A position-specific distance-dependent statistical potential for protein structure and functional study

By Feng Zhao, Jinbo Xu. 2012

53 citations recorded by the source corpus.

Summary Although studied extensively, designing highly accurate protein energy potential is still challenging. A lot of knowledge-based statistical potentials are derived from the inverse of the Boltzmann law and consist of two major components: observed atomic interacting probability and reference state. These potentials mainly distinguish themselves in the reference state and use a similar simple counting method to estimate the observed probability, which is usually assumed to correlate with only atom types. This paper takes a rather different view on the observed probability and parameterizes it by the protein sequence profile context of the atoms and the radius of the gyration, in addition to atom types. Experiments confirm that our position-specific statistical potential outperforms currently the popular ones in several decoy discrimination tests. Our results imply that in addition to reference state, the observed probability also makes energy potentials different and evolutionary information greatly boost performance of energy potentials.

Subfields: 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 Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianzhu Ma
4. David T. Jones
5. Zhiyong Wang
6. Shiwang Liu
7. Badri Adhikari
8. Renyu Zhang

In Computational Biology & Bioinformatics

1. Jinbo Xu
2. Sheng Wang
3. Jianzhu Ma
4. David T. Jones
5. Zhiyong Wang
6. Badri Adhikari
7. Shiwang Liu
8. Renyu Zhang

Citations in the corpus

• MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “ one column does not exist (when or ), zero is used. We predict ( ) using a probabilistic neural network (PNN) implemented in our contextspecific distance-dependent statistical potential package EPAD [36]. EPAD takes as input sequence contexts and mutual information and then yields inter-residue dis”
• Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019, methodology): “Contact predictionhas been extensively used instructure prediction 16,20,29,31 but previous work has also made residue distance predictions, 3,35 and these were used for structure prediction in CASP13 by other groups in distance geometry approaches.”
• Protein threading using residue co-variation and deep learning (2018, methodology): “Specifically, PROSPECT and RAPTOR make use of contact potential; MRFalign makes use of inter-residue distance predicted by a shallow neural network from mutual information and sequence profile (Zhao and Xu, 2012); EigenTHREADER makes use of contacts predicted by MetaPSICOV (Jones, et al.”
• Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, background): “EPAD: a context-specific distance-dependent statistical potential (Zhao and Xu, 2012), derived from protein evolutionary information.”
• Protein Homology Detection Through Alignment of Markov Random Fields (2015, methodology): “The CASP10 results show that EPAD can successfully fold some targets with unusual fold (according to the CASP10 Free Modeling assessor Dr. BK Lee).”
• Protein threading using context-specific alignment potential (2013, methodology): “Our large-scale experimental test also indicates EPAD is much better than those context-independent distance-based pairwise potentials, such as DOPE (Shen and Sali, 2009), DFIRE (Zhou and Zhou, 2009) and RW (Zhang and Zhang, 2010) in ranking protein alignments (or templatebased models) generated by ”
• Predicting protein contact map using evolutionary and physical constraints by integer programming (2013, background): “…of any two residues forming a contact using the following input features: EPAD (a context-specific distance-based statistical potential) (Zhao and Xu, 2012), PSIBLAST sequence profile (Altschul and Koonin, 1998), secondary structure predicted by PSIPRED (Jones, 1999), pairwise contact…”
• CoinFold: a web server for protein contact prediction and contact-assisted protein folding (2016, methodology): “In addition to co-evolution information, CoinFold uses the following features for supervised contact prediction: sequence profile (16), contact or distance potential (17), and some non-evolutionary information (18) (see our method paper (12) for their description).”