ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence.

By John L. Klepeis, Christodoulos A. Floudas. 2003

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In Molecular Biology

Citations in the corpus

Toward optimal fragment generations for ab initio protein structure assembly (2012, background): “For long-range contacts, fragments in the range of [9, 20] have the best accuracy.”
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, methodology): “utilize the information, that can be inferred from the structure of a contact map using advanced machine learning methods including deep learning approaches [22-26] or constraint satisfaction of rules describing protein like contacts maps using linear programming [27].”
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, methodology): “In addition, a couple of methods also use physical constraints, such as PhyCMAP (Wang and Xu, 2013) and Astro-Fold (Klepeis and Floudas, 2003).”
Knowledge-based machine learning methods for macromolecular 3D structure prediction (2016, methodology): “Our method also differs from the first-principle method (e.g. Astro-Fold) in that we exploit evolutionary information from several aspects (e.g. MI, contextspecific distance potential and sequence profile) to significantly improve prediction accuracy.”
The protein folding problem. (2008, methodology): “Physical potential models have also been sampled using non-Boltzmann stochastic and deterministic optimization strategies (121, 174, 207, 220).”
Ab initio modeling of small proteins by iterative TASSER simulations (2007, unknown): “Historically, protein structure prediction was classified into three categories: (i) comparative modeling [3,4], (ii) threading [5-9], and (iii) ab initio folding [10-15].”
A review of recent advances in global optimization (2009, background): “Klepeis et al. (1998) and Klepeis and Floudas (1999a) proposed new global optimization approaches for the structure prediction of solvated peptides using area and volume accessible to the solvent models.”
Mixed-integer nonlinear optimization*† (2013, background): “, 2008), and protein folding (Klepeis and Floudas, 2003) are addressed using mixed-integer optimization.”
Predicting protein contact map using evolutionary and physical constraints by integer programming (2013, methodology): “Astro-Fold (Klepeis and Floudas, 2003) possibly is the first method that applies physical constraints, which implicitly imply the sparsity constraint used by PSICOV and Evfold, to contact map prediction.”