Protein Structure Prediction and Structural Genomics

By David Baker, Andrej Sali. 2001

1,655 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Toward the estimation of the absolute quality of individual protein structure models (2010, background): “Typical sources of errors range from misplaced side chains, incorrect loop conformations, backbone distortions, alignment errors, to choice of a template with incorrect fold (Baker and Sali, 2001; Bordoli et al., 2009; Koh et al., 2003).”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XII (2017, background): “Different objectives require different levels of accuracy 38,39 : at one extreme, identifying peptides that might be used as epitopes in eliciting an immune response requires only an approximate structure, while at the other, drug design will probably require multiple high-resolution ones.”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Progress and New directions in Round XI (2016, background): “50 Thus, one important criterion on which to judge a model is the degree to which it answers the relevant questions.”
Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields (2015, methodology): “Our method differs from Zhou’s deep learning method (denoted as ICML2014)36 in the following aspects: (1) our method places only input features in a visible layer and treats the SS labels as hidden states while Zhou’s method places both the input features and SS labels in a visible layer; (2) our me”
TM-align: a protein structure alignment algorithm based on the TM-score (2005, neutral): “Protein structure comparisons are employed in almost all branches of contemporary structural biology, ranging from protein fold classification (1,2), protein structure modeling (3) to structure-based protein function annotation (4,5).”
SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information (2014, methodology): “An indispensable part of every modelling procedure is the estimation of a protein model’s accuracy, directly providing the user with information regarding the range of its possible applications (11,20,21).”
JPred4: a protein secondary structure prediction server (2015, background): “…structure alone is not as useful as a full three-dimensional model, secondary structure predictions provide important constraints for fold-recognition techniques (13–17) as well as in homology modelling (18,19), ab initio (20–24) and constraint-based tertiary structure prediction methods (25–27).”
Protein threading using residue co-variation and deep learning (2018, background): “Computational protein structure prediction remains one of the most challenging problems in structural bioinformatics and has been extensively studied in the past decades (Baker and Sali, 2001; Bowie, et al., 1991; Dill and MacCallum, 2012; Jones, et al., 1992).”
Template-based protein structure modeling using the RaptorX web server (2012, methodology): “Current methods do, however, become unreliable when there are no homologs with solved structures in PDB or when templates under consideration are distant homologs 16 .”
LOMETS: A local meta-threading-server for protein structure prediction (2007, background): “Because of the differences in the available computer resources among different laboratories, it is difficult to quickly collect all the threading results from the individual servers, which influences its usefulness in the large-scale protein structure prediction (7,8).”
Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019, background): “Given the rapidly increasing gap between the number of known protein sequences and the number of known structures, the demand for high-resolution, computer-based protein structure prediction has risen dramatically [1,2].”
Ranking predicted protein structures with support vector regression (2007, methodology): “Scoring functions can be derived with one of the following three approaches: (1) physical potentials, (2) probability distribution-based potentials, and (3) machine learning-based scores.”
Toward better refinement of comparative models: Predicting loops in inexact environments (2008, background): “Two requirements for successful comparative model refinement are (1) efficient methods for sampling degrees of freedom that enable near-native configurations to be located from the starting structure; and (2) an energy function capable of identifying near-native conformations.”
EvoDesign: Designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function (2019, background): “This approach is, however, often limited by the inability of physical energy functions to accurately recapitulate inter-atomic interactions or recognize correct folds, which has also been manifested in various protein folding and structure prediction studies [9, 10].”