Publications by authors named "Minkyung Baek"

23 Publications

  • Page 1 of 1

Structure of the phosphoinositide 3-kinase (PI3K) p110γ-p101 complex reveals molecular mechanism of GPCR activation.

Sci Adv 2021 Aug 27;7(35). Epub 2021 Aug 27.

Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada.

The class IB phosphoinositide 3-kinase (PI3K), PI3Kγ, is a master regulator of immune cell function and a promising drug target for both cancer and inflammatory diseases. Critical to PI3Kγ function is the association of the p110γ catalytic subunit to either a p101 or p84 regulatory subunit, which mediates activation by G protein-coupled receptors. Here, we report the cryo-electron microscopy structure of a heterodimeric PI3Kγ complex, p110γ-p101. This structure reveals a unique assembly of catalytic and regulatory subunits that is distinct from other class I PI3K complexes. p101 mediates activation through its Gβγ-binding domain, recruiting the heterodimer to the membrane and allowing for engagement of a secondary Gβγ-binding site in p110γ. Mutations at the p110γ-p101 and p110γ-adaptor binding domain interfaces enhanced Gβγ activation. A nanobody that specifically binds to the p101-Gβγ interface blocks activation, providing a novel tool to study and target p110γ-p101-specific signaling events in vivo.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/sciadv.abj4282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397274PMC
August 2021

Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.

Proteins 2021 Jul 30. Epub 2021 Jul 30.

Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.26194DOI Listing
July 2021

Protein oligomer modeling guided by predicted interchain contacts in CASP14.

Proteins 2021 Jul 29. Epub 2021 Jul 29.

Department of Biochemistry, University of Washington, Seattle, Washington, USA.

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).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.26197DOI Listing
July 2021

Accurate prediction of protein structures and interactions using a three-track neural network.

Science 2021 08 15;373(6557):871-876. Epub 2021 Jul 15.

Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/science.abj8754DOI Listing
August 2021

GalaxyHeteromer: protein heterodimer structure prediction by template-based and ab initio docking.

Nucleic Acids Res 2021 07;49(W1):W237-W241

Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.

Protein-protein interactions play crucial roles in diverse biological processes, including various disease progressions. Atomistic structural details of protein-protein interactions may provide important information that can facilitate the design of therapeutic agents. GalaxyHeteromer is a freely available automatic web server (http://galaxy.seoklab.org/heteromer) that predicts protein heterodimer complex structures from two subunit protein sequences or structures. When subunit structures are unavailable, they are predicted by template- or distance-prediction-based modelling methods. Heterodimer complex structures can be predicted by both template-based and ab initio docking, depending on the template's availability. Structural templates are detected from the protein structure database based on both the sequence and structure similarities. The templates for heterodimers may be selected from monomer and homo-oligomer structures, as well as from hetero-oligomers, owing to the evolutionary relationships of heterodimers with domains of monomers or subunits of homo-oligomers. In addition, the server employs one of the best ab initio docking methods when heterodimer templates are unavailable. The multiple heterodimer structure models and the associated scores, which are provided by the web server, may be further examined by user to test or develop functional hypotheses or to design new functional molecules.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkab422DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262733PMC
July 2021

Improved protein structure refinement guided by deep learning based accuracy estimation.

Nat Commun 2021 02 26;12(1):1340. Epub 2021 Feb 26.

Department of Biochemistry and Institute for Protein Design, University of Washington, Washington, WA, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-21511-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910447PMC
February 2021

Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein-Ligand Docking.

J Chem Theory Comput 2021 Mar 12;17(3):2000-2010. Epub 2021 Feb 12.

Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.jctc.0c01184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218654PMC
March 2021

Modeling Protein Homo-Oligomer Structures with GalaxyHomomer Web Server.

Methods Mol Biol 2020 ;2165:127-137

Department of Chemistry, Seoul National University, Seoul, Republic of Korea.

Cellular processes, such as metabolism, signal transduction, or immunity, often depend on the homo-oligomerization of proteins. Detailed structural knowledge of the homo-oligomer structure is therefore crucial for molecular-level understanding of protein functions and their regulation. In this chapter, we introduce the GalaxyHomomer server, which supports easy-to-use web interfaces for general users. It is freely accessible at http://galaxy.seoklab.org/homomer . GalaxyHomomer carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality by performing symmetric loop modeling and overall structure refinement. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state, and locations of unreliable/flexible loops or termini.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-0716-0708-4_7DOI Listing
March 2021

Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45.

Proteins 2020 08 10;88(8):1009-1017. Epub 2019 Dec 10.

Department of Chemistry, Seoul National University, Seoul, Republic of Korea.

We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25859DOI Listing
August 2020

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

Proteins 2019 12 25;87(12):1200-1221. Epub 2019 Oct 25.

School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25838DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274794PMC
December 2019

Prediction of protein oligomer structures using GALAXY in CASP13.

Proteins 2019 12 9;87(12):1233-1240. Epub 2019 Oct 9.

Department of Chemistry, Seoul National University, Seoul, Republic of Korea.

Many proteins need to form oligomers to be functional, so oligomer structures provide important clues to biological roles of proteins. Prediction of oligomer structures therefore can be a useful tool in the absence of experimentally resolved structures. In this article, we describe the server and human methods that we used to predict oligomer structures in the CASP13 experiment. Performances of the methods on the 42 CASP13 oligomer targets consisting of 30 homo-oligomers and 12 hetero-oligomers are discussed. Our server method, Seok-assembly, generated models with interface contact similarity measure greater than 0.2 as model 1 for 11 homo-oligomer targets when proper templates existed in the database. Model refinement methods such as loop modeling and molecular dynamics (MD)-based overall refinement failed to improve model qualities when target proteins have domains not covered by templates or when chains have very small interfaces. In human predictions, additional experimental data such as low-resolution electron microscopy (EM) map were utilized. EM data could assist oligomer structure prediction by providing a global shape of the complex structure.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25814DOI Listing
December 2019

Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning.

Proteins 2019 12 30;87(12):1351-1360. Epub 2019 Aug 30.

Department of Chemistry, Seoul National University, Seoul, Republic of Korea.

Scoring model structure is an essential component of protein structure prediction that can affect the prediction accuracy tremendously. Users of protein structure prediction results also need to score models to select the best models for their application studies. In Critical Assessment of techniques for protein Structure Prediction (CASP), model accuracy estimation methods have been tested in a blind fashion by providing models submitted by the tertiary structure prediction servers for scoring. In CASP13, model accuracy estimation results were evaluated in terms of both global and local structure accuracy. Global structure accuracy estimation was evaluated by the quality of the models selected by the global structure scores and by the absolute estimates of the global scores. Residue-wise, local structure accuracy estimations were evaluated by three different measures. A new measure introduced in CASP13 evaluates the ability to predict inaccurately modeled regions that may be improved by refinement. An intensive comparative analysis on CASP13 and the previous CASPs revealed that the tertiary structure models generated by the CASP13 servers show very distinct features. Higher consensus toward models of higher global accuracy appeared even for free modeling targets, and many models of high global accuracy were not well optimized at the atomic level. This is related to the new technology in CASP13, deep learning for tertiary contact prediction. The tertiary model structures generated by deep learning pose a new challenge for EMA (estimation of model accuracy) method developers. Model accuracy estimation itself is also an area where deep learning can potentially have an impact, although current EMA methods have not fully explored that direction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25804DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851486PMC
December 2019

GalaxyDock3: Protein-ligand docking that considers the full ligand conformational flexibility.

J Comput Chem 2019 12 19;40(31):2739-2748. Epub 2019 Aug 19.

Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea.

Predicting conformational changes of both the protein and the ligand is a major challenge when a protein-ligand complex structure is predicted from the unbound protein and ligand structures. Herein, we introduce a new protein-ligand docking program called GalaxyDock3 that considers the full ligand conformational flexibility by explicitly sampling the ligand ring conformation and allowing the relaxation of the full ligand degrees of freedom, including bond angles and lengths. This method is based on the previous version (GalaxyDock2) which performs the global optimization of a designed score function. Ligand ring conformation is sampled from a ring conformation library constructed from structure databases. The GalaxyDock3 score function was trained with an additional bonded energy term for the ligand on a large set of complex structures. The performance of GalaxyDock3 was improved compared to GalaxyDock2 when predicted ligand conformation was used as the input for docking, especially when the input ligand conformation differs significantly from the crystal conformation. GalaxyDock3 also compared favorably with other available docking programs on two benchmark tests that contained diverse ligand rings. The program is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html. © 2019 Wiley Periodicals, Inc.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jcc.26050DOI Listing
December 2019

GalaxyTongDock: Symmetric and asymmetric ab initio protein-protein docking web server with improved energy parameters.

J Comput Chem 2019 10 7;40(27):2413-2417. Epub 2019 Jun 7.

Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.

Protein-protein docking methods are spotlighted for their roles in providing insights into protein-protein interactions in the absence of full structural information by experiment. GalaxyTongDock is an ab initio protein-protein docking web server that performs rigid-body docking just like ZDOCK but with improved energy parameters. The energy parameters were trained by iterative docking and parameter search so that more native-like structures are selected as top rankers. GalaxyTongDock performs asymmetric docking of two different proteins (GalaxyTongDock_A) and symmetric docking of homo-oligomeric proteins with C and D symmetries (GalaxyTongDock_C and GalaxyTongDock_D). Performance tests on an unbound docking benchmark set for asymmetric docking and a model docking benchmark set for symmetric docking showed that GalaxyTongDock is better or comparable to other state-of-the-art methods. Experimental and/or evolutionary information on binding interfaces can be easily incorporated by using block and interface options. GalaxyTongDock web server is freely available at http://galaxy.seoklab.org/tongdock. © 2019 Wiley Periodicals, Inc.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jcc.25874DOI Listing
October 2019

Novel Compound Heterozygote Mutation in IL10RA in a Patient With Very Early-Onset Inflammatory Bowel Disease.

Inflamm Bowel Dis 2019 02;25(3):498-509

Department of Pediatrics, Asan Medical Center Children's Hospital, University of Ulsan College of Medicine, Seoul, Korea.

Background: Very early-onset inflammatory bowel disease (VEO-IBD) is often associated with monogenetic disorders. IL-10RA deficiency is one of the major causal mutations in VEO-IBD. Here, we aimed to identify the causal mutation associated with severe IBD in a 1-year-old patient, validate the pathogenicity of the mutation, and characterize the mutant protein.

Methods: To identify the causal mutation, targeted exome sequencing (ES) was performed using the genomic DNA from the patient. To validate the pathogenicity, IL-10RA functional tests were performed using the patient's peripheral blood mononuclear cells (PBMCs). Additionally, flow cytometry analysis, confocal microscopy on overexpressed green fluorescent protein-fused mutants, and computational analysis on the structures of IL-10RA proteins were performed.

Results: We identified a novel compound heterozygote mutation p.[Tyr91Cys];[Pro146Alafs*40] in the IL10RA gene of the patient. The missense variant p.Tyr91Cys was previously identified but not functionally tested, and a frameshift variant, p.Pro146Alafs*40, is novel and nonfunctional. PBMCs from the patient showed defective signal transducer and activator of transcription 3 activation. The p.Tyr91Cys mutant protein failed to properly localize on the plasma membrane. The p.Tyr91Cys mutation seems to disrupt the hydrophobic core structure surrounding the tyrosine 91 residue, causing structural instability.

Conclusions: Targeted ES and linkage analysis identified novel compound heterozygous mutations p.[Tyr91Cys];[Pro146Alafs*40] in the IL10RA gene of a child with severe VEO-IBD. p.Tyr91Cys proteins were functionally defective in IL-10RA signaling and failed to properly localize on the plasma membrane, probably due to its structural instability.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/ibd/izy353DOI Listing
February 2019

The challenge of modeling protein assemblies: the CASP12-CAPRI experiment.

Proteins 2018 03 26;86 Suppl 1:257-273. Epub 2017 Nov 26.

VIB Structural Biology Research Center, VUB, Pleinlaan 2, Brussels, Belgium.

We present the quality assessment of 5613 models submitted by predictor groups from both CAPRI and CASP for the total of 15 most tractable targets from the second joint CASP-CAPRI protein assembly prediction experiment. These targets comprised 12 homo-oligomers and 3 hetero-complexes. The bulk of the analysis focuses on 10 targets (of CAPRI Round 37), which included all 3 hetero-complexes, and whose protein chains or the full assembly could be readily modeled from structural templates in the PDB. On average, 28 CAPRI groups and 10 CASP groups (including automatic servers), submitted models for each of these 10 targets. Additionally, about 16 groups participated in the CAPRI scoring experiments. A range of acceptable to high quality models were obtained for 6 of the 10 Round 37 targets, for which templates were available for the full assembly. Poorer results were achieved for the remaining targets due to the lower quality of the templates available for the full complex or the individual protein chains, highlighting the unmet challenge of modeling the structural adjustments of the protein components that occur upon binding or which must be accounted for in template-based modeling. On the other hand, our analysis indicated that residues in binding interfaces were correctly predicted in a sizable fraction of otherwise poorly modeled assemblies and this with higher accuracy than published methods that do not use information on the binding partner. Lastly, the strengths and weaknesses of the assessment methods are evaluated and improvements suggested.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25419DOI Listing
March 2018

GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking.

J Comput Aided Mol Des 2017 Jul 16;31(7):653-666. Epub 2017 Jun 16.

Department of Chemistry, Seoul National University, Seoul, Republic of Korea.

Protein-ligand docking is a useful tool for providing atomic-level understanding of protein functions in nature and design principles for artificial ligands or proteins with desired properties. The ability to identify the true binding pose of a ligand to a target protein among numerous possible candidate poses is an essential requirement for successful protein-ligand docking. Many previously developed docking scoring functions were trained to reproduce experimental binding affinities and were also used for scoring binding poses. However, in this study, we developed a new docking scoring function, called GalaxyDock BP2 Score, by directly training the scoring power of binding poses. This function is a hybrid of physics-based, empirical, and knowledge-based score terms that are balanced to strengthen the advantages of each component. The performance of the new scoring function exhibits significant improvement over existing scoring functions in decoy pose discrimination tests. In addition, when the score is used with the GalaxyDock2 protein-ligand docking program, it outperformed other state-of-the-art docking programs in docking tests on the Astex diverse set, the Cross2009 benchmark set, and the Astex non-native set. GalaxyDock BP2 Score and GalaxyDock2 with this score are freely available at http://galaxy.seoklab.org/softwares/galaxydock.html .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10822-017-0030-9DOI Listing
July 2017

GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure.

Nucleic Acids Res 2017 07;45(W1):W320-W324

Department of Chemistry, Seoul National University, Seoul 151-747, Korea.

Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkx246DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570155PMC
July 2017

Template-based modeling and ab initio refinement of protein oligomer structures using GALAXY in CAPRI round 30.

Proteins 2017 03 4;85(3):399-407. Epub 2016 Nov 4.

Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea.

Many proteins function as homo- or hetero-oligomers; therefore, attempts to understand and regulate protein functions require knowledge of protein oligomer structures. The number of available experimental protein structures is increasing, and oligomer structures can be predicted using the experimental structures of related proteins as templates. However, template-based models may have errors due to sequence differences between the target and template proteins, which can lead to functional differences. Such structural differences may be predicted by loop modeling of local regions or refinement of the overall structure. In CAPRI (Critical Assessment of PRotein Interactions) round 30, we used recently developed features of the GALAXY protein modeling package, including template-based structure prediction, loop modeling, model refinement, and protein-protein docking to predict protein complex structures from amino acid sequences. Out of the 25 CAPRI targets, medium and acceptable quality models were obtained for 14 and 1 target(s), respectively, for which proper oligomer or monomer templates could be detected. Symmetric interface loop modeling on oligomer model structures successfully improved model quality, while loop modeling on monomer model structures failed. Overall refinement of the predicted oligomer structures consistently improved the model quality, in particular in interface contacts. Proteins 2017; 85:399-407. © 2016 Wiley Periodicals, Inc.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25192DOI Listing
March 2017

Absolute binding free energies for octa-acids and guests in SAMPL5 : Evaluating binding free energies for octa-acid and guest complexes in the SAMPL5 blind challenge.

J Comput Aided Mol Des 2017 01 30;31(1):107-118. Epub 2016 Sep 30.

Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung, and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA.

As part of the SAMPL5 blind prediction challenge, we calculate the absolute binding free energies of six guest molecules to an octa-acid (OAH) and to a methylated octa-acid (OAMe). We use the double decoupling method via thermodynamic integration (TI) or Hamiltonian replica exchange in connection with the Bennett acceptance ratio (HREM-BAR). We produce the binding poses either through manual docking or by using GalaxyDock-HG, a docking software developed specifically for this study. The root mean square deviations for our most accurate predictions are 1.4 kcal mol for OAH with TI and 1.9 kcal mol for OAMe with HREM-BAR. Our best results for OAMe were obtained for systems with ionic concentrations corresponding to the ionic strength of the experimental solution. The most problematic system contains a halogenated guest. Our attempt to model the σ-hole of the bromine using a constrained off-site point charge, does not improve results. We use results from molecular dynamics simulations to argue that the distinct binding affinities of this guest to OAH and OAMe are due to a difference in the flexibility of the host. We believe that the results of this extensive analysis of host-guest complexes will help improve the protocol used in predicting binding affinities for larger systems, such as protein-substrate compounds.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10822-016-9965-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472255PMC
January 2017

Absolute binding free energy calculations of CBClip host-guest systems in the SAMPL5 blind challenge.

J Comput Aided Mol Des 2017 01 27;31(1):71-85. Epub 2016 Sep 27.

Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA.

Herein, we report the absolute binding free energy calculations of CBClip complexes in the SAMPL5 blind challenge. Initial conformations of CBClip complexes were obtained using docking and molecular dynamics simulations. Free energy calculations were performed using thermodynamic integration (TI) with soft-core potentials and Bennett's acceptance ratio (BAR) method based on a serial insertion scheme. We compared the results obtained with TI simulations with soft-core potentials and Hamiltonian replica exchange simulations with the serial insertion method combined with the BAR method. The results show that the difference between the two methods can be mainly attributed to the van der Waals free energies, suggesting that either the simulations used for TI or the simulations used for BAR, or both are not fully converged and the two sets of simulations may have sampled difference phase space regions. The penalty scores of force field parameters of the 10 guest molecules provided by CHARMM Generalized Force Field can be an indicator of the accuracy of binding free energy calculations. Among our submissions, the combination of docking and TI performed best, which yielded the root mean square deviation of 2.94 kcal/mol and an average unsigned error of 3.41 kcal/mol for the ten guest molecules. These values were best overall among all participants. However, our submissions had little correlation with experiments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10822-016-9968-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5241186PMC
January 2017

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

Proteins 2016 09 1;84 Suppl 1:323-48. Epub 2016 Jun 1.

Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030136PMC
September 2016

Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.

Methods Mol Biol 2016 ;1414:33-45

Department of Chemistry, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, Republic of Korea.

We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-1-4939-3569-7_3DOI Listing
December 2016
-->