On the importance of the distance measures used to train and test knowledge-based potentials for proteins.

Martin Carlsen,Patrice Koehl,Peter Røgen,Yang Zhang

doi:10.1371/journal.pone.0109335

Martin Carlsen, Patrice Koehl + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0109335

Copy DOI

Abstract

Knowledge-based potentials are energy functions derived from the analysis of databases of protein structures and sequences. They can be divided into two classes. Potentials from the first class are based on a direct conversion of the distributions of some geometric properties observed in native protein structures into energy values, while potentials from the second class are trained to mimic quantitatively the geometric differences between incorrectly folded models and native structures. In this paper, we focus on the relationship between energy and geometry when training the second class of knowledge-based potentials. We assume that the difference in energy between a decoy structure and the corresponding native structure is linearly related to the distance between the two structures. We trained two distance-based knowledge-based potentials accordingly, one based on all inter-residue distances (PPD), while the other had the set of all distances filtered to reflect consistency in an ensemble of decoys (PPE). We tested four types of metric to characterize the distance between the decoy and the native structure, two based on extrinsic geometry (RMSD and GTD-TS*), and two based on intrinsic geometry (Q* and MT). The corresponding eight potentials were tested on a large collection of decoy sets. We found that it is usually better to train a potential using an intrinsic distance measure. We also found that PPE outperforms PPD, emphasizing the benefits of capturing consistent information in an ensemble. The relevance of these results for the design of knowledge-based potentials is discussed.

Highlights

Proteins are the essential macromolecules inside cells that perform most cellular functions
Recent reports on the advancements of ab initio techniques clearly show that the protein structure prediction community is making progress, but that the quality of the models they generate do not meet yet the stringent accuracy requirements to become useful to the biologists [1]
Two main classes of distance measures have been proposed, those based on a Euclidean distance between the positions of the atoms of the two proteins, and those based on the intrinsic geometry of the structures

Summary

Introduction

Proteins are the essential macromolecules inside cells that perform most cellular functions. Structural biologists have embarked upon the challenge of finding the structures of all proteins, in hopes of unraveling this relationship between geometry and biological activity and learn in the process how cells function. Recent reports on the advancements of ab initio techniques clearly show that the protein structure prediction community is making progress, but that the quality of the models they generate do not meet yet the stringent accuracy requirements to become useful to the biologists [1]. The series of Critical Assessment of protein Structure Prediction (CASP) meetings have highlighted that while the methods for generating models of protein structures have improved significantly [2], identifying the native-like conformations among the large collections of model structures ( called decoys) remains a significant challenge [3,4].

Methods

Results

Conclusion