Statistical potentials for 3D structure evaluation: From proteins to RNAs*

Abstract

Structure evaluation is critical to in silico 3-dimensional structure predictions for biomacromolecules such as proteins and RNAs. For proteins, structure evaluation has been paid attention over three decades along with protein folding problem, and statistical potentials have been shown to be effective and efficient in protein structure prediction and evaluation. In recent two decades, RNA folding problem has attracted much attention and several statistical potentials have been developed for RNA structure evaluation, partially with the aid of the progress in protein structure prediction. In this review, we will firstly give a brief overview on the existing statistical potentials for protein structure evaluation. Afterwards, we will introduce the recently developed statistical potentials for RNA structure evaluation. Finally, we will emphasize the perspective on developing new statistical potentials for RNAs in the near future.

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

“…Of course, rsRNASP (and other “traditional” statistical potentials) can be furtherly improved for more accurate evaluation on RNA 3D structures. First, the reference states can be circumvented through some treatments such as iterative technique since the employed reference states may still deviate largely from the ideal one (52, 55). Second, other geometric parameters such as torsional angle and orientation beyond atom-atom distance can be involved to enhance the description of the relative relationship between atoms or atom groups (29, 55, 63).…”

“…First, the reference states can be circumvented through some treatments such as iterative technique since the employed reference states may still deviate largely from the ideal one (52, 55). Second, other geometric parameters such as torsional angle and orientation beyond atom-atom distance can be involved to enhance the description of the relative relationship between atoms or atom groups (29, 55, 63). Third, the classification of interaction ranges can become more subtle and more interaction ranges would bring more weight parameters and consequently more accurate evaluation for structures (55, 71).…”

“…Second, other geometric parameters such as torsional angle and orientation beyond atom-atom distance can be involved to enhance the description of the relative relationship between atoms or atom groups (29, 55, 63). Third, the classification of interaction ranges can become more subtle and more interaction ranges would bring more weight parameters and consequently more accurate evaluation for structures (55, 71). Fourth, a multi-body (three- or four-body) potential can be supplemented to a pair-wise one which would improve the description on atom-atom distributions (65, 66).…”

“…Fourth, a multi-body (three- or four-body) potential can be supplemented to a pair-wise one which would improve the description on atom-atom distributions (65, 66). Finally, a statistical potential is still limited by the limited RNA native structures in PDB, and it can be improved with the increase of the number of RNA structures deposited in PDB database (55). Additionally, for a statistical potential from training through neural network, more extensive training decoy set built from extensive methods would effectively diminish the training bias of a statistical potential and certainly improve its ability in evaluating RNA 3D structures.…”

“…In contrast, knowledge-based statistical potentials derived from the experimental structures deposited in the PDB database have been shown to be very efficient and effective for the quality assessment of protein 3D structures, protein-protein and protein-ligand docking (44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54). The core difference between various statistical potentials mainly originates from the choice of different reference states (55), and up to now, six representative reference states have been proposed to build distancedependent pairwise statistical potentials for proteins, i.e., averaging (48), quasi-chemical approximation (56), atomshuffled (57), finite-ideal-gas (58), spherical-non-interacting (59) and random-walk-chain reference states (60).…”