Unveiling Novel Urease Inhibitors for Helicobacter pylori: A Multi-Methodological Approach from Virtual Screening and ADME to Molecular Dynamics Simulations.

Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining pharmacophore- and structure-based virtual screening, molecular dynamics simulations, and MM-GBSA calculations was employed to identify novel inhibitors for Hp urease (HpU). A refined dataset of 8,271,505 small molecules from the ZINC15 database underwent pharmacokinetic and physicochemical filtering, resulting in 16% of compounds for pharmacophore-based virtual screening. Molecular docking simulations were performed in successive stages, utilizing HTVS, SP, and XP algorithms. Subsequent energetic re-scoring with MM-GBSA identified promising candidates interacting with distinct urease variants. Lys219, a residue critical for urea catalysis at the urease binding site, can manifest in two forms, neutral (LYN) or carbamylated (KCX). Notably, the evaluated molecules demonstrated different interaction and energetic patterns in both protein variants. Further evaluation through ADMET predictions highlighted compounds with favorable pharmacological profiles, leading to the identification of 15 candidates. Molecular dynamics simulations revealed comparable structural stability to the control DJM, with candidates 5, 8 and 12 (CA5, CA8, and CA12, respectively) exhibiting the lowest binding free energies. These inhibitors suggest a chelating capacity that is crucial for urease inhibition. The analysis underscores the potential of CA5, CA8, and CA12 as novel HpU inhibitors. Finally, we compare our candidates with the chemical space of urease inhibitors finding physicochemical similarities with potent agents such as thiourea.

Calcium-Alginate-Chitosan Nanoparticle as a Potential Solution for Pesticide Removal, a Computational Approach.

Pesticides have a significant negative impact on the environment, non-target organisms, and human health. To address these issues, sustainable pest management practices and government regulations are necessary. However, biotechnology can provide additional solutions, such as the use of polyelectrolyte complexes to encapsulate and remove pesticides from water sources. We introduce a computational methodology to evaluate the capture capabilities of Calcium-Alginate-Chitosan (CAC) nanoparticles for a broad range of pesticides. By employing ensemble-docking and molecular dynamics simulations, we investigate the intermolecular interactions and absorption/adsorption characteristics between the CAC nanoparticles and selected pesticides. Our findings reveal that charged pesticide molecules exhibit more than double capture rates compared to neutral counterparts, owing to their stronger affinity for the CAC nanoparticles. Non-covalent interactions, such as van der Waals forces, π-π stacking, and hydrogen bonds, are identified as key factors which stabilized the capture and physisorption of pesticides. Density profile analysis confirms the localization of pesticides adsorbed onto the surface or absorbed into the polymer matrix, depending on their chemical nature. The mobility and diffusion behavior of captured compounds within the nanoparticle matrix is assessed using mean square displacement and diffusion coefficients. Compounds with high capture levels exhibit limited mobility, indicative of effective absorption and adsorption. Intermolecular interaction analysis highlights the significance of hydrogen bonds and electrostatic interactions in the pesticide-polymer association. Notably, two promising candidates, an antibiotic derived from tetracycline and a rodenticide, demonstrate a strong affinity for CAC nanoparticles. This computational methodology offers a reliable and efficient screening approach for identifying effective pesticide capture agents, contributing to the development of eco-friendly strategies for pesticide removal.

A description of the epidemiological dynamics of Chagas disease via mathematical modeling.

Chagas disease is caused by the protozoan Trypanosoma cruzi, which parasitizes many mammals, including humans. Its vectors are blood-feeding hematophagous triatomine insects of different species, which vary according to the geographical area. One of the 17 neglected diseases targeted by the World Health Organization, Chagas disease is endemic to the Americas, but has spread to other countries due to human migratory movements. In this study, we describe the epidemiological dynamics of Chagas disease in an endemic area, considering the main transmission mechanisms and the demographic effects of birth, mortality, and human migration in this phenomenon. We apply mathematical models as a methodological approach to simulate the interactions between reservoirs, vectors, and humans using a system of ordinary differential equations. The results show that the Chagas disease control measures currently in place cannot be relaxed without endangering the progress achieved to date.

MDSCAN: RMSD-based HDBSCAN clustering of long molecular dynamics.

MOTIVATION: The term clustering designates a comprehensive family of unsupervised learning methods allowing to group similar elements into sets called clusters. Geometrical clustering of molecular dynamics (MD) trajectories is a well-established analysis to gain insights into the conformational behavior of simulated systems. However, popular variants collapse when processing relatively long trajectories because of their quadratic memory or time complexity. From the arsenal of clustering algorithms, HDBSCAN stands out as a hierarchical density-based alternative that provides robust differentiation of intimately related elements from noise data. Although a very efficient implementation of this algorithm is available for programming-skilled users (HDBSCAN*), it cannot treat long trajectories under the de facto molecular similarity metric RMSD. RESULTS: Here, we propose MDSCAN, an HDBSCAN-inspired software specifically conceived for non-programmers users to perform memory-efficient RMSD-based clustering of long MD trajectories. Methodological improvements over the original version include the encoding of trajectories as a particular class of vantage-point tree (decreasing time complexity), and a dual-heap approach to construct a quasi-minimum spanning tree (reducing memory complexity). MDSCAN was able to process a trajectory of 1 million frames using the RMSD metric in about 21 h with <8 GB of RAM, a task that would have taken a similar time but more than 32 TB of RAM with the accelerated HDBSCAN* implementation generally used. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of MDSCAN are free and publicly available on GitHub (https://github.com/LQCT/MDScan.git) and as a PyPI package (https://pypi.org/project/mdscan/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural Insights into the Inhibition Site in the Phosphorylcholine Phosphatase Enzyme of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a highly pathogenic Gram-negative microorganism associated with high mortality levels in burned or immunosuppressed patients or individuals affected by cystic fibrosis. Studies support a colonization mechanism whereby P. aeruginosa can breakdown the host cell membrane phospholipids through the sequential action of two enzymes: (I) hemolytic phospholipase C acting upon phosphatidylcholine or sphingomyelin to produce phosphorylcholine (Pcho) and (II) phosphorylcholine phosphatase (PchP) that hydrolyzes Pcho to generate choline and inorganic phosphate. This coordinated action provides the bacteria with carbon, nitrogen, and inorganic phosphate to support growth. Furthermore, PchP exhibits a distinctive inhibition mechanism by high substrate concentration. Here, we combine kinetic assays and computational approaches such as molecular docking, molecular dynamics, and free-energy calculations to describe the inhibitory site of PchP, which shares specific residues with the enzyme's active site. Our study provides insights into a coupled inhibition mechanism by the substrate, allowing us to postulate that the integrity of the inhibition site is needed to the correct functioning of the active site. Our results allow us to gain a better understanding of PchP function and provide the basis for a rational drug design that might contribute to the treatment of infections caused by this important opportunistic pathogen.

Multi-omics data integration approaches for precision oncology.

High-throughput technologies used in molecular biology have been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context, appropriate data integration strategies are required to gain new insights from omics high-dimensional data. Yet, in order to extract valuable knowledge from this kind of information in an efficient manner, different approaches to reduce data dimensionality should be considered in multi-omics data integration pipelines. Multi-omics data integration approaches are mainly classified according to the label availability. Unsupervised data integration only draws inference from inputs without prior labels, whereas its supervised counterpart models allow incorporating known phenotype labels to improve the accuracy of high-throughput biomedical data analyses. However, the real value of the above mentioned approaches lies in their sequential combination with machine learning methods. It represents a major challenge for implementing multi-omics data analysis pipelines but it can certainly improve the decision-making process in the diagnosis and clinical management of cancer. The present review addresses the impact of current multi-omics data integration approaches, and their synergy with machine learning approaches, on the precision oncology field.

Structural Effects of pH Variation and Calcium Amount on the Microencapsulation of Glutathione in Alginate Polymers.

Reduced glutathione (GSH) has a high antioxidant capacity and is present in nearly every cell in the body, playing important roles in nutrient metabolism, antioxidant defense, and regulation of cellular events. Conversely, alginate is a macromolecule that has been widely used in the food, pharmaceutical, biomedical, and textile industries due to its biocompatibility, biodegradability, nontoxicity, and nonimmunogenicity as well as for its capabilities of retaining water and stabilizing emulsions. The primary goal of this study was to characterize and optimize the formation of a molecular complex of calcium alginate with GSH using a computational approach. As methods, we evaluated the influence of varying the amount of calcium cations at two different pHs on the structural stability of Ca2+-alginate complexes and thus on GSH liberation from these types of nanostructures. The results showed that complex stabilization depends on pH, with the system having a lower Ca2+ amount that produces the major GSH release. The systems at pH 2.5 retain more molecules within the calcium-alginate complex, which release GSH more slowly when embedded in more acidic media. In conclusions, this study demonstrates the dependence of the amount of calcium and the stabilizing effect of pH on the formation and subsequent maintenance of an alginate nanostructure. The results presented in this study can help to develop better methodological frameworks in industries where the release or capture of compounds, such as GSH in this case, depends on the conditions of the alginate nanoparticle.

RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics.

MOTIVATION: Density Peaks is a widely spread clustering algorithm that has been previously applied to Molecular Dynamics (MD) simulations. Its conception of cluster centers as elements displaying both a high density of neighbors and a large distance to other elements of high density, particularly fits the nature of a geometrical converged MD simulation. Despite its theoretical convenience, implementations of Density Peaks carry a quadratic memory complexity that only permits the analysis of relatively short trajectories. RESULTS: Here, we describe DP+, an exact novel implementation of Density Peaks that drastically reduces the RAM consumption in comparison to the scarcely available alternatives designed for MD. Based on DP+, we developed RCDPeaks, a refined variant of the original Density Peaks algorithm. Through the use of DP+, RCDPeaks was able to cluster a one-million frames trajectory using less than 4.5 GB of RAM, a task that would have taken more than 2 TB and about 3× more time with the fastest and less memory-hunger alternative currently available. Other key features of RCDPeaks include the automatic selection of parameters, the screening of center candidates and the geometrical refining of returned clusters. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of RCDPeaks are free and publicly available on GitHub (https://github.com/LQCT/RCDPeaks.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases.

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.

BitQT: a graph-based approach to the quality threshold clustering of molecular dynamics.

MOTIVATION: Classical Molecular Dynamics (MD) is a standard computational approach to model time-dependent processes at the atomic level. The inherent sparsity of increasingly huge generated trajectories demands clustering algorithms to reduce other post-simulation analysis complexity. The Quality Threshold (QT) variant is an appealing one from the vast number of available clustering methods. It guarantees that all members of a particular cluster will maintain a collective similarity established by a user-defined threshold. Unfortunately, its high computational cost for processing big data limits its application in the molecular simulation field. RESULTS: In this work, we propose a methodological parallel between QT clustering and another well-known algorithm in the field of Graph Theory, the Maximum Clique Problem. Molecular trajectories are represented as graphs whose nodes designate conformations, while unweighted edges indicate mutual similarity between nodes. The use of a binary-encoded RMSD matrix coupled to the exploitation of bitwise operations to extract clusters significantly contributes to reaching a very affordable algorithm compared to the few implementations of QT for MD available in the literature. Our alternative provides results in good agreement with the exact one while strictly preserving the collective similarity of clusters. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of BitQT are free and publicly available on GitHub (https://github.com/LQCT/BitQT.git) and ReadTheDocs (https://bitqt.readthedocs.io/en/latest/), respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-scale simulation reveals that an amino acid substitution increases photosensitizing reaction inputs in Rhodopsins.

Evaluating the availability of molecular oxygen (O2 ) and energy of excited states in the retinal binding site of rhodopsin is a crucial challenging first step to understand photosensitizing reactions in wild-type (WT) and mutant rhodopsins by absorbing visible light. In the present work, energies of the ground and excited states related to 11-cis-retinal and the O2 accessibility to the ß-ionone ring are evaluated inside WT and human M207R mutant rhodopsins. Putative O2 pathways within rhodopsins are identified by using molecular dynamics simulations, Voronoi-diagram analysis, and implicit ligand sampling while retinal energetic properties are investigated through density functional theory, and quantum mechanical/molecular mechanical methods. Here, the predictions reveal that an amino acid substitution can lead to enough energy and O2 accessibility in the core hosting retinal of mutant rhodopsins to favor the photosensitized singlet oxygen generation, which can be useful in understanding retinal degeneration mechanisms and in designing blue-lighting-absorbing proteic photosensitizers.

Elucidating the Structural Basis of the Intracellular pH Sensing Mechanism of TASK-2 K2P Channels.

Two-pore domain potassium (K2P) channels maintain the cell's background conductance by stabilizing the resting membrane potential. They assemble as dimers possessing four transmembrane helices in each subunit. K2P channels were crystallized in "up" and "down" states. The movements of the pore-lining transmembrane TM4 helix produce the aperture or closure of side fenestrations that connect the lipid membrane with the central cavity. When the TM4 helix is in the up-state, the fenestrations are closed, while they are open in the down-state. It is thought that the fenestration states are related to the activity of K2P channels and the opening of the channels preferentially occurs from the up-state. TASK-2, a member of the TALK subfamily of K2P channels, is opened by intracellular alkalization leading the deprotonation of the K245 residue at the end of the TM4 helix. This charge neutralization of K245 could be sensitive or coupled to the fenestration state. Here, we describe the relationship between the states of the intramembrane fenestrations and K245 residue in TASK-2 channel. By using molecular modeling and simulations, we show that the protonated state of K245 (K245+) favors the open fenestration state and, symmetrically, that the open fenestration state favors the protonated state of the lysine residue. We show that the channel can be completely blocked by Prozac, which is known to induce fenestration opening in TREK-2. K245 protonation and fenestration aperture have an additive effect on the conductance of the channel. The opening of the fenestrations with K245+ increases the entrance of lipids into the selectivity filter, blocking the channel. At the same time, the protonation of K245 introduces electrostatic potential energy barriers to ion entrance. We computed the free energy profiles of ion penetration into the channel in different fenestration and K245 protonation states, to show that the effects of the two transformations are summed up, leading to maximum channel blocking. Estimated rates of ion transport are in qualitative agreement with experimental results and support the hypothesis that the most important barrier for ion transport under K245+ and open fenestration conditions is the entrance of the ions into the channel.

BitClust: Fast Geometrical Clustering of Long Molecular Dynamics Simulations.

The growing computational capacity allows the investigation of large biomolecular systems by increasingly extensive molecular dynamics simulations. The resulting huge trajectories demand efficient partition methods to discern relevant structural dissimilarity. Clustering algorithms are available to address this task, but their implementations still need to be improved to gain in computational speed and to reduce the consumption of random access memory. We propose the BitClust code which, based on a combination of Python and C programming languages, performs fast structural clustering of long molecular trajectories. BitClust takes advantage of bitwise operations applied to a bit-encoded pairwise similarity matrix. Our approach allowed us to process a half-million frame trajectory in 6 h using less than 35 GB, a task that is not affordable with any of the similar alternatives.

Structure/Activity Analysis of TASK-3 Channel Antagonists Based on a 5,6,7,8 tetrahydropyrido[4,3-d]pyrimidine.

TASK-3 potassium (K+) channels are highly expressed in the central nervous system, regulating the membrane potential of excitable cells. TASK-3 is involved in neurotransmitter action and has been identified as an oncogenic K+ channel. For this reason, the understanding of the action mechanism of pharmacological modulators of these channels is essential to obtain new therapeutic strategies. In this study we describe the binding mode of the potent antagonist PK-THPP into the TASK-3 channel. PK-THPP blocks TASK-1, the closest relative channel of TASK-3, with almost nine-times less potency. Our results confirm that the binding is influenced by the fenestrations state of TASK-3 channels and occurs when they are open. The binding is mainly governed by hydrophobic contacts between the blocker and the residues of the binding site. These interactions occur not only for PK-THPP, but also for the antagonist series based on 5,6,7,8 tetrahydropyrido[4,3-d]pyrimidine scaffold (THPP series). However, the marked difference in the potency of THPP series compounds such as 20b, 21, 22 and 23 (PK-THPP) respect to compounds such as 17b, inhibiting TASK-3 channels in the micromolar range is due to the presence of a hydrogen bond acceptor group that can establish interactions with the threonines of the selectivity filter.