NMR-Based Methods for Protein Analysis

Lysine methylation: A strategy to improve in-cell NMR spectroscopy of proteins

BACKGROUND

In-cell NMR is a valuable technique for investigating protein structure and function in cellular environments. However, challenges arise due to highly crowded cellular environment, where nonspecific interactions between the target protein and other cellular components can lead to signals broadening or disappearance in NMR spectra.

RESULTS

We implemented chemical reduction methylation to selectively modify lysine residues on protein surfaces aiming to weaken charge interactions and recover obscured NMR signals. This method was tested on six proteins varying in molecular size and lysine content. While methylation did not disrupt the protein's native conformation, it successful restored some previously obscured in-cell NMR signals, particularly for proteins with high isoelectric points that decreased post-methylation.

SIGNIFICANCE

This study affirms lysine methylation as a feasible approach to enhance the sensitivity of in-cell NMR spectra for protein studies. By mitigating signal loss due to nonspecific interactions, this method expands the utility of in-cell NMR for investigating proteins in their natural cellular environment, potentially leading to more accurate structural and functional insights.

Isotope Reverse-Labeled Infrared Spectroscopy as a Probe of In-Cell Protein Structure

While recent years have seen great progress in determining the three-dimensional structure of isolated proteins, monitoring protein structure inside live cells remains extremely difficult. Here, we examine the utility of Fourier transform infrared (FTIR) spectroscopy as a probe of protein structure in live bacterial cells. Selective isotope enrichment is used both to distinguish recombinantly expressed NuG2b protein from the cellular background and to examine the conformation of specific residues in the protein. To maximize labeling flexibility and to improve spectral resolution between label and main-band peaks, we carry out isotope-labeling experiments in "reverse-labeling" mode: cells are initially grown in 13C-enriched media, with specific 12C-labeled amino acids added when protein expression is induced. 1 Because FTIR measurements require only around 20 μL of sample and each measurement takes only a few minutes to complete, isotope-labeling costs are minimal, allowing us to label multiple different residues in parallel in simultaneously grown cultures. For the stable NuG2b protein, isotope difference spectra from live bacterial cultures are nearly identical to spectra from isolated proteins, confirming that the structure of the protein is unperturbed by the cellular environment. By combining such measurements with site-directed mutagenesis, we further demonstrate that the local conformation of individual amino acids can be monitored, allowing us to determine, for example, whether a specific site in the protein contributes to α-helix or β-sheet structures.

Exploring the conformational landscape of protein kinases

Protein kinases are dynamic enzymes that display complex regulatory mechanisms. Although they possess a structurally conserved catalytic domain, significant conformational dynamics are evident both within a single kinase and across different kinases in the kinome. Here, we highlight methods for exploring this conformational space and its dynamics using kinase domains from ABL1 (Abelson kinase), PKA (protein kinase A), AurA (Aurora A), and PYK2 (proline-rich tyrosine kinase 2) as examples. Such experimental approaches combined with AI-driven methods, such as AlphaFold, will yield discoveries about kinase regulation, the catalytic process, substrate specificity, the effect of disease-associated mutations, as well as new opportunities for structure-based drug design.

Advances in the Clinical Study of Nuclear Overhauser Enhancement

Nuclear overhauser enhancement is a confounding factor arising from the in vivo application of a chemical exchange saturation transfer technique in which two nuclei in close proximity undergo dipole cross-relaxation. Several studies have shown applicability and efficacy of nuclear overhauser enhancement in observing tumors and other lesions in vivo. Thus, this effect could become an emerging molecular imaging research tool for many diseases. Moreover, nuclear overhauser enhancement has the advantages of simplicity, noninvasiveness, and high resolution and has become a major focus of current research. In this review, we summarize the principles and applications of nuclear overhauser enhancement. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Detection of Intrinsically Disordered Peptides by Biological Nanopore

Intrinsically disordered protein regions (IDPRs) are pivotal in regulation of transcription and facilitation of signal transduction. Because of their multiple conformational states of structure, characterizing the highly flexible structures of IDPRs becomes challenging. Herein, we employed the wild-type (WT) aerolysin nanopore as a real-time biosensor for identification and monitoring of long peptides containing IDPRs. This sensor successfully identified three intrinsically disordered peptides, with the lengths up to 43 amino acids, by distinguishing the unique signatures of blockade current and duration time. The analysis of the binding constant revealed that interactions between the nanopore and peptides are critical for peptide translocation, which suggests that mechanisms beyond mere volume exclusion. Furthermore, we were able to compare the conformational stabilities of various IDPRs by examining the detailed current traces of blockade events. Our approach can detect the conformational changes of IDPR in a confined nanopore space. These insights broaden the understanding of peptide structural changes. The nanopore biosensor showed the potential to study the conformations change of IDPRs, IDPRs transmembrane interactions, and protein drug discovery.

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Engineered Proteins and Materials Utilizing Residue-Specific Noncanonical Amino Acid Incorporation

The incorporation of noncanonical amino acids into proteins and protein-based materials has significantly expanded the repertoire of available protein structures and chemistries. Through residue-specific incorporation, protein properties can be globally modified, resulting in the creation of novel proteins and materials with diverse and tailored characteristics. In this review, we highlight recent advancements in residue-specific incorporation techniques as well as the applications of the engineered proteins and materials. Specifically, we discuss their utility in bio-orthogonal noncanonical amino acid tagging (BONCAT), fluorescent noncanonical amino acid tagging (FUNCAT), threonine-derived noncanonical amino acid tagging (THRONCAT), cross-linking, fluorination, and enzyme engineering. This review underscores the importance of noncanonical amino acid incorporation as a tool for the development of tailored protein properties to meet diverse research and industrial needs.

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

AI-Driven Deep Learning Techniques in Protein Structure Prediction

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Natural triterpenoid-aided identification of the druggable interface of HMGB1 occupied by TLR4

HMGB1 interacts with TLR4 to activate the inflammatory cascade response, contributing to the pathogenesis of endogenous tissue damage and infection. The immense importance of HMGB1-TLR4 interaction in the immune system has made its binding interface an area of significant interest. To map the binding interface of HMGB1 occupied by TLR4, triterpenoids that disrupt the HMGB1-TLR4 interaction and interfere with HMGB1-induced inflammation were developed. Using the unique triterpenoid PT-22 as a probe along with photoaffinity labeling and site-directed mutagenesis, we found that the binding interface of HMGB1 was responsible for the recognition of TLR4 located on the "L" shaped B-box with K114 as a crucial hot-spot residue. Amazingly, this highly conserved interaction surface overlapped with the antigen-recognition epitope of an anti-HMGB1 antibody. Our findings propose a novel strategy for better understanding the druggable interface of HMGB1 that interacts with TLR4 and provide insights for the rational design of HMGB1-TLR4 PPI inhibitors to fine tune immune responses.

Exploring treatment options in cancer: Tumor treatment strategies

Traditional therapeutic approaches such as chemotherapy and radiation therapy have burdened cancer patients with onerous physical and psychological challenges. Encouragingly, the landscape of tumor treatment has undergone a comprehensive and remarkable transformation. Emerging as fervently pursued modalities are small molecule targeted agents, antibody-drug conjugates (ADCs), cell-based therapies, and gene therapy. These cutting-edge treatment modalities not only afford personalized and precise tumor targeting, but also provide patients with enhanced therapeutic comfort and the potential to impede disease progression. Nonetheless, it is acknowledged that these therapeutic strategies still harbour untapped potential for further advancement. Gaining a comprehensive understanding of the merits and limitations of these treatment modalities holds the promise of offering novel perspectives for clinical practice and foundational research endeavours. In this review, we discussed the different treatment modalities, including small molecule targeted drugs, peptide drugs, antibody drugs, cell therapy, and gene therapy. It will provide a detailed explanation of each method, addressing their status of development, clinical challenges, and potential solutions. The aim is to assist clinicians and researchers in gaining a deeper understanding of these diverse treatment options, enabling them to carry out effective treatment and advance their research more efficiently.

Structure-based investigation of a DNA aptamer targeting PTK7 reveals an intricate 3D fold guiding functional optimization

DNA aptamers have emerged as novel molecular tools in disease theranostics owing to their high binding affinity and specificity for protein targets, which rely on their ability to fold into distinctive three-dimensional (3D) structures. However, delicate atomic interactions that shape the 3D structures are often ignored when designing and modeling aptamers, leading to inefficient functional optimization. Challenges persist in determining high-resolution aptamer-protein complex structures. Moreover, the experimentally determined 3D structures of DNA molecules with exquisite functions remain scarce. These factors impede our comprehension and optimization of some important DNA aptamers. Here, we performed a streamlined solution NMR-based structural investigation on the 41-nt sgc8c, a prominent DNA aptamer used to target membrane protein tyrosine kinase 7, for cancer theranostics. We show that sgc8c prefolds into an intricate three-way junction (3WJ) structure stabilized by long-range tertiary interactions and extensive base-base stackings. Delineated by NMR chemical shift perturbations, site-directed mutagenesis, and 3D structural information, we identified essential nucleotides constituting the key functional elements of sgc8c that are centralized at the core of 3WJ. Leveraging the well-established structure-function relationship, we efficiently engineered two sgc8c variants by modifying the apical loop and introducing L-DNA base pairs to simultaneously enhance thermostability, biostability, and binding affinity for both protein and cell targets, a feat not previously attained despite extensive efforts. This work showcases a simplified NMR-based approach to comprehend and optimize sgc8c without acquiring the complex structure, and offers principles for the sophisticated structure-function organization of DNA molecules.

Protecting Lyophilized Escherichia coli Adenylate Kinase

Drying protein-based drugs, usually via lyophilization, can facilitate storage at ambient temperature and improve accessibility but many proteins cannot withstand drying and must be formulated with protective additives called excipients. However, mechanisms of protection are poorly understood, precluding rational formulation design. To better understand dry proteins and their protection, we examine Escherichia coli adenylate kinase (AdK) lyophilized alone and with the additives trehalose, maltose, bovine serum albumin, cytosolic abundant heat soluble protein D, histidine, and arginine. We apply liquid-observed vapor exchange NMR to interrogate the residue-level structure in the presence and absence of additives. We pair these observations with differential scanning calorimetry data of lyophilized samples and AdK activity assays with and without heating. We show that the amino acids do not preserve the native structure as well as sugars or proteins and that after heating the most stable additives protect activity best.

Cellular and Biophysical Applications of Genetic Code Expansion

Despite their diverse functions, proteins are inherently constructed from a limited set of building blocks. These compositional constraints pose significant challenges to protein research and its practical applications. Strategically manipulating the cellular protein synthesis system to incorporate novel building blocks has emerged as a critical approach for overcoming these constraints in protein research and application. In the past two decades, the field of genetic code expansion (GCE) has achieved significant advancements, enabling the integration of numerous novel functionalities into proteins across a variety of organisms. This technological evolution has paved the way for the extensive application of genetic code expansion across multiple domains, including protein imaging, the introduction of probes for protein research, analysis of protein-protein interactions, spatiotemporal control of protein function, exploration of proteome changes induced by external stimuli, and the synthesis of proteins endowed with novel functions. In this comprehensive Review, we aim to provide an overview of cellular and biophysical applications that have employed GCE technology over the past two decades.

Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms.

Structural studies of intrinsically disordered MLL-fusion protein AF9 in complex with peptidomimetic inhibitors

AF9 (MLLT3) and its paralog ENL(MLLT1) are members of the YEATS family of proteins with important role in transcriptional and epigenetic regulatory complexes. These proteins are two common MLL fusion partners in MLL-rearranged leukemias. The oncofusion proteins MLL-AF9/ENL recruit multiple binding partners, including the histone methyltransferase DOT1L, leading to aberrant transcriptional activation and enhancing the expression of a characteristic set of genes that drive leukemogenesis. The interaction between AF9 and DOT1L is mediated by an intrinsically disordered C-terminal ANC1 homology domain (AHD) in AF9, which undergoes folding upon binding of DOT1L and other partner proteins. We have recently reported peptidomimetics that disrupt the recruitment of DOT1L by AF9 and ENL, providing a proof-of-concept for targeting AHD and assessing its druggability. Intrinsically disordered proteins, such as AF9 AHD, are difficult to study and characterize experimentally on a structural level. In this study, we present a successful protein engineering strategy to facilitate structural investigation of the intrinsically disordered AF9 AHD domain in complex with peptidomimetic inhibitors by using maltose binding protein (MBP) as a crystallization chaperone connected with linkers of varying flexibility and length. The strategic incorporation of disulfide bonds provided diffraction-quality crystals of the two disulfide-bridged MBP-AF9 AHD fusion proteins in complex with the peptidomimetics. These successfully determined first series of 2.1-2.6 Å crystal complex structures provide high-resolution insights into the interactions between AHD and its inhibitors, shedding light on the role of AHD in recruiting various binding partner proteins. We show that the overall complex structures closely resemble the reported NMR structure of AF9 AHD/DOT1L with notable difference in the conformation of the β-hairpin region, stabilized through conserved hydrogen bonds network. These first series of AF9 AHD/peptidomimetics complex structures are providing insights of the protein-inhibitor interactions and will facilitate further development of novel inhibitors targeting the AF9/ENL AHD domain.

An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction

In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.

Unnatural Amino Acids for Biological Spectroscopy and Microscopy

Due to advances in methods for site-specific incorporation of unnatural amino acids (UAAs) into proteins, a large number of UAAs with tailored chemical and/or physical properties have been developed and used in a wide array of biological applications. In particular, UAAs with specific spectroscopic characteristics can be used as external reporters to produce additional signals, hence increasing the information content obtainable in protein spectroscopic and/or imaging measurements. In this Review, we summarize the progress in the past two decades in the development of such UAAs and their applications in biological spectroscopy and microscopy, with a focus on UAAs that can be used as site-specific vibrational, fluorescence, electron paramagnetic resonance (EPR), or nuclear magnetic resonance (NMR) probes. Wherever applicable, we also discuss future directions.

Evolution of a Combined UV/Vis and NMR Setup with Fixed Pathlengths for Mass-limited Samples

The investigation of reaction mechanisms is a complex task that usually requires the use of several techniques. To obtain as much information as possible on the reaction and any intermediates - possibly invisible to one technique - the combination of techniques is a solution. In this work we present a new setup for combined UV/Vis and NMR spectroscopy and compare it to an established alternative. The presented approach allows a versatile usage of different commercially-available components like mirrors and fiber bundles as well as different fixed pathlengths according to double transmission or single transmission measurements. While a previous approach is based on a dip-probe setup for conventional NMR probes, the new one is based on a micro-Helmholtz coil array (LiquidVoxel™). This makes the use of rectangular cuvettes possible, which ensure well-defined pathlengths allowing for quantification of species. Additionally, very low quantities of compound can be analyzed due to the microfabrication and small cuvette size used. As proof-of-principle this new setup for combined UV/Vis and NMR spectroscopy is used to examine a well-studied photochromic system of the dithienylethene compound class. A thorough comparison of the pros and cons of the two setups for combined UV/Vis and NMR measurements is performed.

Controlling the incorporation of fluorinated amino acids in human cells and its structural impact

Fluorinated aromatic amino acids (FAAs) are promising tools when studying protein structure and dynamics by NMR spectroscopy. The incorporation FAAs in mammalian expression systems has been introduced only recently. Here, we investigate the effects of FAAs incorporation in proteins expressed in human cells, focusing on the probability of incorporation and its consequences on the 19 F NMR spectra. By combining 19 F NMR, direct MS and x-ray crystallography, we demonstrate that the probability of FAA incorporation is only a function of the FAA concentration in the expression medium and is a pure stochastic phenomenon. In contrast with the MS data, the x-ray structures of carbonic anhydrase II reveal that while the 3D structure is not affected, certain positions lack fluorine, suggesting that crystallization selectively excludes protein molecules featuring subtle conformational modifications. This study offers a predictive model of the FAA incorporation efficiency and provides a framework for controlling protein fluorination in mammalian expression systems.

Characterization of mRNA Lipid Nanoparticles by Electron Density Mapping Reconstruction: X-ray Scattering with Density from Solution Scattering (DENSS) Algorithm

PURPOSE

This study aimed to test the feasibility of using Small Angle X-ray Scattering (SAXS) coupled with Density from Solution Scattering (DENSS) algorithm to characterize the internal architecture of messenger RNA-containing lipid nanoparticles (mRNA-LNPs).

METHODS

The DENSS algorithm was employed to construct a three-dimensional model of average individual mRNA-LNP. The reconstructed models were cross validated with cryogenic transmission electron microscopy (cryo-TEM), and dynamic light scattering (DLS) to assess size, morphology, and internal structure.

RESULTS

Cryo-TEM and DLS complemented SAXS, revealed a core-shell mRNA-LNP structure with electron-rich mRNA-rich region at the core, surrounded by lipids. The reconstructed model, utilizing the DENSS algorithm, effectively distinguishes mRNA and lipids via electron density mapping. Notably, DENSS accurately models the morphology of the mRNA-LNPs as an ellipsoidal shape with a "bleb" architecture or a two-compartment structure with contrasting electron densities, corresponding to mRNA-filled and empty lipid compartments, respectively. Finally, subtle changes in the LNP structure after three freeze-thaw cycles were detected by SAXS, demonstrating an increase in radius of gyration (Rg) associated with mRNA leakage.

CONCLUSION

Analyzing SAXS profiles based on DENSS algorithm to yield a reconstructed electron density based three-dimensional model can be a useful physicochemical characterization method in the toolbox to study mRNA-LNPs and facilitate their development.

Evolutionary and Structural Analysis of PP16 in Viridiplantae

Members of the phloem protein 16 (PP16) gene family are induced by elicitors in rice and the corresponding proteins from cucurbits, which display RNA binding and intercellular transport activities, are accumulated in phloem sap. These proteins facilitate the movement of protein complexes through the phloem translocation flow and may be involved in the response to water deficit, among other functions. However, there is scant information regarding their function in other plants, including the identification of paralog genes in non-vascular plants and chlorophytes. In the present work, an evolutionary and structural analysis of the PP16 family in green plants (Viridiplantae) was carried out. Data mining in different databases indicated that PP16 likely originated from a larger gene present in an ancestral lineage that gave rise to chlorophytes and multicellular plants. This gene encodes a protein related to synaptotagmin, which is involved in vesicular transport in animal systems, although other members of this family play a role in lipid turnover in endomembranes and organelles. These proteins contain a membrane-binding C2 domain shared with PP16 proteins in vascular plants. In silico analysis of the predicted structure of the PP16 protein family identified several β-sheets, one α-helix, and intrinsically disordered regions. PP16 may have been originally involved in vesicular trafficking and/or membrane maintenance but specialized in long-distance signaling during the emergence of the plant vascular system.

Fluorinated captopril analogues inhibit metallo-β-lactamases and facilitate structure determination of NDM-1 binding pose

Bacterial resistance to the majority of clinically used β-lactam antibiotics is a global health threat and, consequently, the driving force for the development of metallo-β-lactamase (MBL) inhibitors. The rapid evolution of new MBLs calls for new strategies and tools for inhibitor development. In this study, we designed and developed a series of trifluoromethylated captopril analogues as probes for structural studies of enzyme-inhibitor binding. The new compounds showed activity comparable to the non-fluorinated inhibitors against the New Delhi Metallo-β-lactamase-1 (NDM-1). The most active compound, a derivative of D-captopril, exhibited an IC50 value of 0.3 μM. Several compounds demonstrated synergistic effects, restoring the effect of meropenem and reducing the minimum inhibitory concentration (MIC) values in NDM-1 (up to 64-fold), VIM-2 (up to 8-fold) and IMP-26 (up to 8-fold) harbouring Escherichia coli. NMR spectroscopy and molecular docking of one representative inhibitor determined the binding pose in NDM-1, demonstrating that fluorinated analogues of inhibitors are a valuable tool for structural studies of MBL-inhibitor complexes.

Automated Virtual Screening

Computational methods in modern drug discovery have become ubiquitous, with methods that cover most of the discovery stages: from hit finding and lead identification to lead optimization. The overall aim of these computational methods is to obtain a more efficient discovery process, by reducing the number of "wet" experiments required to produce therapeutics that have higher probability of succeeding in clinical development and subsequently benefitting end patients by developing highly effective therapeutics having minimal side effects. Virtual Screening is usually applied at the early stage of drug discovery, looking to find chemical matter having desired properties, such as molecular shape, electrostatics, and pharmacophores at desired three-dimensional positions. The aim of this stage is to search in a wide chemical space, including chemistry available from commercial suppliers and virtual databases of predicted reaction products, to identify molecules that would exert a particular biochemical response. This initial stage of the discovery process is very important since the subsequent stages will use the initial chemical motifs that have been found at the hit finding stage, and therefore the most suitable the compound is found, the more likely it is that subsequent stages will be successful and less time and resource consuming. This chapter provides a summary of various Virtual Screening methods, including shape match and molecular docking, and these methods are used in a Virtual Screening workflow that is provided as an example which is described to be run automatically in cloud resources. This automatic in-depth exploration of the chemical space using validated Virtual Screening methods can lead to a more streamlined and efficient discovery process, aiming to deliver chemical matter of high quality and maximizing the required biological effects while minimizing adverse effects. Surely, Virtual Screening pipelines of this nature will continue to play a central role in producing much needed therapeutics for the health challenges of the future.

Molecular Profiling of Peanut under Raw, Roasting, and Autoclaving Conditions Using High-Resolution Magic Angle Spinning and Solution 1H NMR Spectroscopy

Higher rates of peanut allergy have been observed in countries that commonly roast peanuts prior to consumption. Despite the importance of understanding the role of thermal processing in allergy and on peanut composition, studies toward generating signatures that identify molecular contents following processing are scant. Here, we identified spectral signatures to track changes and differences in the molecular composition of peanuts under raw, roasted, and high-pressure and high-temperature autoclaved conditions. We analyzed both the solid flesh of the seed and solutions derived from soaking peanuts using High-Resolution Magic Angle Spinning (HR-MAS) and solution 1H Nuclear Magnetic Resonance (NMR) spectroscopy, respectively. The NMR spectra of intact peanuts revealed triglycerides as the dominant species, assigned on the basis of multiplets at 4.1 and 4.3 ppm, and corresponding defatted flours revealed the presence of sugars. Sucrose assigned based on a doublet at 5.4 ppm (anomeric proton), and triglycerides were the most abundant small molecules observed, with little variation between conditions. Soaked peanut solutions were devoid of lipids, and their resulting spectra matched the profiles of defatted peanuts. Spectral signatures resulting from autoclaving differed strikingly between those from raw and roasted peanuts, with considerable line-broadening in regions corresponding to proteins and amino-acid side chains, from 0.5 to 2.0 ppm and 6.5 to 8.5 ppm. Taken together, by using complementary NMR methods to obtain a fingerprint of the molecular components in peanuts, we demonstrated that autoclaving led to a distinct composition, likely resulting from the hydrolytic cleavage of proteins, the most important molecule of the allergic reaction.

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

INTRODUCTION

Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra.

OBJECTIVES

The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals.

METHODS

We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS).

RESULTS

The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites.

CONCLUSION

This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Single-Molecule X-ray Scattering Used to Visualize the Conformation Distribution of Biological Molecules via Single-Object Scattering Sampling

Biological macromolecules, the fundamental building blocks of life, exhibit dynamic structures in their natural environment. Traditional structure determination techniques often oversimplify these multifarious conformational spectra by capturing only ensemble- and time-averaged molecular structures. Addressing this gap, in this work, we extend the application of the single-object scattering sampling (SOSS) method to diverse biological molecules, including RNAs and proteins. Our approach, referred to as "Bio-SOSS", leverages ultrashort X-ray pulses to capture instantaneous structures. In Bio-SOSS, we employ two gold nanoparticles (AuNPs) as labels, which provide strong contrast in the X-ray scattering signal, to ensure precise distance determinations between labeled sites. We generated hypothetical Bio-SOSS images for RNAs, proteins, and an RNA-protein complex, each labeled with two AuNPs at specified positions. Subsequently, to validate the accuracy of Bio-SOSS, we extracted distances between these nanoparticle labels from the images and compared them with the actual values used to generate the Bio-SOSS images. Specifically, for a representative RNA (1KXK), the standard deviation in distance discrepancies between molecular dynamics snapshots and Bio-SOSS retrievals was found to be optimally around 0.2 Å, typically within 1 Å under practical experimental conditions at state-of-the-art X-ray free-electron laser facilities. Furthermore, we conducted an in-depth analysis of how various experimental factors, such as AuNP size, X-ray properties, and detector geometry, influence the accuracy of Bio-SOSS. This comprehensive investigation highlights the practicality and potential of Bio-SOSS in accurately capturing the diverse conformation spectrum of biological macromolecules, paving the way for deeper insights into their dynamic natures.

Biophysical screening and characterisation in medicinal chemistry

In the last two decades the use of biophysical assays and methods in medicinal chemistry has increased significantly, to meet the demands of the novel targets and modalities that drug discoverers are looking to tackle. The desire to obtain accurate affinities, kinetics, thermodynamics and structural data as early as possible in the drug discovery process has fuelled this innovation. This review introduces the principles underlying the techniques in common use and provides a perspective on the weaknesses and strengths of different methods. Case studies are used to further illustrate some of the applications in medicinal chemistry and a discussion of the emerging biophysical methods on the horizon is presented.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

What's the defect? Using mass defects to study oligomerization of membrane proteins and peptides in nanodiscs with native mass spectrometry

Many membrane proteins form functional complexes that are either homo- or hetero-oligomeric. However, it is challenging to characterize membrane protein oligomerization in intact lipid bilayers, especially for polydisperse mixtures. Native mass spectrometry of membrane proteins and peptides inserted in lipid nanodiscs provides a unique method to study the oligomeric state distribution and lipid preferences of oligomeric assemblies. To interpret these complex spectra, we developed novel data analysis methods using macromolecular mass defect analysis. Here, we provide an overview of how mass defect analysis can be used to study oligomerization in nanodiscs, discuss potential limitations in interpretation, and explore strategies to resolve these ambiguities. Finally, we review recent work applying this technique to studying formation of antimicrobial peptide, amyloid protein, and viroporin complexes with lipid membranes.

SARS-CoV-2 proteins structural studies using synchrotron radiation

In the process of the development of structural biology, both the size and the complexity of the determined macromolecular structures have grown significantly. As a result, the range of application areas for the results of structural studies of biological macromolecules has expanded. Significant progress in the development of structural biology methods has been largely achieved through the use of synchrotron radiation. Modern sources of synchrotron radiation allow to conduct high-performance structural studies with high temporal and spatial resolution. Thus, modern techniques make it possible to obtain not only static structures, but also to study dynamic processes, which play a key role in understanding biological mechanisms. One of the key directions in the development of structural research is the drug design based on the structures of biomolecules. Synchrotron radiation offers insights into the three-dimensional time-resolved structure of individual viral proteins and their complexes at atomic resolution. The rapid and accurate determination of protein structures is crucial for understanding viral pathogenicity and designing targeted therapeutics. Through the application of experimental techniques, including X-ray crystallography and small-angle X-ray scattering (SAXS), it is possible to elucidate the structural details of SARS-CoV-2 virion containing 4 structural, 16 nonstructural proteins (nsp), and several accessory proteins. The most studied potential targets for vaccines and drugs are the structural spike (S) protein, which is responsible for entering the host cell, as well as nonstructural proteins essential for replication and transcription, such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp). This article provides a brief overview of structural analysis techniques, with focus on synchrotron radiation-based methods applied to the analysis of SARS-CoV-2 proteins.

Comparative Study of SERS-Spectra of NQ21 Peptide on Silver Particles and in Gold-Coated "Nanovoids"

The NQ21 peptide has relatively recently attracted attention in the biomedical sphere due to its prospects for facilitating the engineering of the HIV1 vaccine and ELISA test. Today, there is still a need for a reliable and fast methodology that reveals the secondary structure of this analyte at the low concentrations conventionally used in vaccines and immunological assays. The present research determined the differences between the surface-enhanced Raman scattering (SERS) spectra of NQ21 peptide molecules adsorbed on solid SERS-active substrates depending on their geometry and composition. The ultimate goal of our research was to propose an algorithm and SERS-active material for structural analysis of peptides. Phosphate buffer solutions of the 30 µg/mL NQ21 peptide at different pH levels were used for the SERS measurements, with silver particles on mesoporous silicon and gold-coated "nanovoids" in macroporous silicon. The SERS analysis of the NQ21 peptide was carried out by collecting the SERS spectra maps. The map assessment with an originally developed algorithm resulted in defining the effect of the substrate on the secondary structure of the analyte molecules. Silver particles are recommended for peptide detection if it is not urgent to precisely reveal all the characteristic bands, because they provide greater enhancement but are accompanied by analyte destruction. If the goal is to carefully study the secondary structure and composition of the peptide, it is better to use SERS-active gold-coated "nanovoids". Objective results can be obtained by collecting at least three 15 × 15 maps of the SERS spectra of a given peptide on substrates from different batches.

Single stop analysis of a protein surface using molecular probe electrochemistry

Visualization of a protein in its native form and environment without any interference has always been a challenging task. Contrary to the assumption that protein surfaces are smooth, they are in fact highly irregular with undulating surfaces. Hence, in this study, we have tackled this ambiguous nature of the 'surface' of a protein by considering the 'effective' protein surface (EPS) with respect to its interaction with the geometrically well-defined and structurally inert anionic molecule [3,3'-Co(1,2-C2B9H11)2]-, abbreviated as [o-COSAN]-, whose stability, propensity for amine residues, and self-assembling abilities are well reported. This study demonstrates the intricacies of protein surfaces exploiting simple electrochemical measurements using a 'small molecule' redox-active probe. This technique offers the advantage of not utilizing any harsh experimental conditions that could alter the native structure of the protein and hence the protein integrity is retained. Identification of the amino acid residues which are most involved in the interactions with [3,3'-Co(1,2-C2B9H11)2]- and how a protein's environment affects these interactions can help in gaining insights into how to modify proteins to optimize their interactions particularly in the fields of drug design and biotechnology. In this research, we have demonstrated that [3,3'-Co(1,2-C2B9H11)2]- anionic small molecules are excellent candidates for studying and visualizing protein surfaces in their natural environment and allow proteins to be classified according to the surface composition, which imparts their properties. [3,3'-Co(1,2-C2B9H11)2]- 'viewed' each protein surface differently and hence has the potential to act as a simple and easy to handle cantilever for measuring and picturing protein surfaces.

Comparison, Analysis, and Molecular Dynamics Simulations of Structures of a Viral Protein Modeled Using Various Computational Tools

The structural analysis of proteins is a major domain of biomedical research. Such analysis requires resolved three-dimensional structures of proteins. Advancements in computer technology have led to progress in biomedical research. In silico prediction and modeling approaches have facilitated the construction of protein structures, with or without structural templates. In this study, we used three neural network-based de novo modeling approaches-AlphaFold2 (AF2), Robetta-RoseTTAFold (Robetta), and transform-restrained Rosetta (trRosetta)-and two template-based tools-the Molecular Operating Environment (MOE) and iterative threading assembly refinement (I-TASSER)-to construct the structure of a viral capsid protein, hepatitis C virus core protein (HCVcp), whose structure have not been fully resolved by laboratory techniques. Templates with sufficient sequence identity for the homology modeling of complete HCVcp are currently unavailable. Therefore, we performed domain-based homology modeling for MOE simulations. The templates for each domain were obtained through sequence-based searches on NCBI and the Protein Data Bank. Then, the modeled domains were assembled to construct the complete structure of HCVcp. The full-length structure and two truncated forms modeled using various computational tools were compared. Molecular dynamics (MD) simulations were performed to refine the structures. The root mean square deviation of backbone atoms, root mean square fluctuation of Cα atoms, and radius of gyration were calculated to monitor structural changes and convergence in the simulations. The model quality was evaluated through ERRAT and phi-psi plot analysis. In terms of the initial prediction for protein modeling, Robetta and trRosetta outperformed AF2. Regarding template-based tools, MOE outperformed I-TASSER. MD simulations resulted in compactly folded protein structures, which were of good quality and theoretically accurate. Thus, the predicted structures of certain proteins must be refined to obtain reliable structural models. MD simulation is a promising tool for this purpose.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

Protein aggregation and neurodegenerative disease: Structural outlook for the novel therapeutics

Before the controversial approval of humanized monoclonal antibody lecanemab, which binds to the soluble amyloid-β protofibrils, all the treatments available earlier, for Alzheimer's disease (AD) were symptomatic. The researchers are still struggling to find a breakthrough in AD therapeutic medicine, which is partially attributable to lack in understanding of the structural information associated with the intrinsically disordered proteins and amyloids. One of the major challenges in this area of research is to understand the structural diversity of intrinsically disordered proteins under in vitro conditions. Therefore, in this review, we have summarized the in vitro applications of biophysical methods, which are aimed to shed some light on the heterogeneity, pathogenicity, structures and mechanisms of the intrinsically disordered protein aggregates associated with proteinopathies including AD. This review will also rationalize some of the strategies in modulating disease-relevant pathogenic protein entities by small molecules using structural biology approaches and biophysical characterization. We have also highlighted tools and techniques to simulate the in vivo conditions for native and cytotoxic tau/amyloids assemblies, urge new chemical approaches to replicate tau/amyloids assemblies similar to those in vivo conditions, in addition to designing novel potential drugs.

Strategies for acquisition of resonance assignment spectra of highly dynamic membrane proteins: a GPCR case study

In protein nuclear magnetic resonance (NMR), chemical shift assignment provides a wealth of information. However, acquisition of high-quality solid-state NMR spectra depends on protein-specific dynamics. For membrane proteins, bilayer heterogeneity further complicates this observation. Since the efficiency of cross-polarization transfer is strongly entwined with protein dynamics, optimal temperatures for spectral sensitivity and resolution will depend not only on inherent protein dynamics, but temperature-dependent phase properties of the bilayer environment. We acquired 1-, 2-, and 3D homo- and heteronuclear experiments of the chemokine receptor CCR3 in a 7:3 phosphatidylcholine:cholesterol lipid environment. 1D direct polarization, cross polarization (CP), and T2' experiments indicate sample temperatures below - 25 °C facilitate higher CP enhancement and longer-lived transverse relaxation times. T1rho experiments indicate intermediate timescales are minimized below a sample temperature of - 20 °C. 2D DCP NCA experiments indicated optimal CP efficiency and resolution at a sample temperature of - 30 °C, corroborated by linewidth analysis in 3D NCACX at - 30 °C compared to - 5 °C. This optimal temperature is concluded to be directly related the lipid phase transition, measured to be between - 20 and 15 °C based on rINEPT signal of all-trans and trans-gauche lipid acyl conformations. Our results have critical implications in acquisition of SSNMR membrane protein assignment spectra, as we hypothesize that different lipid compositions with different phase transition properties influence protein dynamics and therefore the optimal acquisition temperature.

Molecular Glue Discovery: Current and Future Approaches

The intracellular interactions of biomolecules can be maneuvered to redirect signaling, reprogram the cell cycle, or decrease infectivity using only a few dozen atoms. Such "molecular glues," which can drive both novel and known interactions between protein partners, represent an enticing therapeutic strategy. Here, we review the methods and approaches that have led to the identification of small-molecule molecular glues. We first classify current FDA-approved molecular glues to facilitate the selection of discovery methods. We then survey two broad discovery method strategies, where we highlight the importance of factors such as experimental conditions, software packages, and genetic tools for success. We hope that this curation of methodologies for directed discovery will inspire diverse research efforts targeting a multitude of human diseases.

Toward the analysis of functional proteoforms using mass spectrometry-based stability proteomics

Functional proteomics aims to elucidate biological functions, mechanisms, and pathways of proteins and proteoforms at the molecular level to examine complex cellular systems and disease states. A series of stability proteomics methods have been developed to examine protein functionality by measuring the resistance of a protein to chemical or thermal denaturation or proteolysis. These methods can be applied to measure the thermal stability of thousands of proteins in complex biological samples such as cell lysate, intact cells, tissues, and other biological fluids to measure proteome stability. Stability proteomics methods have been popularly applied to observe stability shifts upon ligand binding for drug target identification. More recently, these methods have been applied to characterize the effect of structural changes in proteins such as those caused by post-translational modifications (PTMs) and mutations, which can affect protein structures or interactions and diversify protein functions. Here, we discussed the current application of a suite of stability proteomics methods, including thermal proteome profiling (TPP), stability of proteomics from rates of oxidation (SPROX), and limited proteolysis (LiP) methods, to observe PTM-induced structural changes on protein stability. We also discuss future perspectives highlighting the integration of top-down mass spectrometry and stability proteomics methods to characterize intact proteoform stability and understand the function of variable protein modifications.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.

In silico modelling of the function of disease-related CAZymes

In silico modelling of proteins comprises a diversity of computational tools aimed to obtain structural, electronic, and/or dynamic information about these biomolecules, capturing mechanistic details that are challenging to experimental approaches, such as elusive enzyme-substrate complexes, short-lived intermediates, and reaction transition states (TS). The present article gives the reader insight on the use of in silico modelling techniques to understand complex catalytic reaction mechanisms of carbohydrate-active enzymes (CAZymes), along with the underlying theory and concepts that are important in this field. We start by introducing the significance of carbohydrates in nature and the enzymes that process them, CAZymes, highlighting the conformational flexibility of their carbohydrate substrates. Three commonly used in silico methods (classical molecular dynamics (MD), hybrid quantum mechanics/molecular mechanics (QM/MM), and enhanced sampling techniques) are described for nonexpert readers. Finally, we provide three examples of the application of these methods to unravel the catalytic mechanisms of three disease-related CAZymes: β-galactocerebrosidase (GALC), responsible for Krabbe disease; α-mannoside β-1,6-N-acetylglucosaminyltransferase V (MGAT5), involved in cancer; and O-fucosyltransferase 1 (POFUT1), involved in several human diseases such as leukemia and the Dowling-Degos disease.

Probing the interactions between amyloidogenic proteins and bio-membranes

Protein misfolding diseases (PMDs) in humans are characterized by the deposition of protein aggregates in tissues, including Alzheimer's disease, Parkinson's disease, type 2 diabetes, and amyotrophic lateral sclerosis. Misfolding and aggregation of amyloidogenic proteins play a central role in the onset and progression of PMDs, and these processes are regulated by multiple factors, especially the interaction between proteins and bio-membranes. Bio-membranes induce conformational changes in amyloidogenic proteins and affect their aggregation; on the other hand, the aggregates of amyloidogenic proteins may cause membrane damage or dysfunction leading to cytotoxicity. In this review, we summarize the factors that affect the binding of amyloidogenic proteins and membranes, the effects of bio-membranes on the aggregation of amyloidogenic proteins, mechanisms of membrane disruption by amyloidogenic aggregates, technical approaches for detecting these interactions, and finally therapeutic strategies targeting membrane damage caused by amyloidogenic proteins.

Ligands-induced open-close conformational change during DapE catalysis: Insights from molecular dynamics simulations

The microbial enzyme DapE plays a critical role in the lysine biosynthetic pathway and is considered as a potentially safe antibiotic target. In this study, atomistic simulations are employed to identify the modes of essential dynamics that define the conformational response of the enzyme to ligand binding and unbinding. The binding modes and the binding affinities of the products to the DapE enzyme are estimated from the MM-PBSA method, and the residues contributing to the ligand binding are identified. Various structural analyses and the principal component analysis of the molecular dynamics trajectories reveal that the removal of products from the active site causes a significant change in the overall enzyme structure. Both Cartesian and dihedral principal component analyses are used to characterize the structural changes in terms of domain unfolding and domain twisting motions. In the most dominant mode, that is, the domain unfolding motion, the two catalytic domains move away from the two dimerization domains of the dimeric enzyme, representing a closed-to-open conformational change. The conformational changes are initiated by the coordinated movement of three loops (Asp75-Pro82, Gly240-Asn244, and Thr347-Glu353) that trigger a domain-level movement. From multiple short trajectories, the time constant associated with the domain opening motion is estimated as 43.6 ns. Physiologically, this close-to-open conformational change is essential for the regeneration of the initial state of the enzyme for the subsequent cycle of catalytic action and provides the apo enzyme enough flexibility for efficient substrate binding.

Computational and Functional Insights of Protein Misfolding in Neurodegeneration

Protein folding is the process by which a polypeptide chain self-assembles into the correct three-dimensional structure, so that it ends up in the biologically active, native state. Under conditions of proteotoxic stress, mutations, or cellular aging, proteins can begin to aggregate into non-native structures such as ordered amyloid fibrils and plaques. Many neurodegenerative diseases involve the misfolding and aggregation of specific proteins into abnormal, toxic species. Experimental approaches including crystallography and AFM (atomic force microscopy)-based force spectroscopy are used to exploit the folding and structural characterization of protein molecules. At the same time, computational techniques through molecular dynamics, fold recognition, and structure prediction are widely applied in this direction. Benchmarking analysis for combining and comparing computational methodologies with functional studies can decisively unravel robust interactions between the side groups of the amino acid sequence and monitor alterations in intrinsic protein dynamics with high precision as well as adequately determine potent conformations of the folded patterns formed in the polypeptide structure.

Adaptable Singlet-Filtered Nuclear Magnetic Resonance Spectroscopy for Chemical and Biological Applications

Proton nuclear magnetic resonance (¹H NMR) spectroscopy presents a powerful detection tool for studying chemical compositions and molecular structures. In practical chemical and biological applications, ¹H NMR experiments are generally confronted with the challenge of spectral congestions caused by abundant observable components and intrinsic limitations of a narrow frequency distribution range and extensive J coupling splitting. Herein, a one-dimensional (1D) general NMR method is proposed to individually extract the signals of targeted proton groups based on their endogenous spin singlet states excited from J coupling interactions, and it is suitable for high-resolution detections on complex chemical and biological samples. The applicability of the proposed method is demonstrated by experimental observations on chemical solutions containing different coupled components, intact grape tissues subjected to crowded resonances, and in vitro pig brain with various metabolites. Moreover, the proposed method is further exploited for magnetic resonance spectroscopy applications by directly combining the spatial localization module, showing promise in in vivo biological metabolite studies.

Synthesis of 13C/19F/2H labeled indoles for use as tryptophan precursors for protein NMR spectroscopy

Synthesis of indoles labeled with 13C-1H and 13C-19F spin pairs is described. All syntheses utilize inexpensive carbon-13C dioxide as the 13C isotope source. Ruthenium-mediated ring-closing metathesis is the key step in construction of the 13C containing indole carbocycle. Fluorine is introduced via electrophilic fluorination at the 7-position and via palladium-mediated cross-coupling at the 4-position. Indole and fluoroindoles are viable tryptophan precursors for in vivo protein expression. We show that they are viable also in in vitro protein synthesis using standard E. coli S30 extracts. Incorporation of the synthesized 13C-1H and 13C-19F spin pair labeled tryptophans into proteins enables high-resolution and high-sensitivity nuclear magnetic resonance (NMR) spectroscopy.

Label-Free Fluorescence Molecular Beacon Probes Based on G-Triplex DNA and Thioflavin T for Protein Detection

Protein detection plays an important role in biological and biomedical sciences. The immunoassay based on fluorescence labeling has good specificity but a high labeling cost. Herein, on the basis of G-triplex molecular beacon (G3MB) and thioflavin T (ThT), we developed a simple and label-free biosensor for protein detection. The biotin and streptavidin were used as model enzymes. In the presence of target streptavidin (SA), the streptavidin hybridized with G3MB-b (biotin-linked-G-triplex molecular beacon) perfectly and formed larger steric hindrance, which hindered the hydrolysis of probes by exonuclease III (Exo III). In the absence of target streptavidin, the exonuclease III successively cleaved the stem of G3MB-b and released the G-rich sequences which self-assembled into a G-triplex and subsequently activated the fluorescence signal of thioflavin T. Compared with the traditional G-quadruplex molecular beacon (G4MB), the G3MB only needed a lower dosage of exonuclease III and a shorter reaction time to reach the optimal detection performance, because the concise sequence of G-triplex was good for the molecular beacon design. Moreover, fluorescence experiment results exhibited that the G3MB-b had good sensitivity and specificity for streptavidin detection. The developed label-free biosensor provides a valuable and general platform for protein detection.