in development of sensors for this compound. We used computational protein design to generate protein models with binding sites for 17-OHP containing an extended, nonpolar, shape-complementary binding pocket for the four-ring core of the compound, and hydrogen bonding residues at the base of the pocket to interact with carbonyl and hydroxyl groups at the more polar end of the ligand. Eight of 16 designed proteins experimentally tested bind 17-OHP with micromolar affinity. A co-crystal structure of one of the designs revealed that 17-OHP is rotated 180° around a pseudo-two-fold axis in the compound and displays multiple binding modes within the pocket, while still interacting with all of the designed residues in the engineered site. Subsequent rounds of mutagenesis and binding selection improved the ligand affinity to nanomolar range, while appearing to constrain the ligand to a single bound conformation that maintains the same "flipped" orientation relative to the original design. We trace the discrepancy in the design calculations to two sources: first, a failure to model subtle backbone changes which alter the distribution of sidechain rotameric states and second, an underestimation of the energetic cost of desolvating the carbonyl and hydroxyl groups of the ligand. The difference between design model and crystal structure thus arises from both sampling limitations and energy function inaccuracies that are exacerbated by the near two-fold symmetry of the molecule.


Assuntos
Sítios de Ligação , Biologia Computacional/métodos , Modelos Moleculares , Mutagênese Sítio-Dirigida/métodos , 17-alfa-Hidroxiprogesterona/química , 17-alfa-Hidroxiprogesterona/metabolismo , Sítios de Ligação/genética , Sítios de Ligação/fisiologia , Desenho de Fármacos , Ligação de Hidrogênio , Interações Hidrofóbicas e Hidrofílicas , Ligantes , Biblioteca de Peptídeos , Ligação Proteica/genética , Ligação Proteica/fisiologia , Conformação Proteica
17.
J Chem Theory Comput ; 13(6): 3031-3048, 2017 Jun 13.
Artigo em Inglês | MEDLINE | ID: mdl-28430426

RESUMO

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.


Assuntos
Substâncias Macromoleculares/química , Simulação de Dinâmica Molecular , Protease de HIV/química , Protease de HIV/genética , Protease de HIV/metabolismo , Substâncias Macromoleculares/metabolismo , Mutação , Conformação Proteica , Eletricidade Estática , Termodinâmica
18.
Science ; 355(6322): 294-298, 2017 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-28104891

RESUMO

Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.


Assuntos
Biologia Computacional/métodos , Metagenoma , Proteínas/química , Algoritmos , Sequência de Aminoácidos , Cristalografia por Raios X , Bases de Dados de Proteínas , Evolução Molecular , Modelos Moleculares , Conformação Proteica , Dobramento de Proteína , Proteínas/genética , Análise de Sequência de Proteína , Software
19.
J Chem Theory Comput ; 12(12): 6201-6212, 2016 Dec 13.
Artigo em Inglês | MEDLINE | ID: mdl-27766851

RESUMO

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking have been parametrized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties.


Assuntos
Proteínas/química , Ligação de Hidrogênio , Ligantes , Simulação de Acoplamento Molecular , Ligação Proteica , Estrutura Terciária de Proteína , Proteínas/metabolismo , Eletricidade Estática , Termodinâmica
20.
Proteins ; 84 Suppl 1: 181-8, 2016 09.
Artigo em Inglês | MEDLINE | ID: mdl-26857542

RESUMO

In CASP11 we generated protein structure models using simulated ambiguous and unambiguous nuclear Overhauser effect (NOE) restraints with a two stage protocol. Low resolution models were generated guided by the unambiguous restraints using continuous chain folding for alpha and alpha-beta proteins, and iterative annealing for all beta proteins to take advantage of the strand pairing information implicit in the restraints. The Rosetta fragment/model hybridization protocol was then used to recombine and regularize these models, and refine them in the Rosetta full atom energy function guided by both the unambiguous and the ambiguous restraints. Fifteen out of 19 targets were modeled with GDT-TS quality scores greater than 60 for Model 1, significantly improving upon the non-assisted predictions. Our results suggest that atomic level accuracy is achievable using sparse NOE data when there is at least one correctly assigned NOE for every residue. Proteins 2016; 84(Suppl 1):181-188. © 2016 Wiley Periodicals, Inc.


Assuntos
Biologia Computacional/estatística & dados numéricos , Modelos Moleculares , Modelos Estatísticos , Proteínas/química , Software , Algoritmos , Motivos de Aminoácidos , Biologia Computacional/métodos , Simulação por Computador , Bases de Dados de Proteínas , Cooperação Internacional , Internet , Ressonância Magnética Nuclear Biomolecular , Conformação Proteica em alfa-Hélice , Conformação Proteica em Folha beta , Dobramento de Proteína , Domínios e Motivos de Interação entre Proteínas
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