NNcon: improved protein contact map prediction using 2D-recursive neural networks

By Allison N. Tegge, Zheng Wang, Jesse Eickholt, Jianlin Cheng. 2009

164 citations recorded by the source corpus.

Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. Both NNcon server and software are available at http://casp.rnet.missouri.edu/nncon.html.

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

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. Jinbo Xu
2. Sheng Wang
3. Jianlin Cheng
4. Yang Zhang
5. Robin Pearce
6. Jianzhu Ma
7. András Szilágyi
8. Zhiyong Wang

In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianlin Cheng
4. Yang Zhang
5. Badri Adhikari
6. Robin Pearce
7. Jianzhu Ma
8. Jie Hou

In Computational Biology & Bioinformatics

1. Jinbo Xu
2. Sheng Wang
3. Yang Zhang
4. Robin Pearce
5. Badri Adhikari
6. Jianlin Cheng
7. Jie Hou
8. Jianzhu Ma

Citations in the corpus

• Deep learning in neural networks: An overview (2014, background): “, 2013) as well as protein contact maps (Tegge et al., 2009), also in conjunction with a growing deep FNN (Di Lena et al.”
• A position-specific distance-dependent statistical potential for protein structure and functional study (2012, neutral): “…(Joo et al., 2009), HHpredA (Hildebrand et al., 2009), Jiang_Assembly (Hu et al., 2011), MULTICOM-CLUSTER (Tegge et al., 2009), MULTI- COM-NOVEL (Tegge et al., 2009), Pcomb (Larsson et al., 2008), Phyre2 (Kelley and Sternberg, 2009), pro-sp3-TASSER (Zhou and Skolnick, 2009), QUARK (Xu et al.,…”
• Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, methodology): “To implement this, we model a set of related protein families using Gaussian graphical models and then coestimate their parameters by maximum-likelihood, subject to the constraint that these parameters shall be similar to some degree.”
• Improving protein structure prediction using multiple sequence-based contact predictions. (2011, methodology): “…and Alexov, 2006; Olmea and Valencia, 1997; Vicatos et al., 2005) or generated 2011 Elsevier Ltd All rights reserved 1189 by other machine learning methods such as neural networks (Shackelford and Karplus, 2007; Tegge et al., 2009) would probably further improve the performance of the method.”
• Predicting protein contact map using evolutionary and physical constraints by integer programming (2013, background): “Experimental results confirm that PhyCMAP outperforms currently popular methods no matter how many sequence homologs are available for the protein under consideration.”
• ConEVA: a toolbox for comprehensive assessment of protein contacts (2016, methodology): “Currently, for evaluating predicted contacts, the three most widely used evaluation measures are precision, coverage and distance distribution score (X d ) [3, 12, 14, 18 – 22].”
• Toward the solution of the protein structure prediction problem (2021, methodology): “Following the fi rst iteration of machine-learning-based contact predictors, more complex neural network architectures were developed (153 – 156).”
• A new ensemble coevolution system for detecting HIV-1 protein coevolution (2015, methodology): “We used the contact map for our evaluation purpose because it is still one of the most widely-used standard measurements in the coevolution field, to compare different sequence-based methods [25,48,50] .”