Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins.

By S. Tanaka, Harold A. Scheraga. 1976

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Direct-coupling analysis of residue coevolution captures native contacts across many protein families (2011, background): “…are consistent with the biophysics of molecular contacts: The first peak presumably arises from short-ranged interactions like hydrogen bonding or pairings involved in secondary structure formation, while the second peak likely corresponds to long-ranged, possibly water mediated, contacts (28-30).”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, background): “…that knowledge-based statistical potentials (Lu et al., 2008; Miyazawa and Jernigan, 1985; Shen and Sali, 2006; Simons et al., 1997; Sippl, 1990; Tanaka and Scheraga, 1976; Zhang and Zhang, 2010; Zhou and Zhou, 2002) compare favorably to physics-based potentials (Brooks et al., 2009; Bryngelson…”
Energy-based models for atomic-resolution protein conformations (2020, methodology): “Our work is a logical extension of statistical potential methods (Tanaka & Scheraga, 1976; Sippl, 1990; Lazaridis & Karplus, 2000) that ﬁt energetic terms from data, which, in combination with physically motivated force Published as a conference paper at ICLR 2020 ﬁelds, have contributed to the…”
Statistical potential for assessment and prediction of protein structures (2006, background): “…energy functions Kour-anov et . related residue the free a scoring function whose global minimum corresponds to the native structure from a sample of structures of different sequences (Tanaka and 1976; Miyazawa and Jernigan 1985; Sippl deposited in the Protein Data Bank (PDB) (Kour-anov 2006).”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “The energy function can be either derived from the general physical principles (like a number of popular force ﬁelds (1–4)) or based on diverse sets of known protein structures (various knowledge-based or statistical potentials (5–8)).”
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized (2010, methodology): “The use of KBPs originates from the work of Tanaka and Scheraga [4] who were the first to extract effective interactions from the frequency of contacts in X-ray structures of native proteins.”
Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction (2002, result): “2002), however, yielded results comparable to knowledgebased statistical potentials that extract interactions directly from known protein structures (Tanaka and Scheraga 1976).”
Why similar protein sequences encode similar three-dimensional structures? (2010, methodology): “Globular proteins are usually well packed with buried hydrophobic residues and exposed hydrophilic ones [11]; some residue pairs are often in contact [ 12 ] and some sequences have preferences for different types of secondary structures [13, 14].”
An iterative knowledge‐based scoring function for protein–protein recognition (2008, background): “38–40 The fourth type of scoring methods for protein–protein docking are referred to as knowledge-based potentials,41–49 which are derived from known structures of protein–protein complexes in a database such as PDB, etc.6,50–52 The interaction potentials are directly converted from the statistical…”
Statistical potentials extracted from protein structures: how accurate are they? (1996, methodology): “In one approach, the interactions between amino acids are assumed to be short-ranged, and are approximated using a ‘‘contact potential’’ (Tanaka & Scheraga, 1976; Miyazawa & Jernigan, 1985).”
Accurate and efficient loop selections by the DFIRE‐based all‐atom statistical potential (2004, background): “…to obtain energy function is the knowledge-based statistical potential that extracts interaction energies directly from known protein structures (Tanaka and Scheraga 1976) Knowledge-based statistical potentials are attractive because they are simple and computationally efficient For example, de…”