Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes

By M. Betancourt, D. Thirumalai. 2008

337 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

Folding membrane proteins by deep transfer learning (2017, methodology): “The pairwise features include co-evolution strength generated by CCMpred (Seemayer et al., 2014), mutual information and pairwise contact potential (Miyazawa and Jernigan, 1985, Betancourt and Thirumalai, 1999).”
Statistical potentials for fold assessment (2002, neutral): “1995), and a large variety of knowledge-based potentials of mean force or statistical potentials (Hendlich et al. 1990; Casari and Sippl 1992; Colovos and Yeates 1993; Bauer and Beyer 1994; Kocher et al. 1994; Rooman and Wodak 1995; Jernigan and Bahar 1996; Jones and Thornton 1996; Park and Levitt 1”
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017, background): “The pairwise features include direct co-evolutionary information generated by CCMpred [9], mutual information and pairwise contact potential [37, 38].”
Statistical potential for assessment and prediction of protein structures (2006, background): “Subsequently, a large number of different statistical potentials were described and tested (Hendlich et al. 1990; Colovos and Yeates 1993; Sippl 1993a; Kocher et al. 1994; Huang et al. 1995; Rooman and Wodak 1995; Jernigan and Bahar 1996; Jones and Thornton 1996; Miyazawa and Jernigan 1996; Moult 19”
Developing optimal non-linear scoring function for protein design (2004, background): “, three-body or four-body contacts) should be incorporated in protein description (Betancourt and Thirumalai, 1999; Munson and Singh, 1997; Zheng et al., 1997).”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “Oneof the common approaches to developing statistical potentials is to calculate the probability of various structural features observed in a set of experimental protein structures relative to a reference state (7,25).”
The scoring of poses in protein-protein docking: current capabilities and future directions (2013, neutral): “CP_BT [151] r”