Jens Meiler

Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules

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 Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines.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 chapter 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.

Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution.

Protein dynamics are essential for protein function, and yet it has been challenging to access the underlying atomic motions in solution on nanosecond-to-microsecond time scales. We present a structural ensemble of ubiquitin, refined against residual dipolar couplings (RDCs), comprising solution dynamics up to microseconds. The ensemble covers the complete structural heterogeneity observed in 46 ubiquitin crystal structures, most of which are complexes with other proteins. Conformational selection, rather than induced-fit motion, thus suffices to explain the molecular recognition dynamics of ubiquitin. Marked correlations are seen between the flexibility of the ensemble and contacts formed in ubiquitin complexes. A large part of the solution dynamics is concentrated in one concerted mode, which accounts for most of ubiquitins molecular recognition heterogeneity and ensures a low entropic complex formation cost.

Computational Methods in Drug Discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator

Completing the Set G protein–coupled receptors (GPCRs) are membrane proteins that transduce extracellular signals to activate diverse signaling pathways. Significant insight into GPCR function has come from structures of three of four classes of GPCRs—A, B, and Frizzled. Wu et al. (p. 58, published online 6 March) complete the picture by reporting the structure of metabotropic glutamate receptor 1, a class C GPCR. The structure shows differences in the seven-transmembrane (7TM) domain between class C and other classes; however, the overall fold is preserved. Class C GPCRs are known to form dimers through their extracellular domains; however, the structure suggests additional interactions between the 7TM domains mediated by cholesterol. Insight into the activation mechanism of a human neuronal G protein–coupled receptor. The excitatory neurotransmitter glutamate induces modulatory actions via the metabotropic glutamate receptors (mGlus), which are class C G protein–coupled receptors (GPCRs). We determined the structure of the human mGlu1 receptor seven-transmembrane (7TM) domain bound to a negative allosteric modulator, FITM, at a resolution of 2.8 angstroms. The modulator binding site partially overlaps with the orthosteric binding sites of class A GPCRs but is more restricted than most other GPCRs. We observed a parallel 7TM dimer mediated by cholesterols, which suggests that signaling initiated by glutamate’s interaction with the extracellular domain might be mediated via 7TM interactions within the full-length receptor dimer. A combination of crystallography, structure-activity relationships, mutagenesis, and full-length dimer modeling provides insights about the allosteric modulation and activation mechanism of class C GPCRs.

ROSETTALIGAND: Protein-Small Molecule Docking with Full Side-Chain Flexibility

Protein–small molecule docking algorithms provide a means to model the structure of protein–small molecule complexes in structural detail and play an important role in drug development. In recent years the necessity of simulating protein side‐chain flexibility for an accurate prediction of the protein–small molecule interfaces has become apparent, and an increasing number of docking algorithms probe different approaches to include protein flexibility. Here we describe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side‐chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimental small molecule binding energy with a correlation coefficient of 0.63 across a diverse set of 229 protein– small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 Å in 71 out of 100 protein–small molecule crystal structure complexes (self‐docking). In cross‐docking calculations in which both protein side‐chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 Å in 14 of 20 test cases. Proteins 2006.

Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package Rosetta for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with Rosetta. For each of these six tasks, we provide a tutorial that illustrates a basic Rosetta protocol. The Rosetta method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 Å. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 Å. In addition to de novo structure prediction, Rosetta also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. Rosetta has been used to accurately design a novel protein structure, predict the structure of protein−protein complexes, design altered specificity protein−protein and protein−DNA interactions, and stabilize proteins and protein complexes. Most recently, Rosetta has been used to solve the X-ray crystallographic phase problem.

New algorithms and an in silico benchmark for computational enzyme design

The creation of novel enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Here we describe two new algorithms for enzyme design that employ hashing techniques to allow searching through large numbers of protein scaffolds for optimal catalytic site placement. We also describe an in silico benchmark, based on the recapitulation of the active sites of native enzymes, that allows rapid evaluation and testing of enzyme design methodologies. In the benchmark test, which consists of designing sites for each of 10 different chemical reactions in backbone scaffolds derived from 10 enzymes catalyzing the reactions, the new methods succeed in identifying the native site in the native scaffold and ranking it within the top five designs for six of the 10 reactions. The new methods can be directly applied to the design of new enzymes, and the benchmark provides a powerful in silico test for guiding improvements in computational enzyme design.

Coupled prediction of protein secondary and tertiary structure

The strong coupling between secondary and tertiary structure formation in protein folding is neglected in most structure prediction methods. In this work we investigate the extent to which nonlocal interactions in predicted tertiary structures can be used to improve secondary structure prediction. The architecture of a neural network for secondary structure prediction that utilizes multiple sequence alignments was extended to accept low-resolution nonlocal tertiary structure information as an additional input. By using this modified network, together with tertiary structure information from native structures, the Q3-prediction accuracy is increased by 7–10% on average and by up to 35% in individual cases for independent test data. By using tertiary structure information from models generated with the rosetta de novo tertiary structure prediction method, the Q3-prediction accuracy is improved by 4–5% on average for small and medium-sized single-domain proteins. Analysis of proteins with particularly large improvements in secondary structure prediction using tertiary structure information provides insight into the feedback from tertiary to secondary structure.

Rosetta Predictions in CASP5: Successes, Failures, and Prospects for Complete Automation

We describe predictions of the structures of CASP5 targets using Rosetta. The Rosetta fragment insertion protocol was used to generate models for entire target domains without detectable sequence similarity to a protein of known structure and to build long loop insertions (and N‐and C‐terminal extensions) in cases where a structural template was available. Encouraging results were obtained both for the de novo predictions and for the long loop insertions; we describe here the successes as well as the failures in the context of current efforts to improve the Rosetta method. In particular, de novo predictions failed for large proteins that were incorrectly parsed into domains and for topologically complex (high contact order) proteins with swapping of segments between domains. However, for the remaining targets, at least one of the five submitted models had a long fragment with significant similarity to the native structure. A fully automated version of the CASP5 protocol produced results that were comparable to the human‐assisted predictions for most of the targets, suggesting that automated genomic‐scale, de novo protein structure prediction may soon be worthwhile. For the three targets where the human‐assisted predictions were significantly closer to the native structure, we identify the steps that remain to be automated. Proteins 2003;53:457–468.

Rosettascripts: A scripting language interface to the Rosetta Macromolecular modeling suite

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.