Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to improvement of the Rosetta energy function. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function. The optE tool optimizes the weights on the different components of the energy function by maximizing the recapitulation of a wide range of experimental observations. We use the tools to examine three proposed modifications to the Rosetta energy function: improving the unfolded state energy model (reference energies), using bicubic spline interpolation to generate knowledge based torisonal potentials, and incorporating the recently developed Dunbrack 2010 rotamer library (Shapovalov and Dunbrack, 2011).

Virtually every process in a cell is carried out by macromolecular complexes whose actions need to be perfectly orchestrated. The synchronization and regulation of these biological functions is indeed critical and is usually carried out by complex networks of transient protein interactions. Here, we review some of the many strategies that proteins in regulatory networks use to achieve the dynamic plasticity necessary to rapidly respond to diverse cellular needs. More specifically, we present recent work on the molecular bases of transient peptide‐mediated interactions and the role that post‐translational modifications and disordered regions might play. Finally, in light of some recent findings, we speculate on the possibility of a new regulatory code for intrinsically disordered proteins and the potential biophysical and functional advantages that disorder might provide.

The development and validation of computational macromolecular modeling and design methods depend on suitable benchmark datasets and informative metrics for comparing protocols. In addition, if a method is intended to be adopted broadly in diverse biological applications, there needs to be information on appropriate parameters for each protocol, as well as metrics describing the expected accuracy compared to experimental data. In certain disciplines, there exist established benchmarks and public resources where experts in a particular methodology are encouraged to supply their most efficient implementation of each particular benchmark. We aim to provide such a resource for protocols in macromolecular modeling and design. We present a freely accessible web resource (https://kortemmelab.ucsf.edu/benchmarks) to guide the development of protocols for protein modeling and design. The site provides benchmark datasets and metrics to compare the performance of a variety of modeling protocols using different computational sampling methods and energy functions, providing a “best practice” set of parameters for each method. Each benchmark has an associated downloadable benchmark capture archive containing the input files, analysis scripts, and tutorials for running the benchmark. The captures may be run with any suitable modeling method; we supply command lines for running the benchmarks using the Rosetta software suite. We have compiled initial benchmarks for the resource spanning three key areas: prediction of energetic effects of mutations, protein design, and protein structure prediction, each with associated state-of-the-art modeling protocols. With the help of the wider macromolecular modeling community, we hope to expand the variety of benchmarks included on the website and continue to evaluate new iterations of current methods as they become available.