PKAD: a database of experimentally measured pKa values of ionizable groups in proteins

Acid-sensing ion channel 3 is a new potential therapeutic target for the control of glioblastoma cancer stem cells growth

Glioblastoma (GBM) is the most common malignant primary brain cancer that, despite recent advances in the understanding of its pathogenesis, remains incurable. GBM contains a subpopulation of cells with stem cell-like properties called cancer stem cells (CSCs). Several studies have demonstrated that CSCs are resistant to conventional chemotherapy and radiation thus representing important targets for novel anti-cancer therapies. Proton sensing receptors expressed by CSCs could represent important factors involved in the adaptation of tumours to the extracellular environment. Accordingly, the expression of acid-sensing ion channels (ASICs), proton-gated sodium channels mainly expressed in the neurons of peripheral (PNS) and central nervous system (CNS), has been demonstrated in several tumours and linked to an increase in cell migration and proliferation. In this paper we report that the ASIC3 isoform, usually absent in the CNS and present in the PNS, is enriched in human GBM CSCs while poorly expressed in the healthy human brain. We propose here a novel therapeutic strategy based on the pharmacological activation of ASIC3, which induces a significant GBM CSCs damage while being non-toxic for neurons. This approach might offer a promising and appealing new translational pathway for the treatment of glioblastoma.

Chemoselective Reagents for the Traceless Bioreversible Modification of Native Proteins

Nature utilizes bioreversible post-translational modifications (PTMs) to spatiotemporally diversify protein function. Mimicking Nature's approach, chemists have developed a variety of chemoselective regents for traceless, bioreversible modification of native proteins. These strategies have found utility in the development of reversible covalent inhibitors and degraders as well as the synthesis of functional protein conjugates for delivery into cells. This Viewpoint provides a snapshot of such tools, which currently cover Cys, Ser, Thr, Lys, Asp, and Glu residues and the N terminus. Additionally, we explore how bioreversible reagents, originally developed by research communities with differing objectives, can be utilized synergistically. Looking forward, we discuss the need for developing bioreversible reagents for labeling His, Tyr, Arg, Trp, Asn, Gln, and Met residues and the C-terminus as well as the installation of dynamic PTMs. Finally, to broaden the applicability of these tools, we point out the importance of developing modular release scaffolds with tunable release times and responsiveness to multiple endogenous triggers. We anticipate that this Viewpoint will catalyze further research and technological breakthroughs in this rapidly evolving field.

A Matter of Charge: Electrostatically Tuned Coassembly of Amphiphilic Peptides

Coassembly of peptide biomaterials offers a compelling avenue to broaden the spectrum of hierarchically ordered supramolecular nanoscale structures that may be relevant for biomedical and biotechnological applications. In this work coassemblies of amphiphilic and oppositely charged, anionic and cationic, β-sheet peptides are studied, which may give rise to a diverse range of coassembled forms. Mixtures of the peptides show significantly lower critical coassembly concentration (CCC) values compared to those of the individual pure peptides. Intriguingly, the highest formation of coassembled fibrils is found to require excess of the cationic peptide whereas equimolar mixtures of the peptides exhibited the maximum folding into β-sheet structures. Mixtures of the peptides coassembled sequentially from solutions at concentrations surpassing each peptide's intrinsic critical assembly concentration (CAC), are also found to require a higher portion of the cationic peptide to stabilize hydrogels. This study illuminates a systematic investigation of oppositely charged β-sheet peptides over a range of concentrations, in solutions and in hydrogels. The results may be relevant to the fundamental understanding of such intricate charge-driven assembly systems and to the formulation of peptide-based nanostructures with diverse functionalities.

Accurate Protein pKa Prediction with Physical Organic Chemistry Guided 3D Protein Representation

Protein pKa is a fundamental physicochemical parameter that dictates protein structure and function. However, accurately determining protein site-pKa values remains a substantial challenge, both experimentally and theoretically. In this study, we introduce a physical organic approach, leveraging a protein structural and physical-organic-parameter-based representation (P-SPOC), to develop a rapid and intuitive model for protein pKa prediction. Our P-SPOC model achieves state-of-the-art predictive accuracy, with a mean absolute error (MAE) of 0.33 pKa units. Furthermore, we have incorporated advanced protein structure prediction models, like AlphaFold2, to approximate structures for proteins lacking three-dimensional representations, which enhances the applicability of our model in the context of structure-undetermined protein research. To promote broader accessibility within the research community, an online prediction interface was also established at isyn.luoszgroup.com.

Emerging and Re-emerging Warheads for Targeted Covalent Inhibitors: An Update

Covalent inhibitors and other types of covalent modalities have seen a revival in the past two decades, with a variety of new targeted covalent drugs having been approved in recent years. A key feature of such molecules is an intrinsically reactive group, typically a weak electrophile, which enables the irreversible or reversible formation of a covalent bond with a specific amino acid of the target protein. This reactive group, often called the "warhead", is a critical determinant of the ligand's activity, selectivity, and general biological properties. In 2019, we summarized emerging and re-emerging warhead chemistries to target cysteine and other amino acids (Gehringer, M.; Laufer, S. A. J. Med. Chem. 2019, 62, 5673-5724; DOI: 10.1021/acs.jmedchem.8b01153). Since then, the field has rapidly evolved. Here we discuss the progress on covalent warheads made since our last Perspective and their application in medicinal chemistry and chemical biology.

Coprecipitation of Fe/Cr Hydroxides at Organic-Water Interfaces: Functional Group Richness and (De)protonation Control Amounts and Compositions of Coprecipitates

Iron/chromium hydroxide coprecipitation controls the fate and transport of toxic chromium (Cr) in many natural and engineered systems. Organic coatings on soil and engineered surfaces are ubiquitous; however, mechanistic controls of these organic coatings over Fe/Cr hydroxide coprecipitation are poorly understood. Here, Fe/Cr hydroxide coprecipitation was conducted on model organic coatings of humic acid (HA), sodium alginate (SA), and bovine serum albumin (BSA). The organics bonded with SiO2 through ligand exchange with carboxyl (-COOH), and the adsorbed amounts and pKa values of -COOH controlled surface charges of coatings. The adsorbed organic films also had different complexation capacities with Fe/Cr ions and Fe/Cr hydroxide particles, resulting in significant differences in both the amount (on HA > SA(-COOH) ≫ BSA(-NH2)) and composition (Cr/Fe molar ratio: on BSA(-NH2) ≫ HA > SA(-COOH)) of heterogeneous precipitates. Negatively charged -COOH attracted more Fe ions and oligomers of hydrolyzed Fe/Cr species and subsequently promoted heterogeneous precipitation of Fe/Cr hydroxide nanoparticles. Organic coatings containing -NH2 were positively charged at acidic pH because of the high pKa value of the functional group, limiting cation adsorption and formation of coprecipitates. Meanwhile, the higher local pH near the -NH2 coatings promoted the formation of Cr(OH)3. This study advances fundamental understanding of heterogeneous Fe/Cr hydroxide coprecipitation on organics, which is essential for successful Cr remediation and removal in both natural and engineered settings, as well as the synthesis of Cr-doped iron (oxy)hydroxides for material applications.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

In silico molecular study of hepatitis B virus X protein as a therapeutic target

The Hepatitis B virus is a leading cause of liver cirrhosis and hepatocellular carcinoma. HBx viral protein is considered a contributor to pathogenesis and hepatocarcinogenesis. This study aimed to screen the effect of some antiviral compounds to target HBx protein for inhibition of its function. Here, molecular docking, molcular dynsmic simulation, MM/GBSA and T-SNE methods were applied to study the complex stability and to cluster the conformations that generated in the simulation. Among the 179 compounds screened in this study, three antiviral agents (SC75741, Punicalagin, and Ledipasvir) exhibited the lowest docking energy and best interaction. Among these compounds, SC75741 was identified as a potent inhibitor of HBx that showed the best and most stable interaction during molecular dynamic simulation, and blocking a region near to HBx helix resides (aa 88-100) that is associated with cell invasion. The analysis of relative binding free energy through MM/GBSA for molecular dynamic simulation results revealed binding energy -9.9 kcal/mol for SC75741, -11 kcal/mol for Punicalagin, and -10.1 kcal/mol for Ledipasvir. These results elucidate the possible use of these compounds in the research for targeting HBx.Communicated by Ramaswamy H. Sarma.

Automated preparation of nanoscopic structures: Graph-based sequence analysis, mismatch detection, and pH-consistent protonation with uncertainty estimates

Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input structures. If extracted from experiment, these structures often suffer from severe uncertainties, of which the lack of information on hydrogen atoms is a prominent example. Hence, experimental structures require careful review and curation, which is a time-consuming and error-prone process. Here, we present a fast and robust protocol for the automated structure analysis and pH-consistent protonation, in short, ASAP. For biomolecules as a target, the ASAP protocol integrates sequence analysis and error assessment of a given input structure. ASAP allows for pK a  prediction from reference data through Gaussian process regression including uncertainty estimation and connects to system-focused atomistic modeling described in Brunken and Reiher (J. Chem. Theory Comput. 16, 2020, 1646). Although focused on biomolecules, ASAP can be extended to other nanoscopic objects, because most of its design elements rely on a general graph-based foundation guaranteeing transferability. The modular character of the underlying pipeline supports different degrees of automation, which allows for (i) efficient feedback loops for human-machine interaction with a low entrance barrier and for (ii) integration into autonomous procedures such as automated force field parametrizations. This facilitates fast switching of the pH-state through on-the-fly system-focused reparametrization during a molecular simulation at virtually no extra computational cost.

Peptide-Based Covalent Inhibitors Bearing Mild Electrophiles to Target a Conserved His Residue of the Bacterial Sliding Clamp

Peptide-based covalent inhibitors targeted to nucleophilic protein residues have recently emerged as new modalities to target protein-protein interactions (PPIs) as they may provide some benefits over more classic competitive inhibitors. Covalent inhibitors are generally targeted to cysteine, the most intrinsically reactive amino acid residue, and to lysine, which is more abundant at the surface of proteins but much less frequently to histidine. Herein, we report the structure-guided design of targeted covalent inhibitors (TCIs) able to bind covalently and selectively to the bacterial sliding clamp (SC), by reacting with a well-conserved histidine residue located on the edge of the peptide-binding pocket. SC is an essential component of the bacterial DNA replication machinery, identified as a promising target for the development of new antibacterial compounds. Thermodynamic and kinetic analyses of ligands bearing different mild electrophilic warheads confirmed the higher efficiency of the chloroacetamide compared to Michael acceptors. Two high-resolution X-ray structures of covalent inhibitor-SC adducts were obtained, revealing the canonical orientation of the ligand and details of covalent bond formation with histidine. Proteomic studies were consistent with a selective SC engagement by the chloroacetamide-based TCI. Finally, the TCI of SC was substantially more active than the parent noncovalent inhibitor in an in vitro SC-dependent DNA synthesis assay, validating the potential of the approach to design covalent inhibitors of protein-protein interactions targeted to histidine.

Spontaneous Transition of Spherical Coacervate to Vesicle-Like Compartment

Numerous biological systems contain vesicle-like biomolecular compartments without membranes, which contribute to diverse functions including gene regulation, stress response, signaling, and skin barrier formation. Coacervation, as a form of liquid-liquid phase separation (LLPS), is recognized as a representative precursor to the formation and assembly of membrane-less vesicle-like structures, although their formation mechanism remains unclear. In this study, a coacervation-driven membrane-less vesicle-like structure is constructed using two proteins, GG1234 (an anionic intrinsically disordered protein) and bhBMP-2 (a bioengineered human bone morphogenetic protein 2). GG1234 formed both simple coacervates by itself and complex coacervates with the relatively cationic bhBMP-2 under acidic conditions. Upon addition of dissolved bhBMP-2 to the simple coacervates of GG1234, a phase transition from spherical simple coacervates to vesicular condensates occurred via the interactions between GG1234 and bhBMP-2 on the surface of the highly viscoelastic GG1234 simple coacervates. Furthermore, the shell structure in the outer region of the GG1234/bhBMP-2 vesicular condensates exhibited gel-like properties, leading to the formation of multiphasic vesicle-like compartments. A potential mechanism is proposed for the formation of the membrane-less GG1234/bhBMP-2 vesicle-like compartments. This study provides a dynamic process underlying the formation of biomolecular multiphasic condensates, thereby enhancing the understanding of these biomolecular structures.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

A Non-Functional Carbon Dioxide-Mediated Post-Translational Modification on Nucleoside Diphosphate Kinase of Arabidopsis thaliana

The carbamate post-translational modification (PTM), formed by the nucleophilic attack of carbon dioxide by a dissociated lysine epsilon-amino group, is proposed as a widespread mechanism for sensing this biologically important bioactive gas. Here, we demonstrate the discovery and in vitro characterization of a carbamate PTM on K9 of Arabidopsis nucleoside diphosphate kinase (AtNDK1). We demonstrate that altered side chain reactivity at K9 is deleterious for AtNDK1 structure and catalytic function, but that CO2 does not impact catalysis. We show that nucleotide substrate removes CO2 from AtNDK1, and the carbamate PTM is functionless within the detection limits of our experiments. The AtNDK1 K9 PTM is the first demonstration of a functionless carbamate. In light of this finding, we speculate that non-functionality is a possible feature of the many newly identified carbamate PTMs.

YidC from Escherichia coli Forms an Ion-Conducting Pore upon Activation by Ribosomes

The universally conserved protein YidC aids in the insertion and folding of transmembrane polypeptides. Supposedly, a charged arginine faces its hydrophobic lipid core, facilitating polypeptide sliding along YidC's surface. How the membrane barrier to other molecules may be maintained is unclear. Here, we show that the purified and reconstituted E. coli YidC forms an ion-conducting transmembrane pore upon ribosome or ribosome-nascent chain complex (RNC) binding. In contrast to monomeric YidC structures, an AlphaFold parallel YidC dimer model harbors a pore. Experimental evidence for a dimeric assembly comes from our BN-PAGE analysis of native vesicles, fluorescence correlation spectroscopy studies, single-molecule fluorescence photobleaching observations, and crosslinking experiments. In the dimeric model, the conserved arginine and other residues interacting with nascent chains point into the putative pore. This result suggests the possibility of a YidC-assisted insertion mode alternative to the insertase mechanism.

The expanding repertoire of covalent warheads for drug discovery

The reactive functionalities of drugs that engage in covalent interactions with the enzyme/receptor residue in either a reversible or an irreversible manner are called 'warheads'. Covalent warheads that were previously neglected because of safety concerns have recently gained center stage as a result of their various advantages over noncovalent drugs, including increased selectivity, increased residence time, and higher potency. With the approval of several covalent inhibitors over the past decade, research in this area has accelerated. Various strategies are being continuously developed to tune the characteristics of warheads to improve their potency and mitigate toxicity. Here, we review research progress in warhead discovery over the past 5 years to provide valuable insights for future drug discovery.

DNA Condensation by Peptide-Conjugated PAMAM Dendrimers. Influence of Peptide Charge

Nucleic acid delivery to cells is an important therapeutic strategy that requires the transport of nucleic acids to intracellular compartments and their protection from enzymatic degradation. This can be achieved through the complexation of the nucleic acids with polycations. Poly(amidoamine) (PAMAM) dendrimers and peptide-conjugated dendrimers have been investigated as delivery vectors. Inspired by these studies and the role of flexible peptide domains in protein-DNA interactions, we studied the impact of conjugating two peptides (tails) to generation 2 (G2) PAMAM dendrimers on DNA condensation and polyplex formation. Using gel electrophoresis, dye exclusion assays, atomic force microscopy, and Monte Carlo simulations, it is shown that the steric impact of neutral peptide tails is to hinder the formation of DNA-G2 polyplexes composed of multiple DNA chains. If the tails are negatively charged, which results in overall neutral G2 conjugates, then the interaction of G2 with DNA is hindered. Increasing the net positive charge of the tails resulted in the complexation capacity of G2 with the DNA being restored. While DNA complexation is obtained for a similar net charge balance for G2 and G2 conjugates with positive tails, fewer of the latter are required to achieve a comparable condensation degree. Furthermore, it is shown that about 40% of the DNA remains accessible to binding by small molecules. Overall, this shows that tuning the net charge of peptide tails conjugated to PAMAM dendrimers offers a handle to control the complexation capacity of DNA, which can be explored as a novel route for optimization as gene delivery vehicles.

Assessing Squarates as Amine-Reactive Probes

Probes that covalently label protein targets facilitate the identification of ligand-binding sites. Lysine residues are prevalent in the proteome, making them attractive substrates for covalent probes. However, identifying electrophiles that undergo amine-specific, regioselective reactions with binding site lysine residues is challenging. Squarates can engage in two sequential conjugate addition-elimination reactions with amines. Nitrogen donation reduces the second reaction rate, making the mono squaramide a mild electrophile. We postulated that this mild electrophilicity would demand a longer residence time near the amine, affording higher selectivity for binding site lysines. Therefore, we compared the kinetics of squarate and monosquaramide amine substitution to alternative amine bioconjugation handles. The data revealed that N-hydroxy succinimidyl esters react 4 orders of magnitude faster, consistent with their labeling promiscuity. Squarate reactivity can be tuned by a substitution pattern. Electron-withdrawing groups on the vinylogous ester or amide increase reaction rates. Dithionosquarates react more rapidly than squarates, while vinylogous thioester analogs, dithiosquarates, react more slowly. We assessed squarate selectively using the UDP-sugar processing enzyme GlfT2 from Mycobacterium tuberculosis, which possesses 21 surface-exposed lysines. The reaction predominately modified one lysine proximal to a binding site to afford covalent inhibition. These findings demonstrate the selectivity of squaric esters and squaramides, which is a critical feature for affinity-based chemoproteomic probes.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

Reliable and accurate prediction of basic pK[Formula: see text] values in nitrogen compounds: the pK[Formula: see text] shift in supramolecular systems as a case study

This article presents a quantitative structure-activity relationship (QSAR) approach for predicting the acid dissociation constant (pK[Formula: see text]) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R[Formula: see text]) of 0.9905, low standard error (s) of 0.3066, and high Fisher statistic (F) of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK[Formula: see text] prediction, offering valuable insights for drug design and supramolecular system optimization.

Comparative Performance of High-Throughput Methods for Protein pKa Predictions

The medically relevant field of protein-based therapeutics has triggered a demand for protein engineering in different pH environments of biological relevance. In silico engineering workflows typically employ high-throughput screening campaigns that require evaluating large sets of protein residues and point mutations by fast yet accurate computational algorithms. While several high-throughput pKa prediction methods exist, their accuracies are unclear due to the lack of a current comprehensive benchmarking. Here, seven fast, efficient, and accessible approaches including PROPKA3, DeepKa, PKAI, PKAI+, DelPhiPKa, MCCE2, and H++ were systematically tested on a nonredundant subset of 408 measured protein residue pKa shifts from the pKa database (PKAD). While no method outperformed the null hypotheses with confidence, as illustrated by statistical bootstrapping, DeepKa, PKAI+, PROPKA3, and H++ had utility. More specifically, DeepKa consistently performed well in tests across multiple and individual amino acid residue types, as reflected by lower errors, higher correlations, and improved classifications. Arithmetic averaging of the best empirical predictors into simple consensuses improved overall transferability and accuracy up to a root-mean-square error of 0.76 pKa units and a correlation coefficient (R2) of 0.45 to experimental pKa shifts. This analysis should provide a basis for further methodological developments and guide future applications, which require embedding of computationally inexpensive pKa prediction methods, such as the optimization of antibodies for pH-dependent antigen binding.

Charge Regulation in a Rieske Proton Pump Pinpoints Zero, One, and Two Proton-Coupled Electron Transfer

The degree to which redox-driven proton pumps regulate net charge during electron transfer (ΔZET) remains undetermined due to difficulties in measuring the net charge of solvated proteins. Values of ΔZET can reflect reorganization energies or redox potentials associated with ET and can be used to distinguish ET from proton(s)-coupled electron transfer (PCET). Here, we synthesized protein "charge ladders" of a Rieske [2Fe-2S] subunit from Thermus thermophilus (truncTtRp) and made 120 electrostatic measurements of ΔZET across pH. Across pH 5-10, truncTtRp is suspected of transitioning from ET to PCET, and then to two proton-coupled ET (2PCET). Upon reduction, we found that truncTtRp became more negative at pH 6.0 by one unit (ΔZET = -1.01 ± 0.14), consistent with single ET; was isoelectric at pH 8.8 (ΔZET = -0.01 ± 0.45), consistent with PCET; and became more positive at pH 10.6 (ΔZET = +1.37 ± 0.60), consistent with 2PCET. These ΔZET values are attributed to protonation of H154 and H134. Across pH, redox potentials of TtRp (measured previously) correlated with protonation energies of H154 and H134 and ΔZET for truncTtRp, supporting a discrete proton pumping mechanism for Rieske proteins at the Fe-coordinating histidines.

Basis for Accurate Protein pKa Prediction with Machine Learning

pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pK_a's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pK_a prediction is crucial. Here we present a theoretical pK_a data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pK_a orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pK_a prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pK_a predictor and thus can be applied immediately to, for example, pK_a database construction, protein design, drug discovery, and so on.

Tools for analyzing protonation states and for tracing proton transfer pathways with examples from the Rb. sphaeroides photosynthetic reaction centers

Protons participate in many reactions. In proteins, protons need paths to move in and out of buried active sites. The vectorial movement of protons coupled to electron transfer reactions establishes the transmembrane electrochemical gradient used for many reactions, including ATP synthesis. Protons move through hydrogen bonded chains of waters and hydroxy side chains via the Grotthuss mechanism and by proton binding and release from acidic and basic residues. MCCE analysis shows that proteins exist in a large number of protonation states. Knowledge of the equilibrium ensemble can provide a rational basis for setting protonation states in simulations that fix them, such as molecular dynamics (MD). The proton path into the Q_B site in the bacterial reaction centers (RCs) of Rb. sphaeroides is analyzed by MD to provide an example of the benefits of using protonation states found by the MCCE program. A tangled web of side chains and waters link the cytoplasm to Q_B. MCCE analysis of snapshots from multiple trajectories shows that changing the input protonation state of a residue in MD biases the trajectory shifting the proton affinity of that residue. However, the proton affinity of some residues is more sensitive to the input structure. The proton transfer networks derived from different trajectories are quite robust. There are some changes in connectivity that are largely restricted to the specific residues whose protonation state is changed. Trajectories with Q_B^•- are compared with earlier results obtained with Q_B [Wei et. al Photosynthesis Research volume 152, pages153-165 (2022)] showing only modest changes. While introducing new methods the study highlights the difficulty of establishing the connections between protein conformation.

Engineering protein A ligands to mitigate antibody loss during high-pH washes in protein A chromatography

Protein A chromatography is a workhorse in monoclonal antibody (mAb) manufacture since it provides effective separation of mAbs from impurities such as host-cell proteins (HCPs) in a single capture step. HCP clearance can be aided by the inclusion of a wash step prior to low-pH elution. Although high-pH washes can be effective in removing additional HCPs from the loaded column, they may also contribute to a reduced mAb yield. In this work we show that this yield loss is reflected in a pH-dependent variation of the equilibrium binding capacity of the protein A resin, which is also observed for the capacity of the Fc fragments alone and therefore not a result of steric interactions involving the Fab fragments in the intact mAbs. We therefore hypothesized that the high-pH wash loss was due to protonation or deprotonation of ionizable residues on the protein A ligand. To evaluate this, we applied a rational protein engineering approach to the Z domain (the Fc-binding component of most commercial protein A ligands) and expressed engineered mutants in E. coli. Biolayer interferometry and affinity chromatography experiments showed that some of the Z domain mutants were able to mitigate wash loss at high pH while maintaining similar binding characteristics at neutral pH. These experiments enabled elucidation of the roles of specific interactions in the Z domain - Fc complex, but more importantly offer a route to ameliorating the disadvantages of high-pH washes in protein A chromatography.

Benchmarking In Silico Tools for Cysteine pKa Prediction

Accurate estimation of the pK_a's of cysteine residues in proteins could inform targeted approaches in hit discovery. The pK_a of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug discovery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pK_a's relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pK_a predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pK_a prediction. Here, we report the performance of several computational pK_a methods, including single-structure and ensemble-based approaches, on a diverse test set of experimental cysteine pK_a's retrieved from the PKAD database. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pK_a values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method (MOE) yielded a mean absolute error of 2.3 pK units, highlighting the need for improvement of existing pK_a methods for accurate cysteine pK_a estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

Sequence-based prediction of pH-dependent protein solubility using CamSol

Solubility is a property of central importance for the use of proteins in research in molecular and cell biology and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are material intensive and time consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of solubility for large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the development of one such method to include in the predictions the effect of the pH on solubility. We illustrate the resulting pH-dependent predictions on a variety of antibodies and other proteins to demonstrate that these predictions achieve an accuracy comparable with that of experimental methods. We make this method publicly available at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolph, as the version 3.0 of CamSol.

Molecular determinants of acrylamide neurotoxicity through covalent docking

Acrylamide (ACR) is formed during food processing by Maillard reaction between sugars and proteins at high temperatures. It is also used in many industries, from water waste treatment to manufacture of paper, fabrics, dyes and cosmetics. Unfortunately, cumulative exposure to acrylamide, either from diet or at the workplace, may result in neurotoxicity. Such adverse effects arise from covalent adducts formed between acrylamide and cysteine residues of several neuronal proteins via a Michael addition reaction. The molecular determinants of acrylamide reactivity and its impact on protein function are not completely understood. Here we have compiled a list of acrylamide protein targets reported so far in the literature in connection with neurotoxicity and performed a systematic covalent docking study. Our results indicate that acrylamide binding to cysteine is favored in the presence of nearby positively charged amino acids, such as lysines and arginines. For proteins with more than one reactive Cys, docking scores were able to discriminate between the primary ACR modification site and secondary sites modified only at high ACR concentrations. Therefore, docking scores emerge as a potential filter to predict Cys reactivity against acrylamide. Inspection of the ACR-protein complex structures provides insights into the putative functional consequences of ACR modification, especially for non-enzyme proteins. Based on our study, covalent docking is a promising computational tool to predict other potential protein targets mediating acrylamide neurotoxicity.

Divalent metal ion binding to Staphylococcus aureus FeoB transporter regions

Transition metal ions such as iron, copper, zinc, manganese or, nickel are essential in many biological processes. Bacteria have developed a number of mechanisms for their acquisition and transport, in which numerous of proteins and smaller molecules are involved. One of the representatives of these proteins is FeoB, which belongs to the Feo (ferrous ion transporter) family. Although ferrous iron transport system is widespread among microorganisms, it is still poorly described in Gram-positive pathogens, such as Staphylococcus aureus. In this work, combined potentiometric and spectroscopic studies (UV-Vis, CD and EPR) were carried out to determine Cu(II), Fe(II) and Zn(II) binding modes to FeoB fragments (Ac-IDYHKLMK-NH₂, Ac-ETSHDKY-NH₂, and Ac-SFLHMVGS-NH₂). For the first time iron(II) complexes with peptides were characterized by potentiometry. All studied ligands are able to form a variety of thermodynamically stable complexes with transition metal ions. It was concluded that among the studied systems, the most effective metal ion binding is observed for the Ac-ETSHDKY-NH₂ peptide. Moreover, comparing preferences of all ligands towards different metal ions, copper(II) complexes are the most stable ones at physiological pH.

Scanning aldoxime dehydratase sequence space and characterization of a new aldoxime dehydratase from Fusarium vanettenii

The aim of this work was to map the sequence space of aldoxime dehydratases (Oxds) as enzymes with great potential for nitrile synthesis. Microbes contain an abundance of putative Oxds but fewer than ten Oxds were characterized in total and only two in fungi. In this work, we prepared and characterized a new Oxd (protein gb|EEU37245.1 named OxdFv) from Fusarium vanettenii 77-13-4. OxdFv is distant from the characterized Oxds with a maximum of 36% identity. Moreover, the canonical Oxd catalytic triad RSH is replaced by R141-E187-E303 in OxdFv. R141A and E187A mutants did not show significant activities, but mutant E303A showed a comparable activity as the wild-type enzyme. According to native mass spectrometry, OxdFv contained almost 1 mol of heme per 1 mol of protein, and was composed of approximately 88% monomer (41.8 kDa) and 12% dimer. A major advantage of this enzyme is its considerable activity under aerobic conditions (25.0 ± 4.3 U/mg for E,Z-phenylacetaldoxime at pH 9.0 and 55 °C). Addition of sodium dithionite (reducing agent) and Fe²⁺ was required for this activity. OxdFv favored (aryl)aliphatic aldoximes over aromatic aldoximes. Substrate docking in the homology model of OxdFv showed a similar substrate specificity. We conclude that OxdFv is the first characterized Oxd of the REE type.

Analysis of protonation equilibria of some alanyl dipeptides in water and aqueous ethanol mixtures

The protonation constants are one of the most fundamental properties of biological molecules. The determination of the constants of the dipeptide is interesting and necessary for a full understanding of its activities in biological process. In this study, the protonation constants of some aliphatic alanine dipeptides (alanyl-alanine, alanyl-phenylalanine, alanyl-valine, alanyl-leucine, and alanyl-methionine) were studied in water and ethanol-water mixtures (20%ethanol-80%water;40%ethanol-60%water;60%ethanol-40%water, (v/v)) at 25 ± 0.1 °C under nitrogen atmosphere and ionic strength at 0.10 mol L^-1 by potentiometry. The constants of the systems were calculated using Best computer program. The effects of the different amino acids bound to the alanine on the acidity of the alanyl dipeptides were investigated. The constants were influenced by changes in solvent composition and their variations were discussed in terms of solvent and structural properties.

A pH-dependent anti-CD47 antibody that selectively targets solid tumors and improves therapeutic efficacy and safety

BACKGROUND

The antiphagocytic molecule CD47 is overexpressed in a wide variety of cancer cells, and antibodies targeting CD47 for cancer therapies are currently under intensive investigation. However, owing to the ubiquitous expression of CD47 on healthy cells, anti-CD47 therapies often achieve only weak therapeutic benefits and can induce severe side effects. Here, we report the generation of a pH-dependent anti-CD47 antibody (BC31M4) which selectively binds to tumors under the acidic solid tumor microenvironment.

METHODS

BC31M4 was generated using antibody phage display and a pH-dependent selection strategy. The pH-dependent binding and blocking activities of BC31M4 were verified using in vitro assays, and the structural basis of the pH-dependent binding property was characterized. BC31M4's antitumor effect was confirmed by both phagocytosis assays and studies in xenograft models. The tumor selectivity, mechanism of action, PK properties, side effects, and therapeutic efficacy were further evaluated in humanized (hCD47 and its receptor hSIRPα) immunocompetent syngeneic mouse models.

RESULTS

The crystal structure reveals that two histidines locate within the CDRs of the light chain directly contribute to the pH-dependent binding of BC31M4. BC31M4 promotes macrophage phagocytosis of tumor cells more potently at acidic-pH than at physiological-pH. Our hCD47/hSIRPα humanized syngeneic mouse model results demonstrated that BC31M4 selectively accumulates in tumors but not in normal tissues. BC31M4 causes minimal side effects and exhibits superior PK properties as compared to the other examined anti-CD47 antibodies. When combined with adoptive T cell transfer, BC31M4 efficiently promotes adaptive immune responses against tumors and also induces immune memory. Moreover, we show that BC31M4's antitumor effects rely on an Fc that mediates strong effector functions.

CONCLUSIONS

Our study illustrates that the development of a tumor-selective, pH-dependent anti-CD47 antibody safely confers strong therapeutic effects against solid tumors, thus providing a promising therapeutic strategy to overcome the challenges of anti-CD47 therapy.

Electrostatic interactions mediate the nucleation and growth of a bacterial functional amyloid

Bacterial biofilm formation can have severe impacts on human and environmental health. Enteric bacteria produce functional amyloid fibers called curli that aid in biofilm formation and host colonization. CsgA is the major proteinaceous component of curli amyloid fibers and is conserved in many gram-negative enteric bacteria. The CsgA amyloid core consists of five imperfect repeats (R1-R5). R2, R3, and R4 have aspartic acid (D) and glycine (G) residues that serve as "gatekeeper" residues by modulating the intrinsic aggregation propensity of CsgA. Here, using mutagenesis, salt-mediated charge screening, and by varying pH conditions, we show that the ability of CsgA variants to nucleate and form amyloid fibers is dictated by the charge state of the gatekeeper residues. We report that in Citrobacter youngae CsgA, certain arginine (R) and lysine (K) residues also act as gatekeeper residues. A mechanism of gatekeeping is proposed wherein R and K residues electrostatically interact with negatively charged D residues, tempering CsgA fiber formation.

Origins of the pH-Responsive Photoluminescence of Peptide-Functionalized Au Nanoclusters

Ultrasmall peptide-protected gold nanoclusters are a promising class of bioresponsive material exhibiting pH-sensitive photoluminescence. We present a theoretical insight into the effect peptide-ligand environment has on pH-responsive fluorescence, with the aim of enhancing the rational design of gold nanoclusters for bioapplications. Employing a hybrid quantum/classical computational methodology, we systematically calculate deprotonation free energies of N-terminal cysteine amine groups in proximity to the inherently fluorescent core of Au₂₅(Peptide)₁₈ nanoclusters. We find that subtle changes in hexapeptide sequence alter the electrostatic environment and significantly shift the conventional N-terminal amine pK_a expected for amino acids free-in-solution. Our findings provide an insight into how the deprotonation equilibrium of N-terminal amine and side chain carboxyl groups cooperatively respond to solution pH changes, explaining the experimentally observed, yet elusive, pH-responsive fluorescence of peptide-functionalized Au₂₅ clusters.

Reliable and Accurate Prediction of Single-Residue pKa Values through Free Energy Perturbation Calculations

Accurate prediction of the pK_a's of protein residues is crucial to many applications in biological simulation and drug discovery. Here, we present the use of free energy perturbation (FEP) calculations for the prediction of single-protein residue pK_a values. We begin with an initial set of 191 residues with experimentally determined pK_a values. To isolate sampling limitations from force field inaccuracies, we develop an algorithm to classify residues whose environments are significantly affected by crystal packing effects. We then report an approach to identify buried histidines that require significant sampling beyond what is achieved in typical FEP calculations. We therefore define a clean data set not requiring algorithms capable of predicting major conformational changes on which other pK_a prediction methods can be tested. On this data set, we report an RMSE of 0.76 pK_a units for 35 ASP residues, 0.51 pK_a units for 44 GLU residues, and 0.67 pK_a units for 76 HIS residues.

Reversed-Phase Liquid Chromatography of Peptides for Bottom-Up Proteomics: A Tutorial

The performance of the current bottom-up liquid chromatography hyphenated with mass spectrometry (LC-MS) analyses has undoubtedly been fueled by spectacular progress in mass spectrometry. It is thus not surprising that the MS instrument attracts the most attention during LC-MS method development, whereas optimizing conditions for peptide separation using reversed-phase liquid chromatography (RPLC) remains somewhat in its shadow. Consequently, the wisdom of the fundaments of chromatography is slowly vanishing from some laboratories. However, the full potential of advanced MS instruments cannot be achieved without highly efficient RPLC. This is impossible to attain without understanding fundamental processes in the chromatographic system and the properties of peptides important for their chromatographic behavior. We wrote this tutorial intending to give practitioners an overview of critical aspects of peptide separation using RPLC to facilitate setting the LC parameters so that they can leverage the full capabilities of their MS instruments. After briefly introducing the gradient separation of peptides, we discuss their properties that affect the quality of LC-MS chromatograms the most. Next, we address the in-column and extra-column broadening. The last section is devoted to key parameters of LC-MS methods. We also extracted trends in practice from recent bottom-up proteomics studies and correlated them with the current knowledge on peptide RPLC separation.

Constant pH molecular dynamics simulations: Current status and recent applications

Many important protein functions are carried out through proton-coupled conformational dynamics. Thus, the ability to accurately model protonation states dynamically has wide-ranging implications. Over the past two decades, two main types of constant pH methods (discrete and continuous) have been developed to enable proton-coupled molecular dynamics (MD) simulations. In this short review, we discuss the current status of the development and highlight recent applications that have advanced our understanding of protein structure-function relationships. We conclude the review by outlining the remaining challenges in the method development and projecting important areas for future applications.

Covalent drugs targeting histidine - an unexploited opportunity?

Covalent drugs and chemical probes often possess pharmacological advantages over reversible binding ligands, such as enhanced potency and pharmacodynamic duration. The highly nucleophilic cysteine thiol is commonly targeted using acrylamide electrophiles, but the amino acid is rarely present in protein binding sites. Sulfonyl exchange chemistry has expanded the covalent drug discovery toolkit by enabling the rational design of irreversible inhibitors targeting tyrosine, lysine, serine and threonine. Probes containing the sulfonyl fluoride warhead have also been shown to serendipitously label histidine residues in proteins. Histidine targeting is an attractive prospect because the residue is frequently proximal to protein small molecule ligands and the imidazole side chain possesses desirable nucleophilicity. We recently reported the design of cereblon molecular glues to site-selectively modify a histidine in the thalidomide binding site using sulfonyl exchange chemistry. We believe that histidine targeting holds great promise for future covalent drug development and this Opinion highlights these opportunities.

The Nϵ-Rule for Serine, but Not Cysteine Catalytic Triads

Catalytic triads, composed of a serine or cysteine nucleophile, a histidine, and a third triad residue (typically Asp/Glu/Asn), are common in enzyme active sites and catalyze a wide variety of chemical reactions. Two types of triads can be distinguished: We refer to them as Nδ- or Nϵ-configured, depending on whether the histidine imidazole Nδ or Nϵ atom is close to the nucleophile Oγ/Sγ. In this study, we have analyzed triad configuration. In structural triads, the more stable Nδ-configuration predominates. For catalytic triads, the configuration depends on the nucleophile. When it is a cysteine residue, both configuration types occur, depending on the family. However, when the nucleophile is a serine residue, the less stable Nϵ-configuration is almost exclusively found. We posit that the energetically less favored conformation is selected for in serine triads to facilitate the otherwise difficult proton transfer from the nucleophile to the histidine residue.

Extending the Stochastic Titration CpHMD to CHARMM36m

The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art methodology to deal with these effects. However, it still lacks widespread adoption by the scientific community. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and E. coli thioredoxins. All proteins were conformationally stable in the simulations, even at extreme pH values. The RMSE values (pK_a prediction vs experimental) obtained were very encouraging, in particular for lysozyme and human thioredoxin. We have also identified a few residues that challenged the CpHMD simulations, highlighting scenarios where the method still needs improvement independently of the force field. The CHARMM36m all-atom implementation was more computationally efficient when compared with the GROMOS 54A7, taking advantage of a shorter nonbonded interaction cutoff and a less frequent neighboring list update. The new extension will allow the study of pH effects in many systems for which this force field is particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Cysteine Oxidation in Proteins: Structure, Biophysics, and Simulation

Cysteine side chains can exist in distinct oxidation states depending on the pH and redox potential of the environment, and cysteine oxidation plays important yet complex regulatory roles. Compared with the effects of post-translational modifications such as phosphorylation, the effects of oxidation of cysteine to sulfenic, sulfinic, and sulfonic acid on protein structure and function remain relatively poorly characterized. We present an analysis of the role of cysteine reactivity as a regulatory factor in proteins, emphasizing the interplay between electrostatics and redox potential as key determinants of the resulting oxidation state. A review of current computational approaches suggests underdeveloped areas of research for studying cysteine reactivity through molecular simulations.

Unravelling interactions between active site residues and DMAP in the initial steps of prenylated flavin mononucleotide biosynthesis catalyzed by PaUbiX

BACKGROUND

Prenylated flavin mononucleotide (prFMN) is a recently discovered, heavily modified flavin compound. It is the only known cofactor that enables enzymatic 1,3-dipolar cycloaddition reactions. It is produced by enzymes from the UbiX family, from flavin mononucleotide and either dimethylallyl mono- or diphosphate. prFMN biosynthesis is currently reported to be initiated by protonation of the substrate by Glu140.

METHODS

Computational chemistry methods are applied herein - Constant pH MD, classical MD simulations, and QM cluster optimizations.

RESULTS

Glu140 competes for a single proton with Lys129 prior to prFMN biosynthesis, but it is the latter that adopted a protonated state. Once the prenyl-FMN adduct is formed, Glu140 occurs in a protonated state far more often, while the occupancy of protonated Lys129 does not change. Lys129, Glu140, and Arg122 seem to play a key role in either stabilizing or protonating DMAP phosphate group within the PaUbiX active site throughout initial steps of prFMN biosynthesis.

CONCLUSIONS

The role of Lys129 in the functioning of PaUbiX is reported for the first time. Glu140 is unlikely to act as a proton donor in prFMN biosynthesis. Instead, Lys129 and Arg122 fulfil this role. Glu140 still plays a role in contributing to hydrogen-bond network. This behavior is most likely conserved throughout the UbiX family due to the structural similarity of the active sites of those proteins.

SIGNIFICANCE

Mechanistic insights into a crucial biochemical process, the biosynthesis of prFMN, are provided. This study, although purely computational, extends and perfectly complements the knowledge obtained in classical laboratory experiments.

Structural Characterization of Per Os Infectivity Factor 5 (PIF5) Reveals the Essential Role of Intramolecular Interactions in Baculoviral Oral Infectivity

Baculoviruses initiate oral infection in the highly alkaline midgut of insects via a group of envelope proteins called per os infectivity factors (PIFs). To date, no high-resolution structural information has been reported for any PIF. Here, we present the crystal structure of the PIF5 ectodomain (PIF5e) from Autographa californica multiple nucleopolyhedrovirus (AcMNPV) at a 2.2-Å resolution. It revealed an open cavity between the N-terminal E1 domain and the C-terminal E2 domain and a cysteine-rich region with three pairs of disulfide bonds in the E2 domain. Multiple conserved intramolecular interactions within PIF5 are essential for maintaining its tertiary structure. Two conserved arginines (Arg8 and Arg74) play critical roles in E1-E2 interactions, and mutagenesis analysis supported their crucial role in oral infection. Importantly, the reduction in the oral infectivity of the Arg8, Arg74, or cysteine mutant viruses was related to the proteolytic cleavage of PIF5 by the endogenous protease embedded in occlusion bodies during alkaline treatment. This suggested that the structural stability of PIF5 under physiological conditions in the insect midgut is critical for baculoviral oral infectivity. IMPORTANCEPer os infection mediated by PIFs is the highly complex mechanism by which baculoviruses initiate infection in insects. Previous studies revealed that multiple PIF proteins form a large PIF complex on the envelope of virions, while PIF5 functions independently of the PIF complex. Here, we report the crystal structure of AcMNPV PIF5e, which, to our knowledge, is the first atomic structure reported for a PIF protein. The structure revealed the precise locations of three previously proposed disulfide bonds and other conserved intramolecular interactions, which are important for the structural stability of PIF5 and are also essential for oral infectivity. These findings advance our understanding of the molecular mechanism of baculovirus oral infection under alkaline conditions.

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins

Existing computational methods for estimating pK_a values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined pK_a shifts to train deep learning models, which are shown to rival the physics-based predictors. These neural networks managed to infer the electrostatic contributions of different chemical groups and learned the importance of solvent exposure and close interactions, including hydrogen bonds. Although trained only using theoretical data, our pKAI+ model displayed the best accuracy in a test set of ∼750 experimental values. Inference times allow speedups of more than 1000× compared to physics-based methods. By combining speed, accuracy, and a reasonable understanding of the underlying physics, our models provide a game-changing solution for fast estimations of macroscopic pK_a values from ensembles of microscopic values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations.

Creation of Cross-Linked Crystals With Intermolecular Disulfide Bonds Connecting Symmetry-Related Molecules Allows Retention of Tertiary Structure in Different Solvent Conditions

Protein crystals are generally fragile and sensitive to subtle changes such as pH, ionic strength, and/or temperature in their crystallization mother liquor. Here, using T4 phage lysozyme as a model protein, the three-dimensional rigidification of protein crystals was conducted by introducing disulfide cross-links between neighboring molecules in the crystal. The effect of cross-linking on the stability of the crystals was evaluated by microscopic observation and X-ray diffraction. When soaking the obtained cross-linked crystals into a precipitant-free solution, the crystals held their shape without dissolution and diffracted to approximately 1.1 Å resolution, comparable to that of the non-cross-linked crystals. Such cross-linked crystals maintained their diffraction even when immersed in other solutions with pH values from 4 to 10, indicating that the disulfide cross-linking made the packing contacts enforced and resulted in some mechanical strength in response to changes in the preservation conditions. Furthermore, the cross-linked crystals gained stability to permit soaking into solutions containing high concentrations of organic solvents. The results suggest the possibility of obtaining protein crystals for effective drug screening by introducing appropriate cross-linked disulfide bonds.

SpySwitch enables pH- or heat-responsive capture and release for plug-and-display nanoassembly

Proteins can be empowered via SpyTag for anchoring and nanoassembly, through covalent bonding to SpyCatcher partners. Here we generate a switchable version of SpyCatcher, allowing gentle purification of SpyTagged proteins. We introduce numerous histidines adjacent to SpyTag’s binding site, giving moderate pH-dependent release. After phage-based selection, our final SpySwitch allows purification of SpyTag- and SpyTag003-fusions from bacterial or mammalian culture by capture at neutral pH and release at pH 5, with purity far beyond His-tag methods. SpySwitch is also thermosensitive, capturing at 4 °C and releasing at 37 °C. With flexible choice of eluent, SpySwitch-purified proteins can directly assemble onto multimeric scaffolds. 60-mer multimerization enhances immunogenicity and we use SpySwitch to purify receptor-binding domains from SARS-CoV-2 and 11 other sarbecoviruses. For these receptor-binding domains we determine thermal resilience (for mosaic vaccine development) and cross-recognition by antibodies. Antibody EY6A reacts across all tested sarbecoviruses, towards potential application against new coronavirus pandemic threats.

The SpyCatcher-SpyTag system allows protein anchoring and nanoassembly. Here, the authors engineer SpySwitch, a dually switchable Catcher which allows gentle purification of SpyTagged proteins prior to downstream applications such as the assembly of virus-like particles.