Template-based Modeling Research Articles

Protein alignment based on higher order conditional random fields for template-based modeling.

The query-template alignment of proteins is one of the most critical steps of template-based modeling methods used to predict the 3D structure of a query protein. This alignment can be interpreted as a temporal classification or structured prediction task and first order Conditional Random Fields have been proposed for protein alignment and proven to be rather successful. Some other popular structured prediction problems, such as speech or image classification, have gained from the use of higher order Conditional Random Fields due to the well known higher order correlations that exist between their labels and features. In this paper, we propose and describe the use of higher order Conditional Random Fields for query-template protein alignment. The experiments carried out on different public datasets validate our proposal, especially on distantly-related protein pairs which are the most difficult to align.

Cloning, overexpression, and purification of a gene of unknown function of prophage loci from ‘Candidatus Liberibacter asiaticus,’ the destructive bacterial pathogen of huanglongbing disease in citrus plants

Citrus Huanglongbing (HLB) or citrus greening is one of the most destructive diseases affecting citrus industry worldwide. The causal agent in Asia is a phloem-limited, Gram-negative bacterium, 'Candidatus Liberibacter asiaticus' (CLas). Within the genome of CLas lies prophage regions, classified as Type-A, B, C, and D. In particular, Type-D has been indicated to correlate with the blotchy-mottle symptoms of citrus trees. Here we reported the cloning, overexpression, and purification of the ORF1, an open reading frame from the partial Type-D region of CLas obtained from an infected lime tree (Citrus aurantifolia Swingle). Overexpression of the ORF1 was toxic to the E. coli BL21(DE3), and the transient expression of ORF1 in Arabidopsis seedlings by Agrobacterium-mediated transformation exhibited rapid and total chlorosis of the seedlings within two days post-transformation. The native-PAGE of the purified protein showed multiple bands, indicative of various conformations in solution. The ESI-TOF mass spectrum confirmed the molecular weight of the purified ORF1 to be 15,364.3150 Da, corresponding to the [M+1]+ of the ORF1 without an N-terminal methionine. The protein predominantly consisted of α-helix as evidenced by circular dichroism (CD), and the transition toward random coil structure upon heating was reversible. The template-based modeling (I-TASSER) of the ORF1 indicated eight α-helices connected through variable loops. The simulated CD spectrum, generated from the atomic coordinates of the I-TASSER model, was notably similar to the experimental spectrum. Our report offers the basis for understanding the contributions of genes within Type-D prophage region toward the disease pathogenicity of citrus HLB.

Characterization of Two Endo-β-1, 4-Xylanases from Myceliophthora thermophila and Their Saccharification Efficiencies, Synergistic with Commercial Cellulase.

The xylanases with high specific activity and resistance to harsh conditions are of high practical value for biomass utilization. In the present study, two new GH11 xylanase genes, MYCTH_56237 and MYCTH_49824, have been cloned from thermophilic fungus Myceliophthora thermophila and expressed in Pichia pastoris. The specific activities of purified xylanases reach approximately 1,533.7 and 1,412.5 U/mg, respectively. Based on multiple template-based homology modeling, the structures of their catalytic domains are predicted. Enzyme activity was more effective in 7.5 L fermentor, yielding 2,010.4 and 2,004.2 U/mL, respectively. Both enzymes exhibit optimal activity at 60°C with pH of 6.0 and 7.0, respectively. Their activities are not affected by EDTA and an array of metal ions. The kinetic constants have been determined for MYCTH_56237 (Km = 8.80 mg/mL, Vmax = 2,380 U/mg) and MYCTH_49824 (Km = 5.67 mg/mL, Vmax = 1,750 U/mg). More importantly, both xylanases significantly cooperate with the commercial cellulase Celluclast 1.5 L in terms of the saccharification efficiency. All these biochemical properties of the xylanases offer practical potential for future applications.

DeepSF: deep convolutional neural network for mapping protein sequences to folds.

MotivationProtein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice.ResultsWe develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein sequence into one of 1195 known folds, which is useful for both fold recognition and the study of sequence–structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and maps it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding an average classification accuracy of 75.3%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 73.0%. We compare our method with a top profile–profile alignment method—HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 12.63–26.32% higher than HHSearch on template-free modeling targets and 3.39–17.09% higher on hard template-based modeling targets for top 1, 5 and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Availability and implementationThe DeepSF server is publicly available at: http://iris.rnet.missouri.edu/DeepSF/.Supplementary information Supplementary data are available at Bioinformatics online.

Template-based quaternary structure prediction of proteins using enhanced profile-profile alignments.

Proteins often exist as their multimeric forms when they function as so‐called biological assemblies consisting of the specific number and arrangement of protein subunits. Consequently, elucidating biological assemblies is necessary to improve understanding of protein function. Template‐Based Modeling (TBM), based on known protein structures, has been used widely for protein structure prediction. Actually, TBM has become an increasingly useful approach in recent years because of the increased amounts of information related to protein amino acid sequences and three‐dimensional structures. An apparently similar situation exists for biological assembly structure prediction as protein complex structures in the PDB increase, although the inference of biological assemblies is not a trivial task. Many methods using TBM, including ours, have been developed for protein structure prediction. Using enhanced profile–profile alignments, we participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12), as the FONT team (Group # 480). Herein, we present experimental procedures and results of retrospective analyses using our approach for the Quaternary Structure Prediction category of CASP12. We performed profile–profile alignments of several types, based on FORTE, our profile–profile alignment algorithm, to identify suitable templates. Results show that these alignment results enable us to find templates in almost all possible cases. Moreover, we have come to understand the necessity of developing a model selection method that provides improved accuracy. Results also demonstrate that, to some extent, finding templates of protein complexes is useful even for MEDIUM and HARD assembly prediction.

Protein structure modeling and refinement by global optimization in CASP12.

For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded.

Review of Patient-Specific Vascular Modeling: Template-Based Isogeometric Framework and the Case for CAD

We review the literature on patient-specific vascular modeling, with particular attention paid to three-dimensional arterial networks. Patient-specific vascular modeling typically involves three main steps: image processing, analysis suitable model generation, and computational analysis. Analysis suitable model generation techniques that are currently utilized suffer from several difficulties and complications, which often necessitate manual intervention and crude approximations. Because the modeling pipeline spans multiple disciplines, the benefits of integrating a computer-aided design (CAD) component for the geometric modeling tasks has been largely overlooked. Upon completion of our review, we adopt this philosophy and present a CAD-integrated template-based modeling framework that streamlines the construction of solid non-uniform rational B-spline vascular models for performing isogeometric finite element analysis. Examples of arterial models for mouse and human circles of Willis and a porcine coronary tree are presented.

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

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.

Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successfully folds three medium-size FM targets that have more than 150 residues, even though the interplay between the two pipelines still awaits further optimization. Newly developed sequence-based contact prediction by NeBcon plays a critical role to enhance the quality of models, particularly for FM targets, by the new pipelines. The inclusion of NeBcon predicted contacts as restraints in the QUARK simulations results in an average TM-score of 0.41 for the best in top five predicted models, which is 37% higher than that by the QUARK simulations without contacts. In particular, there are seven targets that are converted from non-foldable to foldable (TM-score >0.5) due to the use of contact restraints in the simulations. Another additional feature in the current pipelines is the local structure quality prediction by ResQ, which provides a robust residue-level modeling error estimation. Despite the success, significant challenges still remain in ab initio modeling of multi-domain proteins and folding of β-proteins with complicated topologies bound by long-range strand-strand interactions. Improvements on domain boundary and long-range contact prediction, as well as optimal use of the predicted contacts and multiple threading alignments, are critical to address these issues seen in the CASP12 experiment.

Definition and classification of evaluation units for tertiary structure prediction in CASP12 facilitated through semi-automated metrics.

For assessment purposes, CASP targets are split into evaluation units. We herein present the official definition of CASP12 evaluation units (EUs) and their classification into difficulty categories. Each target can be evaluated as one EU (the whole target) or/and several EUs (separate structural domains or groups of structural domains). The specific scenario for a target split is determined by the domain organization of available templates, the difference in server performance on separate domains versus combination of the domains, and visual inspection. In the end, 71 targets were split into 96 EUs. Classification of the EUs into difficulty categories was done semi-automatically with the assistance of metrics provided by the Prediction Center. These metrics account for sequence and structural similarities of the EUs to potential structural templates from the Protein Data Bank, and for the baseline performance of automated server predictions. The metrics readily separate the 96 EUs into 38 EUs that should be straightforward for template-based modeling (TBM) and 39 that are expected to be hard for homology modeling and are thus left for free modeling (FM). The remaining 19 borderline evaluation units were dubbed FM/TBM, and were inspected case by case. The article also overviews structural and evolutionary features of selected targets relevant to our accompanying article presenting the assessment of FM and FM/TBM predictions, and overviews structural features of the hardest evaluation units from the FM category. We finally suggest improvements for the EU definition and classification procedures.

Homology modeling in a dynamical world.

A key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.

Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions.

We report our group's performance for protein-protein complex structure prediction and scoring in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), an objective assessment of protein-protein complex modeling. We demonstrated noticeable improvement in both prediction and scoring compared to previous rounds of CAPRI, with our human predictor group near the top of the rankings and our server scorer group at the top. This is the first time in CAPRI that a server has been the top scorer group. To predict protein-protein complex structures, we used both multi-chain template-based modeling (TBM) and our protein-protein docking program, LZerD. LZerD represents protein surfaces using 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. Because 3DZD are a soft representation of the protein surface, LZerD is tolerant to small conformational changes, making it well suited to docking unbound and TBM structures. The key to our improved performance in CAPRI Round 37 was to combine multi-chain TBM and docking. As opposed to our previous strategy of performing docking for all target complexes, we used TBM when multi-chain templates were available and docking otherwise. We also describe the combination of multiple scoring functions used by our server scorer group, which achieved the top rank for the scorer phase.

Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods

Our aim in CASP12 was to improve our Template-Based Modeling (TBM) methods through better model selection, accuracy self-estimate (ASE) scores and refinement. To meet this aim, we developed two new automated methods, which we used to score, rank, and improve upon the provided server models. Firstly, the ModFOLD6_rank method, for improved global Quality Assessment (QA), model ranking and the detection of local errors. Secondly, the ReFOLD method for fixing errors through iterative QA guided refinement. For our automated predictions we developed the IntFOLD4-TS protocol, which integrates the ModFOLD6_rank method for scoring the multiple-template models that were generated using a number of alternative sequence-structure alignments. Overall, our selection of top models and ASE scores using ModFOLD6_rank was an improvement on our previous approaches. In addition, it was worthwhile attempting to repair the detected errors in the top selected models using ReFOLD, which gave us an overall gain in performance. According to the assessors' formula, the IntFOLD4 server ranked 3rd/5th (average Z-score > 0.0/-2.0) on the server only targets, and our manual predictions (McGuffin group) ranked 1st/2nd (average Z-score > -2.0/0.0) compared to all other groups.

Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta.

SummaryFoldit Standalone is an interactive graphical interface to the Rosetta molecular modeling package. In contrast to most command-line or batch interactions with Rosetta, Foldit Standalone is designed to allow easy, real-time, direct manipulation of protein structures, while also giving access to the extensive power of Rosetta computations. Derived from the user interface of the scientific discovery game Foldit (itself based on Rosetta), Foldit Standalone has added more advanced features and removed the competitive game elements. Foldit Standalone was built from the ground up with a custom rendering and event engine, configurable visualizations and interactions driven by Rosetta. Foldit Standalone contains, among other features: electron density and contact map visualizations, multiple sequence alignment tools for template-based modeling, rigid body transformation controls, RosettaScripts support and an embedded Lua interpreter.Availability and ImplementationFoldit Standalone is available for download at https://fold.it/standalone, under the Rosetta license, which is free for academic and non-profit users. It is implemented in cross-platform C ++ and binary executables are available for Windows, macOS and Linux.

Exploring the binding properties and structural stability of an opsin in the chytrid Spizellomyces punctatus using comparative and molecular modeling.

BackgroundOpsin proteins are seven transmembrane receptor proteins which detect light. Opsins can be classified into two types and share little sequence identity: type 1, typically found in bacteria, and type 2, primarily characterized in metazoa. The type 2 opsins (Rhodopsins) are a subfamily of G-protein coupled receptors (GPCRs), a large and diverse class of seven transmembrane proteins and are generally restricted to metazoan lineages. Fungi use light receptors including opsins to sense the environment and transduce signals for developmental or metabolic changes. Opsins characterized in the Dikarya (Ascomycetes and Basidiomycetes) are of the type 1 bacteriorhodopsin family but the early diverging fungal lineages have not been as well surveyed. We identified by sequence similarity a rhodopsin-like GPCR in genomes of early diverging chytrids and examined the structural characteristics of this protein to assess its likelihood to be homologous to animal rhodopsins and bind similar chromophores.MethodsWe used template-based structure modeling, automated ligand docking, and molecular modeling to assess the structural and binding properties of an identified opsin-like protein found in Spizellomyces punctatus, a unicellular, flagellated species belonging to Chytridiomycota, one of the earliest diverging fungal lineages. We tested if the sequence and inferred structure were consistent with a solved crystal structure of a type 2 rhodopsin from the squid Todarodes pacificus.ResultsOur results indicate that the Spizellomyces opsin has structural characteristics consistent with functional animal type 2 rhodopsins and is capable of maintaining a stable structure when associated with the retinaldehyde chromophore, specifically the 9-cis-retinal isomer. Together, these results support further the homology of Spizellomyces opsins to animal type 2 rhodopsins.DiscussionThis represents the first test of structure/function relationship of a type 2 rhodopsin identified in early branching fungal lineages, and provides a foundation for future work exploring pathways and components of photoreception in early fungi.

EigenTHREADER: analogous protein fold recognition by efficient contact map threading.

MotivationProtein fold recognition when appropriate, evolutionarily-related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can be detected, fold recognition is a notoriously difficult problem (Moult et al., 2014). Here we present EigenTHREADER, a novel fold recognition method capable of identifying folds where no homologous structures can be identified. EigenTHREADER takes a query amino acid sequence, generates a map of intra-residue contacts, and then searches a library of contact maps of known structures. To allow the contact maps to be compared, we use eigenvector decomposition to resolve the principal eigenvectors these can then be aligned using standard dynamic programming algorithms. The approach is similar to the Al-Eigen approach of Di Lena et al. (2010), but with improvements made both to speed and accuracy. With this search strategy, EigenTHREADER does not depend directly on sequence homology between the target protein and entries in the fold library to generate models. This in turn enables EigenTHREADER to correctly identify analogous folds where little or no sequence homology information is.ResultsEigenTHREADER outperforms well-established fold recognition methods such as pGenTHREADER and HHSearch in terms of True Positive Rate in the difficult task of analogous fold recognition. This should allow template-based modelling to be extended to many new protein families that were previously intractable to homology based fold recognition methods.Availability and implementationAll code used to generate these results and the computational protocol can be downloaded from https://github.com/DanBuchan/eigen_scripts. EigenTHREADER, the benchmark code and the data this paper is based on can be downloaded from: http://bioinfadmin.cs.ucl.ac.uk/downloads/eigenTHREADER/.

ReFOLD: a server for the refinement of 3D protein models guided by accurate quality estimates

ReFOLD is a novel hybrid refinement server with integrated high performance global and local Accuracy Self Estimates (ASEs). The server attempts to identify and to fix likely errors in user supplied 3D models of proteins via successive rounds of refinement. The server is unique in providing output for multiple alternative refined models in a way that allows users to quickly visualize the key residue locations, which are likely to have been improved. This is important, as global refinement of a full chain model may not always be possible, whereas local regions, or individual domains, can often be much improved. Thus, users may easily compare the specific regions of the alternative refined models in which they are most interested e.g. key interaction sites or domains. ReFOLD was used to generate hundreds of alternative refined models for the CASP12 experiment, boosting our group's performance in the main tertiary structure prediction category. Our successful refinement of initial server models combined with our built-in ASEs were instrumental to our second place ranking on Template Based Modeling (TBM) and Free Modeling (FM)/TBM targets. The ReFOLD server is freely available at: http://www.reading.ac.uk/bioinf/ReFOLD/.

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

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.

Structure-Function Studies Link Class II Viral Fusogens with the Ancestral Gamete Fusion Protein HAP2

The conserved transmembrane protein, HAP2/GCS1, has been linked to fertility in a wide range of taxa and is hypothesized to be an ancient gamete fusogen. Using template-based structural homology modeling, we now show that the ectodomain of HAP2 orthologs from Tetrahymena thermophila and other species adopt a protein fold remarkably similar to the dengue virus E glycoprotein and related class II viral fusogens. To test the functional significance of this predicted structure, we developed a flow-cytometry-based assay that measures cytosolic exchange across the conjugation junction to rapidly probe the effects of HAP2 mutations in the Tetrahymena system. Using this assay, alterations to a region in and around a predicted "fusion loop" in T.thermophila HAP2 were found to abrogate membrane pore formation in mating cells. Consistent with this, a synthetic peptide corresponding to the HAP2 fusion loop was found to interact directly with model membranes in a variety of biophysical assays. These results raise interesting questions regarding the evolutionary relationships of class II membrane fusogens and harken back to a long-held argument that eukaryotic sex arose as the byproduct of selection for the horizontal transfer of a "selfish" genetic element from cell to cell via membrane fusion.

Template-Based Prediction of Protein-Peptide Interactions by Using GalaxyPepDock.

We introduce a web server called GalaxyPepDock that predicts protein-peptide interactions based on templates. With the continuously increasing size of the protein structure database, the probability of finding related proteins for templates is increasing. GalaxyPepDock takes a protein structure and a peptide sequence as input and returns protein-peptide complex structures as output. Templates for protein-peptide complex structures are selected from the structure database considering similarity to the target protein structure and to putative protein-peptide interactions as estimated by protein structure alignment and peptide sequence alignment. Complex structures are then built from the template structures by template-based modeling. By further structure refinement that performs energy-based optimization, structural aspects that are missing in the template structures or that are not compatible with the given protein and peptide are refined. During the refinement, flexibilities of both protein and peptide induced by binding are considered. The atomistic protein-peptide interactions predicted by GalaxyPepDock can offer important clues for designing new peptides with desired binding properties.