Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

Skolnick, Jeffrey; Zhou, Hongyi; Gao, Mu

doi:10.1016/j.sbi.2013.01.009

Cited by 26 publications

(15 citation statements)

References 68 publications

(103 reference statements)

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“…The usefulness of homology models in structure based virtual screening has been demonstrated in various retrospective analyses on a broad variety of different targets (Costanzi, 2013; Kairys et al, 2006; McGovern and Shoichet, 2003; Oshiro et al, 2004; Skolnick et al, 2013). One class of proteins with particular interest for structure based drug discovery are GPCRs, where recent advances in experimental structure determination have brought a wide range of receptors within range for comparative modeling techniques (Carlsson et al, 2011; Kobilka and Schertler, 2008).…”

Section: Application Of Homology Models In Structure-based Drug Discomentioning

confidence: 99%

Protein Modeling: What Happened to the “Protein Structure Gap”?

2013

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Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing vision in structural biology as it holds the promise to bypass part of the laborious process of experimental structure solution. Over the last two decades, a paradigm shift has occurred: starting from a situation where the “structure knowledge gap” between the huge number of protein sequences and small number of known structures has hampered the widespread use of structure-based approaches in life science research, today some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. With the scientific focus of interest moving towards larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows studying large and complex molecular machines. Computational modeling and prediction techniques are still facing a number of challenges which hamper the more widespread use by the non-expert scientist. For example, it is often difficult to convey the underlying assumptions of a computational technique, as well as the expected accuracy and structural variability of a specific model. However, these aspects are crucial to understand the limitations of a model, and to decide which interpretations and conclusions can be supported.

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Section: Application Of Homology Models In Structure-based Drug Discomentioning

confidence: 99%

Protein Modeling: What Happened to the “Protein Structure Gap”?

2013

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“…An important question asked by protein modelers, and biological researchers in general, is: what kind of structural information can be obtained from the docking of protein models and what is the reliability of such predictions? Protein models were shown to have significant utility in studies of protein–ligand interactions and characterization of functional sites . Protein–protein docking of models by information driven approach was validated on a set of CAPRI targets .…”

Section: Introductionmentioning

confidence: 99%

“…Protein models were shown to have significant utility in studies of protein-ligand interactions and characterization of functional sites. [13][14][15] Protein-protein docking of models by information driven approach was validated on a set of CAPRI targets. 16 High-resolution free docking was recently tested on a diverse set of protein models to reveal that meaningful predictions can be obtained even for models with significant distortions, although at significantly lower success rates.…”

Section: Introductionmentioning

confidence: 99%

Modeling complexes of modeled proteins

2016

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Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such “double” modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å Cα RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5 – 6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking.

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“…However, this situation is changing and homology modeling is currently making structural information available for large numbers of proteins [3]. Moreover, it has been shown that modeled proteins can be effectively used to annotate function [4,5,6,7]. …”

Section: Introductionmentioning

confidence: 99%

Template-based prediction of protein function

Petrey

Chen

Deng

et al. 2015

Current Opinion in Structural Biology

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We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that can’t be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.

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Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

Cited by 26 publications

References 68 publications

Protein Modeling: What Happened to the “Protein Structure Gap”?

Protein Modeling: What Happened to the “Protein Structure Gap”?

Modeling complexes of modeled proteins

Template-based prediction of protein function

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