Macromolecular modeling and design in Rosetta: recent methods and frameworks.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Ultrafast photoinduced dynamics in Prussian blue analogues.

New magnetic materials and methods for controlling them are needed to improve data storage technologies. Recent progress has enabled optical detection and manipulation of spins in molecule-based magnets on the femtosecond timescale, which is promising for both increasing the read/write speed but also the data storage density. Experimental developments in femtosecond X-ray free-electron lasers (XFELs) and magneto-optics, in combination with theory advances, have opened up several new avenues to investigate molecule-based magnets. This review discusses the literature concerning ultrafast photoinduced dynamics in Prussian blue analogues (PBAs), which are molecule-based magnets. In PBAs spin-flips and lattice distortions can happen on the 100 fs timescale, which in some analogues lead to photoinduced changes in the long-range magnetic order. The literature and themes covered in this review are of relevance for ultrafast optical control of new multifunctional materials.

Improved specificity of TALE-based genome editing using an expanded RVD repertoire.

Transcription activator-like effector (TALE) proteins have gained broad appeal as a platform for targeted DNA recognition, largely owing to their simple rules for design. These rules relate the base specified by a single TALE repeat to the identity of two key residues (the repeat variable diresidue, or RVD) and enable design for new sequence targets via modular shuffling of these units. A key limitation of these rules is that their simplicity precludes options for improving designs that are insufficiently active or specific. Here we address this limitation by developing an expanded set of RVDs and applying them to improve the performance of previously described TALEs. As an extreme example, total conversion of a TALE nuclease to new RVDs substantially reduced off-target cleavage in cellular studies. By providing new RVDs and design strategies, these studies establish options for developing improved TALEs for broader application across medicine and biotechnology.

Tracking nuclear motion in single-molecule magnets using femtosecond X-ray absorption spectroscopy.

The development of new data storage solutions is crucial for emerging digital technologies. Recently, all-optical magnetic switching has been achieved in dielectrics, proving to be faster than traditional methods. Despite this, single-molecule magnets (SMMs), which are an important class of magnetic materials due to their nanometre size, remain underexplored for ultrafast photomagnetic switching. Herein, we report femtosecond time-resolved K-edge X-ray absorption spectroscopy (TR-XAS) on a Mn(III)-based trinuclear SMM. Exploiting the elemental specificity of XAS, we directly track nuclear dynamics around the metal ions and show that the ultrafast dynamics upon excitation of a crystal-field transition are dominated by a magnetically active Jahn-Teller mode. Our results, supported by simulations, reveal minute bond length changes from 0.01 to 0.05 Å demonstrating the sensitivity of the method. These geometrical changes are discussed in terms of magneto-structural relationships and consequently our results illustrate the importance of TR-XAS for the emerging area of ultrafast molecular magnetism.

Single-cell analysis of habituation in Stentor coeruleus.

Although learning is often viewed as a unique feature of organisms with complex nervous systems, single-celled organisms also demonstrate basic forms of learning. The giant ciliate Stentor coeruleus responds to mechanical stimuli by contracting into a compact shape, presumably as a defense mechanism. When a Stentor cell is repeatedly stimulated at a constant level of force, it will learn to ignore that stimulus but will still respond to stronger stimuli. Prior studies of habituation in Stentor reported a graded response, suggesting that cells transition through a continuous range of response probabilities. By analyzing single cells using an automated apparatus to deliver calibrated stimuli, we find that habituation occurs via a single step-like switch in contraction probability within each cell, with the graded response in a population arising from the random distribution of switching times in individual cells. This step-like response allows Stentor behavior to be represented by a simple two-state model whose parameters can be estimated from experimental measurements. We find that transition rates depend on stimulus force and also on the time between stimuli. The ability to measure the behavior of the same cell to the same stimulus allowed us to quantify the functional heterogeneity among single cells. Together, our results suggest that the behavior of Stentor is governed by a two-state stochastic machine whose transition rates are sensitive to the time series properties of the input stimuli.

In-situ Field Gamma Spectrometry in a Radionuclide Air Sampler.

ABSTRACT: The work being presented is on the development of a system to measure the speciation of airborne radionuclide emissions from the environment during a nuclear emergency. On-site air sampling measurements that were conducted during the Fukushima Daiichi accident were limited because field teams had to be sent out to run the sampling systems and retrieve the filters for gamma spectrometry analysis in a separate laboratory. The start of air sampling was delayed, and it was impossible for emergency responders to use the information about the airborne radionuclide composition in a timely way. The goal of the current study is to develop a system that could provide live, near real-time information about the concentrations of different radionuclides in the air without having to rely on human intervention. The development of the prototype in the current work is largely being enabled by Cd-Zn-Te spectrometers, which provide reasonably high-resolution spectrometry given that it is a room temperature sensor, and allow the measurements to be conducted in the field. A custom filter cartridge has been designed to hold a pair of aerosol and iodine filters in place while keeping the gamma spectrometers as close as possible in order to obtain high count rate efficiencies. A single cartridge holds both filters and has an internal flow channel directing the air flow between them. The cartridge design also facilitates replacing the filters as the accumulated radioactivity on the filters becomes too high. An automation system can move a filter cartridge from the fresh cartridge storage bank to the sampling location (filtration and gamma spectrometry) and return the used filter cartridge to the used cartridge storage bank. The radionuclide air sampling system prototype has been designed and constructed. It has been tested with fixed sources located on the respective aerosol and iodine filters. The real-time data capture aspects of the system were also demonstrated with a live 131I capture experiment. The projected performance of the system during a reactor accident was also simulated, emulating the characteristic detector efficiencies and projecting how the airborne concentrations could be reconstructed. The study has designed and constructed a radionuclide air sampler that could be used for measuring airborne radioactivity in emissions from a nuclear accident. Because the gamma spectrometry measurements are done in situ with good resolution and the system is automated, it would allow data to be transmitted back to an emergency operations center immediately rather than having to wait for additional laboratory analysis.

Photoinduced Jahn-Teller switch in Mn(III) terpyridine complexes.

Ultrafast transient absorption spectra were recorded for [Mn(terpy)X3], where X = Cl, F, and N3, to explore photoinduced switching from axial to equatorial Jahn-Teller (JT) distortion. Strong oscillations were observed in the transients, corresponding to a wavepacket on the excited-state potential energy surface with oscillation frequency around 115 cm-1 for all three complexes. Multireference quantum chemistry calculations indicate that the reaction coordinate is a pincer-like motion of the terpyridine ligand arising from bond length changes in the excited state due to the JT switch. We observed long dephasing times of the wavepacket, with times of 620 fs for [Mn(terpy)Cl3], 450 fs for [Mn(terpy)F3], and 370 fs for [Mn(terpy)(N3)3]. The dephasing time of these coherences decreases with an increasing number of vibrational modes at lower energy than the mode dominating the reaction coordinate, suggesting they act as an effective bath to dissipate the excess energy obtained from photoexcitation.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Deamidation and isomerization liability analysis of 131 clinical-stage antibodies.

Contemporary in vivo and in vitro discovery platform technologies greatly increase the odds of identifying high-affinity monoclonal antibodies (mAbs) towards essentially any desired biologically relevant epitope. Lagging discovery throughput is the ability to select for highly developable mAbs with drug-like properties early in the process. Upstream consideration of developability metrics should reduce the frequency of failures in later development stages. As the field moves towards incorporating biophysical screening assays in parallel to discovery processes, similar approaches should also be used to ensure robust chemical stability. Optimization of chemical stability in the early stages of discovery has the potential to reduce complications in formulation development and improve the potential for successful liquid formulations. However, at present, our knowledge of the chemical stability characteristics of clinical-stage therapeutic mAbs is fragmented and lacks comprehensive comparative assessment. To address this knowledge gap, we produced 131 mAbs with amino acid sequences corresponding to the variable regions of clinical-stage mAbs, subjected these to low and high pH stresses and identified the resulting modifications at amino acid-level resolution via tryptic peptide mapping. Among this large set of mAbs, relatively high frequencies of asparagine deamidation events were observed in CDRs H2 and L1, while CDRs H3, H2 and L1 contained relatively high frequencies of instances of aspartate isomerization.

Computational design of a modular protein sense-response system.

Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.

Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.

Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.

Extending chemical perturbations of the ubiquitin fitness landscape in a classroom setting reveals new constraints on sequence tolerance.

Although the primary protein sequence of ubiquitin (Ub) is extremely stable over evolutionary time, it is highly tolerant to mutation during selection experiments performed in the laboratory. We have proposed that this discrepancy results from the difference between fitness under laboratory culture conditions and the selective pressures in changing environments over evolutionary timescales. Building on our previous work (Mavor et al., 2016), we used deep mutational scanning to determine how twelve new chemicals (3-Amino-1,2,4-triazole, 5-fluorocytosine, Amphotericin B, CaCl2, Cerulenin, Cobalt Acetate, Menadione, Nickel Chloride, p-Fluorophenylalanine, Rapamycin, Tamoxifen, and Tunicamycin) reveal novel mutational sensitivities of ubiquitin residues. Collectively, our experiments have identified eight new sensitizing conditions for Lys63 and uncovered a sensitizing condition for every position in Ub except Ser57 and Gln62. By determining the ubiquitin fitness landscape under different chemical constraints, our work helps to resolve the inconsistencies between deep mutational scanning experiments and sequence conservation over evolutionary timescales.

Design of Light-Controlled Protein Conformations and Functions.

In recent years, interest in controlling protein function with light has increased. Light offers a number of unique advantages over other methods, including spatial and temporal control and high selectivity. Here, we describe a general protocol for engineering a protein to be controllable with light via reaction with an exogenously introduced photoisomerizable small molecule and illustrate our protocol with two examples from the literature: the engineering of the calcium affinity of the cell-cell adhesion protein cadherin, which is an example of a protein that switches from a native to a disrupted state (Ritterson et al. J Am Chem Soc (2013) 135:12516-12519), and the engineering of the opening and closing of the chaperonin Mm-cpn, an example of a switch between two functional states (Hoersch et al.: Nat Nanotechn (2013) 8:928-932). This protocol guides the user from considering which proteins may be most amenable to this type of engineering, to considerations of how and where to make the desired changes, to the assays required to test for functionality.

Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting.

Ubiquitin is essential for eukaryotic life and varies in only 3 amino acid positions between yeast and humans. However, recent deep sequencing studies indicate that ubiquitin is highly tolerant to single mutations. We hypothesized that this tolerance would be reduced by chemically induced physiologic perturbations. To test this hypothesis, a class of first year UCSF graduate students employed deep mutational scanning to determine the fitness landscape of all possible single residue mutations in the presence of five different small molecule perturbations. These perturbations uncover 'shared sensitized positions' localized to areas around the hydrophobic patch and the C-terminus. In addition, we identified perturbation specific effects such as a sensitization of His68 in HU and a tolerance to mutation at Lys63 in DTT. Our data show how chemical stresses can reduce buffering effects in the ubiquitin proteasome system. Finally, this study demonstrates the potential of lab-based interdisciplinary graduate curriculum.

A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design.

The development and validation of computational macromolecular modeling and design methods depend on suitable benchmark datasets and informative metrics for comparing protocols. In addition, if a method is intended to be adopted broadly in diverse biological applications, there needs to be information on appropriate parameters for each protocol, as well as metrics describing the expected accuracy compared to experimental data. In certain disciplines, there exist established benchmarks and public resources where experts in a particular methodology are encouraged to supply their most efficient implementation of each particular benchmark. We aim to provide such a resource for protocols in macromolecular modeling and design. We present a freely accessible web resource (https://kortemmelab.ucsf.edu/benchmarks) to guide the development of protocols for protein modeling and design. The site provides benchmark datasets and metrics to compare the performance of a variety of modeling protocols using different computational sampling methods and energy functions, providing a "best practice" set of parameters for each method. Each benchmark has an associated downloadable benchmark capture archive containing the input files, analysis scripts, and tutorials for running the benchmark. The captures may be run with any suitable modeling method; we supply command lines for running the benchmarks using the Rosetta software suite. We have compiled initial benchmarks for the resource spanning three key areas: prediction of energetic effects of mutations, protein design, and protein structure prediction, each with associated state-of-the-art modeling protocols. With the help of the wider macromolecular modeling community, we hope to expand the variety of benchmarks included on the website and continue to evaluate new iterations of current methods as they become available.

A TALE nuclease architecture for efficient genome editing.

Nucleases that cleave unique genomic sequences in living cells can be used for targeted gene editing and mutagenesis. Here we develop a strategy for generating such reagents based on transcription activator-like effector (TALE) proteins from Xanthomonas. We identify TALE truncation variants that efficiently cleave DNA when linked to the catalytic domain of FokI and use these nucleases to generate discrete edits or small deletions within endogenous human NTF3 and CCR5 genes at efficiencies of up to 25%. We further show that designed TALEs can regulate endogenous mammalian genes. These studies demonstrate the effective application of designed TALE transcription factors and nucleases for the targeted regulation and modification of endogenous genes.