Inflation and Contact Predictions of Composite Tire Shell Carcass

1987 ◽  
Vol 24 (12) ◽  
pp. 849-855 ◽  
Author(s):  
Sunil Saigal ◽  
T. Y. Yang ◽  
W. Soedel
Keyword(s):  
Contact Predictions

Use of spatial, frequency and curvature attributes for reservoir, fluid and contact predictions

2008 ◽  
Author(s):  
Willem Flierman ◽  
Jan Gabe van der Weide ◽  
Andries Wever ◽  
Friso Brouwer ◽  
Arnaud Huck
Keyword(s):  
Spatial Frequency ◽  
Reservoir Fluid ◽  
Contact Predictions

scholarly journals Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age

2017 ◽  
Vol 86 ◽  
pp. 51-66 ◽  
Author(s):  
Joerg Schaarschmidt ◽  
Bohdan Monastyrskyy ◽  
Andriy Kryshtafovych ◽  
Alexandre M.J.J. Bonvin
Keyword(s):  
Deep Learning ◽  
Coming Of Age ◽  
Contact Predictions

scholarly journals Large-scale structure prediction by improved contact predictions and model quality assessment

2017 ◽  
Author(s):  
Mirco Michel ◽  
David Menéndez Hurtado ◽  
Karolis Uziela ◽  
Arne Elofsson
Keyword(s):  
Structure Prediction ◽  
Large Scale ◽  
Supplementary Information ◽  
Model Quality ◽  
Contact Maps ◽  
Folding Algorithm ◽  
Unknown Structure ◽  
Supplementary Material ◽  
Direct Coupling Analysis ◽  
Contact Predictions

AbstractMotivationAccurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known.ResultsWe present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these 415 have not been reported before.AvailabilityDatasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/. All programs used here are freely [email protected] informationNo supplementary data

scholarly journals Residue contacts predicted by evolutionary covariance extend the application ofab initiomolecular replacement to larger and more challenging protein folds

IUCrJ ◽  
2016 ◽  
Vol 3 (4) ◽  
pp. 259-270 ◽  
Author(s):  
Felix Simkovic ◽  
Jens M. H. Thomas ◽  
Ronan M. Keegan ◽  
Martyn D. Winn ◽  
Olga Mayans ◽  
...  
Keyword(s):  
Ab Initio ◽  
Structure Prediction ◽  
Sequence Information ◽  
Protein Targets ◽  
Residue Contact ◽  
Residue Contacts ◽  
Structure Solution ◽  
Improved Performance ◽  
Model Ensembles ◽  
Contact Predictions

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurateab initio(non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here,AMPLE, an MR pipeline that assembles search-model ensembles fromab initiostructure predictions (`decoys'), is employed to assess the value of contact-assistedab initiomodels to the crystallographer. It is demonstrated that evolutionary covariance-derived residue–residue contact predictions improve the quality ofab initiomodels and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simpleRosettadecoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

scholarly journals Large-scale structure prediction by improved contact predictions and model quality assessment

2017 ◽  
Vol 33 (14) ◽  
pp. i23-i29 ◽  
Author(s):  
Mirco Michel ◽  
David Menéndez Hurtado ◽  
Karolis Uziela ◽  
Arne Elofsson
Keyword(s):  
Quality Assessment ◽  
Structure Prediction ◽  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Model Quality ◽  
Model Quality Assessment ◽  
Contact Predictions

scholarly journals Accurate contact-based modelling of repeat proteins predicts the structure of Curlin and SPW repeats

2019 ◽  
Author(s):  
Claudio Bassot ◽  
Arne Elofsson
Keyword(s):  
Deep Learning ◽  
High Accuracy ◽  
Unique Sequence ◽  
Amino Acid Sequences ◽  
Primary Sequence ◽  
Structure Information ◽  
Repeat Proteins ◽  
Eukaryotic Proteomes ◽  
Direct Coupling Analysis ◽  
Contact Predictions

AbstractRepeat proteins are an abundant class in eukaryotic proteomes. They are involved in many eukaryotic specific functions, including signalling. For many of these families, the structure is not known. Recently, it has been shown that the structure of many protein families can be predicted by using contact predictions from direct coupling analysis and deep learning. However, their unique sequence features present in repeat proteins is a challenge for contact predictions DCA-methods. Here, we show that using the deep learning-based PconsC4 is more effective for predicting both intra and interunit contacts among a comprehensive set of repeat proteins. In a benchmark dataset of 819 repeat proteins about one third can be correctly modelled and among 51 PFAM families lacking a protein structure, we produce models of five families with estimated high accuracy.Author SummaryRepeat proteins are widespread among organisms and particularly abundant in eukaryotic proteomes. Their primary sequence present repetition in the amino acid sequences that origin structures with repeated folds/domains. Although the repeated units are easy to be recognized in primary sequence, often structure information are missing. Here we used contact prediction for predicting the structure of repeats protein directly from their primary sequences. We benchmark our method on a dataset comprehensive of all the known repeated structures. We evaluate the contact predictions and the obtained models set for different classes of proteins and different lengths of the target, and we benchmark the quality assessment of the models on repeats proteins. Finally, we applied the methods on the repeat PFAM families missing of resolved structures, five of them modelled with high accuracy.

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