VADAR: a web server for quantitative evaluation of protein structure quality

Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.

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Improving NMR protein structure quality by Rosetta refinement: A molecular replacement study

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.22229 ◽

2008 ◽

Vol 75 (1) ◽

pp. 147-167 ◽

Cited By ~ 49

Author(s):

Theresa A. Ramelot ◽

Srivatsan Raman ◽

Alexandre P. Kuzin ◽

Rong Xiao ◽

Li-Chung Ma ◽

...

Keyword(s):

Protein Structure ◽

Molecular Replacement ◽

Structure Quality ◽

Replacement Study

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KoBaMIN: a knowledge-based minimization web server for protein structure refinement

Nucleic Acids Research ◽

10.1093/nar/gks376 ◽

2012 ◽

Vol 40 (W1) ◽

pp. W323-W328 ◽

Cited By ~ 96

Author(s):

J. P. G. L. M. Rodrigues ◽

M. Levitt ◽

G. Chopra

Keyword(s):

Protein Structure ◽

Structure Refinement ◽

Web Server ◽

Protein Structure Refinement ◽

Knowledge Based

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MADOKA: an ultra-fast approach for large-scale protein structure similarity searching

BMC Bioinformatics ◽

10.1186/s12859-019-3235-1 ◽

2019 ◽

Vol 20 (S19) ◽

Cited By ~ 2

Author(s):

Lei Deng ◽

Guolun Zhong ◽

Chenzhe Liu ◽

Judong Luo ◽

Hui Liu

Keyword(s):

Protein Structure ◽

Large Scale ◽

Parallel Implementation ◽

Structural Alignment ◽

3D Structure ◽

Web Server ◽

Structure Alignment ◽

Two Phase ◽

Fast Approach ◽

Structure Similarity

Abstract Background Protein comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging. Results Here, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. Then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6–100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database (About 360,000 protein chains in total), as well as additional features such as 3D structure alignment visualization. The MADOKA web server is freely available at: http://madoka.denglab.org/ Conclusions MADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs.

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