A unified coarse-grained model of three major classes of biological molecules—proteins, nucleic acids, and polysaccharides—has been developed. It is based on the observations that the repeated units of biopolymers (peptide groups, nucleic acid bases, sugar rings) are highly polar and their charge distributions can be represented crudely as point multipoles. The model is an extension of the united residue (UNRES) coarse-grained model of proteins developed previously in our laboratory. The respective force fields are defined as the potentials of mean force of biomacromolecules immersed in water, where all degrees of freedom not considered in the model have been averaged out. Reducing the representation to one center per polar interaction site leads to the representation of average site–site interactions as mean-field dipole–dipole interactions. Further expansion of the potentials of mean force of biopolymer chains into Kubo’s cluster-cumulant series leads to the appearance of mean-field dipole–dipole interactions, averaged in the context of local interactions within a biopolymer unit. These mean-field interactions account for the formation of regular structures encountered in biomacromolecules, e.g., α-helices and β-sheets in proteins, double helices in nucleic acids, and helicoidally packed structures in polysaccharides, which enables us to use a greatly reduced number of interacting sites without sacrificing the ability to reproduce the correct architecture. This reduction results in an extension of the simulation timescale by more than four orders of magnitude compared to the all-atom representation. Examples of the performance of the model are presented. Components of the Unified Coarse Grained Model (UCGM) of biological macromolecules

Significance: With the example of the coarse-grained United Residue model of polypeptide chains, this paper demonstrates that the physics-based approach for protein-structure prediction can lead to exceptionally good results when correct domain packing is an issue, even for a highly homologous target. The reason for this is probably that emphasis is placed on energetically favorable residue–residue interactions, including those with residues in relatively flexible linker regions; these regions are usually very different in the target compared with those of proteins in the databases used for template-based modeling. The results suggest that a combination of bioinformatics and a physics-based approach could result in a major increase in the prediction capacity of existing approaches.

Summary: Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Exercise on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently-improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully-extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed three-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino-acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the CαRMSD was 3.8 Å for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template based modeling (TBM) methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes.

The amyloid cascade hypothesis states that senile plaques, composed of amyloid β (Aβ) fibrils, play a key role in Alzheimer's disease (AD). However, recent experiments have shown that Aβ oligomers are more toxic to neurons than highly ordered fibrils. The molecular mechanism underlying this observation remains largely unknown. One of the possible scenarios for neurotoxicity is that Aβ peptides create pores in the lipid membrane that allow Ca²⁺ ions to enter cells, resulting in a signal of cell apoptosis. Hence, one might think that oligomers are more toxic due to their higher ability to create ion channels than fibrils. In this work, we study the effect of Aβ42 dodecamer and fibrils on a neuronal membrane, which is similar to that observed in AD patients, using all-atom molecular dynamics simulations. Due to short simulation times, we cannot observe the formation of pores, but useful insight on the early events of this process has been obtained. Namely, we showed that dodecamer distorts the lipid membrane to a greater extent than fibrils, which may indicate that ion channels can be more easily formed in the presence of oligomers. Based on this result, we anticipate that oligomers are more toxic than mature fibrils, as observed experimentally. Moreover, the Aβ-membrane interaction was found to be governed by the repulsive electrostatic interaction between Aβ and the ganglioside GM1 lipid. We calculated the bending and compressibility modulus of the membrane in the absence of Aβ and obtained good agreement with the experiment. We predict that the dodecamer will increase the compressibility modulus but has little effect on the bending modulus. Due to the weak interaction with the membrane, fibrils insignificantly change the membrane elastic properties.