Post-Doctoral Fellow in the Baker Lab at the University of Washington, Seattle
De Novo Computational Design of Novel Enzymes
I am interested in protein design as applied to the creation of new enzymes for interesting small-molecule chemical reactions and for biotechnological and medical applications. Enzymes are responsible for the wide variety of metabolic function in all living organisms, and have proven useful in the synthesis of pharmaceuticals and industrial products, for example fructose by glucose isomerase. While many enzymes are medically and industrially useful most reactions cannot be catalyzed by natural enzymes, and even those are generally limited by a very narrow range of substrate specificity. It would therefore be very useful to be able to design enzymes to catalyze reactions which previously were not amenable to biocatalysis, or to re-design existing enzymes to broaden or alter their specificity. Using new algorithms to search for appropriate active sites and design atomically-accurate pockets to bind the transition states (TS) of particular chemical reactions, the Baker lab has succeeded in producing de novo constructed enzymes for a Retro-Aldol condensation and the Kemp elimination.
I have extended these techniques to the design of a protein catalyst for the Morita-Baylis-Hillman reaction from an inert scaffold, and the re-design of a natural small-molecule ligase for fluorescent applications (manuscript in preparation with the Ting lab at MIT). I have also developed novel algorithms and benchmarks for protein design, including a technique that is now standard for the preparation of protein crystal structures from the PDB for computational design, and a method to apply more computationally intensive tools during design. I am currently applying these tools to the design of proteins with novel fluorescence properties for in vivo labeling.
Automating Human Intuition for Protein Design
|In the design of new enzymes and binding proteins, human intuition is often used to modify computationally designed amino acid sequences prior to experimental characterization. The manual sequence changes involve both reversions of amino acid mutations back to the identity present in the parent scaffold and the introduction of residues making additional interactions with the binding partner or backing up first shell interactions. Automation of this manual sequence refinement process would allow more systematic evaluation, and considerably reduce the amount of human designer effort involved. Here we introduce a benchmark for evaluating the ability of automated methods to recapitulate the sequence changes made to computer-generated models by human designers, and use it to assess alternative computational methods. We find the best performance for a greedy one-position-at-a-time optimization protocol that utilizes metrics (such as shape complementarity) and local refinement methods too computationally expensive for global Monte Carlo sequence optimization. This protocol should be broadly useful for improving the stability and function of designed binding proteins.||
A Pareto-Optimal Refinement Method for Protein Design Scaffolds
|Computational design of protein function involves a search for amino acids with the lowest energy subject to a set of constraints specifying function. In many cases a set of natural protein backbone structures, or "scaffolds", are searched to find regions where functional sites (an enzyme active site, ligand binding pocket, protein-protein interaction region, etc.) can be placed, and the identities of the surrounding amino acids are optimized to satisfy functional contraints. Input native protein structures almost invariably have regions that score very poorly with the design force field, and any design based on these unmodified structures may result in mutations away from the native sequence solely as a result of the energetic strain. Because the input structure is already a stable protein, it is desirable to keep the total number of mutations to a minimum and to avoid mutations resulting from poorly-scoring input structures. Here we describe a protocol using cycles of minimization with combined backbone/sidechain restraints that is Pareto-optimal with respect to RMSD to the native structure and energetic strain reduction. The protocol should be broadly useful in the preparation of scaffold libraries for functional site design.||
Computational Design of Enone-Binding Proteins with Catalytic Activity for the Morita-Baylis-Hillman Reaction
|The Morita-Baylis-Hillman reaction forms a carbon-carbon bond between the α-carbon of a conjugated carbonyl compound and a carbon electrophile. The reaction mechanism involves Michael addition of a nucleophile catalyst at the carbonyl β-carbon, followed by bond formation with the eletrophile and catalyst disassociation to release the product. We used Rosetta to design 48 proteins containing active sites predicted to carry out this mechanism, of which two show catalytic activity by mass spectrometry (MS). Substrate labeling measured by MS and site-directed mutagenesis experiments how that the designed active-site residues are responsible for activity, although rate acceration over background is modest. To characterize the designed proteins, we developed a fluorescence-based screen for intermediate formation in cell lysates, carried out microsecond molecular dynamics simulations, and solved X-ray crystal structures. These data indicate a partially formed active site and suggest several clear avenues for designing more active catalysts.||
In my graduate work I studied RNA Folding using Single Molecule Fluorescence and Monte Carlo Simulation methods. I have used these techniques to study the folding of a Group I intron, the ribozyme bI5, the smaller hairpin ribozyme, and a small non-catalytic hairpin loop RNA. Through simulation we aim to interpret single molecule experiment and offer avenues for new experimental work. Introductory descriptions and references for these projects are listed below.
In previous work I studied NMR, using the technique to solve the structure of the prion protein from cats (responsible for a wasting disease in cats); the physics of forced DNA movement through nanometer scale pores; and the neuroscience of learning and memory. References are provided below.
All-Atom Simulation of Hairpin Ribozyme Folding
|The hairpin ribozyme is known to undergo a structural
transition between the docked and undocked states, and this transition is
recapitulated in our Go simulations of the ribozyme. We seek to use the
all-atom nature of these simulations to build a simulated model of the
transition state for folding, or docking, of the ribozyme, from its folding
intermediate to the native state. The structure of the transition state for
docking has been studied in previous single-molecule work by targeted
mutations, salt variation, and urea titration (Bokinsky et al. PNAS vol.
100, 9302). These experimental studies will be useful for comparison with
our simulation results.
We analyze the structure of the transition state (or set of structures, the transition state ensemble, TSE) by conducting repeated folding simulations starting from the undocked conformation. In our Go model, the tertiary interaction strength and secondary interaction strength are independently adjustable. By changing the tertiary strength, one stabilizes or destabilizes tertiary structure. By determining the folding behavior as a function of tertiary strength, we learn about the relative importance of tertiary structure in the TS, as described below. Then, by carrying out further all-atom simulations, we can identify an atomically detailed model of the TSE using what are called Pfold simulations. In the image at right, nucleotides determined to be important in the TSE are colored in orange or red.
Folding of an RNA with its Protein Co-Factor
|Like most cellular RNA enzymes, the bI5 group I intron requires stable binding by a protein cofactor to fold correctly. We use fluorescence resonance energy transfer to observe the structural dynamics of the bI5 RNA as it assembles with its CBP2 protein cofactor. The single-molecule trajectories reveal highly dynamic structures of the RNA in the presence of CBP2. CBP2 binds to the target RNA in two distinct modes with apparently opposite effects: a nonspecific mode that forms rapidly and induces large conformational fluctuations in the RNA (image at right) and a specific mode that forms slowly and stabilizes the native RNA structure. Upon binding of CBP2, the bI5 RNA folds though multiple pathways toward the native state, typically traversing highly fluctuating intermediate states induced by nonspecific binding of CBP2. These results suggest that the protein cofactor-assisted RNA folding involves sequential nonspecific and specific protein-RNA interactions. We propose that the nonspecific interaction increases the local protein concentration and the number of conformations accessible to the RNA, thereby promoting the formation of specific RNA-protein interactions.|
Simulation of the Folding of an RNA tetraloop, GCAA
We report a detailed all-atom simulation of the folding of the GCAA RNA Tetraloop. The GCAA Tetraloop motif is a very common and thermodynamically stable secondary structure in natural RNAs. We use our simulation methods to study the folding behavior of a 12-base GCAA tetraloop structure with a four-base helix adjacent to the tetraloop proper. We implement an all-atom Monte Carlo (MC) simulation of RNA structural dynamics using a Go potential. Molecular Dynamics (MD) simulation of RNA and protein has realistic energetics and sterics, but is extremely expensive in terms of computational time. By coarsely treating non-covalent energetics, but retaining all-atom sterics and entropic effects, all-atom MC techniques are a useful method for the study of protein and now RNA. We observe a sharp folding transition for this structure, and in simulations at room temperature the state histogram shows three distinct minima: An unfolded state (U), a more narrow intermediated state (I), and a narrow folded state (F). The intermediate consists primarily of structures with the GCAA loop and some helix Hydrogen Bonds formed. Repeated kinetic folding simulations reveal that the number of helix base-pairs forms a simple 1-D reaction coordinate for the I-->N transition (Structure of a member of TSE displayed at right, with GCAA turn in green).
All-Atom Monte Carlo Simulation of GCAA RNA folding. JMB 344, 29.
Computational Protein Design
"Automating Human Intuition for Protein Design"(Nivon, Bjelic, King and Baker, Proteins: Structure Function and Bioinformatics. 82(5) 858-866). 2014.
"A Pareto-Optimal Refinement Method for Protein Design Scaffolds" (Nivon, Moretti, Baker. PLoS ONE 8(4): e59004). 2013.
"Computational Design of enone-binding proteins with catalytic activity for the Morita-Baylis-Hillman Reaction" (Bjelic, Nivon, Michael, Rosewall, Lovick, Seetharamn, Lew, Celebi-Olcum, Houk, Montelione and Baker. ACS Chem. Bio. 8 749). 2013.
Single-Molecule RNA Folding:
"Two Distinct Binding Modes of a Protein Cofactor with its Target RNA" (Bokinsky, Nivon, Liu, Chai, Hong, Weeks and Zhuang, J. Mol. Biol. 361 771-784). 2006.
RNA Folding Simulation:
"Thermodynamics and Kinetics of the Hairpin Ribozyme from Atomistic Folding/Unfolding Simulations" (Nivon and Shakhnovich, J. Mol. Biol. 411 1128-1144). 2011.
"All-Atom Monte Carlo Simulation of GCAA RNA folding" (Nivon and Shakhnovich, J. Mol. Biol. 344 29-45). 2004.
Prion protein Structure Determination:
"Prion protein NMR structures of cats, dogs, pigs, and sheep" (Lysek, Schorn, Nivon, Esteve-Moya, Christen, Calzolai, von Schroetter, Fiorito, Herrmann, Guentert and Wuthrich, Proc. Nat. Acad. Sci., USA, 102, 640-645). 2005.
"Amino Acid Sequence of the Felis catus prion protein" (Lysek, Nivon, and Wuthrich, Gene 341 249-253). 2004.
Nanopore DNA Sequencing:
"Voltage-driven DNA translocations through a nanopore " (Meller, Nivon, and Branton. Phys. Rev. Lett., 86, 3435-38). 2001.
"Rapid nanopore discrimination between single polynucleotide molecules" (Meller, Nivon, et al., Proc. Nat. Acad. Sci., USA, 97, 1079-1084). 2000.
Neuroscience of Learning and Memory:
"Effects of Chronic Stress on Hippocampal Long Term Potentiation (LTP)" (C. Pavlides, L. Nivon and B. McEwen. Hippocampus, 12, 245-257). 2002.
Rosetta Introductory Tutorial. Lawrence Berkeley National Lab. November 5-6, 2012. (Full 2 day Rosetta basics tutorial, including structure prediction, sequence design, protein-protein interface design, enzyme design and small-molecule preparation).
"Computational Design of a Resorufin-Peptide Ligase by Re-Design of E. coli Lipoic Acid Ligase". (Nivon, Richter, Liu, Ting and Baker). Protein Society 2011. Protein Science vol. 20 Sup. 1, p. 192, Boston, MA. July 2011.
"Kinetic Folding Pathway of the Hairpin Ribozyme from All-Atom Simulation". (Nivon and Shakhnovich). Platform Session Talk at the 51st Biophysical Society Meeting, Baltimore, MD. March 2007.
"Thermodynamics and Kinetic Folding Pathway of the Hairpin Ribozyme From All-Atom Simulation: Prediction of a Thermodynamic Intermediate State". (Nivon and Shakhnovich). Poster Presentation at the 50th Biophysical Society Meeting, Salt Lake City, UT. February 2006.
[G. Bokinsky, presenter]"Protein-assisted RNA folding occurs through multiple pathways with highly dynamic intermediate states". (Bokinsky, G.[presenter], L. Nivon, S. Liu, K. Weeks and X. Zhuang) Platform Session Talk at Biophysical Society Meeting, Long Beach, CA. February 2005.
"Single molecule analysis of an RNA-protein complex reveals a rugged folding landscape". (Bokinsky, G., L. Nivon, K. Weeks and X. Zhuang) Platform Session Talk at Biophysical Society Meeting, Baltimore, MD. February 2004. BIOPHYSICAL JOURNAL 86 (1): 352A-352A Part 2 Suppl. S, January 2004
"All-Atom Monte Carlo Simulation Methodology for RNA and Folding Dynamics of the GCAA Tetraloop". (Nivon and Shakhnovich). Poster Presentation at the 5th International Conference on Biological Physics, Gothenburg, Sweden. August, 2004.
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