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We have recently completed a full re-architecturing of the Rosetta molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy to use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as Rosetta3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This document describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform.

Topology has been shown to be an important determinant of many features of protein folding; however, the delineation of sequence effects on folding remains obscure. Furthermore, differentiation between the two influences proves difficult due to their intimate relationship. To investigate the effect of sequence in the absence of significant topological differences, we examined the folding mechanisms of segment B1 peptostreptococcal protein L and segment B1 of streptococcal protein G. These proteins share the same highly symmetrical topology. Despite this symmetry, neither protein folds through a symmetrical transition state. We analyzed the origins of this difference using theoretical models. We found that the strength of the interactions present in the N-terminal hairpin of protein L causes this hairpin to form ahead of the C-terminal hairpin. The difference in chain entropy associated with the formation of the hairpins of protein G proves sufficient to beget initiation of folding at the shorter C-terminal hairpin. Our findings suggest that the mechanism of folding may be understood by examination of the free energy associated with the formation of partially folded microstates.Keywords: Protein folding; symmetry breaking; protein L; protein G; folding pathway; transition state Supplemental material: See www.proteinscience.org.Most proteins perform their biological function in a compact, native state that is reached through folding from a wide array of unfolded states. Despite considerable progress made in the past several decades through experimental and theoretical approaches (Dobson and Karplus 1999), much remains to be learned about the folding process. A complete knowledge of the mechanism by which this process takes place will have broad reaching implications, ranging from understanding the formation and propagation of prion and amyloid diseases (Kelly 1998) to protein design and the prediction of structure from sequence (Jackson 1998).In recent years, topology has been shown to be a key determinant of many attributes in folding. Simplified protein models which capture little but the topology of the native protein are in some cases able to correctly reproduce many important features of folding Galzitskaya and Finkelstein 1999;Munoz and Eaton 1999;Shea et al. 1999). Furthermore, folding rates of two-state proteins have been shown to correlate very well with contact order, a quantity linked to topology (Plaxco et al. 1998). This correlation has been used to argue that topology may also be a main determinant of transition state structure (Plaxco et al. 2000), and hence the mechanism of protein folding. While this is not true for all proteins, it has been shown to hold in a comparison of two SH3 domains Riddle et al. 1999) and a comparison of acylphosphatase with human procarboxypeptidase A2 activation domain (Villegas et al. 1998;Chiti et al. 1999).One of the next challenges facing the protein folding community is to push our understanding of protein folding beyond topological effects. While topology c...

The Rosetta software suite for macromolecular modeling, docking, and design is widely used in pharmaceutical, industrial, academic, non-profit, and government laboratories. Despite its broad modeling capabilities, Rosetta remains consistently among leading software suites when compared to other methods created for highly specialized protein modeling and design tasks. Developed for over two decades by a global community of over 60 laboratories, Rosetta has undergone multiple refactorings, and now comprises over three million lines of code. Here we discuss methods developed in the last five years in Rosetta, involving the latest protocols for structure prediction; protein-protein and protein-small molecule docking; protein structure and interface design; loop modeling; the incorporation of various types of experimental data; modeling of peptides, antibodies and proteins in the immune system, nucleic acids, non-standard chemistries, carbohydrates, and membrane proteins. We briefly discuss improvements to the energy function, user interfaces, and usability of the software. Rosetta is available at www.rosettacommons.org.

Based on the crystal structure of the cross-␤ spine formed by the peptide NNQQNY, we have developed a computational approach for identifying those segments of amyloidogenic proteins that themselves can form amyloid-like fibrils. The approach builds on experiments showing that hexapeptides are sufficient for forming amyloid-like fibrils. Each six-residue peptide of a protein of interest is mapped onto an ensemble of templates, or 3D profile, generated from the crystal structure of the peptide NNQQNY by small displacements of one of the two intermeshed ␤-sheets relative to the other. The energy of each mapping of a sequence to the profile is evaluated by using ROSETTADESIGN, and the lowest energy match for a given peptide to the template library is taken as the putative prediction. If the energy of the putative prediction is lower than a threshold value, a prediction of fibril formation is made. This method can reach an accuracy of Ϸ80% with a P value of Ϸ10 ؊12 when a conservative energy threshold is used to separate peptides that form fibrils from those that do not. We see enrichment for positive predictions in a set of fibril-forming segments of amyloid proteins, and we illustrate the method with applications to proteins of interest in amyloid research.amyloid ͉ prediction ͉ ROSETTADESIGN ͉ lysozyme ͉ myoglobin A myloid-like fibrils of protein are common to deposition diseases such as Alzheimer's, the spongiform encephalopathies including Creutzfeldt-Jakob disease and bovine spongiform encephalopathy, and the protein-based heredity of [PSI ϩ ] and other prions in yeast. Thus understanding the range of protein sequences that can undergo fibrillization and the basis for stability of fibrils could have wide significance. We address these problems with a computational method for predicting which segments of a given protein might form the cross-␤ spine in the fibrillar form.The ability to form amyloid fibers is not restricted to those proteins associated with amyloid or prion disease. Otherwise innocuous proteins can be fibrillized by altering the pH, temperature, or composition of their native solvent (1-3). In addition, numerous short peptides (e.g., four to seven residues) are found to form amyloid-like fibrils in isolation from the rest of the protein (4-11). De novo-designed synthetic peptides have also been shown to form fibers (12)(13)(14).The question of how both full proteins and short peptides can form fibrils was illuminated by the crystal structures (11) of NNQQNY and GNNQQNY, which showed that the fundamental structure of the protofibril is a pair of ␤-sheets, which mate at a dry interface where their side chains tightly interdigitate in a ''steric zipper.'' To form this steric zipper, the strands in the sheets need be only four to six residues in length. Therefore, we would expect that short peptides with a tendency to fibrillize can do so, either when cleaved from the rest of the protein chain, as for the ␤-amyloid (Abeta) peptide of Alzheimer's disease, or when they are unmasked from an inaccessible ...