Concordance of X-ray and AlphaFold2 Models of SARS-CoV-2 Main Protease with Residual Dipolar Couplings Measured in Solution

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed that these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly fivefold enriched in conditionally folded IDRs over IDRs in general and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.

Computational modeling multiple conformational states of proteins with residual dipolar coupling data

Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, residual dipolar couplings (RDCs) provide access to amplitudes and directions of motion, characteristics that are important to the function of these molecules. Although observed in the 1960s, the acquisition and computational analysis of RDCs has gained significant momentum in recent years, and particularly applications to motion in proteins have become more numerous. This trend may well continue as RDCs can easily leverage structures produced by new computational methods (e.g., AlphaFold) to produce functional descriptions. In this report, we provide examples and a summary of the ways that RDCs have been used to confirm the existence of internal dynamics, characterize the type of dynamics, and recover atomic-scale structural ensembles that define the full range of conformational sampling.

Modulation of Rotational Dynamics in Halogen-Bonded Cocrystalline Solids

Dynamic processes are responsible for the functionality of a range of materials, biomolecules, and catalysts. We report a detailed systematic study of the modulation of methyl rotational dynamics via the direct and the indirect influence of noncovalent halogen bonds. For this purpose, a novel series of cocrystalline architectures featuring halogen bonds (XB) to tetramethylpyrazine (TMP) is designed and prepared using gas-phase, solution, and solid-state mechanochemical methods. Single-crystal X-ray diffraction reveals the capacity of molecular bromine as well as weak chloro-XB donors to act as robust directional structure-directing elements. Methyl rotational barriers (Ea) measured using variable-temperature deuterium solid-state NMR range from 3.75 ± 0.04 kJ mol-1 in 1,3,5-trichloro-2,4,6-trifluorobenzene·TMP to 7.08 ± 0.15 kJ mol-1 in 1,4-dichlorotetrafluorobenzene·TMP. Ea data for a larger series of TMP cocrystals featuring chloro-, bromo-, and iodo-XB donors are shown to be governed by a combination of steric and electronic factors. The average number of carbon-carbon close contacts to the methyl group is found to be a key steric metric capable of rationalizing the observed trends within each of the Cl, Br, and I series. Differences between each series are accounted for by considering the strength of the σ-hole on the XB donor. One possible route to modulating dynamics is therefore via designer cocrystals of variable stoichiometry, maintaining the core chemical features of interest between a given donor and acceptor while simultaneously modifying the number of carbon close contacts affecting dynamics. These principles may provide design opportunities to modulate more complex geared or cascade dynamics involving larger functional groups.

Improved methodology for protein NMR structure calculation using hydrogen bond restraints and ANSURR validation: The SH2 domain of SH2B1

Protein structures calculated using NMR data are less accurate and less well-defined than they could be. Here we use the program ANSURR to show that this deficiency is at least in part due to a lack of hydrogen bond restraints. We describe a protocol to introduce hydrogen bond restraints into the structure calculation of the SH2 domain from SH2B1 in a systematic and transparent way and show that the structures generated are more accurate and better defined as a result. We also show that ANSURR can be used as a guide to know when the structure calculation is good enough to stop.

Synergism between x-ray crystallography and NMR residual dipolar couplings in characterizing protein dynamics

The important role of structural dynamics in protein function is widely recognized. Thermal or B-factors and their anisotropy, seen in x-ray analysis of protein structures, report on the presence of atomic coordinate heterogeneity that can be attributed to motion. However, their quantitative evaluation in terms of protein dynamics by x-ray ensemble refinement remains challenging. NMR spectroscopy provides quantitative information on the amplitudes and time scales of motional processes. Unfortunately, with a few exceptions, the NMR data do not provide direct insights into the atomic details of dynamic trajectories. Residual dipolar couplings, measured by solution NMR, are very precise parameters reporting on the time-averaged bond-vector orientations and may offer the opportunity to derive correctly weighted dynamic ensembles of structures for cases where multiple high-resolution x-ray structures are available. Applications to the SARS-CoV-2 main protease, M^pro, and ubiquitin highlight this complementarity of NMR and crystallography for quantitative assessment of internal motions.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

Validation of X-ray Crystal Structure Ensemble Representations of SARS-CoV-2 Main Protease by Solution NMR Residual Dipolar Couplings

Considerable debate has focused on whether sampling of molecular dynamics trajectories restrained by crystallographic data can be used to develop realistic ensemble models for proteins in their natural, solution state. For the SARS-CoV-2 main protease, Mpro, we evaluated agreement between solution residual dipolar couplings (RDCs) and various recently reported multi-conformer and dynamic-ensemble crystallographic models. Although Phenix-derived ensemble models showed only small improvements in crystallographic Rfree, substantially improved RDC agreement over fits to a conventionally refined 1.2-Å X-ray structure was observed, in particular for residues with above average disorder in the ensemble. For a set of six lower resolution (1.55-2.19 Å) Mpro X-ray ensembles, obtained at temperatures ranging from 100 to 310 K, no significant improvement over conventional two-conformer representations was found. At the residue level, large differences in motions were observed among these ensembles, suggesting high uncertainties in the X-ray derived dynamics. Indeed, combining the six ensembles from the temperature series with the two 1.2-Å X-ray ensembles into a single 381-member "super ensemble" averaged these uncertainties and substantially improved agreement with RDCs. However, all ensembles showed excursions that were too large for the most dynamic fraction of residues. Our results suggest that further improvements to X-ray ensemble refinement are feasible, and that RDCs provide a sensitive benchmark in such endeavors. Remarkably, a weighted ensemble of 350 PDB Mpro X-ray structures provided slightly better cross-validated agreement with RDCs than any individual ensemble refinement, implying that differences in lattice confinement also limit the fit of RDCs to X-ray coordinates.

Using AlphaFold Predictions in Viral Research

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

Recombinant Expression and Chemical Amidation of Isotopically Labeled Native Melittin

Post-translational modifications are ubiquitous in the eukaryotic proteome. However, these modifications are rarely incorporated in NMR studies of eukaryotic proteins, which are typically produced through recombinant expression in E. coli. Melittin is the primary peptide in honey bee venom. Its native C-terminal amide significantly affects its equilibrium structure and dynamics in solution and is thus a prerequisite for studying its native structure and function. Here, we present a method for producing triply isotopically labeled (2H, 13C, and 15N) native melittin through recombinant expression followed by chemical amidation. We then show that structural models produced with AlphaFold-Multimer are in even better agreement with experimental residual dipolar couplings than the 2.0 Å resolution X-ray crystal structure for residues G3-K23.

Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Highlights

AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

Large-scale investigation of zoonotic viruses in the era of high-throughput sequencing

Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly caused approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS have revealed hidden viral infections. This review introduces the history of viral searches using HTS, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using the HTS technology.

Novel computational pipelines in antiviral structure‑based drug design (Review)

Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals in silico provides a solution to the arduous and costly process of conventional drug development pipelines. Furthermore, rapid advances in high-throughput computing, along with the growth of available biomolecular and biochemical data, enable the development of novel computational pipelines in the hunt of antivirals. The incorporation of modern methods, such as deep-learning and artificial intelligence, has the potential to revolutionize the structure-based design and repurposing of antiviral compounds, with minimal side effects and high efficacy. The present review aims to provide an outline of both traditional computational drug design and emerging, high-level computing strategies.

Polymer-Nanodiscs as a Novel Alignment Medium for High-Resolution NMR-Based Structural Studies of Nucleic Acids

Residual dipolar couplings (RDCs) are increasingly used for high-throughput NMR-based structural studies and to provide long-range angular constraints to validate and refine structures of various molecules determined by X-ray crystallography and NMR spectroscopy. RDCs of a given molecule can be measured in an anisotropic environment that aligns in an external magnetic field. Here, we demonstrate the first application of polymer-based nanodiscs for the measurement of RDCs from nucleic acids. Polymer-based nanodiscs prepared using negatively charged SMA-EA polymer and zwitterionic DMPC lipids were characterized by size-exclusion chromatography, 1H NMR, dynamic light-scattering, and 2H NMR. The magnetically aligned polymer-nanodiscs were used as an alignment medium to measure RDCs from a 13C/15N-labeled fluoride riboswitch aptamer using 2D ARTSY-HSQC NMR experiments. The results showed that the alignment of nanodiscs is stable for nucleic acids and nanodisc-induced RDCs fit well with the previously determined solution structure of the riboswitch. These results demonstrate that SMA-EA-based lipid-nanodiscs can be used as a stable alignment medium for high-resolution structural and dynamical studies of nucleic acids, and they can also be applicable to study various other biomolecules and small molecules in general.

The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database

The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

NMR Observation of Sulfhydryl Signals in SARS-CoV-2 Main Protease Aids Structural Studies

The 68‐kDa homodimeric 3C‐like protease of SARS‐CoV‐2, M^pro (3CL^pro/Nsp5), is a key antiviral drug target. NMR spectroscopy of this large system proved challenging and resonance assignments have remained incomplete. Here we present the near‐complete (>97 %) backbone assignments of a C145A variant of M^pro (M^pro_C145A) both with, and without, the N‐terminal auto‐cleavage substrate sequence, in its native homodimeric state. We also present SILLY (Selective Inversion of thioL and Ligand for NOESY), a simple yet effective pseudo‐3D NMR experiment that utilizes NOEs to identify interactions between Cys‐thiol or aliphatic protons, and their spatially proximate backbone amides in a perdeuterated protein background. High protection against hydrogen exchange is observed for 10 of the 11 thiol groups in M^pro_C145A, even those that are partially accessible to solvent. A combination of SILLY methods and high‐resolution triple‐resonance NMR experiments reveals site‐specific interactions between M^pro, its substrate peptides, and other ligands, which present opportunities for competitive binding studies in future drug design efforts.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and ¹⁵N-¹H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

Isolation, Purification, and Characterization of a Laccase-Degrading Aflatoxin B1 from Bacillus amyloliquefaciens B10

Aflatoxins, widely found in feed and foodstuffs, are potentially harmful to human and animal health because of their high toxicity. In this study, a strain of Bacillus amyloliquefaciens B10 with a strong ability to degrade aflatoxin B1 (AFB1) was screened; it could degrade 2.5 μg/mL of AFB1 within 96 h. The active substances of Bacillus amyloliquefaciens B10 for the degradation of AFB1 mainly existed in the culture supernatant. A new laccase with AFB1-degrading activity was separated by ammonium sulfate precipitation, diethylaminoethyl (DEAE) and gel filtration chromatography. The results of molecular docking showed that B10 laccase and aflatoxin had a high docking score. The coding sequence of the laccase was successfully amplified from cDNA by PCR and cloned into E. coli. The purified laccase could degrade 79.3% of AFB1 within 36 h. The optimum temperature for AFB1 degradation was 40 °C, and the optimum pH was 6.0–8.0. Notably, Mg²⁺ and dimethyl sulfoxide (DMSO) could enhance the AFB1-degrading activity of B10 laccase. Mutation of the three key metal combined sites of B10 laccase resulted in the loss of AFB1-degrading activity, indicating that these three metal combined sites of B10 laccase play an essential role in the catalytic degradation of AFB1.

Utilization of Soybean Oil Waste for a High-Level Production of Ceramide by a Novel Phospholipase C as an Environmentally Friendly Process

Ceramide is a natural functional ingredient as food additive and medicine that has attracted extensive attention in the food, medical, and cosmetic industries. Here, we developed a biotechnological strategy based on a recombinant whole-cell biocatalyst for efficiently producing ceramide from crude soybean oil sediment (CSOS) waste. A novel phospholipase C (PLC_ac) from Acinetobacter calcoaceticus isolated from soil samples was identified and characterized. Furthermore, recombinant Komagataella phaffii displaying PLC_ac (dPLC_ac) on the cell surface was constructed as a whole-cell biocatalyst with better thermostability (30-60 °C) and pH stability (8.0-10.0) to successfully produce ceramide. After synergistical optimization of reaction time and dPLC_ac dose, the ceramide yield of hydrolyzing from CSOS using dPLC_ac was 51% (the theoretical maximum yield of converting sphingomyelin, ∼70%) and the relative yield was over 50% after seven consecutive 4 h batches under the optimized conditions. Our study provides a potentially promising strategy for the commercial production of ceramide.

Computer-aided discovery, design, and investigation of COVID-19 therapeutics

Coronavirus disease 2019 (COVID-19) pandemic is currently the most serious public health threat faced by mankind. Thus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is being intensively investigated. Several vaccines are now available for clinical use. However, owing to the highly mutated nature of RNA viruses, the SARS-CoV-2 is changing at a rapid speed. Breakthrough infections by SARS-CoV-2 variants have been seen in vaccinated individuals. As a result, effective therapeutics for treating COVID-19 patients is urgently required. With the advance of computer technology, computational methods have become increasingly powerful in the biomedical research and pharmaceutical drug discovery. The applications of these techniques have largely reduced the costs and simplified processes of pharmaceutical drug developments. Intensive and extensive studies on SARS-CoV-2 proteins have been carried out and three-dimensional structures of the major SARS-CoV-2 proteins have been resolved and deposited in the Protein Data Bank. These structures provide the foundations for drug discovery and design using the structure-based computations, such as molecular docking and molecular dynamics simulations. In this review, introduction to the applications of computational methods in the discovery and design of novel drugs and repurposing of existing drugs for the treatments of COVID-19 is given. The examples of computer-aided investigations and screening of COVID-19 effective therapeutic compounds, functional peptides, as well as effective molecules from the herb medicines are discussed.