RosettaCommons makes close collaboration between laboratories the norm, even with single code modules. This allows for rapid sharing of enhancements and promotes the values of team science.
RosettaCommons has a unique agreement among member universities. The source code belongs to the RosettaCommons members and is a collaborative effort among research institutions, a model that promotes shared development and discoveries.
Rosetta partners include government laboratories, institutaes, research centers, and partner corporations also use Rosetta software. Select a partner to learn more about their involvement with RosettaCommons.
The primary goals of the research in the Baker group over the past several years have been to predict the structures of naturally occurring biomolecules and interactions and to design new molecules with new and useful functions. These prediction and design challenges have direct relevance for biomedicine and provide stringent and objective tests of our understanding of the fundamental underpinnings of molecular biology.
To carry out the prediction and design calculations, we have been developing a computer program called Rosetta. At the core of Rosetta are potential functions for computing the energies of interactions within and between macromolecules, and methods for finding the lowest energy structure for a protein or RNA sequence (structure prediction) and for finding the lowest energy sequence for a protein or given structure or function (design) (Das and Baker, 2008). Feedback from the prediction and design tests is used continually to improve the potential functions and the search algorithms. Development of one computer program to treat these diverse problems has considerable advantages: first, the different applications provide complementary tests of the underlying physical model (the fundamental physics/physical chemistry is, of course, the same in all cases); second, many problems of current interest, such as flexible backbone protein design and protein-protein docking with backbone flexibility, involve a combination of the different optimization methods.
Macromolecular modeling for the advancement and understanding of human health and disease.
The Rosetta Design Group performs contract R&D in the pharmaceutical and biotech industries and collaborates with academic labs to advance macromolecular modeling and engineering. Additionally, the Rosetta Design Group supports the Rosetta Commons by hosting RosettaCon and providing training and support services for the Rosetta Macromolecular Modeling Suite.
Modeling and design of large, symmetric protein complexes.
Large protein complexes carry out some of the most complex functions in biology. Such structures are often assembled spontaneously from individual components through the process of self-assembly. A fundamental challenge in biology is to understand how protein subunits have evolved the remarkable ability to spontaneously self-assembly into complex structures and to characterize the interactions, assembly pathway and three-dimensional structures of such protein assemblies. The concept of self-assembly is relevant not only in the study of naturally occurring systems, but as a design principle in the engineering of novel protein assemblies.
We develop computational methods to predict the 3D structure of protein assemblies at high resolution, and to rationally design novel protein assemblies. These are then characterized using experimental methods.
Modeling and design of transmembrane proteins.
Our long-term goal is to understand how ligand/ membrane receptor/ downstream effector systems transmit specific signals across biological membranes and to exploit this knowledge to reengineer signaling pathways.
We address these objectives using a combination of computational and experimental approaches to model, design and reprogram ligand/ receptor/ effector interaction networks. Our long-term goal is to deconstruct the complex function and quantitatively describe the basic principles underlying these signaling networks.
Ab initio protein structure prediction, modeling and design of peptide-based ligands.
My lab is focused on a number of computational biology problems that, if solved, would remove key bottlenecks in biology and systems biology. We focus on two main categories of computational biology: learning networks from functional genomics data and predicting and modeling protein structure. In both areas I have played key roles in solving unsolved problems and achieving critical field-wide milestones.
In the area of structure prediction we were early contributors to the Rosetta code; a platform for structure prediction, design and docking. In the area of network inference we worked on two computational methods that were used to demonstrate the first predictive genome-wide model of regulatory dynamics (i.e. the first case where a genome-wide model could predict the whole transcriptional state of cells at future time points not part of the training set). Both network inference and protein structure prediction remain grand challenges and in spite of our progress much exciting work remains to be done in the coming years as we continue to improve, scale and apply these methods.
Ab initio protein structure prediction; modeling and design of protein-DNA & protein-peptide interactions
My group's research is focused on developing predictive models of molecular recognition using high-resolution structural modeling. We are currently working to predict the specificity of protein-DNA and protein-peptide interactions. We develop and apply new algorithms for molecular modeling within the framework of the Rosetta software package, a set of tools for the prediction and design of protein structures and interactions.
My lab studies protein folding and design. Understanding the pathway of protein folding may be the key to better algorithms for protein design. We are focusing our protein design work on green fluorescent protein (GFP), with the goal of understanding its folding and its dynamics, and to harness its intrinsic reporting ability to create programmable fluorescent biosensors, capable of specifically detecting peptides and proteins. We develop software that runs on massively parallel clusters such as CCNI. We synthesize and screen computationally designed molecules in the lab using molecular biological techniques, and then subject the proteins to biophysical analysis and X-ray crystallography.
Modeling and Design of RNA.
Our lab seeks an agile and predictive understanding of how nucleic acids and proteins code for the activities of living systems. We are creating new computational and chemical tools to tackle structure prediction of protein and RNA puzzles, the biophysics of functional and random RNAs, and the design of new RNA shapes and switches.
Structure-determination from sparse and noisy experimental data; crystal structure refinement. Methods for conformational sampling and protein forcefield development
Description of proteins using methods of Bayesian statistics applied to the PDB.
The Dunbrack group concentrates on research in computational structural biology, including homology modeling, fold recognition, molecular dynamics simulations, statistical analysis of the PDB, and bioinformatics. In developing these methods, we use modern methods of Bayesian statistics and extensive benchmarking. We are interested in applying comparative modeling to important problems in various areas of biology, especially in cancer research.
Computational design and experimental characterization of novel protein function
Design principles of molecular recognition in antibodies and enzymes
Molecular recognition and design of interactions in biological membranes
Our lab is developing and applying computational and experimental methods to design and discover new compounds for applications in chemical biology and drug discovery and design, with a focus on covalent inhibition and kinase signaling.
Modeling and design of peptide-protein interactions.
Our laboratory consists of interacting scientists that are interested in improving our basic understanding and manipulation of interactions between proteins.
This embraces different levels of resolution and scale: starting from the basic atom - level details of interactions; continuing to prediction and characterization of specific interactions; and finally addressing the ultimate question of their role within the context of a cell and a whole organism.
We use computational tools, including structure-based computational prediction and manipulation of specific interactions, analysis of evolutionary signals hidden in sequences, and large-scale integration of this data by machine-learning approaches.
Protein docking, antibody design, protein-surface interactions.
The goal of our research is to create and apply protein structure prediction methods to solve practical problems in self-assembly and function in biomolecular engineering.
In particular, we specialize in protein-protein docking, therapeutic antibodies, protein-solid surface interactions, and allostery. Rapid conformational search methods and accurate free energy functions have enabled unprecedented new abilities in structure prediction. We are pushing these techniques toward realistic biological and engineering problems and in the process developing additional methods capable of handling, for example, large proteins, proteins for which homology models must be used for docking, proteins which change conformation to affect function (allostery), and proteins which undergo conformational change upon binding.
Modeling of DNA-protein interactions, design of specific interactions.
The Havranek group seeks to understand the determinants of specificity in DNA-binding proteins. Our long-term goal is to develop a quantitative model for describing the interactions between proteins and their DNA binding sites, with applications in prediction and engineering.
We are currently studying DNA-binding specificity across a family of bacterial transcription factors. Computationally, we are using sequence analysis and structural modeling to assess the differences and similarities of the binding profiles for these proteins. Experimentally, we are combining high-throughput sequencing of binding sites selected from randomized DNA libraries with fluorescence-based binding assays to characterize relative and absolute binding preferences within this family of transcription factors.
Protein and small molecule design.
Our primary goal is to develop structure-based approaches for modulating protein function using small-molecules. We are exploring two parallel paths towards this overarching goal: the first is re-engineering proteins so that a small-molecule can be used to “turn on” function, and the second is identifying small-molecules that naturally complement and occlude a protein surface to “turn off” function. We apply these tools to understand how specific protein interactions are responsible for normal and aberrant signal transduction in cells.
Protein Enzyme design.
We are pursuing a research program at the interface of computational and experimental biophysics, enzymology and molecular biology. We use computational protein design and directed (laboratory) evolution to understand the structural, biophysical and evolutionary bases of molecular recognition phenomena in protein function such as enzyme activity, specificity and conformational changes.
Foldit - a protein structure prediction game.
Foldit is a revolutionary new game, in which you play to solve puzzles, and we test your solutions to work on curing cancer, AIDS, and a host of diseases.
One main goal for FoldIt is protein structure prediction, where human folders work on proteins that do not have a known structure. This would require first attracting the attention of scientists and biotech companies and convincing them that the process is effective. Another goal is to take folding strategies that human players have come up with while playing the game, and automate these strategies to make protein-prediction software more effective. These two goals are more or less independent and either or both may happen.
The more interesting goal for Foldit, perhaps, is not in protein prediction but protein design. Designing new proteins may be more directly practical than protein prediction, as the problem you must solve as a protein designer is basically an engineering problem (protein engineering), whether you are trying to disable a virus or scrub carbon dioxide from the atmosphere. It's also a relatively new field compared to protein prediction. There aren't a lot of automated approaches to protein design, so Foldit's human folders will have less competition from the machines.
Redesign of regulated protein interactions.
We are interested in how biological molecules communicate with each other, and how this communication encodes the processing of information. How do biomolecules recognize one another, and how do their interactions transduce signals? How do molecules build up "modules" that act as "adaptors", "switches" and feedback-loops? How are modules wired together into the networks responsible for regulation and decision processes observed in biology?
Computationally, we have developed a simple physical energy function for the prediction and design of protein-protein interactions, at the atomic level. Experimentally, we have applied this model to the computational redesign of a protein interface and have created an artificial DNA binding protein with new specificity. More recently, we have developed a computational strategy for the redesign of protein complexes to generate new pairs of interacting proteins.
We are now applying and extending our computational model at different "resolution", ranging from details of atom-atom interactions to cellular communication networks. We are aiming to develop more accurate methods to model the structural details of molecular interactions. Can new interactions and modules with defined properties be engineered? Ultimately we would like to apply computational and experimental methods to better understand how cellular processes are regulated by molecular communication.
Design of proteins and protein interactions.
We use a combination of computational and experimental methods to design proteins.
Currently we are focusing on a variety of design goals including the creation of novel protein-protein interactions, protein structures and light activatable protein switches. Central to all of our projects is the Rosetta program for protein modeling. In collaboration with developers from a variety of universities, we are continually adding new features to Rosetta as well as testing it on new problems.
NMR-guided modeling of protein structure.
We are interested in understanding the basic principles that govern conformational motion to enable a rational modulation of dynamic behavior of engineered biomolecules such as artificial enzymes and inhibitors.
As a stepping stone we strive to rationally design simple model systems that undergo specific conformational changes. To detect and characterize conformational changes we use Nuclear Magnetic Resonance (NMR) Spectroscopy. We will also continue our effort to develop computational tools to determine structure and dynamics of proteins with the help of sparse NMR data.
Algorithm development for structure prediction and design.
1. Design and rewiring of cellular signaling networks
2. Systematic analysis of protein drug targets
3. Drug (small molecules, peptides, antibodies) optimization and design
Sergey is a Software Engineer in the Gray Lab. He is a Lead Test Engineer for the Rosetta Project and a Build Engineer for the PyRosetta Project
Proteome-scale modeling of protein structures.
Systems biology is an information intensive science and we are using cutting-edge information management strategies, high-performance computing, computational modeling, machine learning and statistics to gain insight.
Modeling and design of protein-ligand interactions.
Research in our laboratory seeks to fuse computational and experimental efforts to investigate proteins, the fundamental molecules of biology, and their interactions with small molecule substrates, therapeutics, or probes. We develop computational methods with three major ambitions in mind.
A) To enable protein structure elucidation of membrane proteins the primary target of most therapeutics and large macromolecular complexes such as viruses;
B) Design proteins with novel structure and/or function to explore novel approaches to protein therapeutics and deepen our understanding of protein folding pathways.
C) Understand the relation between chemical structure and biological activity quantitatively in order to design more efficient and more specific drugs.
Crucial for our success is the experimental validation of our computational approaches which we pursue in our laboratory or in collaboration with other scientists.
The Biomolecular Science Group's research goal is to enable increasingly cost-competitive advanced lignocellulosic biofuels that can be produced at sufficient scale to impact the transportation fuel market. The primary research focus is understanding the molecular basis of biomass recalcitrance and developing enzymatic, microbial, plant biomass modification, and chemical systems to overcome recalcitrance and make possible more competitive and scalable conversion technologies.
Our research approach aims for understanding in three important areas: plant cell wall structure and chemistry across multiple scales; the structure, function, and dynamics of plant cell wall deconstruction biocatalysts; and, importantly, the interactions between substrate (plant cell walls) and the catalysts.
Vaccine design, de novo design of protein interactions.
The Schief lab works on grafting epitopes from HIV into other proteins in the search for an HIV vaccine.
Our goal is to live up to the classic tagline "Better Living Through Chemistry", which we achieve through computational enzyme engineering. Enzymes are the primary means by which biology performs chemical transformations. Naturally evolved enzymes have been optimized over long periods of time to address challenges biological systems face in nature. However, modern society faces additional challenges in food, energy, and health. To address these challenges, novel catalysts are needed. The Siegel lab focuses on the use of computational, genetic, and chemical methods to design, build, and test enzyme catalysts tailored for today’s challenges.
The goal of our research is to predict protein structure to understand how proteins function and interact. To reach this goal we use advanced data mining and machine learning techniques from bioinformatics together with molecular based energy functions and sampling to predict and study molecular interactions.
Protein docking, modeling and design of metal-binding proteins.
Our research programs focus on the development and application of multi-disciplinary tools in chemoproteomics, biochemistry, bioinformatics and computational structure biology to 1) globally uncover novel funtional sites in enzymes that are post-translationally regulated by endogenous reactive metabolites or targeted by drug compounds; 2) interrogate the underlying molecular mechanisms by which these modifications regulate protein function to perturb key cellular signaling pathways and 3) develop computational tools to predict, model and design such protein-small molecule interactions. These studies have the great potential to provide penetrating mechanistic insights into the molecular basis for numerous diseases functionally linked to metabolic disorder as well as to integrate and streamline efforts in inhibitor discovery, drug design and the functional annotation of uncharacterized enzymes in the post-genomic era.
Protein design assisted by in vitro evolution.
We are a young lab that designs and engineers functional proteins. We are focused on the development of computational and experimental tools as well as two major areas for the application of these methods: (1) the microbial-mediated conversion of biomass to fuels and chemicals that more closely approximate petroleum-derived feedstocks; and (2) development of antibody and antibody-like molecules for use as protein therapeutics against viral pathogens.
Modeling of transmembrane proteins.
My research interests and expertise encompass neuroscience, protein structure, computational biology, and evolution. Main focus of my research group is on structure and function studies of voltage-gated ion channels, computational design and chemical synthesis of subtype-specific modulators of voltage-gated ion channels, development of computational methods for membrane protein structure prediction and design, and analysis of evolution of human voltage-gated ion channels.
Foldit, citizen science, and crowdsourcing.
Foldit is a video game that allows players to contribute to protein structure prediction and design. We are exploring how humans and computers working together can lead to solutions to challenging problems.
The Lindert group engages in computational biophysics research. Research in the lab focuses on the development and application of computational techniques for modeling biological systems of varying sizes. We are particularly active in the field of protein structure prediction using sparse experimental data, protein dynamics as well as drug discovery.
Directed evolution and structure prediction of transmembrane proteins
The Procko lab uses the tools of directed evolution to inform computational modeling of transmembrane proteins. We are particularly interested in G protein-coupled receptors with large extracellular domains for the recognition of small molecule ligands. Such receptors are abundantly expressed in the nervous system for the detection of neurotransmitters, pheromones, and sweet or savory tasting substances. The structures of individual domains for these receptors are known, but how they are arranged in a full receptor is unclear. We are developing experimental methods to map the sequence-fitness landscape of these receptors, which can be used to constrain conformational sampling during structure prediction of resting or active states.
We are interested in designing allosteric transcription factors as small molecule biosensors. We apply these designer biosensors toward engineering microbes for biosynthesis of valuable chemicals fuels, and for functional mining of environmental microbiota to identify bioremediation pathways. We are developing highly multiplexed screening and directed evolution approaches to address these challenges. We are also interested in studying mechanism of allosteric regulation at molecular resolution using systems biology principles.
The Kulp lab focuses on rational vaccine and therapeutic antibody design for a variety of NIAID priority infectious diseases (e.g. Lassa Virus) and cancer targets. The ultimate test of our understanding of B cell immune responses is to design new immunogens that drive predictable antibody maturation. To that end, we are interested in the development and application of protein engineering methods for modifying antigen/cell receptor interfaces, antigen/antibody interfaces, antigen surface properties and core stabilization.