Auto-regulation of Secretory Flux by Sensing and Responding to the Folded Cargo Protein Load in the Endoplasmic Reticulum.

Maintaining the optimal performance of cell processes and organelles is the task of auto-regulatory systems. Here we describe an auto-regulatory device that helps to maintain homeostasis of the endoplasmic reticulum (ER) by adjusting the secretory flux to the cargo load. The cargo-recruiting subunit of the coatomer protein II (COPII) coat, Sec24, doubles as a sensor of folded cargo and, upon cargo binding, acts as a guanine nucleotide exchange factor to activate the signaling protein Gα12 at the ER exit sites (ERESs). This step, in turn, activates a complex signaling network that activates and coordinates the ER export machinery and attenuates proteins synthesis, thus preventing large fluctuations of folded and potentially active cargo that could be harmful to the cell or the organism. We call this mechanism AREX (autoregulation of ER export) and expect that its identification will aid our understanding of human physiology and diseases that develop from secretory dysfunction.

PKD-dependent PARP12-catalyzed mono-ADP-ribosylation of Golgin-97 is required for E-cadherin transport from Golgi to plasma membrane.

Adenosine diphosphate (ADP)-ribosylation is a posttranslational modification involved in key regulatory events catalyzed by ADP-ribosyltransferases (ARTs). Substrate identification and localization of the mono-ADP-ribosyltransferase PARP12 at the trans-Golgi network (TGN) hinted at the involvement of ARTs in intracellular traffic. We find that Golgin-97, a TGN protein required for the formation and transport of a specific class of basolateral cargoes (e.g., E-cadherin and vesicular stomatitis virus G protein [VSVG]), is a PARP12 substrate. PARP12 targets an acidic cluster in the Golgin-97 coiled-coil domain essential for function. Its mutation or PARP12 depletion, delays E-cadherin and VSVG export and leads to a defect in carrier fission, hence in transport, with consequent accumulation of cargoes in a trans-Golgi/Rab11-positive intermediate compartment. In contrast, PARP12 does not control the Golgin-245-dependent traffic of cargoes such as tumor necrosis factor alpha (TNFα). Thus, the transport of different basolateral proteins to the plasma membrane is differentially regulated by Golgin-97 mono-ADP-ribosylation by PARP12. This identifies a selective regulatory mechanism acting on the transport of Golgin-97- vs. Golgin-245-dependent cargoes. Of note, PARP12 enzymatic activity, and consequently Golgin-97 mono-ADP-ribosylation, depends on the activation of protein kinase D (PKD) at the TGN during traffic. PARP12 is directly phosphorylated by PKD, and this is essential to stimulate PARP12 catalytic activity. PARP12 is therefore a component of the PKD-driven regulatory cascade that selectively controls a major branch of the basolateral transport pathway. We propose that through this mechanism, PARP12 contributes to the maintenance of E-cadherin-mediated cell polarity and cell-cell junctions.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database.

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative.

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Exscalate4CoV: Innovative High Performing Computing (HPC) Strategies to Tackle Pandemic Crisis.

This Special Issue was intended as a dissemination forum where the major results pursued by the EXSCALATE4CoV project (E4C, https://www [...].

Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel.

(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.

Structural Evolution of Delta (B.1.617.2) and Omicron (BA.1) Spike Glycoproteins.

The vast amount of epidemiologic and genomic data that were gathered as a global response to the COVID-19 pandemic that was caused by SARS-CoV-2 offer a unique opportunity to shed light on the structural evolution of coronaviruses and in particular on the spike (S) glycoprotein, which mediates virus entry into the host cell by binding to the human ACE2 receptor. Herein, we carry out an investigation into the dynamic properties of the S glycoprotein, focusing on the much more transmissible Delta and Omicron variants. Notwithstanding the great number of mutations that have accumulated, particularly in the Omicron S glycoprotein, our data clearly showed the conservation of some structural and dynamic elements, such as the global motion of the receptor binding domain (RBD). However, our studies also revealed structural and dynamic alterations that were concentrated in the aa 627-635 region, on a small region of the receptor binding motif (aa 483-485), and the so-called "fusion-peptide proximal region". In particular, these last two S regions are known to be involved in the human receptor ACE2 recognition and membrane fusion. Our structural evidence, therefore, is likely involved in the observed different transmissibility of these S mutants. Finally, we highlighted the role of glycans in the increased RBD flexibility of the monomer in the up conformation of Omicron.

The Spike Mutants Website: A Worldwide Used Resource against SARS-CoV-2.

A large number of SARS-CoV-2 mutations in a short period of time has driven scientific research related to vaccines, new drugs, and antibodies to combat the new variants of the virus. Herein, we present a web portal containing the structural information, the tridimensional coordinates, and the molecular dynamics trajectories of the SARS-CoV-2 spike protein and its main variants. The Spike Mutants website can serve as a rapid online tool for investigating the impact of novel mutations on virus fitness. Taking into account the high variability of SARS-CoV-2, this application can help the scientific community when prioritizing molecules for experimental assays, thus, accelerating the identification of promising drug candidates for COVID-19 treatment. Below we describe the main features of the platform and illustrate the possible applications for speeding up the drug discovery process and hypothesize new effective strategies to overcome the recurrent mutations in SARS-CoV-2 genome.

Altered Local Interactions and Long-Range Communications in UK Variant (B.1.1.7) Spike Glycoprotein.

The COVID-19 pandemic is caused by SARS-CoV-2. Currently, most of the research efforts towards the development of vaccines and antibodies against SARS-CoV-2 were mainly focused on the spike (S) protein, which mediates virus entry into the host cell by binding to ACE2. As the virus SARS-CoV-2 continues to spread globally, variants have emerged, characterized by multiple mutations of the S glycoprotein. Herein, we employed microsecond-long molecular dynamics simulations to study the impact of the mutations of the S glycoprotein in SARS-CoV-2 Variant of Concern 202012/01 (B.1.1.7), termed the "UK variant", in comparison with the wild type, with the aim to decipher the structural basis of the reported increased infectivity and virulence. The simulations provided insights on the different dynamics of UK and wild-type S glycoprotein, regarding in particular the Receptor Binding Domain (RBD). In addition, we investigated the role of glycans in modulating the conformational transitions of the RBD. The overall results showed that the UK mutant experiences higher flexibility in the RBD with respect to wild type; this behavior might be correlated with the increased transmission reported for this variant. Our work also adds useful structural information on antigenic "hotspots" and epitopes targeted by neutralizing antibodies.

Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on The hTRPM8 Channel.

BACKGROUND: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 µs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets.

SARS-CoV-2 Entry Inhibitors: Small Molecules and Peptides Targeting Virus or Host Cells.

The pandemic evolution of SARS-CoV-2 infection is forcing the scientific community to unprecedented efforts to explore all possible approaches against COVID-19. In this context, targeting virus entry is a promising antiviral strategy for controlling viral infections. The main strategies pursued to inhibit the viral entry are considering both the virus and the host factors involved in the process. Primarily, direct-acting antivirals rely on inhibition of the interaction between ACE2 and the receptor binding domain (RBD) of the Spike (S) protein or targeting the more conserved heptad repeats (HRs), involved in the membrane fusion process. The inhibition of host TMPRSS2 and cathepsins B/L may represent a complementary strategy to be investigated. In this review, we discuss the development entry inhibitors targeting the S protein, as well as the most promising host targeting strategies involving TMPRSS2 and CatB/L, which have been exploited so far against CoVs and other related viruses.

Computational Studies of SARS-CoV-2 3CLpro: Insights from MD Simulations.

Given the enormous social and health impact of the pandemic triggered by severe acute respiratory syndrome 2 (SARS-CoV-2), the scientific community made a huge effort to provide an immediate response to the challenges posed by Coronavirus disease 2019 (COVID-19). One of the most important proteins of the virus is an enzyme, called 3CLpro or main protease, already identified as an important pharmacological target also in SARS and Middle East respiratory syndrome virus (MERS) viruses. This protein triggers the production of a whole series of enzymes necessary for the virus to carry out its replicating and infectious activities. Therefore, it is crucial to gain a deeper understanding of 3CLpro structure and function in order to effectively target this enzyme. All-atoms molecular dynamics (MD) simulations were performed to examine the different conformational behaviors of the monomeric and dimeric form of SARS-CoV-2 3CLpro apo structure, as revealed by microsecond time scale MD simulations. Our results also shed light on the conformational dynamics of the loop regions at the entry of the catalytic site. Studying, at atomic level, the characteristics of the active site and obtaining information on how the protein can interact with its substrates will allow the design of molecules able to block the enzymatic function crucial for the virus.

A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0.

(1) Background: Virtual screening studies on the therapeutically relevant proteins of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) require a detailed characterization of their druggable binding sites, and, more generally, a convenient pocket mapping represents a key step for structure-based in silico studies; (2) Methods: Along with a careful literature search on SARS-CoV-2 protein targets, the study presents a novel strategy for pocket mapping based on the combination of pocket (as performed by the well-known FPocket tool) and docking searches (as performed by PLANTS or AutoDock/Vina engines); such an approach is implemented by the Pockets 2.0 plug-in for the VEGA ZZ suite of programs; (3) Results: The literature analysis allowed the identification of 16 promising binding cavities within the SARS-CoV-2 proteins and the here proposed approach was able to recognize them showing performances clearly better than those reached by the sole pocket detection; and (4) Conclusions: Even though the presented strategy should require more extended validations, this proved successful in precisely characterizing a set of SARS-CoV-2 druggable binding pockets including both orthosteric and allosteric sites, which are clearly amenable for virtual screening campaigns and drug repurposing studies. All results generated by the study and the Pockets 2.0 plug-in are available for download.

Targeting the minor pocket of C5aR for the rational design of an oral allosteric inhibitor for inflammatory and neuropathic pain relief.

Chronic pain resulting from inflammatory and neuropathic disorders causes considerable economic and social burden. Pharmacological therapies currently available for certain types of pain are only partially effective and may cause severe adverse side effects. The C5a anaphylatoxin acting on its cognate G protein-coupled receptor (GPCR), C5aR, is a potent pronociceptive mediator in several models of inflammatory and neuropathic pain. Although there has long been interest in the identification of C5aR inhibitors, their development has been complicated, as for many peptidomimetic drugs, mostly by poor drug-like properties. Herein, we report the de novo design of a potent and selective C5aR noncompetitive allosteric inhibitor, DF2593A, guided by the hypothesis that an allosteric site, the "minor pocket," previously characterized in CXC chemokine receptors-1 and -2, is functionally conserved in the GPCR class. In vitro, DF2593A potently inhibited C5a-induced migration of human and rodent neutrophils. In vivo, oral administration of DF2593A effectively reduced mechanical hyperalgesia in several models of acute and chronic inflammatory and neuropathic pain, without any apparent side effects. Mechanical hyperalgesia after spared nerve injury was also reduced in C5aR(-/-) mice compared with WT mice. Furthermore, treatment of C5aR(-/-) mice with DF2593A did not produce any further antinociceptive effect compared with C5aR(-/-) mice treated with vehicle. The successful medicinal chemistry strategy confirms that a conserved minor pocket is amenable for the rational design of selective inhibitors and the pharmacological results support that the allosteric blockade of the C5aR represents a highly promising therapeutic approach to control chronic inflammatory and neuropathic pain.

Molecular mechanism and functional role of brefeldin A-mediated ADP-ribosylation of CtBP1/BARS.

ADP-ribosylation is a posttranslational modification that modulates the functions of many target proteins. We previously showed that the fungal toxin brefeldin A (BFA) induces the ADP-ribosylation of C-terminal-binding protein-1 short-form/BFA-ADP-ribosylation substrate (CtBP1-S/BARS), a bifunctional protein with roles in the nucleus as a transcription factor and in the cytosol as a regulator of membrane fission during intracellular trafficking and mitotic partitioning of the Golgi complex. Here, we report that ADP-ribosylation of CtBP1-S/BARS by BFA occurs via a nonconventional mechanism that comprises two steps: (i) synthesis of a BFA-ADP-ribose conjugate by the ADP-ribosyl cyclase CD38 and (ii) covalent binding of the BFA-ADP-ribose conjugate into the CtBP1-S/BARS NAD(+)-binding pocket. This results in the locking of CtBP1-S/BARS in a dimeric conformation, which prevents its binding to interactors known to be involved in membrane fission and, hence, in the inhibition of the fission machinery involved in mitotic Golgi partitioning. As this inhibition may lead to arrest of the cell cycle in G2, these findings provide a strategy for the design of pharmacological blockers of cell cycle in tumor cells that express high levels of CD38.

Corrigendum to "Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities" [Comput Struct Biotechnol J vol. 23 (2024) 2141-2151].

[This corrects the article DOI: 10.1016/j.csbj.2024.05.024.].

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.

LiGen: a high performance workflow for chemistry driven de novo design.

Tools for molecular de novo design are actively sought incorporating sets of chemical rules for fast and efficient identification of structurally new chemotypes endowed with a desired set of biological properties. In this paper, we present LiGen, a suite of programs which can be used sequentially or as stand-alone tools for specific purposes. In its standard application, LiGen modules are used to define input constraints, either structure-based, through active site identification, or ligand-based, through pharmacophore definition, to docking and to de novo generation. Alternatively, individual modules can be combined in a user-defined manner to generate project-centric workflows. Specific features of LiGen are the use of a pharmacophore-based docking procedure which allows flexible docking without conformer enumeration and accurate and flexible reactant mapping coupled with reactant tagging through substructure searching. The full description of LiGen functionalities is presented.

Use of experimental design to optimize docking performance: the case of LiGenDock, the docking module of LiGen, a new de novo design program.

On route toward a novel de novo design program, called LiGen, we developed a docking program, LiGenDock, based on pharmacophore models of binding sites, including a non-enumerative docking algorithm. In this paper, we present the functionalities of LiGenDock and its accompanying module LiGenPocket, aimed at the binding site analysis and structure-based pharmacophore definition. We also report the optimization procedure we have carried out to improve the cognate docking and virtual screening performance of LiGenDock. In particular, we applied the design of experiments (DoE) methodology to screen the set of user-adjustable parameters to identify those having the largest influence on the accuracy of the results (which ensure the best performance in pose prediction and in virtual screening approaches) and then to choose their optimal values. The results are also compared with those obtained by two popular docking programs, namely, Glide and AutoDock for pose prediction, and Glide and DOCK6 for Virtual Screening.

Ensemble of structure and ligand-based classification models for hERG liability profiling.

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.