Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

Khoury, George; Tamamis, Phanourios; Pinnaduwage, Neesha; Smadbeck, James B.; Kieslich, Chris A.; Floudas, Christodoulos A.

doi:10.1002/prot.24459

Cited by 21 publications

(47 citation statements)

References 69 publications

(130 reference statements)

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“…Herein we describe the Princeton_TIGRESS 2.0 protocol. The overall method progression is similar to Princeton_TIGRESS with enhancements to the SVM‐driven classification step and the final MD refinement step. The final method is shown in Figure with the changes highlighted.…”

Section: Methodsmentioning

confidence: 99%

“…Geometric constraint derivation follows the procedure of the original Princeton_TIGRESS protocol . As the input structures can come from dozens of different servers, sources, and methods, each with their own scoring/energy functions, rotamer libraries, and van der Waal radii definitions, it is very important to standardize the input.…”

Section: Methodsmentioning

confidence: 99%

“…Initial structure reconstruction follows the procedure described in the original Princeton_TIGRESS protocol . Initial structures are reconstructed by solving feasibility problems using a torsion angle molecular dynamics (TAD) simulated annealing protocol in CYANA 2.1.3 .…”

Section: Methodsmentioning

confidence: 99%

“…Each of the 200 structures generated by CYANA undergo FastRelax to generate 10 models per input structure, totalling 2200 candidate structures. We utilize the score13_env_hb energy function, the ‐relax:thorough flag, which does a larger number of cycles (15) than the default number of cycles (5), and the ‐relax:constrain_relax_to_start_coords flag to disallow large movements from the CYANA reconstructed structure . The FastRelax cycles run side‐chain repacking and minimization while varying the repulsive weight in the forcefield, causing compactions and expansions of the structure…”

Section: Methodsmentioning

confidence: 99%

“…Methods to perform operations to improve model accuracy have been developed and span the entire gamut of sampling techniques including molecular dynamics (for example, FEIG, GalaxyRefine, Kiharalab, LEER), Monte Carlo (for example, Rosetta), anisotropic network models, local (KoBa MIN , 3D refine ) or global optimization, and hybrid methods that combine or utilize the previous techniques in a new way (for example, Princeton_TIGRESS, Seok‐server, BAKER‐REFINESERVER, Schroderlab). These methods also draw from the superset of physics‐based (for example, CHARMM, DREIDING, GROMOS, OPLS‐AA, AMBER, ECEPP), knowledge‐based (for example, dDFIRE, RWPlus, GOAP), or hybrid (for example, Rosetta) forcefields and scoring functions.…”

Section: Introductionmentioning

confidence: 99%

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Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double‐blind predictions during the CASP11 experiment

et al. 2017

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Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during the last four CASP experiments, a majority of the methods continue to degrade models rather than improve them. Princeton_TIGRESS (Khoury et al., Proteins 2014;82:794-814) was developed previously and utilizes separate sampling and selection stages involving Monte Carlo and molecular dynamics simulations and classification using an SVM predictor. The initial implementation was shown to consistently refine protein structures 76% of the time in our own internal benchmarking on CASP 7-10 targets. In this work, we improved the sampling and selection stages and tested the method in blind predictions during CASP11. We added a decomposition of physics-based and hybrid energy functions, as well as a coordinate-free representation of the protein structure through distance-binning Cα-Cα distances to capture fine-grained movements. We performed parameter estimation to optimize the adjustable SVM parameters to maximize precision while balancing sensitivity and specificity across all cross-validated data sets, finding enrichment in our ability to select models from the populations of similar decoys generated for targets in CASPs 7-10. The MD stage was enhanced such that larger structures could be further refined. Among refinement methods that are currently implemented as web-servers, Princeton_TIGRESS 2.0 demonstrated the most consistent and most substantial net refinement in blind predictions during CASP11. The enhanced refinement protocol Princeton_TIGRESS 2.0 is freely available as a web server at http://atlas.engr.tamu.edu/refinement/. Proteins 2017; 85:1078-1098. © 2017 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double‐blind predictions during the CASP11 experiment

et al. 2017

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CASP11 refinement experiments with ROSETTA

2015

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We report new Rosetta-based approaches to tackling the major issues that confound protein structure refinement, and the testing of these approaches in the CASP11 experiment. Automated refinement protocols were developed that integrate a range of sampling methods using parallel computation and multi-objective optimization. In CASP11, we used a more aggressive large scale structure rebuilding approach for poor starting models, and a less aggressive local rebuilding plus core refinement approach for starting models likely to be closer to the native structure. The more incorrectly modeled a structure was predicted to be, the more it was allowed to vary during refinement. The CASP11 experiment revealed strengths and weaknesses of the approaches: the high-resolution strategy incorporating local rebuilding with core refinement consistently improved starting structures, while the low-resolution strategy incorporating the reconstruction of large parts of the structures improved starting models in some cases but often considerably worsened them, largely because of model selection issues. Overall, the results suggest the high-resolution refinement protocol is a promising method orthogonal to other approaches, while the low-resolution refinement method clearly requires further development.

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Template‐free modeling by LEE and LEER in CASP11

et al. 2015

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For the template-free modeling of human targets of CASP11, we utilized two of our modeling protocols, LEE and LEER. The LEE protocol took CASP11-released server models as the input and used some of them as templates for 3D (threedimensional) modeling. The template selection procedure was based on the clustering of the server models aided by a community detection method of a server-model network. Restraining energy terms generated from the selected templates together with physical and statistical energy terms were used to build 3D models. Side-chains of the 3D models were rebuilt using target-specific consensus side-chain library along with the SCWRL4 rotamer library, which completed the LEE protocol. The first success factor of the LEE protocol was due to efficient server model screening. The average backbone accuracy of selected server models was similar to that of top 30% server models. The second factor was that a proper energy function along with our optimization method guided us, so that we successfully generated better quality models than the input template models. In 10 out of 24 cases, better backbone structures than the best of input template structures were generated. LEE models were further refined by performing restrained molecular dynamics simulations to generate LEER models. CASP11 results indicate that LEE models were better than the average template models in terms of both backbone structures and side-chain orientations. LEER models were of improved physical realism and stereo-chemistry compared to LEE models, and they were comparable to LEE models in the backbone accuracy.

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Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

Cited by 21 publications

References 69 publications

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double‐blind predictions during the CASP11 experiment

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double‐blind predictions during the CASP11 experiment

CASP11 refinement experiments with ROSETTA

Template‐free modeling by LEE and LEER in CASP11

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