Insights into the Interaction Mechanism of DTP3 with MKK7 by Using STD-NMR and Computational Approaches

NF-κB: Governing Macrophages in Cancer

Tumor-associated macrophages (TAMs) are the major component of the tumor microenvironment (TME), where they sustain tumor progression and or-tumor immunity. Due to their plasticity, macrophages can exhibit anti- or pro-tumor functions through the expression of different gene sets leading to distinct macrophage phenotypes: M1-like or pro-inflammatory and M2-like or anti-inflammatory. NF-κB transcription factors are central regulators of TAMs in cancers, where they often drive macrophage polarization toward an M2-like phenotype. Therefore, the NF-κB pathway is an attractive therapeutic target for cancer immunotherapy in a wide range of human tumors. Hence, targeting NF-κB pathway in the myeloid compartment is a potential clinical strategy to overcome microenvironment-induced immunosuppression and increase anti-tumor immunity. In this review, we discuss the role of NF-κB as a key driver of macrophage functions in tumors as well as the principal strategies to overcome tumor immunosuppression by targeting the NF-κB pathway.

Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors

Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.

Molecular docking and dynamic approach to screen the drug candidate against the Imipenem-resistant CarO porin in Acinetobacter baumannii

The multidrug-resistant Acinetobacter baumannii is an emerging nosocomial pathogen in the healthcare sector. Intrinsic resistance in A. baumannii is a significant problem framing a perfect treatment regimen. Also, this organism showed more resistance towards the carbapenem antibiotics, especially for imipenem and meropenem. The development of carbapenem-resistant Acinetobacter baumannii is mainly due to the alteration or loss of the porin region in the outer membrane. The most well-known porin in Acinetobacter baumannii is CarO (carbapenem-associated outer membrane protein). The CarO protein, which functions as a porin channel for carbapenem inflow, may contribute to carbapenem resistance. The current study identifies a potent drug candidate with a better binding affinity to the carbapenem-resistant outer membrane protein. We investigated the specificity of carbapenems such as imipenem, meropenem, ertapenem, biapenem, doripenem, and fluoroquinolone drugs such as sitafloxacin against the imipenem-resistant CarO protein was demonstrated using the computational approaches molecular docking and dynamic simulation for 50 ns. As a result, the high to low enzyme-ligand complex's binding affinity exhibited a greater binding affinity for ertapenem -7.76 kcal·mol^-1 and sitafloxacin -7.75 kcal·mol^-1 than biapenem, doripenem, meropenem, and imipenem. The molecular dynamic simulation and the MMPBSA analysis depicted ertapenem -55.431±25.908 kJ/mol and sitafloxacin -47.154 ± 11.052 kJ/mol with better binding affinity and more stability against the imipenem resistant CarO protein when it compared to other antibiotics.

Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors

Studying the binding processes of G protein-coupled receptors (GPCRs) proteins is of particular interest both to better understand the molecular mechanisms that regulate the signaling between the extracellular and intracellular environment and for drug design purposes. In this study, we propose a new computational approach for the identification of the binding site for a specific ligand on a GPCR. The method is based on the Zernike polynomials and performs the ligand-GPCR association through a shape complementarity analysis of the local molecular surfaces. The method is parameter-free and it can distinguish, working on hundreds of experimentally GPCR-ligand complexes, binding pockets from randomly sampled regions on the receptor surface, obtaining an Area Under ROC curve of 0.77. Given its importance both as a model organism and in terms of applications, we thus investigated the olfactory receptors of the C. elegans, building a list of associations between 21 GPCRs belonging to its olfactory neurons and a set of possible ligands. Thus, we can not only carry out rapid and efficient screenings of drugs proposed for GPCRs, key targets in many pathologies, but also we laid the groundwork for computational mutagenesis processes, aimed at increasing or decreasing the binding affinity between ligands and receptors.

Members of the GADD45 Protein Family Show Distinct Propensities to form Toxic Amyloid-Like Aggregates in Physiological Conditions

The three members (GADD45α, GADD45β, and GADD45γ) of the growth arrest and DNA damage-inducible 45 (GADD45) protein family are involved in a myriad of diversified cellular functions. With the aim of unravelling analogies and differences, we performed comparative biochemical and biophysical analyses on the three proteins. The characterization and quantification of their binding to the MKK7 kinase, a validated functional partner of GADD45β, indicate that GADD45α and GADD45γ are strong interactors of the kinase. Despite their remarkable sequence similarity, the three proteins present rather distinct biophysical properties. Indeed, while GADD45β and GADD45γ are marginally stable at physiological temperatures, GADD45α presents the Tm value expected for a protein isolated from a mesophilic organism. Surprisingly, GADD45α and GADD45β, when heated, form high-molecular weight species that exhibit features (ThT binding and intrinsic label-free UV/visible fluorescence) proper of amyloid-like aggregates. Cell viability studies demonstrate that they are endowed with a remarkable toxicity against SHSY-5Y and HepG2 cells. The very uncommon property of GADD45β to form cytotoxic species in near-physiological conditions represents a puzzling finding with potential functional implications. Finally, the low stability and/or the propensity to form toxic species of GADD45 proteins constitute important features that should be considered in interpreting their many functions.

Elucidation of Teicoplanin Interactions with Drug Targets Related to COVID-19

Teicoplanin is a glycopeptide antibiotic effective against several bacterial infections, has exhibited promising therapeutic efficiency against COVID-19 in vitro, and the rationale for its use in COVID-19 is yet to be recognized. Hence, in this study a number of molecular modeling techniques were employed to decrypt the mechanistic insight of teicoplanin interaction with several COVID-19 drug targets. Initially, molecular docking was employed to study the teicoplanin interaction with twenty-five SARS-CoV-2 structural and non-structural proteins which was followed by molecular mechanics/generalized Born surface area (MM/GBSA) computation for binding energy predictions of top ten models from each target. Amongst all macromolecular targets, the N-terminal domain of the nucleocapsid protein displayed the strongest affinity with teicoplanin showing binding energies of −7.4 and −102.13 kcal/mol, in docking and Prime MM/GBSA, respectively. Thermodynamic stability of the teicoplanin-nucleocapsid protein was further probed by molecular dynamics simulations of protein–ligand complex as well as unbounded protein in 100 ns trajectories. Post-simulation MM-GBSA computation of 50 frames extracted from simulated trajectories estimated an average binding energy of −62.52 ± 12.22 kcal/mol. In addition, conformational state of protein in complex with docked teicoplanin displayed stable root-mean-square deviation/fluctuation. In conclusion, computational investigation of the potential targets of COVID-19 and their interaction mechanism with teicoplanin can guide the design of novel therapeutic armamentarium for the treatment of SARS-CoV-2 infection. However, additional studies are warranted to establish the clinical use or relapses, if any, of teicoplanin in the therapeutic management of COVID-19 patients.

Computational optimization of angiotensin-converting enzyme 2 for SARS-CoV-2 Spike molecular recognition

Since the beginning of the Covid19 pandemic, many efforts have been devoted to identifying approaches to neutralize SARS-CoV-2 replication within the host cell. A promising strategy to block the infection consists of using a mutant of the human receptor angiotensin-converting enzyme 2 (ACE2) as a decoy to compete with endogenous ACE2 for the binding to the SARS-CoV-2 Spike protein, which decreases the ability of the virus to enter the host cell. Here, using a computational framework based on the 2D Zernike formalism we investigate details of the molecular binding and evaluate the changes in ACE2-Spike binding compatibility upon mutations occurring in the ACE2 side of the molecular interface. We demonstrate the efficacy of our method by comparing our results with experimental binding affinities changes upon ACE2 mutations, separating ones that increase or decrease binding affinity with an Area Under the ROC curve ranging from 0.66 to 0.93, depending on the magnitude of the effects analyzed. Importantly, the iteration of our approach leads to the identification of a set of ACE2 mutants characterized by an increased shape complementarity with Spike. We investigated the physico-chemical properties of these ACE2 mutants and propose them as bona fide candidates for Spike recognition.