Protein Structure Prediction: Is It Useful?

By Yang Zhang. 2009

287 citations recorded by the source corpus.

Summary Computationally predicted three-dimensional structure of protein molecules has demonstrated the usefulness in many areas of biomedicine, ranging from approximate family assignments to precise drug screening. For nearly 40 years, however, the accuracy of the predicted models has been dictated by the availability of close structural templates. Progress has recently been achieved in refining low-resolution models closer to the native ones; this has been made possible by combining knowledge-based information from multiple sources of structural templates as well as by improving the energy funnel of physics-based force fields. Unfortunately, there has been no essential progress in the development of techniques for detecting remotely homologous templates and for predicting novel protein structures.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

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

In Materials Chemistry

1. Yang Zhang
2. Robin Pearce
3. Michael Feig
4. Jianyi Yang
5. Chengxin Zhang
6. Lim Heo
7. Yang Li
8. Wei Zheng

In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Jianyi Yang
4. Chengxin Zhang
5. Michael Feig
6. Yang Li
7. Wei Zheng
8. Eric W. Bell

Citations in the corpus

• I-TASSER server: new development for protein structure and function predictions (2015, background): “A live-bench based platform has been recently embarked to assess the ligand-binding site prediction (15), which demonstrated the considerable accuracy and usefulness of automated protein function annotations (16).”
• Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, background): “1 Due to an increasing number of experimentally resolved protein structures and progress in template-based modeling methods, it has become possible to predict protein structures with high accuracy.”
• Conformation Dependence of Backbone Geometry in Proteins (2009, methodology): “…underlies the restraints used to guide protein structure refinement (e.g., Evans, 2007) and is also the basis of the rigid-geometry approximation used to simplify model building in the most successful structure-prediction packages such as Rosetta and I-TASSER (Rohl et al., 2004; Zhang, 2009).”
• 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].”
• I-TASSER: a unified platform for automated protein structure and function prediction (2010, methodology): “The biological usefulness of the predicted protein models relies on the accuracy of the structure predictio”
• Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, background): “26 In this regard, it is of particular importance to identify the trustable regions of the predicted models by estimating the residue-level quality.”
• SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models (2010, background): “This limitation likely arises from the vast conformational space of the backbone and the difficulties with high-resolution refinement of protein structures [46,47].”
• ResQ: An approach to unified estimation of B-factor and residue-specific error in protein structure prediction (2015, methodology): “1     ResQ: An approach to unified estimation of B-factor and residue-specific error in protein structure prediction Jianyi Yang, Yan Wang, Yang Zhang”
• Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment (2013, background): “These represent a set of models in a typical range of accuracy of the template-based protein structure predictions (Zhang, 2009).”
• Computational prediction of secondary and supersecondary structures. (2013, background): “5 million nonredundant protein sequences in the RefSeq database (5), and the fact that experimental determination of protein structure is relatively expensive and time-consuming and cannot keep up with the rapid accumulation of the sequence data (6-9).”