About

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.

de novo protein and peptide design, design of protein interactions, design of protein properties

Protein-based medicine is transforming healthcare, and we are working to accelerate this transformation. To do this, we design biologic drugs, as well as create tools to facilitate drug discovery. We design novel proteins and peptides de novo, and we engineer existing proteins to improve or modify their properties. We place a major emphasis on high-throughput science and leverage extensive laboratory automation.

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.

The laboratory is highly interdisciplinary combining techniques across molecular modeling and experiments.

The main goal of our research is to build proteins and small-molecules with therapeutic properties. Generally, we use a combination of computational molecular modeling methods (ROSETTA, MD, docking, computer scripting, etc) to theoretically predict interactions between a designed molecule and a drug target. To validate our modeling efforts, in the next step, our designs are functionally assessed by experimental techniques. Here we employ a range of basic biochemistry techniques for protein expression/purification, molecular biology, determination of molecular interactions, etc.

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.

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 Corn Lab develops and uses next-generation genome editing and regulation technologies. We work on both fundamental biological discovery and potential therapies for human genetic diseases. Our focus is the mechanisms by which cells repair their DNA, maintain and differentiate hematopoietic stem cells, and use ubiquitin signaling to propagate cellular signals. Through technology development, mechanistic cellular biochemistry, and translational projects, we are working to unravel complex cellular phenotypes to further biological understanding and improve human health.

The Correia Group is driven by the passion of expanding nature’s repertoire by designing novel functional proteins to be used for practical purposes such as therapeutics, vaccines, biosensors and others.

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.

Protein docking in small molecule design. Protein design. Structural biology.

The main objective of research in the Fischer lab has been to understand the mechanisms by which cells control protein stability, and to develop novel therapeutic strategies that modulate protein stability. We combine a broad range of technologies from structural biology, biochemistry, cell biology to large scale multi-omics and computational methods.

The Ubiquitin Proteasome System is involved in virtually any cellular process and frequently implicated in human diseases. The Fischer lab combines structural biology, cell biology, and high throughput biology approaches to understand the mechanistic principles that govern signaling through the ubiquitin proteasome system. We leverage these insights to propose and test new avenues of therapeutic intervention, such as targeted protein degradation or SPLiNTs. Our work has contributed to the now widespread application of targeted protein degradation in drug development and as a powerful tool to study biology.

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

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 engineering, glycoproteins, membrane proteins.

My lab’s research focuses on computational protein structure prediction and design, particularly protein- protein docking, therapeutic antibodies, protein-surface interactions, membrane protein interactions, and glycosylation. Our lab plays a leading role in developing methods like RosettaDock, RosettaAntibody, RosettaSurface, RosettaMP (membrane protein modeling), RosettaCarbohydrate, and the ROSIE web server and the PyRosetta interactive platform. Core challenges include protein conformational change upon binding and accurate calculations of energies. An important part of our work is collaborations with diverse research groups to understand basic biology, disease and immunity. We have collaborated on projects on cancer, hypophosphotasia (a bone mineralization disease), Celiac’s disease, Alzheimer’s disease, progeria (related to aging), antibiotic resistance, and many more.

Structural bioinformatics, coarse grained modelling, algorithm development

We focus on computational methods for multiscale modelling of biomacromolecular structure and dynamics. Our lab devised a few computational approaches to study proteins at various level of coarse-graining. We also develop BioShell software suite for structural bioinformatics, alignments and structure modelling, analysis and visualisation.

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.

De novo protein design, machine learning based tool development, molecular motors, protein origami, biosensors

We specializes in the development of novel protein platforms in solving biological problems. The protein design field took ten years to find a foothold in designing de novo structures (i.e. achieving full control of modeling protein backbones and sequences) and finally matured as suggested by the recent wave of publications such that we are now closer to achieving atomic-precision design of custom-made protein structures to address biomedical problems. Many challenges remain, as we are still far from achieving the complexity and functionality that nature displays. ​We are building a strong interdisciplinary research program to bridge this gap. Our research combines both computational and experimental aspects of protein engineering.​

The Horowitz lab studies citizen science and scientific computer gaming using the biochemistry game Foldit. Our work with Foldit focuses on integrating experimental data into the game, and developing new educational tools and approaches. These efforts improve the speed and accuracy of structural biology investigations, as well as providing hands-on science teaching tools used world-wide. Additionally, we study how nucleic acids impact protein folding and aggregation using strategies ranging from genetics to biophysics.

Our group works at the interface of computational protein design and synthetic biology to engineer chimeric proteins and microbes with applications in biomanufacturing, bioremediation and diagnostics. We are interested in design of allosteric biosensors, and protein modules with novel properties such as catalytic activity or binding to a ligand or an antigen.

Jiang lab focuses on computational structural biology and drug design for Alzheimer's, Parkinson's, Lou Gehrig's disease and other degenerative disorders. Current research is driven by two key questions: How do unfolded or misfolded proteins self-associate into abnormal aggregates? How do these aggregates propagate and lead to disease? Ongoing research is to develop new therapeutic approach for neurodegenerative and other brain diseases, which includes: 1) design allosteric BACE inhibitor that specifically blocks the APP cleavage and Abeta production; 2) design protein inhibitor that blocks the prion-like transmission of protein aggregates in neurodegenerative diseases; 3) design and test new protein that crosses the blood-brain barrier via carrier-mediated transport. The findings of the research will identify new drug targets, develop new therapeutics and design new therapeutic compounds or peptides for the treatment of neurodegenerative disorders. All of these will strengthen our ability of designing biological systems with desired properties, provide an alternative perspective for us to ultimately understand our living world, and promise solutions to some of the most pressing problems in human health.

Protein and small molecule design.

Our goal is to develop structure-based approaches for modulating protein function using small-molecules. We apply these new approaches in projects seeking to re-activate disabled tumor suppressors, inhibit cancer-driving RNA-binding proteins, disable key oncogenic kinases, and tune the activity of antibodies used in cancer immunotherapy.

Our lab seeks a molecular understanding of how the cell organizes its information. We are using Rosetta and cryo-EM to interpret, at the atomic level, structures of dynamic complexes that interact with nucleic acids to understand better their biological functions.

Proteins are Nature’s building block of choice for the construction of ‘molecular machines’: stable yet dynamic assemblies with unparalleled abilities in molecular recognition and logic. The King group incorporates these features into the design of functional protein-based nanomaterials with the goal of creating new opportunities for the treatment and prevention of disease. We use computational protein design and a variety of biochemical, biophysical, and structural techniques to produce and characterize our novel materials.

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.

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.

Carbohydrates, post-translational modifications

The overarching goal of my undergraduate research group is to develop computational tools to decipher the language of the cell at the chemical level, focusing on interactions with carbohydrates and other PTMs. Students in my lab learn skills in organic chemistry, biochemistry, and computational modeling.

Our two main projects are computational glycoengineering, where we seek to develop Rosetta toward designing and predicting glycan residues and glycosylation patterns as favorable candidates for experimentation, and virtual post-translational modification of proteins, where we develop tools to read consensus sequences from a database and use this information to build models for a wide range of PTMs, including glycosylation, phosphorylation, and acetylation.

Algorithm development for structure prediction and design.

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 mass spectrometry (covalent labeling, surface induced dissociation, ion mobility) and cryo-EM data. We also study protein dynamics and investigate protein-ligand interactions for drug discovery.

Research interests:

1. Design and rewiring of cellular signaling networks

2. Systematic analysis of protein drug targets

3. Drug (small molecules, peptides, antibodies) optimization and design

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.

Transmembrane protein design

Lu lab mainly focus on computationally design of new generations of functional multipass transmembrane proteins, and design of small proteins targeting disease-related membrane proteins. His group proposes to overcome several key challenges. (i) Use computational protein design methods to develop new generation of transmembrane nanopores and design ion channels with specific selectivity, from scratch. (ii) Extend effort to design transmembrane proteins with cavities that could bind small molecules. One example is to design receptors that could oligomerize and signal upon ligand binding. (iii) Combine computational design effort with massively parallel gene synthesis followed by high-throughput screen to make de novo designed binders for disease-related membrane proteins. Seamless interaction of computational protein design, high-throughput screening, channel activity studies, ligand binding efficacy studies, membrane protein expression and structure determination in Lu lab will provide insights into principles of transmembrane protein 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.

Our research is focused on using computational methods to engineer proteins with novel functions for a variety of chemical and biological applications. Although naturally occurring proteins carry out a wide array of important functions using only twenty standard amino acids, recent technological advances have expanded the genetic codes of several different organisms to include more than 150 amino acids not found in naturally occurring proteins. The chemical functionalities present in these unnatural amino acids could be used to: circumvent current difficulties in protein engineering, provide platforms to study proteins at the molecular level, or generate proteins with functions that would be difficult to achieve with naturally occurring amino acids alone. To achieve these goals we employ computational protein design methods to carry out evolutionary trajectories in silico in the context of a variety of non-canonical amino acids. Our current efforts are divided into the following major areas: design of protein biosensors, design of enzymes and design of functional protein nanomaterials.

Protein structure and function, protein-based materials, crystallization chaperones, enzyme design, radical SAM enzymology

The goal of our research is the apply protein modeling and engineering to better understand enzymes and protein complexes, and to engineer new enzymes, protein polymers, and crystallization chaperones.

In particular, we are working to develop general-use crystallization chaperone strategies to streamline protein crystallization and expand the scope of proteins that can be structurally characterized. We are also developing diffraction-quality protein polymers. We are engaged in the structural enzymology of radical SAM enzymes and their complexes. We are also working on enzyme design of radical SAM enzymes. Radical-mediated enzyme mechanisms enable new synthetic chemistry and we are developing protocols and pipelines to make redesigned radical SAM enzymes more attainable for enzymatic small molecule and drug synthesis applications in industry and medicine.

We are working to understand the molecular design principles of the solar energy conversion machinery in photosynthesis, and use these for building novel solar energy conversion devices by reverse engineering. Our group applies protein design tools in order to construct novel protein-cofactor complexes that serve as minimal versions of the elaborate complexes that comprise the natural photosynthetic apparatus.

Protein-protein interface design, antibody design

The goal of our research is to design proteins that bind to therapeutic target proteins.

Our research includes (i) computational design therapeutic antibodies which bind to a selected surface on target proteins, (ii) de novo design of mini-scaffolds that display a linear binding motif, (iii) and de novo design of small binding protein proteins that enhance the binding capacity of already available protein binders. Since water molecules are usually important mediators at the interface between pairs of binding proteins, we plan to develop methods to incorporate explicit water molecules in the protein-protein interface design protocols.

I work on developing new hybrid computational and experimental methods for protein design. The focus is on modular systems based on designed repeat proteins for spatial control of protein structures and their applications as tools to study and influence cell behaviour.

We develop and apply algorithms to model and design protein-protein interactions. With a major focus on immune recognition and therapeutics, we are particularly interested in T cell receptors, antibodies and vaccine design. Using computational protein design and docking algorithms, such as Rosetta, ZAFFI and ZDOCK, we are exploring ways to improve antibody and TCR targeting of viruses and tumor antigens, and to better predict uncharacterized interactions. Recent efforts in vaccine design include engineering and immunogenicity testing of a number of novel vaccine candidates to effectively target hepatitis C virus.

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.

We develop high-throughput methods for protein biophysics and protein design, with a focus on protein therapeutics. Key questions include: How do protein sequence and structure determine folding stability, conformational dynamics, and resistance to aggregation/degradation-inducing stresses? Can we quantitatively predict these protein "phenotypes" from genotype (sequence) using computational modeling? How do we design protein therapeutics that optimize these phenotypes for a particular application? To answer these questions, we combine large-scale de novo computational protein design with high-throughput methods such as display selections, mass spectrometry proteomics, and next- generation sequencing, enabling us to test thousands of proteins in parallel. By combining these technologies, we seek to develop efficient "design-test-analyze" cycles, iterating our way to an improved, quantitative understanding of protein biophysics and more advanced protein therapeutics.

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.

We study proteins of the immune system that play important roles in human health. To perform their role, these proteins must interact with other biomolecules, such as other protein receptors and smaller peptides. Elucidating these molecular interactions at high resolution will help establish the biochemical basis of immune recognition. Besides obtaining an unprecedented basic science understanding of fundamental biological processes, the knowledge gained from our detailed molecular description will enable us to develop new therapeutic molecules for emerging immunotherapy applications to combat viral infections, autoimmune diseases and cancer.

To achieve these goals, we employ a variety of state-of-the-art biophysical techniques, including X-ray crystallography, fluorescence spectroscopy, solution NMR, and computational modeling, followed upon by experiments using cell lines.

Enzyme design.

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.

In the Shirts group, we design and characterize new materials at the nanoscale through the use of theory and computation. Our focuses include drug design through prediction of physical properties and binding affinities and the design of novel biomimetic materials. We are especially interested in the development of computational tools that can fundamentally change molecular design by making searches through chemical and configurations space much more predictive, reliable and efficient.

We are using Rosetta to study nonbiological foldamers; heteropolymers that, like proteins, have well- defined structure, but which have different chemical functionalities. As part of our research, we are adding both general chemical representations of heteropolymers, removing the restrictions that limit Rosetta to protein-like structures, as well as adding more general coarse-grained functionality, representing these molecules at a larger-than-atomistic scale. We then work to see what these models teach us about how molecules can fold into well-defined secondary and tertiary structures.

Outer Membrane Protein Design.

The location of the membrane allows for the control of interactions between the cell and its environment. This feature gives membrane protein designers access to many targets including: inhibition of antibiotic resistance (via antibiotic efflux), oil spill remediation (via enzymatic activity at the membrane surface), and cancer diagnostics (via voltage-dependent membrane fluorophores). The Slusky lab aims to 1) identify common characteristics of proteins in membranes 2) use these common features to make new membrane proteins that will be useful to society.

The Smith Lab aims to determine the atomic-level mechanisms of how changes in the so-called “second shell” and beyond propagate through the protein and ultimately affect function. This can enable protein activity to be regulated by distant events through a process called allostery. Furthermore, deleterious mutations far removed from the active site make the protein malfunction and cause disease. In other cases, protein engineering has discovered unusually located mutations that enhance activity, making synthetic and therapeutic applications possible. The common mechanistic question about these examples is the following: how do atomic rearrangements propagate from one part of the structure to another? Even more intriguingly, how can a signal propagate without distinct structural changes? There are an increasing number of cases where this communication happens not through a change in the structure of the protein, but in the amount of motion.

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.

Data-driven protein engineering and design, antibodies, molecular recognition, plant synthetic biology

My group designs and engineers proteins. The advent of next-generation sequencing has presented protein scientists with the ability to economically observe entire populations of molecules before, during, and after a high-throughput screen or selection for function. My group leverages this unprecedented wealth of sequence-function information to address key gaps and important fundamental and applied grand challenges in innovative ways:

Design and engineering of protein affinity, specificity, and function. Proteins are designable agents in medicine as shown by the incredible success of antibody and pro- drug enzyme therapies. We have enhanced the efficiency of programming specificity, affinity, and stability into proteins.

Identifying functional constraints on the evolvability of protein sequences. We have investigated fundamental trade-offs between enzyme activity and solubility and sequence determinants to enzyme specificity. We are applying these insights to understand why and how certain protective antibodies can be elicited for vaccines against viruses like Dengue, HIV, and Influenza.

Plant synthetic biology. In collaboration with some of the world’s best plant scientists, we are developing plant sense and respond modules that can turn on and off developmental pathways and metabolism using external, readily available chemicals.

We develop enzymes and microbes for converting renewable biomass to value-added chemicals and bioproducts.

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.

The group of Protein Design and Self-Assembly focuses on dissecting the physical principles of protein self-assembly. While conceptually simple, the phenomenon of self-assembly entails a fine equilibrium of a number of physical properties, which determine the dynamics and structure of the assembly architecture. We combine computational de novo protein design with protein production, in vitro model substrates and biophysical methods to systematically investigate the interplay between different types of interactions in the protein assembly process. Our goal is to create dynamic and responsive protein-based materials, by exploring protein structural flexibility and by coupling assembly to support coating or cargo encapsulation.

The Schiffner lab combines computational protein design with experimental in vitro high-throughput screening methods to develop “smart” vaccine immunogens. We use technologies such as antibody/antigen interface optimization, epitope grafting, protein resurfacing, T-cell epitope engineering, glycan masking, immunogen multimerization, and protein stabilization and apply them to the rational design of next-gen immunogens against a variety of pathogens, such as Coronaviruses, Hepatitis C Virus and Human Immunodeficiency Virus.

Interactions formed on a molecular level define specificity and differentiate between desired and undesired effects in vivo. Our research aims to use protein design to obtain new therapeutic options for infectious, oncological and immunological diseases through the combination of computational design and experimental validation. Using computational protocols such as de novo protein design, redesign of protein domains, epitope grafting, binder design and glycan masking we are sampling molecular interactions that define interactions with the target of interest. Designed proteins are validated using immunological and biochemical assays and methods in structural biology. Vaccine design for influenza and filoviruses, protein design for cancer cell targeting in multiple myeloma and design of adeno-associated virus capsids are of special interest to us.

Our research group combines computational protein design with high-throughput biophysical techniques to study biomolecular conformational changes, towards engineering proteins and protein drugs that change conformations, interact with other molecules, and cross membranes in response to signals. Design principles for building signal-responsive, conformation-switching proteins can guide efforts to control the behavior of living systems and treat diseases without disrupting healthy cells. Our long-term goal is to engineer multi-functional proteins that respond to ligand binding and correct localization via programmed conformational changes. This work serves the public by growing our knowledge of biomolecular behavior and opening new paths to treat disease.

We are using both computational and experimental methodologies to understand, inhibit and re-purpose biological processes on the protein level. Our main focus is on how to diagnose, prevent and treat viral infections with the aim to generate new anti-virals and candidates for vaccination through protein design. For that we develop and use new cutting-edge technologies involving structural design as well as pooled oligo-synthesis and next-generation sequencing. While we are studying viral surface proteins principally to understand how we can target them or provide new immunogens, we also seek to shed light on how protein chemistry is involved in making viruses so successful. Viruses and their surface proteins hold the molecular keys for identifying specific host cells, entering them and re-programming them -- much of what we will need to fight cancer.

The DeGrado lab works on the design of proteins and small molecules to address problems of biomedical interest.