Chemically inducible split protein regulators for mammalian cells.

Chemically inducible systems represent valuable synthetic biology tools that enable the external control of biological processes. However, their translation to therapeutic applications has been limited because of unfavorable ligand characteristics or the immunogenicity of xenogeneic protein domains. To address these issues, we present a strategy for engineering inducible split protein regulators (INSPIRE) in which ligand-binding proteins of human origin are split into two fragments that reassemble in the presence of a cognate physiological ligand or clinically approved drug. We show that the INSPIRE platform can be used for dynamic, orthogonal and multiplex control of gene expression in mammalian cells. Furthermore, we demonstrate the functionality of a glucocorticoid-responsive INSPIRE platform in vivo and apply it for perturbing an endogenous regulatory network. INSPIRE presents a generalizable approach toward designing small-molecule responsive systems that can be implemented for the construction of new sensors, regulatory networks and therapeutic applications.

The Effect of Microwaves on Protein Structure: Molecular Dynamics Approach.

The impact of microwave (MW) irradiation on protein folding, potentially inciting misfolding, was investigated by employing molecular dynamics (MD) simulations. Twenty-nine proteins were subjected to MD simulations under equilibrium (300 K) and MW conditions, where the rotational temperature was elevated to 700 K. The utilized replacement model captures the microwave effects of δ- and γ-relaxation processes (frequency range of ∼300 MHz to ∼20 GHz). The results disclosed that MW heating incited a shift toward more compact protein conformations, as indicated by decreased root-mean-square deviations, root-mean-square fluctuations, head-to-tail distances, and radii of gyration. This compaction was attributed to the intensification of intramolecular electrostatic interactions and hydrogen bonds within the protein caused by MW-destabilized hydrogen bonds between the protein and solvent. The solvent-accessible surface area (SASA), particularly that of polar amino-acid residues, shrank under MW conditions, corresponding to a reduced polarity of the water solvent. However, MW irradiation produced no significant alterations in protein secondary structures; hence, MW heating was observed to primarily affect the protein tertiary structures.

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach.

In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.

Fentanyl and the Fluorinated Fentanyl Derivative NFEPP Elicit Distinct Hydrogen-Bond Dynamics of the Opioid Receptor.

The development of safe therapeutics to manage pain is of central interest for biomedical applications. The fluorinated fentanyl derivative N-(3-fluoro-1-phenethylpiperidin-4-yl)-N-phenylpropionamide (NFEPP) is potentially a safer alternative to fentanyl because unlike fentanyl─which binds to the µ-opioid receptor (MOR) at both physiological and acidic pH─NFEPP might bind to the MOR only at acidic pH typical of inflamed tissue. Knowledge of the protonation-coupled dynamics of the receptor-drug interactions is thus required to understand the molecular mechanism by which receptor activation initiates cell signaling to silence pain. To this end, here we have carried out extensive atomistic simulations of the MOR in different protonation states, in the absence of opioid drugs, and in the presence of fentanyl vs NFEPP. We used graph-based analyses to characterize internal hydrogen-bond networks that could contribute to the activation of the MOR. We find that fentanyl and NFEPP prefer distinct binding poses and that, in their binding poses, fentanyl and NFEPP partake in distinct internal hydrogen-bond networks, leading to the cytoplasmic G-protein-binding region. Moreover, the protonation state of functionally important aspartic and histidine side chains impacts hydrogen-bond networks that extend throughout the receptor, such that the ligand-bound MOR presents at its cytoplasmic G-protein-binding side, a hydrogen-bonding environment where dynamics depend on whether fentanyl or NFEPP is bound, and on the protonation state of specific MOR groups. The exquisite sensitivity of the internal protein-water hydrogen-bond network to the protonation state and to details of the drug binding could enable the MOR to elicit distinct pH- and opioid-dependent responses at its cytoplasmic G-protein-binding site.

Computational design of probes to detect bacterial genomes by multivalent binding.

Rapid methods for diagnosis of bacterial infections are urgently needed to reduce inappropriate use of antibiotics, which contributes to antimicrobial resistance. In many rapid diagnostic methods, DNA oligonucleotide probes, attached to a surface, bind to specific nucleotide sequences in the DNA of a target pathogen. Typically, each probe binds to a single target sequence; i.e., target-probe binding is monovalent. Here we show using computer simulations that the detection sensitivity and specificity can be improved by designing probes that bind multivalently to the entire length of the pathogen genomic DNA, such that a given probe binds to multiple sites along the target DNA. Our results suggest that multivalent targeting of long pieces of genomic DNA can allow highly sensitive and selective binding of the target DNA, even if competing DNA in the sample also contains binding sites for the same probe sequences. Our results are robust to mild fragmentation of the bacterial genome. Our conclusions may also be relevant for DNA detection in other fields, such as disease diagnostics more broadly, environmental management, and food safety.

Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.

Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone ϕ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the ϕ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.

Conserved Water Networks Identification for Drug Design Using Density Clustering Approaches on Positional and Orientational Data.

This work describes the development and testing of a method for the identification and classification of conserved water molecules and their networks from molecular dynamics (MD) simulations. The conserved waters in the active sites of proteins influence protein-ligand binding. Recently, several groups have argued that a water network formed from conserved waters can be used to interpret the thermodynamic signature of the binding site. We implemented a novel methodology in which we apply the complex approach to categorize water molecules extracted from the MD simulation trajectories using clustering approaches. The main advantage of our methodology as compared to current state of the art approaches is the inclusion of the information on the orientation of hydrogen atoms to further inform the clustering algorithm and to classify the conserved waters into different subtypes depending on how strongly certain orientations are preferred. This information is vital for assessing the stability of water networks. The newly developed approach is described in detail as well as validated against known results from the scientific literature including comparisons with the experimental data on thermolysin, thrombin, and Haemophilus influenzae virulence protein SiaP as well as with the previous computational results on thermolysin. We observed excellent agreement with the literature and were also able to provide additional insights into the orientations of the conserved water molecules, highlighting the key interactions which stabilize them. The source code of our approach, as well as the utility tools used for visualization, are freely available on GitHub.

Recent Developments in Electrochemical-Impedimetric Biosensors for Virus Detection.

Viruses, including influenza viruses, MERS-CoV (Middle East respiratory syndrome coronavirus), SARS-CoV (severe acute respiratory syndrome coronavirus), HAV (Hepatitis A virus), HBV (Hepatitis B virus), HCV (Hepatitis C virus), HIV (human immunodeficiency virus), EBOV (Ebola virus), ZIKV (Zika virus), and most recently SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), are responsible for many diseases that result in hundreds of thousands of deaths yearly. The ongoing outbreak of the COVID-19 disease has raised a global concern and intensified research on the detection of viruses and virus-related diseases. Novel methods for the sensitive, rapid, and on-site detection of pathogens, such as the recent SARS-CoV-2, are critical for diagnosing and treating infectious diseases before they spread and affect human health worldwide. In this sense, electrochemical impedimetric biosensors could be applied for virus detection on a large scale. This review focuses on the recent developments in electrochemical-impedimetric biosensors for the detection of viruses.

Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME).

High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Fichert, Ghose, Mozzicconacci, Muegge, Egan, Murcko, Veber, Ro3, Ro4, and Ro5) to facilitate their open access use and compared them. Then, we implemented these filters in the open platform Konstanz Information Miner (KNIME) as a freely accessible and simple workflow compatible with small or large compound databases for the benefit of the readers and for the help in the early drug design steps.

Neuropilin (NRPs) Related Pathological Conditions and Their Modulators.

Neuropilin 1 (NRP1) represents one of the two homologous neuropilins (NRP, splice variants of neuropilin 2 are the other) found in all vertebrates. It forms a transmembrane glycoprotein distributed in many human body tissues as a (co)receptor for a variety of different ligands. In addition to its physiological role, it is also associated with various pathological conditions. Recently, NRP1 has been discovered as a coreceptor for the SARS-CoV-2 viral entry, along with ACE2, and has thus become one of the COVID-19 research foci. However, in addition to COVID-19, the current review also summarises its other pathological roles and its involvement in clinical diseases like cancer and neuropathic pain. We also discuss the diversity of native NRP ligands and perform a joint analysis. Last but not least, we review the therapeutic roles of NRP1 and introduce a series of NRP1 modulators, which are typical peptidomimetics or other small molecule antagonists, to provide the medicinal chemistry community with a state-of-the-art overview of neuropilin modulator design and NRP1 druggability assessment.

Electrochromic Device Demonstrator from Household Materials.

Electrochromism encompasses reversible changes of material's optical properties (color, opacity) under the influence of an external electric current or applied voltage. The effect has been known for decades, but its importance continues to grow due to the rapid development of smart systems and the accompanying demand to build devices that consume less power. Most commercial electrochromic devices (ECDs) require sophisticated chemicals and advanced material preparation techniques. Also, the demonstration of electrochromism in chemistry classes mainly uses expensive WO3 films, intrinsically conductive polymers, and/or optically transparent electrodes (OTEs). The aim of this article is to present a simple and fast educational method to build ECDs from household materials without the need for OTEs: unsharpened kitchen knives are used as electrodes, curcumin from turmeric is used as the electrochromic dye, and baking soda is used as the electrolyte. The laboratory experiments presented will help students gain a deeper understanding of the fundamentals of electrochemistry (electrolysis, pH change) and electrochromism (in our case, color changes due to pH-induced keto-enol tautomerism of curcumin).

CHARMM Force-Field Parameters for Morphine, Heroin, and Oliceridine, and Conformational Dynamics of Opioid Drugs.

Opioid drug binding to specialized G protein-coupled receptors (GPCRs) can lead to analgesia upon activation via downstream Gi protein signaling and to severe side effects via activation of the ß-arrestin signaling pathway. Knowledge of how different opioid drugs interact with receptors is essential, as it can inform and guide the design of safer therapeutics. We performed quantum and classical mechanical computations to explore the potential energy landscape of four opioid drugs: morphine and its derivatives heroin and fentanyl and for the unrelated oliceridine. From potential energy profiles for bond twists and from interactions between opioids and water, we derived a set of force-field parameters that allow a good description of structural properties and intermolecular interactions of the opioids. Potential of mean force profiles computed from molecular dynamics simulations indicate that fentanyl and oliceridine have complex energy landscapes with relatively small energy penalties, suggesting that interactions with the receptor could select different binding poses of the drugs.

Potential Novel Thioether-Amide or Guanidine-Linker Class of SARS-CoV-2 Virus RNA-Dependent RNA Polymerase Inhibitors Identified by High-Throughput Virtual Screening Coupled to Free-Energy Calculations.

SARS-CoV-2, or severe acute respiratory syndrome coronavirus 2, represents a new pathogen from the family of Coronaviridae that caused a global pandemic of COVID-19 disease. In the absence of effective antiviral drugs, research of novel therapeutic targets such as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) becomes essential. This viral protein is without a human counterpart and thus represents a unique prospective drug target. However, in vitro biological evaluation testing on RdRp remains difficult and is not widely available. Therefore, we prepared a database of commercial small-molecule compounds and performed an in silico high-throughput virtual screening on the active site of the SARS-CoV-2 RdRp using ensemble docking. We identified a novel thioether-amide or guanidine-linker class of potential RdRp inhibitors and calculated favorable binding free energies of representative hits by molecular dynamics simulations coupled with Linear Interaction Energy calculations. This innovative procedure maximized the respective phase-space sampling and yielded non-covalent inhibitors representing small optimizable molecules that are synthetically readily accessible, commercially available as well as suitable for further biological evaluation and mode of action studies.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design.

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.

Opioid Receptors and Protonation-Coupled Binding of Opioid Drugs.

Opioid receptors are G-protein-coupled receptors (GPCRs) part of cell signaling paths of direct interest to treat pain. Pain may associate with inflamed tissue characterized by acidic pH. The potentially low pH at tissue targeted by opioid drugs in pain management could impact drug binding to the opioid receptor, because opioid drugs typically have a protonated amino group that contributes to receptor binding, and the functioning of GPCRs may involve protonation change. In this review, we discuss the relationship between structure, function, and dynamics of opioid receptors from the perspective of the usefulness of computational studies to evaluate protonation-coupled opioid-receptor interactions.

Prioritisation of Compounds for 3CLpro Inhibitor Development on SARS-CoV-2 Variants.

COVID-19 represents a new potentially life-threatening illness caused by severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 pathogen. In 2021, new variants of the virus with multiple key mutations have emerged, such as B.1.1.7, B.1.351, P.1 and B.1.617, and are threatening to render available vaccines or potential drugs ineffective. In this regard, we highlight 3CLpro, the main viral protease, as a valuable therapeutic target that possesses no mutations in the described pandemically relevant variants. 3CLpro could therefore provide trans-variant effectiveness that is supported by structural studies and possesses readily available biological evaluation experiments. With this in mind, we performed a high throughput virtual screening experiment using CmDock and the "In-Stock" chemical library to prepare prioritisation lists of compounds for further studies. We coupled the virtual screening experiment to a machine learning-supported classification and activity regression study to bring maximal enrichment and available structural data on known 3CLpro inhibitors to the prepared focused libraries. All virtual screening hits are classified according to 3CLpro inhibitor, viral cysteine protease or remaining chemical space based on the calculated set of 208 chemical descriptors. Last but not least, we analysed if the current set of 3CLpro inhibitors could be used in activity prediction and observed that the field of 3CLpro inhibitors is drastically under-represented compared to the chemical space of viral cysteine protease inhibitors. We postulate that this methodology of 3CLpro inhibitor library preparation and compound prioritisation far surpass the selection of compounds from available commercial "corona focused libraries".

Protein-water hydrogen-bond networks of G protein-coupled receptors: Graph-based analyses of static structures and molecular dynamics.

Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether GPCR structures and GPCR conformational dynamics could be described in terms of conserved hydrogen-bond networks, and alterations of these hydrogen-bond networks along the reaction coordinate of the GPCR. To enable efficient analyses of the hydrogen-bond networks of GPCRs we implemented graph-based algorithms, and applied these algorithms to static GPCR structures from structural biology, and from molecular dynamics simulations of two opioid receptors. We find that static GPCR structures tend to have a conserved, core hydrogen-bond network which, when protein and water dynamics are included with simulations, extends to comprise most of the interior of an inactive receptor. In an active receptor, the dynamic protein-water hydrogen-bond network spans the entire receptor, bridging all functional motifs. Such an extensive, dynamic hydrogen-bond network might contribute to the activation mechanism of the GPCR.

Carcinogenesis of ß-Propiolactone: A Computational Study.

The discovery that ß-propiolactone (BPL), once a commercially important chemical, causes various tumors in experimental animals has led to a significant decrease in its use. However, owing to its efficacy this possible human carcinogen remains to be utilized in vaccines for inactivation of viruses. The focus of the current study was to uncover the mechanisms of ß-propiolactone reactions with both nucleobases and glutathione (GSH) through computer simulations based on quantum chemical methods. Our results, in accordance with in vitro studies, show that among all nucleobases guanine most readily forms adducts with BPL through SN2 reaction mechanism. Acquired activation energies with incorporated solvent effects reveal that alkylation represents an energetically more favorable reaction than acylation for all nucleobases. Comparison of activation free energies of glutathione and guanine reactions with BPL suggest that glutathione may represent an efficient natural scavenger of BPL. Therefore, glutathione present in the organism may provide protection to the DNA and thus prevent BPL's genotoxicity, mutagenicity, and possibly even carcinogenicity.

Potential Energy Function for Fentanyl-Based Opioid Pain Killers.

Opioids are molecules whose binding to specialized G-Protein Coupled Receptors (GPCRs) triggers a signaling cascade that leads to the downregulation of pain pathways. Binding of an opioid to the membrane-embedded GPCR occurs when the opioid molecule is protonated, which provides a potential strategy to design nontoxic opioids that are protonated and bind to the GPCR only at the low pH of injured or inflamed tissue. Excellent model systems to study protonation-dependent binding of opioids to GPCRs are fentanyl, which is protonated and binds to the GPCR at both physiological and low pH, and the fluorinated fentanyl derivative NFEPP, which is protonated and binds to the GPCR only at low pH. The molecular mechanisms of fentanyl and NFEPP binding to the GPCR are largely unknown. To enable atomistic studies of opioid binding to GPCRs, we have carried out extensive quantum mechanical and classical mechanical computations to derive a potential energy function for fentanyl and NFEPP and present force field parameters for both opioid molecules. We find that fluorination alters the electronic ground state properties of fentanyl. As a consequence, fentanyl and NFEPP have distinct torsional and electrostatic properties likely to impact how they bind to receptors.

Protective Effects of [6]-Gingerol Against Chemical Carcinogens: Mechanistic Insights.

[6]-Gingerol from ginger has received considerable attention as a potential cancer therapeutic agent because of its chemopreventive and chemotherapeutic effects, as well as its safety. In the current study, we examined [6]-gingerol as a natural scavenger of nine ultimate chemical carcinogens to which we are frequently exposed: glycidamide, styrene oxide, aflatoxin B1 exo-8,9-epoxide, ß-propiolactone, ethylene oxide, propylene oxide, 2-cyanoethylene oxide, chloroethylene oxide, and vinyl carbamate epoxide. To evaluate [6]-gingerol efficacy, we expanded our research with the examination of glutathione-the strongest natural scavenger in human cells. The corresponding activation free energies were calculated using Hartree-Fock method with three flexible basis sets and two implicit solvation models. According to our results, [6]-gingerol proves to be an extremely effective scavenger of chemical carcinogens of the epoxy type. On the other hand, with the exception of aflatoxin B1 exo-8,9-epoxide, glutathione represents a relatively poor scavenger, whose efficacy could be augmented by [6]-gingerol. Moreover, our quantum mechanical study of the alkylation reactions of chemical carcinogens with [6]-gingerol and glutathione provide valuable insights in the reaction mechanisms and the geometries of the corresponding transition states. Therefore, we strongly believe that our research forms a solid basis for further computational, experimental and clinical studies of anticarcinogenic properties of [6]-gingerol as well as for the development of novel chemoprophylactic dietary supplements. Finally, the obtained results also point to the applicability of quantum chemical methods to studies of alkylation reactions related to chemical carcinogenesis.