The traditional computational modeling of protein structure, dynamics, and interactions remains difficult for many protein systems. It is mostly due to the size of protein conformational spaces and required simulation time scales that are still too large to be studied in atomistic detail. Lowering the level of protein representation from all-atom to coarse-grained opens up new possibilities for studying protein systems. In this review we provide an overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions. A more detailed description is given for applications of coarse-grained models suitable for efficient combinations with all-atom simulations in multiscale modeling strategies.

The Rosetta de novo structure prediction and loop modeling protocols begin with coarse grained Monte Carlo searches in which the moves are based on short fragments extracted from a database of known structures. Here we describe a new object oriented program for picking fragments that greatly extends the functionality of the previous program (nnmake) and opens the door for new approaches to structure modeling. We provide a detailed description of the code design and architecture, highlighting its modularity, and new features such as extensibility, total control over the fragment picking workflow and scoring system customization. We demonstrate that the program provides at least as good building blocks for ab-initio structure prediction as the previous program, and provide examples of the wide range of applications that are now accessible.

Entropy sampling Monte Carlo, the replica method, and the classical Metropolis scheme were applied in numerical studies of the collapse transition in a simple face-centered cubic lattice polymer. The force field of the model consists of pairwise, contact-type, long-range interactions and a short-range potential based on the β-sheet definition assumed in the model. The ability to find the lowest energy conformation by various Monte Carlo methods and the computational cost associated with each was examined. It is shown that all of the methods generally provide the same picture of the collapse transition. However, the most complete thermodynamic description of the transition derives from the results of entropy sampling Monte Carlo simulations, but this is the most time-consuming method. The replica method is shown to be the most effective and efficient in searching for the lowest energy conformation. The possible consequences of these findings for the development of simulation strategies for the folding of model proteins are discussed briefly.

Although experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. For a considerable fraction of proteins whose structures will not be determined experimentally, computational methods can provide valuable information. The value of structural models in biological research depends critically on their quality. Development of high-accuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling. Large sets of structural decoys have been generated using reduced conformational space protein modeling tool CABS. Subsequently, the reduced models were subject to all-atom reconstruction. Then, the resulting detailed models were energy-minimized using state-of-the-art all-atom force field, assuming fixed positions of the alpha carbons. It has been shown that a very short minimization leads to the proper ranking of the quality of the models (distance from the native structure), when the all-atom energy is used as the ranking criterion. Additionally, we performed test on medium and low accuracy decoys built via classical methods of comparative modeling. The test placed our model evaluation procedure among the state-of-the-art protein model assessment methods. These test computations show that a large scale high resolution protein structure prediction is possible, not only for small but also for large protein domains, and that it should be based on a hierarchical approach to the modeling protocol. We employed Molecular Mechanics with fixed alpha carbons to rank-order the all-atom models built on the scaffolds of the reduced models. Our tests show that a physic-based approach, usually considered computationally too demanding for large-scale applications, can be effectively used in such studies.