Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression.

Depression is a common mental health condition that often occurs in association with other chronic illnesses, and varies considerably in severity. Electronic Health Records (EHRs) contain rich information about a patient's medical history and can be used to train, test and maintain predictive models to support and improve patient care. This work evaluated the feasibility of implementing an environment for predicting mental health crisis among people living with depression based on both structured and unstructured EHRs. A large EHR from a mental health provider, Mersey Care, was pseudonymised and ingested into the Natural Language Processing (NLP) platform CogStack, allowing text content in binary clinical notes to be extracted. All unstructured clinical notes and summaries were semantically annotated by MedCAT and BioYODIE NLP services. Cases of crisis in patients with depression were then identified. Random forest models, gradient boosting trees, and Long Short-Term Memory (LSTM) networks, with varying feature arrangement, were trained to predict the occurrence of crisis. The results showed that all the prediction models can use a combination of structured and unstructured EHR information to predict crisis in patients with depression with good and useful accuracy. The LSTM network that was trained on a modified dataset with only 1000 most-important features from the random forest model with temporality showed the best performance with a mean AUC of 0.901 and a standard deviation of 0.006 using a training dataset and a mean AUC of 0.810 and 0.01 using a hold-out test dataset. Comparing the results from the technical evaluation with the views of psychiatrists shows that there are now opportunities to refine and integrate such prediction models into pragmatic point-of-care clinical decision support tools for supporting mental healthcare delivery.

Untangling the threads of cellulose mercerization.

Naturally occurring plant cellulose, our most abundant renewable resource, consists of fibers of long polymer chains that are tightly packed in parallel arrays in either of two crystal phases collectively referred to as cellulose I. During mercerization, a process that involves treatment with sodium hydroxide, cellulose goes through a conversion to another crystal form called cellulose II, within which every other chain has remarkably changed direction. We designed a neutron diffraction experiment with deuterium labelling in order to understand how this change of cellulose chain direction is possible. Here we show that during mercerization of bacterial cellulose, chains fold back on themselves in a zigzag pattern to form crystalline anti-parallel domains. This result provides a molecular level understanding of one of the most widely used industrial processes for improving cellulosic materials.

Capturing the Catalytic Proton of Dihydrofolate Reductase: Implications for General Acid-Base Catalysis.

Acid-base catalysis, which involves one or more proton transfer reactions, is a chemical mechanism commonly employed by many enzymes. The molecular basis for catalysis is often derived from structures determined at the optimal pH for enzyme activity. However, direct observation of protons from experimental structures is quite difficult; thus, a complete mechanistic description for most enzymes remains lacking. Dihydrofolate reductase (DHFR) exemplifies general acid-base catalysis, requiring hydride transfer and protonation of its substrate, DHF, to form the product, tetrahydrofolate (THF). Previous X-ray and neutron crystal structures coupled with theoretical calculations have proposed that solvent mediates the protonation step. However, visualization of a proton transfer has been elusive. Based on a 2.1 Å resolution neutron structure of a pseudo-Michaelis complex of E. coli DHFR determined at acidic pH, we report the direct observation of the catalytic proton and its parent solvent molecule. Comparison of X-ray and neutron structures elucidated at acidic and neutral pH reveals dampened dynamics at acidic pH, even for the regulatory Met20 loop. Guided by the structures and calculations, we propose a mechanism where dynamics are crucial for solvent entry and protonation of substrate. This mechanism invokes the release of a sole proton from a hydronium (H3O+) ion, its pathway through a narrow channel that sterically hinders the passage of water, and the ultimate protonation of DHF at the N5 atom.

Structural plasticity of SARS-CoV-2 3CL Mpro active site cavity revealed by room temperature X-ray crystallography.

The COVID-19 disease caused by the SARS-CoV-2 coronavirus has become a pandemic health crisis. An attractive target for antiviral inhibitors is the main protease 3CL Mpro due to its essential role in processing the polyproteins translated from viral RNA. Here we report the room temperature X-ray structure of unliganded SARS-CoV-2 3CL Mpro, revealing the ligand-free structure of the active site and the conformation of the catalytic site cavity at near-physiological temperature. Comparison with previously reported low-temperature ligand-free and inhibitor-bound structures suggest that the room temperature structure may provide more relevant information at physiological temperatures for aiding in molecular docking studies.

BraggNet: integrating Bragg peaks using neural networks.

Neutron crystallography offers enormous potential to complement structures from X-ray crystallography by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing to the challenge of integrating peak shapes from pulsed-source experiments. To advance existing software, this article demonstrates the use of machine learning to refine peak locations, predict peak shapes and yield more accurate integrated intensities when applied to whole data sets from a protein crystal. The artificial neural network, based on the U-Net architecture commonly used for image segmentation, is trained using about 100 000 simulated training peaks derived from strong peaks. After 100 training epochs (a round of training over the whole data set broken into smaller batches), training converges and achieves a Dice coefficient of around 65%, in contrast to just 15% for negative control data sets. Integrating whole peak sets using the neural network yields improved intensity statistics compared with other integration methods, including k-nearest neighbours. These results demonstrate, for the first time, that neural networks can learn peak shapes and be used to integrate Bragg peaks. It is expected that integration using neural networks can be further developed to increase the quality of neutron, electron and X-ray crystallography data.

Improving the accuracy and resolution of neutron crystallographic data by three-dimensional profile fitting of Bragg peaks in reciprocal space.

Neutron crystallography is a powerful technique for directly visualizing the locations of H atoms in biological macromolecules. This information has provided key new insights into enzyme mechanisms, ligand binding and hydration. However, despite the importance of this information, the application of neutron crystallography in biology has been limited by the relatively low flux of available neutron beams and the large incoherent neutron scattering from hydrogen, both of which contribute to weak diffraction data with relatively low signal-to-background ratios. A method has been developed to fit weak data based on three-dimensional profile fitting of Bragg peaks in reciprocal space by an Ikeda-Carpenter function with a bivariate Gaussian. When applied to data collected from three different proteins, three-dimensional profile fitting yields intensities with higher correlation coefficients (CC1/2) at high resolutions, decreased Rfree factors, extended resolutions and improved nuclear density maps. Importantly, additional features are revealed in nuclear density maps that may provide additional scientific information. These results suggest that three-dimensional profile fitting will help to extend the capabilities of neutron macromolecular crystallography.

Evaluation of models determined by neutron diffraction and proposed improvements to their validation and deposition.

The Protein Data Bank (PDB) contains a growing number of models that have been determined using neutron diffraction or a hybrid method that combines X-ray and neutron diffraction. The advantage of neutron diffraction experiments is that the positions of all atoms can be determined, including H atoms, which are hardly detectable by X-ray diffraction. This allows the determination of protonation states and the assignment of H atoms to water molecules. Because neutrons are scattered differently by hydrogen and its isotope deuterium, neutron diffraction in combination with H/D exchange can provide information on accessibility, dynamics and chemical lability. In this study, the deposited data, models and model-to-data fit for all PDB entries that used neutron diffraction as the source of experimental data have been analysed. In many cases, the reported Rwork and Rfree values were not reproducible. In such cases, the model and data files were analysed to identify the reasons for this mismatch. The issues responsible for the discrepancies are summarized and explained. The analysis unveiled limitations to the annotation, deposition and validation of models and data, and a lack of community-wide accepted standards for the description of neutron models and data, as well as deficiencies in current model refinement tools. Most of the issues identified concern the handling of H atoms. Since the primary use of neutron macromolecular crystallography is to locate and directly visualize H atoms, it is important to address these issues, so that the deposited neutron models allow the retrieval of the maximum amount of information with the smallest effort of manual intervention. A path forward to improving the annotation, validation and deposition of neutron models and hybrid X-ray and neutron models is suggested.

Tension wood structure and morphology conducive for better enzymatic digestion.

BACKGROUND: Tension wood is a type of reaction wood in response to bending or leaning stem as a corrective growth process. Tension wood is formed by both natural and man-made processes. Most attractively, tension wood contains higher glucan content and undergoes higher enzymatic conversion to fermentable sugars. Here, we have employed structural techniques, small-angle neutron scattering (SANS) and wide-angle X-ray diffraction (WAXD) to elucidate structural and morphological aspects of tension wood conducive to higher sugar yields. RESULTS: Small-angle neutron scattering data exhibited a tri-modal distribution of the fibril cross-sectional dimension. The smallest size, 22 Å observed in all samples concurred with the WAXD results of the control and opposite side samples. This smallest and the most abundant occurring size was interpreted as the cellulose elementary microfibril diameter. The intermediate size of 45 Å, which is most pronounced in the tension side sample and consistent with WAXD results for tension side sample, indicates association of neighboring elementary microfibrils to form larger crystallite bundles. The largest size 61 Å observed by SANS was however not observed by WAXD and therefore associated to mesopores. CONCLUSIONS: Structure and morphology of tension wood is different from control wood. Cellulose crystallinity increases, lignin content is lower and the appearance of mesopores with 61 Å diameter is observed. Despite the presence of higher crystalline cellulose content in tension side, the lower lignin content and may be combined with the abundance of mesopores, substantially improves enzyme accessibility leading to higher yields in cellulose digestion.

Neutron scattering in the biological sciences: progress and prospects.

The scattering of neutrons can be used to provide information on the structure and dynamics of biological systems on multiple length and time scales. Pursuant to a National Science Foundation-funded workshop in February 2018, recent developments in this field are reviewed here, as well as future prospects that can be expected given recent advances in sources, instrumentation and computational power and methods. Crystallography, solution scattering, dynamics, membranes, labeling and imaging are examined. For the extraction of maximum information, the incorporation of judicious specific deuterium labeling, the integration of several types of experiment, and interpretation using high-performance computer simulation models are often found to be particularly powerful.

Dynamics of water bound to crystalline cellulose.

Interactions of water with cellulose are of both fundamental and technological importance. Here, we characterize the properties of water associated with cellulose using deuterium labeling, neutron scattering and molecular dynamics simulation. Quasi-elastic neutron scattering provided quantitative details about the dynamical relaxation processes that occur and was supported by structural characterization using small-angle neutron scattering and X-ray diffraction. We can unambiguously detect two populations of water associated with cellulose. The first is "non-freezing bound" water that gradually becomes mobile with increasing temperature and can be related to surface water. The second population is consistent with confined water that abruptly becomes mobile at ~260 K, and can be attributed to water that accumulates in the narrow spaces between the microfibrils. Quantitative analysis of the QENS data showed that, at 250 K, the water diffusion coefficient was 0.85 ± 0.04 × 10-10 m2sec-1 and increased to 1.77 ± 0.09 × 10-10 m2sec-1 at 265 K. MD simulations are in excellent agreement with the experiments and support the interpretation that water associated with cellulose exists in two dynamical populations. Our results provide clarity to previous work investigating the states of bound water and provide a new approach for probing water interactions with lignocellulose materials.

In Vivo Protein Dynamics on the Nanometer Length Scale and Nanosecond Time Scale.

Selectively labeled GroEL protein was produced in living deuterated bacterial cells to enhance its neutron scattering signal above that of the intracellular milieu. Quasi-elastic neutron scattering shows that the in-cell diffusion coefficient of GroEL was (4.7 ± 0.3) × 10-12 m2/s, a factor of 4 slower than its diffusion coefficient in buffer solution. Internal protein dynamics showed a relaxation time of (65 ± 6) ps, a factor of 2 slower compared to the protein in solution. Comparison to the literature suggests that the effective diffusivity of proteins depends on the length and time scale being probed. Retardation of in-cell diffusion compared to the buffer becomes more significant with the increasing probe length scale, suggesting that intracellular diffusion of biomolecules is nonuniform over the cellular volume. The approach outlined here enables investigation of protein dynamics within living cells to open up new lines of research using "in-cell neutron scattering" to study the dynamics of complex biomolecular systems.

Visualizing the Bohr effect in hemoglobin: neutron structure of equine cyanomethemoglobin in the R state and comparison with human deoxyhemoglobin in the T state.

Neutron crystallography provides direct visual evidence of the atomic positions of deuterium-exchanged H atoms, enabling the accurate determination of the protonation/deuteration state of hydrated biomolecules. Comparison of two neutron structures of hemoglobins, human deoxyhemoglobin (T state) and equine cyanomethemoglobin (R state), offers a direct observation of histidine residues that are likely to contribute to the Bohr effect. Previous studies have shown that the T-state N-terminal and C-terminal salt bridges appear to have a partial instead of a primary overall contribution. Four conserved histidine residues [αHis72(EF1), αHis103(G10), αHis89(FG1), αHis112(G19) and ßHis97(FG4)] can become protonated/deuterated from the R to the T state, while two histidine residues [αHis20(B1) and ßHis117(G19)] can lose a proton/deuteron. αHis103(G10), located in the α1:ß1 dimer interface, appears to be a Bohr group that undergoes structural changes: in the R state it is singly protonated/deuterated and hydrogen-bonded through a water network to ßAsn108(G10) and in the T state it is doubly protonated/deuterated with the network uncoupled. The very long-term H/D exchange of the amide protons identifies regions that are accessible to exchange as well as regions that are impermeable to exchange. The liganded relaxed state (R state) has comparable levels of exchange (17.1% non-exchanged) compared with the deoxy tense state (T state; 11.8% non-exchanged). Interestingly, the regions of non-exchanged protons shift from the tetramer interfaces in the T-state interface (α1:ß2 and α2:ß1) to the cores of the individual monomers and to the dimer interfaces (α1:ß1 and α2:ß2) in the R state. The comparison of regions of stability in the two states allows a visualization of the conservation of fold energy necessary for ligand binding and release.

Long-Range Electrostatics-Induced Two-Proton Transfer Captured by Neutron Crystallography in an Enzyme Catalytic Site.

Neutron crystallography was used to directly locate two protons before and after a pH-induced two-proton transfer between catalytic aspartic acid residues and the hydroxy group of the bound clinical drug darunavir, located in the catalytic site of enzyme HIV-1 protease. The two-proton transfer is triggered by electrostatic effects arising from protonation state changes of surface residues far from the active site. The mechanism and pH effect are supported by quantum mechanics/molecular mechanics (QM/MM) calculations. The low-pH proton configuration in the catalytic site is deemed critical for the catalytic action of this enzyme and may apply more generally to other aspartic proteases. Neutrons therefore represent a superb probe to obtain structural details for proton transfer reactions in biological systems at a truly atomic level.

A Structural Study of CESA1 Catalytic Domain of Arabidopsis Cellulose Synthesis Complex: Evidence for CESA Trimers.

A cellulose synthesis complex with a "rosette" shape is responsible for synthesis of cellulose chains and their assembly into microfibrils within the cell walls of land plants and their charophyte algal progenitors. The number of cellulose synthase proteins in this large multisubunit transmembrane protein complex and the number of cellulose chains in a microfibril have been debated for many years. This work reports a low resolution structure of the catalytic domain of CESA1 from Arabidopsis (Arabidopsis thaliana; AtCESA1CatD) determined by small-angle scattering techniques and provides the first experimental evidence for the self-assembly of CESA into a stable trimer in solution. The catalytic domain was overexpressed in Escherichia coli, and using a two-step procedure, it was possible to isolate monomeric and trimeric forms of AtCESA1CatD. The conformation of monomeric and trimeric AtCESA1CatD proteins were studied using small-angle neutron scattering and small-angle x-ray scattering. A series of AtCESA1CatD trimer computational models were compared with the small-angle x-ray scattering trimer profile to explore the possible arrangement of the monomers in the trimers. Several candidate trimers were identified with monomers oriented such that the newly synthesized cellulose chains project toward the cell membrane. In these models, the class-specific region is found at the periphery of the complex, and the plant-conserved region forms the base of the trimer. This study strongly supports the "hexamer of trimers" model for the rosette cellulose synthesis complex that synthesizes an 18-chain cellulose microfibril as its fundamental product.

Protein Kinase A Catalytic Subunit Primed for Action: Time-Lapse Crystallography of Michaelis Complex Formation.

The catalytic subunit of the cyclic AMP-dependent protein kinase A (PKAc) catalyzes the transfer of the γ-phosphate of bound Mg2ATP to a serine or threonine residue of a protein substrate. Here, time-lapse X-ray crystallography was used to capture a series of complexes of PKAc with an oligopeptide substrate and unreacted Mg2ATP, including the Michaelis complex, that reveal important geometric rearrangements in and near the active site preceding the phosphoryl transfer reaction. Contrary to the prevailing view, Mg(2+) binds first to the M1 site as a complex with ATP and is followed by Mg(2+) binding to the M2 site. Concurrently, the target serine hydroxyl of the peptide substrate rotates away from the active site toward the bulk solvent, which breaks the hydrogen bond with D166. Lastly, the serine hydroxyl of the substrate rotates back toward D166 to form the Michaelis complex with the active site primed for phosphoryl transfer.

Production of bacterial cellulose with controlled deuterium-hydrogen substitution for neutron scattering studies.

Isotopic enrichment of biomacromolecules is a widely used technique that enables the investigation of the structural and dynamic properties to provide information not accessible with natural abundance isotopic composition. This study reports an approach for deuterium incorporation into bacterial cellulose. A media formulation for growth of Acetobacter xylinus subsp. sucrofermentans and Gluconacetobacter hansenii was formulated that supports cellulose production in deuterium (D) oxide. The level of D incorporation can be varied by altering the ratio of deuterated and protiated glycerol used during cell growth in the D2O-based growth medium. Spectroscopic analysis and mass spectrometry show that the level of deuterium incorporation is high (>90%) for the perdeuterated form of bacterial cellulose. The small-angle neutron scattering profiles of the cellulose with different amounts of D incorporation are all similar indicating that there are no structural changes in the cellulose due to substitution of deuterium for hydrogen. In addition, by varying the amount of deuterated glycerol in the media it was possible to vary the scattering length density of the deuterated cellulose. The ability to control deuterium content of cellulose extends the range of experiments using techniques such as neutron scattering to reveal information about the structure and dynamics of cellulose, and its interactions with other biomacromolecules as well as synthetic polymers used for development of composite materials.

Direct determination of protonation states and visualization of hydrogen bonding in a glycoside hydrolase with neutron crystallography.

Glycoside hydrolase (GH) enzymes apply acid/base chemistry to catalyze the decomposition of complex carbohydrates. These ubiquitous enzymes accept protons from solvent and donate them to substrates at close to neutral pH by modulating the pKa values of key side chains during catalysis. However, it is not known how the catalytic acid residue acquires a proton and transfers it efficiently to the substrate. To better understand GH chemistry, we used macromolecular neutron crystallography to directly determine protonation and ionization states of the active site residues of a family 11 GH at multiple pD (pD=pH+0.4) values. The general acid glutamate (Glu) cycles between two conformations, upward and downward, but is protonated only in the downward orientation. We performed continuum electrostatics calculations to estimate the pKa values of the catalytic Glu residues in both the apo- and substrate-bound states of the enzyme. The calculated pKa of the Glu increases substantially when the side chain moves down. The energy barrier required to rotate the catalytic Glu residue back to the upward conformation, where it can protonate the glycosidic oxygen of the substrate, is 4.3 kcal/mol according to free energy simulations. These findings shed light on the initial stage of the glycoside hydrolysis reaction in which molecular motion enables the general acid catalyst to obtain a proton from the bulk solvent and deliver it to the glycosidic oxygen.

Phosphoryl Transfer Reaction Snapshots in Crystals: INSIGHTS INTO THE MECHANISM OF PROTEIN KINASE A CATALYTIC SUBUNIT.

To study the catalytic mechanism of phosphorylation catalyzed by cAMP-dependent protein kinase (PKA) a structure of the enzyme-substrate complex representing the Michaelis complex is of specific interest as it can shed light on the structure of the transition state. However, all previous crystal structures of the Michaelis complex mimics of the PKA catalytic subunit (PKAc) were obtained with either peptide inhibitors or ATP analogs. Here we utilized Ca(2+) ions and sulfur in place of the nucleophilic oxygen in a 20-residue pseudo-substrate peptide (CP20) and ATP to produce a close mimic of the Michaelis complex. In the ternary reactant complex, the thiol group of Cys-21 of the peptide is facing Asp-166 and the sulfur atom is positioned for an in-line phosphoryl transfer. Replacement of Ca(2+) cations with Mg(2+) ions resulted in a complex with trapped products of ATP hydrolysis: phosphate ion and ADP. The present structural results in combination with the previously reported structures of the transition state mimic and phosphorylated product complexes complete the snapshots of the phosphoryl transfer reaction by PKAc, providing us with the most thorough picture of the catalytic mechanism to date.