The traditional computational modeling of protein structure, dynamics, and interactions remains difficult for many protein systems. It is mostly due to the size of protein conformational spaces and required simulation time scales that are still too large to be studied in atomistic detail. Lowering the level of protein representation from all-atom to coarse-grained opens up new possibilities for studying protein systems. In this review we provide an overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions. A more detailed description is given for applications of coarse-grained models suitable for efficient combinations with all-atom simulations in multiscale modeling strategies.

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Coarse-Grained Protein Models and Their Applications

We report the results of two fully automated structure prediction pipelines, "Zhang-Server" and "QUARK", in CASP13. The pipelines were built upon the C-I-TASSER and C-QUARK programs, which in turn are based on I-TASSER and QUARK but with three new modules: (a) a novel multiple sequence alignment (MSA) generation protocol to construct deep sequence-profiles for contact prediction; (b) an improved meta-method, NeBcon, which combines multiple contact predictors, including ResPRE that predicts contact-maps by coupling precision-matrices with deep residual convolutional neural-networks; and (c) an optimized contact potential to guide structure assembly simulations. For 50 CASP13 FM domains that lacked homologous templates, average TM-scores of the first models produced by C-I-TASSER and C-QUARK were 28% and 56% higher than those constructed by I-TASSER and QUARK, respectively. For the first time, contact-map predictions demonstrated usefulness on TBM domains with close homologous templates, where TM-scores of C-I-TASSER models were significantly higher than those of I-TASSER models with a P-value <.05. Detailed data analyses showed that the success of C-I-TASSER and C-QUARK was mainly due to the increased accuracy of deep-learning-based contact-maps, as well as the careful balance between sequence-based contact restraints, threading templates, and generic knowledge-based potentials. Nevertheless, challenges still remain for predicting quaternary structure of multi-domain proteins, due to the difficulties in domain partitioning and domain reassembly. In addition, contact prediction in terminal regions was often unsatisfactory due to the sparsity of MSAs. Development of new contact-based domain partitioning and assembly methods and training contact models on sparse MSAs may help address these issues.

Deep-learning contact-map guided protein structure prediction in CASP13.

Motivation Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction. Results In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 A distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length. Availability and implementation The web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11 and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/. Contact chengji@missouri.edu. Supplementary information Supplementary data are available at Bioinformatics online.

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DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

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A practical guide to large-scale docking.

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.

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

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 A to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc.

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Integration of QUARK and I‐TASSER for Ab Initio Protein Structure Prediction in CASP11

We report the structure prediction results of a new composite pipeline for template-based modeling (TBM) in the 11th CASP experiment. Starting from multiple structure templates identified by LOMETS based meta-threading programs, the QUARK ab initio folding program is extended to generate initial full-length models under strong constraints from template alignments. The final atomic models are then constructed by I-TASSER based fragment reassembly simulations, followed by the fragment-guided molecular dynamic simulation and the MQAP-based model selection. It was found that the inclusion of QUARK-TBM simulations as an intermediate modeling step could help improve the quality of the I-TASSER models for both Easy and Hard TBM targets. Overall, the average TM-score of the first I-TASSER model is 12% higher than that of the best LOMETS templates, with the RMSD in the same threading-aligned regions reduced from 5.8 to 4.7 A. Nevertheless, there are nearly 18% of TBM domains with the templates deteriorated by the structure assembly pipeline, which may be attributed to the errors of secondary structure and domain orientation predictions that propagate through and degrade the procedures of template identification and final model selections. To examine the record of progress, we made a retrospective report of the I-TASSER pipeline in the last five CASP experiments (CASP7-11). The data show no clear progress of the LOMETS threading programs over PSI-BLAST; but obvious progress on structural improvement relative to threading templates was witnessed in recent CASP experiments, which is probably attributed to the integration of the extended ab initio folding simulation with the threading assembly pipeline and the introduction of atomic-level structure refinements following the reduced modeling simulations. Proteins 2016; 84(Suppl 1):233-246. © 2015 Wiley Periodicals, Inc.

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Template‐based protein structure prediction in CASP11 and retrospect of I‐TASSER in the last decade

In framework of heavy-diquark$-$light-quark endowed with heavy-pair binding, we explore excited baryons $QQ^{\prime}q$ containing two heavy quarks ($QQ^{\prime}=cc,bb,bc$) by combining the method of Regge trajectory with the perturbative correction due to the heavy-pair interaction in baryons. Two Regge relations, one linear and the other nonlinear, are constructed semi-classically in the QCD string picture, and a mass scaling relation based on the heavy diquark-heavy antiquark symmetry are employed between the doubly heavy baryons and heavy mesons. We employ the ground-state mass estimates compatible with the observed doubly charmed baryon $\Xi_{cc}$ and spectra of the heavy quarkonia to determine the trajectory parameters and the binding energies of the heavy pair, and thereby compute the low-lying masses of the excited baryons $\Xi_{QQ^{\prime}}$ and $\Omega_{QQ^{\prime}}$ up to 2S and 1P excitations of the light quark and heavy diquark. The level spacings of heavy diquark excitations are found to be smaller generally than that of the light-quark excitations, in according with the nature of adiabatic expansion between the heavy and light-quark dynamics. 

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Low-lying doubly heavy baryons: Regge relation and mass scaling

In picture of heavy-diquark-light-quark endowed with heavy-pair binding, we explore the low-lying excited baryons Ξ QQ ′ and Ω QQ ′ containing two heavy quarks( QQ ′ = cc, bb, bc ) by applying relations of Regge trajectory and mass scaling to excitations of light quarks and heavy diquarks, respectively. Two Regge relations, one linear and the other nonlinear, are constructed based on semi-classical analysis of massive QCD string so that they contain the short-distance correction due to heavy-pair interaction when heavy diquark excited internally. Evaluating the short-distance corrections via the binding energy of the pairs estimated by that of the heavy-anti-heavy pairs in the observed doubly heavy mesons, we compute eﬀective masses of heavy diquarks in ground state, 2S- and 1P- waves and thereby predict low-lying mass spectra of the doubly-heavy baryons up to the 2S- and 1P- waves of the light quarks and heavy diquarks. The results indicate that the mass level spacings due to heavy diquark excitations are generally narrower in contrast with that due to the light quark excitations.

Low-lying mass spectra of excited doubly heavy baryons: Regge relation and mass scaling

We introduce an enhanced binding energy $B_{QQ}$ between heavy-heavy quarks $QQ$ and a flux-tube correction into the chromomagnetic interaction model to study nonstrange doubly-heavy tetraquarks $T_{QQ}$ ($Q=c,b)$. A simple relation in terms of baryon masses is proposed to estimate the binding energies $B_{QQ}$ and thereby map the flux-tube corrections in doubly-heavy tetraquarks $T_{cc}$, $T_{bb}$ and $T_{bc}$. Our computation via diagonalization of chromomagnetic interaction predicts the doubly charmed tetraquark $T_{cc}$ (in color rep. $\bar{3}_{c}\otimes 3_{c}$) and $T_{cc}^{\ast }$ (in $6_{c}\otimes \bar{6}_{c}$) with $IJ^{P}=01^{+}$ to have masses of $3879.2$ MeV and $4287.6$ MeV, respectively, with the former being in consistent with the measured mass $3874.7\pm 0.05$ MeV of the doubly charmed tetraquark $T_{cc}(1^{+})=cc\bar{u}\bar{d}$ discovered by LHCb. Further mass predictions are given of the doubly bottom tetraquarks $T_{bb}$ and the bottom-charmed tetraquarks $T_{bc}$ with $J^{P}=0^{+},1^{+},2^{+}$ and $I=0,1$. A chromomagnetical mixing between the color configurations $\bar{3}_{c}\otimes 3_{c}$ and $6_{c}\otimes \bar{6}_{c}$ is noted for the bottom-charmed states $T_{bc}$ with $IJ^{P}=01^{+}$ and $IJ^{P}=1(0^{+},1^{+})$.

Wenxuan Zhang

Papers

Integration of QUARK and I‐TASSER for Ab Initio Protein Structure Prediction in CASP11

Template‐based protein structure prediction in CASP11 and retrospect of I‐TASSER in the last decade

Low-lying doubly heavy baryons: Regge relation and mass scaling

Low-lying mass spectra of excited doubly heavy baryons: Regge relation and mass scaling

Doubly heavy tetraquarks: heavy quark bindings and chromomagnetically mixings