Gyu Rie Lee

Protein Loop Modeling Using a New Hybrid Energy Function and Its Application to Modeling in Inaccurate Structural Environments

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

Effective protein model structure refinement by loop modeling and overall relaxation

Protein structures predicted by state‐of‐the‐art template‐based methods may still have errors when the template proteins are not similar enough to the target protein. Overall target structure may deviate from the template structures owing to differences in sequences. Structural information for some local regions such as loops may not be available when there are sequence insertions or deletions. Those structural aspects that originate from deviations from templates can be dealt with by ab initio structure refinement methods to further improve model accuracy. In the CASP11 refinement experiment, we tested three different refinement methods that utilize overall structure relaxation, loop modeling, and quality assessment of multiple initial structures. From this experiment, we conclude that the overall relaxation method can consistently improve model quality. Loop modeling is the most useful when the initial model structure is high quality, with GDT‐HA >60. The method that used multiple initial structures further refined the already refined models; the minor improvements with this method raise the issue of problem with the current energy function. Future research directions are also discussed. Proteins 2016; 84(Suppl 1):293–301.

Evaluation of GalaxyDock Based on the Community Structure-Activity Resource 2013 and 2014 Benchmark Studies.

We analyze the results of the GalaxyDock protein-ligand docking program in the two recent experiments of Community Structure-Activity Resource (CSAR), CSAR 2013 and 2014. GalaxyDock performs global optimization of a modified AutoDock3 energy function by employing the conformational space annealing method. The energy function of GalaxyDock is quite sensitive to atomic clashes. Such energy functions can be effective for sampling physically correct conformations but may not be effective for scoring when conformations are not fully optimized. In phase 1 of CSAR 2013, we successfully selected all four true binders of digoxigenin along with three false positives. However, the energy values were rather high due to insufficient optimization of the conformations docked to homology models. A posteriori relaxation of the model complex structures by GalaxyRefine improved the docking energy values and differentiated the true binders from the false positives better. In the scoring test of CSAR 2013 phase 2, we selected the best poses for each of the two targets. The results of CSAR 2013 phase 3 suggested that an improved method for generating initial conformations for GalaxyDock is necessary for targets involving bulky ligands. Finally, combining existing binding information with GalaxyDock energy-based optimization may be needed for more accurate binding affinity prediction.

Galaxy7TM: flexible GPCR–ligand docking by structure refinement

G-protein-coupled receptors (GPCRs) play important physiological roles related to signal transduction and form a major group of drug targets. Prediction of GPCR–ligand complex structures has therefore important implications to drug discovery. With previously available servers, it was only possible to first predict GPCR structures by homology modeling and then perform ligand docking on the model structures. However, model structures generated without explicit consideration of specific ligands of interest can be inaccurate because GPCR structures can be affected by ligand binding. The Galaxy7TM server, freely accessible at http://galaxy.seoklab.org/7TM, improves an input GPCR structure by simultaneous ligand docking and flexible structure refinement using GALAXY methods. The server shows better performance in both ligand docking and GPCR structure refinement than commonly used programs AutoDock Vina and Rosetta MPrelax, respectively.

Simultaneous refinement of inaccurate local regions and overall structure in the CASP12 protein model refinement experiment

Advances in protein model refinement techniques are required as diverse sources of protein structure information are available from low‐resolution experiments or informatics‐based computations such as cryo‐EM, NMR, homology models, or predicted residue contacts. Given semi‐reliable or incomplete structural information, structure quality of a protein model has to be improved by ab initio methods such as energy‐based simulation. In this study, we describe a new automatic refinement server method designed to improve locally inaccurate regions and overall structure simultaneously. Locally inaccurate regions may occur in protein structures due to non‐convergent or missing information in template structures used in homology modeling or due to intrinsic structural flexibilities not resolved by experimental techniques. However, such variable or dynamic regions often play important functional roles by participating in interactions with other biomolecules or in transitions between different functional states. The new refinement method introduced here utilizes diverse types of geometric operators which drive both local and global changes, and the effect of structure changes and relaxations are accumulated. This resulted in consistent refinement of both local and global structural features. Performance of this method in CASP12 is discussed.

Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4

The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015–2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers “computational + allosteric.” This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.

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

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.

Structural and functional characterization of the α-catenin·β-catenin binding interface in Caenorhabditis elegans reveals conserved requirements for cell-cell adhesion in metazoans

Stable tissue integrity during embryonic development relies on the function of the cadherin·catenin complex (CCC). The Caenorhabditis elegans CCC is a useful paradigm for analyzing in vivo requirements for specific interactions among the core components of the CCC, and it provides a unique opportunity to examine evolutionarily conserved mechanisms that govern the interaction between α- and β-catenin. HMP-1, unlike its mammalian homolog α-catenin, is constitutively monomeric, and its binding affinity for HMP-2/β-catenin is higher than that of α-catenin for β-catenin. A crystal structure shows that the HMP-1·HMP-2 complex forms a five-helical bundle structure distinct from the structure of the mammalian α-catenin·β-catenin complex. Deletion analysis based on the crystal structure shows that the first helix of HMP-1 is necessary for binding HMP-2 avidly in vitro and for efficient recruitment of HMP-1 to adherens junctions in embryos. HMP-2 Ser-47 and Tyr-69 flank its binding interface with HMP-1, and we show that phosphomimetic mutations at these two sites decrease binding affinity of HMP-1 to HMP-2 by 40–100-fold in vitro. In vivo experiments using HMP-2 S47E and Y69E mutants showed that they are unable to rescue hmp-2(zu364) mutants, suggesting that phosphorylation of HMP-2 on Ser-47 and Tyr-69 could be important for regulating CCC formation in C. elegans. Our data provide novel insights into how cadherin-dependent cell–cell adhesion is modulated in metazoans by conserved elements as well as features unique to specific organisms.

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Biophysical and functional characterization of Norrin signaling through Frizzled4

Significance Wnt signaling has a broad spectrum of effects on cellular physiology and diseases like cancer. Much of the specificity and regulation mechanism of the Frizzled (Fzd) family, the main receptor of the Wnt pathway, remains elusive, but exploiting the activation mechanism of Fzd is pivotal to understanding the Wnt signaling pathway. Here, by using biophysical and biochemical techniques, we identify the key conformational changes that occur as the Norrin ligand binds to Fzd4, and we show the functional implications of the involved regions. We also reveal that the linker region plays an important role in communicating ligand binding with cytoplasmic signaling. Our observations offer insight into the activation mechanism of the Fzd family and the regulation of the Wnt signaling pathway. Wnt signaling is initiated by Wnt ligand binding to the extracellular ligand binding domain, called the cysteine-rich domain (CRD), of a Frizzled (Fzd) receptor. Norrin, an atypical Fzd ligand, specifically interacts with Fzd4 to activate β-catenin–dependent canonical Wnt signaling. Much of the molecular basis that confers Norrin selectivity in binding to Fzd4 was revealed through the structural study of the Fzd4CRD–Norrin complex. However, how the ligand interaction, seemingly localized at the CRD, is transmitted across full-length Fzd4 to the cytoplasm remains largely unknown. Here, we show that a flexible linker domain, which connects the CRD to the transmembrane domain, plays an important role in Norrin signaling. The linker domain directly contributes to the high-affinity interaction between Fzd4 and Norrin as shown by ∼10-fold higher binding affinity of Fzd4CRD to Norrin in the presence of the linker. Swapping the Fzd4 linker with the Fzd5 linker resulted in the loss of Norrin signaling, suggesting the importance of the linker in ligand-specific cellular response. In addition, structural dynamics of Fzd4 associated with Norrin binding investigated by hydrogen/deuterium exchange MS revealed Norrin-induced conformational changes on the linker domain and the intracellular loop 3 (ICL3) region of Fzd4. Cell-based functional assays showed that linker deletion, L430A and L433A mutations at ICL3, and C-terminal tail truncation displayed reduced β-catenin–dependent signaling activity, indicating the functional significance of these sites. Together, our results provide functional and biochemical dissection of Fzd4 in Norrin signaling.