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Open Access Publications from the University of California

Scholarly Works (36 results)

  • Article
  • Peer Reviewed
UC Berkeley Previously Published Works (2015)
We report a Monte Carlo side chain entropy (MC-SCE) method that uses a physical energy function inclusive of long-range electrostatics and hydrophobic potential of mean force, coupled with both backbone variations and a backbone dependent side chain rotamer library, to describe protein conformational ensembles. Using the MC-SCE method in conjunction with backbone variability, we can reliably determine the side chain rotamer populations derived from both room temperature and cryogenically cooled X-ray crystallographic structures for CypA and H-Ras and NMR J-coupling constants for CypA, Eglin-C, and the DHFR product binary complexes E:THF and E:FOL. Furthermore, we obtain near perfect discrimination between a protein's native state ensemble and ensembles of misfolded structures for 55 different proteins, thereby generating far more competitive side chain packings for all of these proteins and their misfolded states.
    Cover page: A Monte Carlo Method for Generating Side Chain Structural Ensembles
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2016)

    Abstract

    Understanding and Improving Designed Enzymes by Computer Simulations

    By

    Asmit Bhowmick

    Doctor of Philosophy in Chemical Engineering

    University of California, Berkeley

    Professor Teresa Head-Gordon, Chair

    The ability to control for protein structure, electrostatics and dynamical motions is a fundamental problem that limits our ability to rationally design catalysts for new chemical reactions not known to have a natural biocatalyst. Current computational approaches for de novo enzyme design seek to engineer a small catalytic construct into an accommodating protein scaffold as exemplified by the Rosetta strategy. Here we consider 3 designed enzymes for the Kemp elimination reaction (KE07, KE70 and KE15) that showed minimal catalytic activity. KE07 and KE70 were subsequently improved by 2 orders of magnitude in catalytic efficiency by directed evolution and highlighted the shortcomings of the design process. This work studies two keys issues plaguing the designs – side chain conformational variability and electrostatics.

    For the first part, a new Monte Carlo sampling method was developed that uses a physical forcefield and coupled with backbone variability and a backbone dependent rotamer library. Using transition state theory with energies/entropies calculated from Monte Carlo simulations, it is shown that in both KE07 and KE70, the initial design was over-optimized to stabilize the enzyme-substrate complex. Mutations introduced by directed evolutions led to destabilization of the enzyme-substrate complex and stabilization of the transition state. Furthermore, analysis of residue correlations via mutual information yielded hotspots, several of which were mutations during directed evolution. Laboratory mutations of these hotspots in the best variant of KE07 led to a drop in catalytic performance, demonstrating their importance. The metrics identified in KE07/KE70 studies were used to predict mutations to improve enzyme KE15 that had not been improved prior to this study. Several mutants, all predicted through computer simulations have now yielded better catalytic activity in the laboratory with the best one 10-fold better than the starting enzyme.

    In order to quantify the role of electrostatics, a new method was developed using the AMOEBA polarizable forcefield that allowed splitting the contribution of electric field at the substrate by residues and solvent. The improvement in KE07 series could be tracked directly through changes in electric field at the substrate. In comparison, KE70 did not show a significant shift in electrostatic field, suggesting other factors like substrate binding may have been the reason for enhancement of activity. However, the common theme in both enzymes was the lack of participation (and in fact detrimental role) of the scaffold in the reaction. Future design efforts would benefit from an expanded theozyme and careful selection of scaffold based on electrostatic properties.

    Generating efficient biocatalysts without using laboratory directed evolution would be an inflection point in the field of enzyme design. This work is a step in that direction.

      Cover page: Understanding and Improving Designed Enzymes by Computer Simulations
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2017)
      We report electric field values relevant to the reactant and transition states of designed Kemp eliminases KE07 and KE70 and their improved variants from laboratory directed evolution (LDE), using atomistic simulations with the AMOEBA polarizable force field. We find that the catalytic base residue contributes the most to the electric field stabilization of the transition state of the LDE variants of the KE07 and KE70 enzymes, whereas the electric fields of the remainder of the enzyme and solvent disfavor the catalytic reaction in both cases. By contrast, we show that the electrostatic environment plays a large and stabilizing role for the naturally occurring enzyme ketosteroid isomerase (KSI). These results suggest that LDE is ultimately a limited strategy for improving de novo enzymes since it is largely restricted to optimization of chemical positioning in the active site, thus yielding a ∼3 order magnitude improvement over the uncatalyzed reaction, which we suggest may be an absolute upper bound estimate based on LDE applied to comparable de novo Kemp eliminases and other enzymes like KSI. Instead de novo enzymatic reactions could more productively benefit from optimization of the electrostatics of the protein scaffold in early stages of the computational design, utilizing electric field optimization as guidance.
        Cover page: The Importance of the Scaffold for de Novo Enzymes: A Case Study with Kemp Eliminase
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2016)
        Side chain entropy and mutual entropy information between residue pairs have been calculated for two de novo designed Kemp eliminase enzymes, KE07 and KE70, and for their most improved versions at the end of laboratory directed evolution (LDE). We find that entropy, not just enthalpy, helped to destabilize the preference for the reactant state complex of the designed enzyme as well as favoring stabilization of the transition state complex for the best LDE enzymes. Furthermore, residues with the highest side chain couplings as measured by mutual information, when experimentally mutated, were found to diminish or annihilate catalytic activity, some of which were far from the active site. In summary, our findings demonstrate how side chain fluctuations and their coupling can be an important design feature for de novo enzymes, and furthermore could be utilized in the computational steps in lieu of or in addition to the LDE steps in future enzyme design projects.
          Cover page: The role of side chain entropy and mutual information for improving the de novo design of Kemp eliminases KE07 and KE70
          • Article
          • Peer Reviewed
          LBL Publications (2019)
          A nonlinear least-squares method for refining a parametric expression describing the estimated errors of reflection intensities in serial crystallographic (SX) data is presented. This approach, which is similar to that used in the rotation method of crystallographic data collection at synchrotrons, propagates error estimates from photon-counting statistics to the merged data. Here, it is demonstrated that the application of this approach to SX data provides better SAD phasing ability, enabling the autobuilding of a protein structure that had previously failed to be built. Estimating the error in the merged reflection intensities requires the understanding and propagation of all of the sources of error arising from the measurements. One type of error, which is well understood, is the counting error introduced when the detector counts X-ray photons. Thus, if other types of random errors (such as readout noise) as well as uncertainties in systematic corrections (such as from X-ray attenuation) are completely understood, they can be propagated along with the counting error, as appropriate. In practice, most software packages propagate as much error as they know how to model and then include error-adjustment terms that scale the error estimates until they explain the variance among the measurements. If this is performed carefully, then during SAD phasing likelihood-based approaches can make optimal use of these error estimates, increasing the chance of a successful structure solution. In serial crystallography, SAD phasing has remained challenging, with the few examples of de novo protein structure solution each requiring many thousands of diffraction patterns. Here, the effects of different methods of treating the error estimates are estimated and it is shown that using a parametric approach that includes terms proportional to the known experimental uncertainty, the reflection intensity and the squared reflection intensity to improve the error estimates can allow SAD phasing even from weak zinc anomalous signal.
            Cover page: SAD phasing of XFEL data depends critically on the error model
            • Article
            • Peer Reviewed
            LBL Publications (2020)
            Most crystallographic data processing methods use pixel integration. In serial femtosecond crystallography (SFX), the intricate interaction between the reciprocal lattice point and the Ewald sphere is integrated out by averaging symmetrically equivalent observations recorded across a large number (104-106) of exposures. Although sufficient for generating biological insights, this approach converges slowly, and using it to accurately measure anomalous differences has proved difficult. This report presents a novel approach for increasing the accuracy of structure factors obtained from SFX data. A physical model describing all observed pixels is defined to a degree of complexity such that it can decouple the various contributions to the pixel intensities. Model dependencies include lattice orientation, unit-cell dimensions, mosaic structure, incident photon spectra and structure factor amplitudes. Maximum likelihood estimation is used to optimize all model parameters. The application of prior knowledge that structure factor amplitudes are positive quantities is included in the form of a reparameterization. The method is tested using a synthesized SFX dataset of ytterbium(III) lysozyme, where each X-ray laser pulse energy is centered at 9034 eV. This energy is 100 eV above the Yb3+ L-III absorption edge, so the anomalous difference signal is stable at 10 electrons despite the inherent energy jitter of each femtosecond X-ray laser pulse. This work demonstrates that this approach allows the determination of anomalous structure factors with very high accuracy while requiring an order-of-magnitude fewer shots than conventional integration-based methods would require to achieve similar results.
              Cover page: Beyond integration: modeling every pixel to obtain better structure factors from stills
              • Article
              • Peer Reviewed
              LBL Publications (2022)
                Creative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                LBL Publications (2023)
                One of the reasons for the high efficiency and selectivity of biological catalysts arise from their ability to control the pathways of substrates and products using protein channels, and by modulating the transport in the channels using the interaction with the protein residues and the water/hydrogen-bonding network. This process is clearly demonstrated in Photosystem II (PS II), where its light-driven water oxidation reaction catalyzed by the Mn4CaO5 cluster occurs deep inside the protein complex and thus requires the transport of two water molecules to and four protons from the metal center to the bulk water. Based on the recent advances in structural studies of PS II from X-ray crystallography and cryo-electron microscopy, in this review we compare the channels that have been proposed to facilitate this mass transport in cyanobacteria, red and green algae, diatoms, and higher plants. The three major channels (O1, O4, and Cl1 channels) are present in all species investigated; however, some differences exist in the reported structures that arise from the different composition and arrangement of membrane extrinsic subunits between the species. Among the three channels, the Cl1 channel, including the proton gate, is the most conserved among all photosynthetic species. We also found at least one branch for the O1 channel in all organisms, extending all the way from Ca/O1 via the 'water wheel' to the lumen. However, the extending path after the water wheel varies between most species. The O4 channel is, like the Cl1 channel, highly conserved among all species while having different orientations at the end of the path near the bulk. The comparison suggests that the previously proposed functionality of the channels in T. vestitus (Ibrahim et al., Proc Natl Acad Sci USA 117:12624-12635, 2020; Hussein et al., Nat Commun 12:6531, 2021) is conserved through the species, i.e. the O1-like channel is used for substrate water intake, and the tighter Cl1 and O4 channels for proton release. The comparison does not eliminate the potential role of O4 channel as a water intake channel. However, the highly ordered hydrogen-bonded water wire connected to the Mn4CaO5 cluster via the O4 may strongly suggest that it functions in proton release, especially during the S0 → S1 transition (Saito et al., Nat Commun 6:8488, 2015; Kern et al., Nature 563:421-425, 2018; Ibrahim et al., Proc Natl Acad Sci USA 117:12624-12635, 2020; Sakashita et al., Phys Chem Chem Phys 22:15831-15841, 2020; Hussein et al., Nat Commun 12:6531, 2021).
                  Cover page: Evolutionary diversity of proton and water channels on the oxidizing side of photosystem II and their relevance to functionCreative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  LBL Publications (2020)
                  Macromolecular crystallography (MX) is the dominant means of determining the three-dimensional structures of biological macromolecules. Over the last few decades, most MX data have been collected at synchrotron beamlines using a large number of different detectors produced by various manufacturers and taking advantage of various protocols and goniometries. These data came in their own formats: sometimes proprietary, sometimes open. The associated metadata rarely reached the degree of completeness required for data management according to Findability, Accessibility, Interoperability and Reusability (FAIR) principles. Efforts to reuse old data by other investigators or even by the original investigators some time later were often frustrated. In the culmination of an effort dating back more than two decades, a large portion of the research community concerned with high data-rate macromolecular crystallography (HDRMX) has now agreed to an updated specification of data and metadata for diffraction images produced at synchrotron light sources and X-ray free-electron lasers (XFELs). This 'Gold Standard' will facilitate the processing of data sets independent of the facility at which they were collected and enable data archiving according to FAIR principles, with a particular focus on interoperability and reusability. This agreed standard builds on the NeXus/HDF5 NXmx application definition and the International Union of Crystallo-graphy (IUCr) imgCIF/CBF dictionary, and it is compatible with major data-processing programs and pipelines. Just as with the IUCr CBF/imgCIF standard from which it arose and to which it is tied, the NeXus/HDF5 NXmx Gold Standard application definition is intended to be applicable to all detectors used for crystallography, and all hardware and software developers in the field are encouraged to adopt and contribute to the standard.
                    Cover page: Gold Standard for macromolecular crystallography diffraction data
                    • Article
                    • Peer Reviewed
                    LBL Publications (2024)
                    X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min -- sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS-NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Completing data analysis with 10 mins is a first for XFEL experiments and an important milestone if we are to keep up with data collection trends.
                      Cover page: Real‐time XFEL data analysis at SLAC and NERSC: A trial run of nascent exascale experimental data analysisCreative Commons 'BY' version 4.0 license
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