Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field

By Dong Xu, Yang Zhang. 2012

875 citations recorded by the source corpus.

Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template‐free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full‐length structure models are then assembled from fragments using replica‐exchange Monte Carlo simulations, which are guided by a composite knowledge‐based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one‐third cases of short proteins up to 100 residues. In the ninth community‐wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z‐score of global distance test‐total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Proteins 2012; © 2012 Wiley Periodicals, Inc.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics, AI for Physical Sciences

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Citations in the corpus

I-TASSER server: new development for protein structure and function predictions (2015, methodology): “The QUARK program has not yet been integrated into the I-TASSER server, which is available as an independent online server for ab initio folding targets at http://zhanglab.ccmb.med.umich.edu/ QUARK/.”
Toward optimal fragment generations for ab initio protein structure assembly (2012, background): “4L predicted contacts are selected for each of the three contact orders, that is, |i – j| in [6, 11], [12, 24], and >24, which result in 1.”
End-to-end differentiable learning of protein structure (2018, background): “, 2015), but protein structure pipelines continue to resemble the ways in which computers tackled vision and speech prior to deep learning, by having many human-engineered stages, each independently optimized (Xu and Zhang, 2012; Yang et al., 2015) (Figure 1).”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, background): “…methods are often not applicable to structure prediction tasks of interest, including orphan proteins, and thus for many novel proteins the field has turned to template-free, or de novo, folding approaches that predict 3D structures using the query sequence alone (Zhang, 2008; Xu and Zhang, 2012).”
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks (2017, methodology): “Successful de novo structure prediction methods, in the recent CASP experiments, have attributed much of their performance to the incorporation of predicted contacts (Ovchinnikov et al., 2016; Xu and Zhang, 2012; Zhang et al., 2016).”
UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling (2016, methodology): “, 2008) to perform conformational search from a continuous space that is free from discretized database-driven search strategies as employed in fragment assembly based structure prediction methods (Simons et al., 1997; Xu and Zhang, 2012).”
De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, methodology): “In QUARK [37], the distance proﬁle information is used as an energy term to improve the accuracy of prediction.”
Toward the solution of the protein structure prediction problem (2021, methodology): “For the cases with no or sparse spatial restraints, more advanced structural assembly and conformational searching approaches have been proven to be necessary due to the roughness of the physics-and knowledge-based energy landscapes (21, 23, 138).”
Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10 (2013, background): “In general, models in QUARK Server are generated by QUARK, with the LOMETS restraints incorporated for the Triv-ial (Triv) and Easy targets.”
Large-scale model quality assessment for improving protein tertiary structure prediction (2015, methodology): “Its total Z-score of first models is around 4 points higher than the best server predictor Zhang-Server, and its total Z-score of the best of five models is > 6 points higher than the best server predictor QUARK.”
Protein storytelling through physics (2020, positive): “For example, Rosetta ( 45 ), Quark ( 46 ), Dock 1 ( 47 ), Cluspro ( 48 ), Haddock ( 49 ), MODELLER ( 50 ), and IMP ( 51 ) do this to outstanding effect for certain structure-based computations.”