2017
DOI: 10.1016/j.csbj.2017.01.011
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Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

Abstract: Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale ci… Show more

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Cited by 21 publications
(15 citation statements)
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“…This idea has been used in a generalized mixture model to iteratively update the smoothing parameter . Schellhase and Kauermann and Najibi et al extended this approach for density estimation. We borrow their formulation and use the parameter estimates in the i t h step to update the tuning parameter, ie, trueλ^i+1, through trueλ^i+11=trace-2pt()trueΘ^iboldRtrueΘ^isans-serifdffalse(trueλ^ifalse)false(a1false), where a is the order of the differences (derivative) used in the penalty matrix R (see Section 2.5).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…This idea has been used in a generalized mixture model to iteratively update the smoothing parameter . Schellhase and Kauermann and Najibi et al extended this approach for density estimation. We borrow their formulation and use the parameter estimates in the i t h step to update the tuning parameter, ie, trueλ^i+1, through trueλ^i+11=trace-2pt()trueΘ^iboldRtrueΘ^isans-serifdffalse(trueλ^ifalse)false(a1false), where a is the order of the differences (derivative) used in the penalty matrix R (see Section 2.5).…”
Section: Methodsmentioning
confidence: 99%
“…From what we have seen in the implementation of the new procedure, updating the tuning parameter within the Newton‐Raphson iterations, on average, does not increase the number of iterations required for convergence. Therefore, the new procedure obtains the final result p times faster than the old procedure, where p is the number of λ s used in the grid search to minimize the AIC (see the work of Najibi et al for details).…”
Section: Methodsmentioning
confidence: 99%
“…() by using non‐parametric density estimation, focusing on modelling loop regions of a protein; see also Najibi et al . (). Motivated by direct modelling of the evolution of a protein, Golden et al .…”
Section: Mathematical Formulation: Unlabelled Shape Analysismentioning
confidence: 99%
“…For example, Boomsma et al (2008) used hidden Markov models as generative models for protein structure. Lennox et al (2009) used Dirichlet process mixtures for density estimation of the distribution of dihedral angles: a problem also considered by Maadooliat et al (2016) by using non-parametric density estimation, focusing on modelling loop regions of a protein; see also Najibi et al (2017). Motivated by direct modelling of the evolution of a protein, Golden et al (2017) described the shape of a protein as a sequence of dihedral angles on the torus; their model captures dependences between sequence and structure evolution through a diffusion process on the torus.…”
Section: Mathematical Formulation: Unlabelled Shape Analysismentioning
confidence: 99%
“…The prediction accuracies have improved gradually over the years [4, 5, 6] and are approaching the ones estimated from NMR chemical shifts [6]. It has been demonstrated that the predicted torsion angles are useful to improve ab initio structure prediction [7, 8], sequence alignment [9], secondary structure prediction [10, 11], template-based tertiary structure prediction and fold recognition [12, 13, 14], protein structure classification and loop modeling [15,16], and protein comformation sampling [17].…”
Section: Introductionmentioning
confidence: 99%