Protein Structure Prediction

The structural prediction of monomeric, soluble proteins is still an unsolved problem, notwithstanding notable recent advances. One important necessity in computational prediction protocols is reducing the high dimensional search space during simulations. An increasingly successful approach is the incorporation of structural restraints derived from phylogeny or low-resolution experiments–both approaches provide valuable but sparse and/or noisy information, and the challenge is to productively use these data. For example, Braun et al. demonstrate that evolutionary information on the protein fold can be discretized as residue-residue “contact maps”, and that these can be combined with iterative sampling techniques for more accurate protein structure prediction [14]. In another example, Huber and colleagues show the integration of Rosetta with sparse EPR constraints to model conformational states in a model protein [15]. One technical issue that arises with the incorporation of multiple experimentally derived restraints is that individual sets are incompatible with each other, thus requiring manual intervention from the coder. To address this problem, Porter et al. developed a computational framework that simplifies combined sampling strategies in Rosetta [16]. They then demonstrated this powerful framework on a range of modeling problems, including domain insertion and ab initio structure prediction with multiple sets of experimental restraints.

Membrane Proteins

The design and modeling of membrane proteins is an emerging research area. Gray and colleagues present an integrated framework for membrane protein modeling and design [17]. In this work they showed application of the modeling framework to predict free energy changes upon mutation, high-resolution structural refinement, protein-protein docking, and assembly of symmetric protein complexes.

Docking

A significant issue limiting the success of both protein-protein and protein-small molecule docking is the large size and ruggedness of the search space. To efficiently sample conformational space, several approximations are made in the Rosetta approach: a low resolution Monte Carlo search, typically with a coarse-grained representation of the molecules and an approximate energy function, is first performed, followed by high resolution Monte Carlo refinement with atomic resolution [18]. In spite of these approximations, sampling remains computationally inefficient. Furthermore, the energy functions used in the high-resolution step, while being more accurate than the low-resolution step, are still built for speed over accuracy, and often suffer from incorrect modeling of interactions between polar groups, and protein with the solvent. More specifically, in the Rosetta high-resolution energy function, the balance of hydrogen bonding, electrostatics and desolvation forces is a known contributor to energy function inaccuracy [8,19]. It should be noted that the limitations in scoring and sampling are related–enhanced sampling allows identification of false positive conformations, where as more accurate scoring increases ease of identification of true positive solutions by more efficient identification of more optimal basins. Several papers tackle the sampling and scoring issues in docking:

Zhang et al. show the application of replica exchange and other advance sampling techniques to increase the efficiency of Monte Carlo search during docking. Using a benchmark set of 20 protein-protein complexes, they identified an advanced sampling strategy showed better performance with equivalent computational resources. A new sampling approach was used by DeLuca et al. [20] to improve the accuracy and decrease the computational cost of the RosettaLigand docking protocol used in the prediction of protein-small molecule interactions [21]. For protein-small docking, the Karanicolas group report several significant improvements to a previously developed “ray casting” docking approach [22] used for the prediction of small molecules that disrupt protein-protein interactions [23]. Bazzoli et al. show that the use of two recent enhancements to the Rosetta energy function–explicitly including a Coulombic electrostatic term, and using a modified form of the implicit solvation potential–can markedly improve the ability to identify small-molecule inhibitors of protein-protein interactions [24].

Protein Multispecificity Design

The design of multi-specificity of proteins is important in applications ranging from structural vaccine design, bispecific antibody therapy, and combinatorial biocatalysis. Many computational design strategies rely on genetic algorithms, which are slow and limit search space. To address this problem, the Meiler group developed a new algorithm that can find multistate minima without reliance on techniques that limit search space like a fixed backbone approximation [25].

Scientific Benchmarks

Many of the above protocols were developed by evaluating performance against a benchmark set. Development of accessible, standard benchmarks for different end uses has the potential to increase the speed of method development, and aid reproducibility. For that reason, the Kortemme lab has developed a centralized web resource for standardized benchmark datasets (https://kortemmelab.ucsf.edu/benchmarks) [26]. This web resource includes analysis scripts, Rosetta commandlines, and tutorials for the given benchmark. There are three main sets of benchmarks in this resource: tests estimating the energetic effects upon mutation, tests for structure prediction, and ones for protein design. As a further example of the utility of benchmark sets, Ollikainen et al. developed a benchmark in order to test different protein design protocols on the re-design of enzyme substrate specificity [27]. They then showed that a protocol coupling backbone with side-chain flexibility improves prediction of sequence recovery over a competing fixed backbone approach.

Taken together, the articles in this collection highlight the utility of the Rosetta approach in tackling wide-ranging problems in biomolecular modeling and design using a common platform that allows the accessible and reproducible re-utilization of software. The common framework also provides an inherent feedback loop where new algorithms for sampling and scoring can be widely utilized and benchmarked for diverse scientific problems, in the process highlighting limitations of the approaches and areas where further developments are needed. We hope that through this collection readers will get a taste of the excitement and the unity in diversity that we enjoyed at RosettaCon 2014!