An evolutionary approach to folding small alpha-helical proteins that uses sequence information and an empirical guiding fitness function.

By J. Bowie, D. Eisenberg. 1994

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yang Zhang
2. Robin Pearce
3. Jianyi Yang
4. Santrupti Nerli
5. David Baker
6. Dong Xu
7. Jian Zhang
8. Mirco Michel

Citations in the corpus

• Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, methodology): “Using this strategy, methods based on fragment-based assembly of proteins can sometimes produce surprisingly accurate models, in particular for small a-helical proteins [1-3].”
• Toward the solution of the protein structure prediction problem (2021, methodology): “Besides molecular dynamics-based methods, many current FM approaches use fragment assembly, an idea pioneered by Bowie and Eisenberg in 1994 (135).”
• Statistical potentials for fold assessment (2002, unknown): “…the different residue or atom types to be exposed to the solvent, or applications, including the ab initio protein structure prediction (Sun 1993; Bowie and Eisenberg 1994; O’Donoghue and Nilges 1997; Chiu and Goldstein 2000; Tobi and Elber 2000), fold recognition or threading (Jones et al.…”
• Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. (1997, methodology): “Better results were obtained by Bowie & Eisenberg (1994), 33% of 200 folding trials yielded structures with less than 4 A Ê dme (distance matrix error) from the native state, but several parameters in the scoring function were trained on this protein.”
• Ab initio protein structure prediction: progress and prospects. (2001, background): “A common approach is to classify sites in proteins according to their degree of solvent exposure (either through the surface area or the number of nearby residues) (11) and to determine the frequencies of occurrence of the amino acids in each type of site.”
• Progress and challenges in protein structure prediction. (2008, background): “The best-known idea for free modeling is probably the one pioneered by Bowie and Eisenberg who assembled new tertiary structures using small fragments (mainly 9-mer) cut from other PDB proteins [43].”
• Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement (2011, background): “20,21 For I-TASSER,22– 24 although the reassembly of structural fragments excised from the threading alignments often results in significantly improved models, the quality of final models is still essentially dependent on that of threading templates identified in the PDB library.”
• Chemical shift-based methods in NMR structure determination (2018, background): “Bowie and Eisenberg pioneered the field of ab initio prediction with their concept of generating protein models from an assembly of short, overlapping backbone fragments derived from a structural database [56] and this idea laid the foundation for several early implementations of ab initio methods [”
• Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “One of the most effective methods for constructing FM models is fragment assembly, an idea originally pioneered by Bowie and Eisenberg in 1994 [11].”