Multibody coarse‐grained potentials for native structure recognition and quality assessment of protein models

Abstract

Summary Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Our goal was to combine long range multi-body potentials and short range potentials to improve recognition of native structure among misfolded decoys. We optimized the weights for four-body non-sequential, four-body sequential and short range potentials in order to obtain optimal model ranking results for threading and have compared these data against results obtained with other potentials. (Twenty six different coarse-grained potentials from the Potentials ‘R’Us web server have been used.) Our optimized multi-body potentials outperform all other contact potentials in the recognition of the native structure among decoys, both for models from homology template-based modeling and from template-free modeling in CASP8 decoy sets. We have compared the results obtained for this optimized coarse-grained potentials, where each residue is represented by a single point, with results obtained by using the DFIRE potential, which takes into account atomic level information of proteins. We found that for all proteins larger than 80 amino acids our optimized coarse-grained potentials yield results comparable to those obtained with the atomic DFIRE potential.

Molecular determinants of cadherin ideal bond formation: Conformation-dependent unbinding on a multidimensional landscape

“…Details on how the weights for the different terms were obtained (35) by minimizing the RMSD of best decoys from homology modeling targets of CASP8 (54) to their corresponding native structures using particle swarm optimization (55) have been described previously.…”

“…The X-dimer first twists along PC2 to give rise to an intermediate, which moves along PC1 to give rise to the WT, which then twists back in the PC2 direction to yield the S-dimer K14E structure. (35). Following this, the energies of structures on the entire landscape were visualized in the form of an energy contour plot and the experimental structures were shown as solid red circles (Fig.…”

Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction?

“…Higher order information and better scoring performances are reported for those multi-body potentials. 8,[26][27][28] To the best of our knowledge, however, all of those multi-body potentials are based on a coarse-grained representation of interacting bodies and are contact-based. In this study, we asked the question if higher order information is also important for distance-dependent statistical potentials that are based on an atomistic representation of the interacting bodies.…”

Pedestrian dynamics at transit stations: an integrated pedestrian flow modeling approach

“…Accordingly, the objective of this paper is to accurately and efficiently model pedestrian operational behavior (focusing on walking behavior), using a combination of the Social Force model (Helbing and Molnár 1995), the behavioral heuristics model (Moussaïd, Helbing, and Theraulaz 2011), and concepts from materials science such us multi-body potential molecular interactions (Gniewek et al 2011). The motivation behind such 'integration' is the hope that the resulting 'integrated model' (IM) will benefit from the attraction/repulsive force concepts offered by the social force models (offering realistic one-toone interactions), the flexibility of the behavioral heuristics (incorporating multiple psychological and physiological pedestrian characteristics), and the theoretical foundation from materials science.…”

“…pedestrians or obstacles), nor all surrounding bodies, but rather between multiple bodies (i.e. multibody potential interactions) within the corresponding field of view (Gniewek et al 2011). In our model, we experimented with considering the closest 1, 2, and 3 nearest pedestrians/obstacles when determining a specific pedestrian's course of action.…”