Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.

By K. Simons, Charles L. Kooperberg, Enoch S. Huang, David Baker. 1997

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Citations in the corpus

Toward optimal fragment generations for ab initio protein structure assembly (2012, background): “…an exhaustive search of the weighting parameters through a five-dimensional grid system and obtained w 1 5 2, w 2 5 6, w 3 5 2.5, w 4 5 12 and w 5 5 10, which resulted in the best average RMSD of fragments on 88 independent training proteins, which are also listed at…”
Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “Rosetta’s protocol to predict protein structures from their sequence alone runs in two phases: a low-resolution phase (abinitio) relying on fragment insertion (Simons et al., 1997) to search through a relatively smooth energy landscape (Simons et al.”
The Rosetta all-atom energy function for macromolecular modeling and design (2017, methodology): “31 Initially, it used statistical potentials describing individual residue environments and frequent residue-pair interactions derived from the Protein Databank (PDB).”
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019, methodology): “With a reference state, 27 this becomes the log likelihood ratio of the distances under the full conditional model and under a background model predicting the distance distributions P ( d ij j length) independent of sequence (trained with the same network architecture on the same set of proteins…”
Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011, neutral): “ROSETTA was initially written in FORTRAN 77 as two separate programs for protein structure prediction (Simons et al. , 1997) and for protein design (Kuhlman and Baker, 2000), merged, mechanically ported to C þþ , and refactored for several years thereafter.”
Protein sequence design by conformational landscape optimization (2021, neutral): “Energy landscapes obtained from ab initio folding calculations are approximate as the sampling procedure is biased, nonexhaustive, and employs an imperfect energy function (17), but these biases should not qualitatively change our conclusions about overall landscape shape.”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, background): “…al., 2005; Skolnick, 2006) indicate that knowledge-based statistical potentials (Lu et al., 2008; Miyazawa and Jernigan, 1985; Shen and Sali, 2006; Simons et al., 1997; Sippl, 1990; Tanaka and Scheraga, 1976; Zhang and Zhang, 2010; Zhou and Zhou, 2002) compare favorably to physics-based…”
LOMETS: A local meta-threading-server for protein structure prediction (2007, methodology): “These data can be easily used as constraints to guide the tertiary structure modeling procedures such as MODELLER (10), ROBETTA (11), TASSER (12,36).”
Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field (2012, background): “The average TM-score of the models by QUARK on the 145 benchmark proteins is 10% higher than that by Rosetta, one of the state-of-the-art algorithms in ab initio protein structure prediction.”
Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014, background): “This process is typically governed by the use of statistical constraints [7], force fields [8] and/or NMR spectra [9].”
Progress and challenges in protein structure prediction. (2008, methodology): “[38 ] first built low-resolution models by ROSETTA [40] using a fragment library enriched by the query-template alignment; the Cb-contact restraints were used to guide the assembly procedure.”
De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, background): “Finally, the novel conformation is executed once fragment assembly [59], [63] (see SF, available online) to generate a mutation conformation C mutant .”
Learning protein constitutive motifs from sequence data (2018, methodology): “We believe protein design with detailed computational modeling methods, such as Rosetta [54, 55], could be efficiently guided by our RBM-based approach, in much the same way protein folding greatly benefited from the inclusion of long-range contacts found by direct-coupling analysis [16, 56].”
Template‐based modeling and free modeling by I‐TASSER in CASP7 (2007, methodology): “17–21 Progress along all these directions is assessed in the CASP7 experiment under the categories of high accuracy (HA), template-based modeling (TBM), and free modeling (FM), respectively.”
Improved recognition of native‐like protein structures using a combination of sequence‐dependent and sequence‐independent features of proteins (1999, methodology): “7) suggests that ab initio folding simulations using the method of Simons et al. 12 and the new scoring function should yield better results with a broad range of small proteins provided that the secondary structure is known.”
3DRobot: automated generation of diverse and well-packed protein structure decoys (2016, background): “Availiablity and implementation: http://zhanglab.ccmb.med.umich.edu/3DRobot/ Contact: jiay@phy.ccnu.edu.cn; zhng@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.”
An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. (1998, methodology): “Since then, knowledge-based discriminatory functions have been used to validate experimentally determined protein structures (Lu ¨ thy et al., 1992; Sippl, 1993; MacArthur et al., 1994), for ab initio protein structure prediction (Sun, 1993; Simons et al., 1997 ), and have proven their worth in bon”