Quantitative Biology

Monolayer-protected gold nanoparticles (Au NPs) are promising biomedical tools with applications to diagnosis and therapy, thanks to their biocompatibility and versatility. Here we show how the NP surface functionalization can drive the mechanism of interaction with lipid membranes. In particular, we show that the spontaneous protonation of anionic carboxylic groups on the NP surface can make the NP-membrane interaction faster and less disruptive.

Neuropeptide SF (NPSF) is a member of RFamide neuropeptides that play diverse roles in central nervous system. Little is know about the effects of NPSF on brain functions. Antidepressant-like effect of NPSF was studied in modified mice FST. NPSF showed the antidepressant-like effects by decreasing the immobility time and increasing the climbing and swimming time. Furthermore, the involvement of the adrenergic, serotonergic, cholinergic or dopaminergic receptors in the antidepressant-like effect of NPSF was studied in modified mice FST. Mice were pretreated with a non-selective {\alpha}-adrenergic receptor antagonist phenoxybenzamine, a \b{eta}-adrenergic receptor antagonist, propranolol, a non-selective 5-HT2 serotonergic receptor antagonist, cyproheptadine, nonselective muscarinic acetylcholine receptor antagonist, atropine, or D2, D3, D4 dopamine receptor antagonist, haloperidol. The present results confirmed that the antidepressant-like effect of NPSF is mediated, at least in part, by an interaction of the {\alpha}-adrenergic, 5-HT2 serotonergic, muscarinic acetylcholine receptors and D2, D3, D4 dopamine receptor in a modified mouse FST.

The steep climbing of victims caused by the new coronavirus disease 2019 (COVID-19) throughout the planet is sparking an unprecedented effort to identify effective therapeutic regimens to tackle the pandemic. The SARS-CoV-2 virus is known to gain entry into various cell types through the binding of one of its surface proteins (spike) to the host Angiotensin-Converting Enzyme 2 (ACE2). Thus, spike-ACE2 interaction represents a major target for vaccines and antiviral drugs. A novel method has been recently described by some of the authors to pharmacologically downregulate the expression of target proteins at the post-translational level. This technology builds on computational advancements in the simulation of folding mechanisms to rationally block protein expression by targeting folding intermediates, hence hampering the folding process. Here, we report the all-atom simulations of the entire sequence of events underlying the folding pathway of ACE2. Our data revealed the existence of a folding intermediate showing two druggable pockets hidden in the native conformation. Both pockets were targeted by a virtual screening repurposing campaign aimed at quickly identifying drugs capable to decrease the expression of ACE2. We identified four compounds capable of lowering ACE2 expression in Vero cells in a dose-dependent fashion. All these molecules were found to inhibit the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein. Importantly, the antiviral activity has been tested against live SARS-CoV-2 (MEX-BC2/2020 strain). One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing antiviral activity against SARS-CoV-2. Ongoing studies are further evaluating the possibility of repurposing these drugs for the treatment of COVID-19.

To shorten the time required to find effective new drugs, like antivirals, a key parameter to consider is membrane permeability, as a compound intended for an intracellular target with poor permeability will have low efficacy. Here, we present a computational model that considers both drug characteristics and membrane properties for the rapid assessment of drugs permeability through the coronavirus envelope and various cellular membranes. We analyze 79 drugs that are considered as potential candidates for the treatment of SARS-CoV-2 and determine their time of permeation in different organelle membranes grouped by viral baits and mammalian processes. The computational results are correlated with experimental data, present in the literature, on bioavailability of the drugs, showing a negative correlation between fast permeation and most promising drugs. This model represents an important tool capable of evaluating how permeability affects the ability of compounds to reach both intended and unintended intracellular targets in an accurate and rapid way. The method is general and flexible and can be employed for a variety of molecules, from small drugs to nanoparticles, as well to a variety of biological membranes.

Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable both for new medication development and for preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds is still required.

Current methods for investigation of receptor - ligand interactions in drug discovery are based on three-dimensional complementarity of receptor and ligand surfaces, and they include pharmacophore modelling, QSAR, molecular docking etc. Those methods only consider short-range molecular interactions (distances <5A), and not include long-range interactions (distances >5A) which are essential for kinetic of biochemical reactions because they influence the number of productive collisions between interacting molecules. Previously was shown that the electron-ion interaction potential (EIIP) represents the physical property which determines the long-range properties of biological molecules. This molecular descriptor served as a base for development of the informational spectrum method (ISM), a virtual spectroscopy method for investigation of protein-protein interactions. In this paper, we proposed a new approach to treat small molecules as linear entities, allowing study of the small molecule - protein interaction by ISM. We analyzed here 21 sets of KEGG drug-protein interactions and showed that this new approach allows an efficient discrimination between biologically active and inactive ligands, and consistence with AA regions of their binding site on the target protein.

Estrogen receptors (ERs) are a group of proteins activated by 17 β -estradiol. The endocrine-disrupting chemicals (EDCs) mimic estrogen action by bind directly to the ligand binding domain of ER. From this perspective, ER represent a good model for identifying and assessing the health risk of potential EDCs. This ability is best reflected by the ligand-ER binding energy. Multilayer fragment molecular orbital (MFMO) calculations were performed which allowed us to obtain the binding energy using a calculation scheme that considers the molecular interactions that occur on the following model systems: the bound and free receptor, 17 β -estradiol and a water cluster. The bound and free receptor and 17 β -estradiol were surrounded by a water shell containing the same number of molecules as the water cluster. The structures required for MFMO calculations were obtained from molecular dynamics simulations and cluster analysis. Attractive dispersion interactions were observed between 17 β -estradiol and the binding site hydrophobic residues. In addition, strong electrostatic interactions were found between 17 β -estradiol and the following charged/polarized residues: Glu 353, His 524 and Arg 394. The FMO2-RHF/STO-3G:MP2/6-31G(d) weighted binding energy was of -67.2 kcal/mol. We hope that the model developed in this study can be useful for identifying and assessing the health risk of potential EDCs.

Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein-ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprintbased methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein-ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein-ligand binding affinity predictions.

Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field.

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.