ASTRO‐FOLD 2.0: An enhanced framework for protein structure prediction

Subramani, A.; Wei, Yinan; Floudas, Christodoulos A.

doi:10.1002/aic.12669

Cited by 21 publications

(17 citation statements)

References 98 publications

(102 reference statements)

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“…Each structure decoy's features are assessed, normalized by sequence length (so they can be compared among dissimilar sequences) and taken as relative values from the template (feat decoyðiÞ 2feat template ) (again, so they can be compared among dissimilar sequences). In addition, the relative GDT_TS to the template was calculated and used as the final feature (7). Then, each structure's features are transformed so as to include the feature, its quadratic value, and every combination of two bilinear products to evaluate the response in terms of the interplay of the individual features.…”

Section: Support Vector Machines Filtering and Selectionmentioning

confidence: 99%

“…Next, LIBSVM 44 was used to train separate models using refinement targets from CASPs (7,8,9), CASPs (7,8,10), CASPs (7,9,10), and CASPs (8,9,10), to be tested respectively on CASP10, CASP9, CASP8, and CASP7. The training and testing was done in this way to ensure the training and testing sets are completely separated to test the utility of such a model rather than its predictive ability on any single dataset.…”

Section: Support Vector Machines Filtering and Selectionmentioning

confidence: 99%

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Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

et al. 2013

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Protein structure refinement aims to perform a set of operations given a predicted structure to improve model quality and accuracy with respect to the native in a blind fashion. Despite the numerous computational approaches to the protein refinement problem reported in the previous three CASPs, an overwhelming majority of methods degrade models rather than improve them. We initially developed a method tested using blind predictions during CASP10 which was officially ranked in 5th place among all methods in the refinement category. Here, we present Princeton_TIGRESS, which when benchmarked on all CASP 7,8,9, and 10 refinement targets, simultaneously increased GDT_TS 76% of the time with an average improvement of 0.83 GDT_TS points per structure. The method was additionally benchmarked on models produced by top performing three-dimensional structure prediction servers during CASP10. The robustness of the Princeton_TIGRESS protocol was also tested for different random seeds. We make the Princeton_TIGRESS refinement protocol freely available as a web server at http://atlas.princeton.edu/refinement. Using this protocol, one can consistently refine a prediction to help bridge the gap between a predicted structure and the actual native structure.

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Section: Support Vector Machines Filtering and Selectionmentioning

confidence: 99%

Section: Support Vector Machines Filtering and Selectionmentioning

confidence: 99%

Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

et al. 2013

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“…The first approach was implemented by Klepeis et al 15,16 This approach utilizes the protein structure prediction framework ASTRO-FOLD, 26,27,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] which is based on deterministic global optimization. This approach is not currently used in the implementation of Protein WISDOM since it is very computationally demanding.…”

confidence: 99%

Protein WISDOM: A Workbench for <em>In silico</em> <em>De novo</em> Design of BioMolecules

Smadbeck

Peterson

Khoury

et al. 2013

JoVE

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The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods. Video LinkThe video component of this article can be found at

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“…A tight distance bound corresponding to false predictions can potentially mislead the conformational search. The predicted contacts from COMSAT are used as distance restraints in tandem with protein 3D structure prediction method, ASTRO‐FOLD . In order to be consistent with the previous works, a contact definition of 14 Å between Cα‐Cα atoms for membrane proteins is used in this article.…”

Section: Methodsmentioning

confidence: 97%

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming

et al. 2016

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In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.

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ASTRO‐FOLD 2.0: An enhanced framework for protein structure prediction

Cited by 21 publications

References 98 publications

Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

Protein WISDOM: A Workbench for <em>In silico</em> <em>De novo</em> Design of BioMolecules

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming

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