A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data.

BACKGROUND: The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles. RESULTS: Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R2 and Q2-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity. CONCLUSIONS: The proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.

A novel machine learning-based screening identifies statins as inhibitors of the calcium pump SERCA.

We report a novel small-molecule screening approach that combines data augmentation and machine learning to identify Food and Drug Administration (FDA)-approved drugs interacting with the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) from skeletal (SERCA1a) and cardiac (SERCA2a) muscle. This approach uses information about small-molecule effectors to map and probe the chemical space of pharmacological targets, thus allowing to screen with high precision large databases of small molecules, including approved and investigational drugs. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in muscle and it represents a major target in both skeletal and cardiac muscle. The machine learning model predicted that SERCA1a and SERCA2a are pharmacological targets for seven statins, a group of FDA-approved 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors used in the clinic as lipid-lowering medications. We validated the machine learning predictions by using in vitro ATPase assays to show that several FDA-approved statins are partial inhibitors of SERCA1a and SERCA2a. Complementary atomistic simulations predict that these drugs bind to two different allosteric sites of the pump. Our findings suggest that SERCA-mediated Ca2+ transport may be targeted by some statins (e.g., atorvastatin), thus providing a molecular pathway to explain statin-associated toxicity reported in the literature. These studies show the applicability of data augmentation and machine learning-based screening as a general platform for the identification of off-target interactions and the applicability of this approach extends to drug discovery.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment.

The use of nanomaterials has been increasing in recent times, and they are widely used in industries such as cosmetics, drugs, food, water treatment, and agriculture. The rapid development of new nanomaterials demands a set of approaches to evaluate the potential toxicity and risks related to them. In this regard, nanosafety has been using and adapting already existing methods (toxicological approach), but the unique characteristics of nanomaterials demand new approaches (nanotoxicology) to fully understand the potential toxicity, immunotoxicity, and (epi)genotoxicity. In addition, new technologies, such as organs-on-chips and sophisticated sensors, are under development and/or adaptation. All the information generated is used to develop new in silico approaches trying to predict the potential effects of newly developed materials. The overall evaluation of nanomaterials from their production to their final disposal chain is completed using the life cycle assessment (LCA), which is becoming an important element of nanosafety considering sustainability and environmental impact. In this review, we give an overview of all these elements of nanosafety.

Yes SIR! On the structure-inactivity relationships in drug discovery.

In analogy with structure-activity relationships (SARs), which are at the core of medicinal chemistry, studying structure-inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish 'activity' and 'inactivity' with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled 'active' and 'inactive' compounds.

Nanomaterial Exposure, Extracellular Vesicle Biogenesis and Adverse Cellular Outcomes: A Scoping Review.

The progressively increasing use of nanomaterials (NMs) has awakened issues related to nanosafety and its potential toxic effects on human health. Emerging studies suggest that NMs alter cell communication by reshaping and altering the secretion of extracellular vesicles (EVs), leading to dysfunction in recipient cells. However, there is limited understanding of how the physicochemical characteristics of NMs alter the EV content and their consequent physiological functions. Therefore, this review explored the relevance of EVs in the nanotoxicology field. The current state of the art on how EVs are modulated by NM exposure and the possible regulation and modulation of signaling pathways and physiological responses were assessed in detail. This review followed the manual for reviewers produced by The Joanna Brigs Institute for Scoping Reviews and the PRISMA extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. The research question, "Do NMs modulate cellular responses mediated by EVs?" was analyzed following the PECO model (P (Population) = EVs, E (Exposure) = NMs, C (Comparator) = EVs without exposure to NMs, O (Outcome) = Cellular responses/change in EVs) to help methodologically assess the association between exposure and outcome. For each theme in the PECO acronym, keywords were defined, organized, and researched in PubMed, Science Direct, Scopus, Web of Science, EMBASE, and Cochrane databases, up to 30 September 2021. In vitro, in vivo, ex vivo, and clinical studies that analyzed the effect of NMs on EV biogenesis, cargo, and cellular responses were included in the analysis. The methodological quality assessment was conducted using the ToxRTool, ARRIVE guideline, Newcastle Ottawa and the EV-TRACK platform. The search in the referred databases identified 2944 articles. After applying the eligibility criteria and two-step screening, 18 articles were included in the final review. We observed that depending on the concentration and physicochemical characteristics, specific NMs promote a significant increase in EV secretion as well as changes in their cargo, especially regarding the expression of proteins and miRNAs, which, in turn, were involved in biological processes that included cell communication, angiogenesis, and activation of the immune response, etc. Although further studies are necessary, this work suggests that molecular investigations on EVs induced by NM exposure may become a potential tool for toxicological studies since they are widely accessible biomarkers that may form a bridge between NM exposure and the cellular response and pathological outcome.

Rationality over fashion and hype in drug design.

The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of in silico tools comes at the risk of using them as "black boxes" without sufficient expert knowledge, leading to widespread misconceptions and problems. For example, results of computations may be taken at face value as "nothing but the truth" and data visualization may be used only to generate "pretty and colorful pictures". Computational experts might come to the rescue and help to re-direct such efforts, for example, by guiding interested novices to conduct meaningful data analysis, make scientifically sound predictions, and communicate the findings in a rigorous manner. However, this is not always ensured. This contribution aims to encourage investigators entering the CADD arena to obtain adequate computational training, communicate or collaborate with experts, and become aware of the fundamentals of computational methods and their given limitations, beyond the hype. By its very nature, this Opinion is partly subjective and we do not attempt to provide a comprehensive guide to the best practices of CADD; instead, we wish to stimulate an open discussion within the scientific community and advocate rational rather than fashion-driven use of computational methods. We take advantage of the open peer-review culture of F1000Research such that reviewers and interested readers may engage in this discussion and obtain credits for their candid personal views and comments. We hope that this open discussion forum will contribute to shaping the future practice of CADD.

An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition.

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.

Conserved Luminal C-Terminal Domain Dynamically Controls Interdomain Communication in Sarcolipin.

Sarcolipin (SLN) mediates Ca2+ transport and metabolism in muscle by regulating the activity of the Ca2+ pump SERCA. SLN has a conserved luminal C-terminal domain that contributes to its functional divergence among homologous SERCA regulators, but the precise mechanistic role of this domain remains poorly understood. We used all-atom molecular dynamics (MD) simulations of SLN totaling 77.5 µs to show that the N- (NT) and C-terminal (CT) domains function in concert. Analysis of the MD simulations showed that serial deletions of the SLN C-terminus do not affect the stability of the peptide nor induce dissociation of SLN from the membrane but promote a gradual decrease in both the tilt angle of the transmembrane helix and the local thickness of the lipid bilayer. Mutual information analysis showed that the NT and CT domains communicate with each other in SLN and that interdomain communication is partially or completely abolished upon deletion of the conserved segment Tyr29-Tyr31 as well as by serial deletions beyond this domain. Phosphorylation of SLN at residue Thr5 also induces changes in the communication between the CT and NT domains, which thus provides additional evidence for interdomain communication within SLN. We found that interdomain communication is independent of the force field used and lipid composition, which thus demonstrates that communication between the NT and CT domains is an intrinsic functional feature of SLN. We propose the novel hypothesis that the conserved C-terminus is an essential element required for dynamic control of SLN regulatory function.

A hallmark of phospholamban functional divergence is located in the N-terminal phosphorylation domain.

Sarcoplasmic reticulum Ca2+ pump (SERCA) is a critical component of the Ca2+ transport machinery in myocytes. There is clear evidence for regulation of SERCA activity by PLB, whose activity is modulated by phosphorylation of its N-terminal domain (residues 1-25), but there is less clear evidence for the role of this domain in PLB's functional divergence. It is widely accepted that only sarcolipin (SLN), a protein that shares substantial homology with PLB, uncouples SERCA Ca2+ transport from ATP hydrolysis by inducing a structural change of its energy-transduction domain; yet, experimental evidence shows that the transmembrane domain of PLB (residues 26-52, PLB26-52) partially uncouples SERCA in vitro. These apparently conflicting mechanisms suggest that PLB's uncoupling activity is encoded in its transmembrane domain, and that it is controlled by the N-terminal phosphorylation domain. To test this hypothesis, we performed molecular dynamics simulations (MDS) of the binary complex between PLB26-52 and SERCA. Comparison between PLB26-52 and wild-type PLB (PLBWT) showed no significant changes in the stability and orientation of the transmembrane helix, indicating that PLB26-52 forms a native-like complex with SERCA. MDS showed that PLB26-52 produces key intermolecular contacts and structural changes required for inhibition, in agreement with studies showing that PLB26-52 inhibits SERCA. However, deletion of the N-terminal phosphorylation domain facilitates an order-to-disorder shift in the energy-transduction domain associated with uncoupling of SERCA, albeit weaker than that induced by SLN. This mechanistic evidence reveals that the N-terminal phosphorylation domain of PLB is a primary contributor to the functional divergence among homologous SERCA regulators.

Structural Basis for the Limited Response to Oxidative and Thiol-Conjugating Agents by Triosephosphate Isomerase From the Photosynthetic Bacteria Synechocystis.

In plants, the ancestral cyanobacterial triosephosphate isomerase (TPI) was replaced by a duplicated version of the cytosolic TPI. This isoform acquired a transit peptide for chloroplast localization and functions in the Calvin-Benson cycle. To gain insight into the reasons for this gene replacement in plants, we characterized the TPI from the photosynthetic bacteria Synechocystis (SyTPI). SyTPI presents typical TPI enzyme kinetics profiles and assembles as a homodimer composed of two subunits that arrange in a (ß-α)8 fold. We found that oxidizing agents diamide (DA) and H2O2, as well as thiol-conjugating agents such as oxidized glutathione (GSSG) and methyl methanethiosulfonate (MMTS), do not inhibit the catalytic activity of SyTPI at concentrations required to inactivate plastidic and cytosolic TPIs from the plant model Arabidopsis thaliana (AtpdTPI and AtcTPI, respectively). The crystal structure of SyTPI revealed that each monomer contains three cysteines, C47, C127, and C176; however only the thiol group of C176 is solvent exposed. While AtcTPI and AtpdTPI are redox-regulated by chemical modifications of their accessible and reactive cysteines, we found that C176 of SyTPI is not sensitive to redox modification in vitro. Our data let us postulate that SyTPI was replaced by a eukaryotic TPI, because the latter contains redox-sensitive cysteines that may be subject to post-translational modifications required for modulating TPI's enzymatic activity.

Structural basis for relief of phospholamban-mediated inhibition of the sarcoplasmic reticulum Ca2+-ATPase at saturating Ca2+ conditions.

Sarcoplasmic reticulum Ca2+-ATPase (SERCA) is critical for cardiac Ca2+ transport. Reversal of phospholamban (PLB)-mediated SERCA inhibition by saturating Ca2+ conditions operates as a physiological rheostat to reactivate SERCA function in the absence of PLB phosphorylation. Here, we performed extensive atomistic molecular dynamics simulations to probe the structural mechanism of this process. Simulation of the inhibitory complex at superphysiological Ca2+ concentrations ([Ca2+] = 10 mm) revealed that Ca2+ ions interact primarily with SERCA and the lipid headgroups, but not with PLB's cytosolic domain or the cytosolic side of the SERCA-PLB interface. At this [Ca2+], a single Ca2+ ion was translocated from the cytosol to the transmembrane transport sites. We used this Ca2+-bound complex as an initial structure to simulate the effects of saturating Ca2+ at physiological conditions ([Ca2+]total ≈ 400 µm). At these conditions, ∼30% of the Ca2+-bound complexes exhibited structural features consistent with an inhibited state. However, in ∼70% of the Ca2+-bound complexes, Ca2+ moved to transport site I, recruited Glu771 and Asp800, and disrupted key inhibitory contacts involving the conserved PLB residue Asn34 Structural analysis showed that Ca2+ induces only local changes in interresidue inhibitory interactions, but does not induce repositioning or changes in PLB structural dynamics. Upon relief of SERCA inhibition, Ca2+ binding produced a site I configuration sufficient for subsequent SERCA activation. We propose that at saturating [Ca2+] and in the absence of PLB phosphorylation, binding of a single Ca2+ ion in the transport sites rapidly shifts the equilibrium toward a noninhibited SERCA-PLB complex.

Preexisting domain motions underlie protonation-dependent structural transitions of the P-type Ca2+-ATPase.

We have performed microsecond molecular dynamics (MD) simulations to determine the mechanism for protonation-dependent structural transitions of the sarco/endoplasmic reticulum Ca2+-ATPase (SERCA), one of the most prominent members of the large P-type ATPase superfamily that transports ions across biological membranes. The release of two H+ from the transport sites activates SERCA by inducing a structural transition between low (E2) and high (E1) Ca2+-affinity states (E2-to-E1 transition), but the structural mechanism by which transport site deprotonation facilitates this transition is unknown. We performed microsecond all-atom MD simulations to determine the effects of transport site protonation on the structural dynamics of the E2 state in solution. We found that the protonated E2 state has structural characteristics that are similar to those observed in crystal structures of E2. Upon deprotonation, a single Na+ ion rapidly (<10 ns) binds to the transmembrane transport sites and induces a kink in M5, disrupts the M3-M5 interface, and increases the mobility of the M3/A-M3 linker. Principal component analysis showed that counter-rotation of the cytosolic N-A domains about the membrane normal axis, which is the primary motion driving the E2-to-E1 transition, is present in both protonated and deprotonated E2 states; however, protonation-dependent structural changes in the transmembrane domain control the hierarchical organization and amplitude of this motion. We propose that preexisting rigid-body domain motions underlie structural transitions of SERCA, where the functionally important directionality is preserved while transport site protonation controls the dominance and amplitude of motion to shift the equilibrium between the E1 and E2 states. We conclude that ligand-induced modulation of preexisting domain motions is likely a common theme in structural transitions of the P-type ATPase superfamily.

Database fingerprint (DFP): an approach to represent molecular databases.

BACKGROUND: Molecular fingerprints are widely used in several areas of chemoinformatics including diversity analysis and similarity searching. The fingerprint-based analysis of chemical libraries, in particular of large collections, usually requires the molecular representation of each compound in the library that may lead to issues of storage space and redundant calculations. In fact, information redundancy is inherent to the data, resulting on binary digit positions in the fingerprint without significant information. RESULTS: Herein is proposed a general approach to represent an entire compound library with a single binary fingerprint. The development of the database fingerprint (DFP) is illustrated first using a short fingerprint (MACCS keys) for 10 data sets of general interest in chemistry. The application of the DFP is further shown with PubChem fingerprints for the data sets used in the primary example but with a larger number of compounds, up to 25,000 molecules. The performance of DFP were studied through differential Shannon entropy, k-mean clustering, and DFP/Tanimoto similarity. CONCLUSIONS: The DFP is designed to capture key information of the compound collection and can be used to compare and assess the diversity of molecular libraries. This Preliminary Communication shows the potential of the novel fingerprint to conduct inter-library relationships. A major future goal is to apply the DFP for virtual screening and developing DFP for other data sets based on several different type of fingerprints.Graphical AbstractDatabase fingerprint captures the key information of molecular databases to perform chemical space characterization and virtual screening.

Design and synthesis of N-benzoyl amino acid derivatives as DNA methylation inhibitors.

The inhibition of human DNA Methyl Transferases (DNMT) is a novel promising approach to address the epigenetic dysregulation of gene expression in different diseases. Inspired by the validated virtual screening hit NSC137546, a series of N-benzoyl amino acid analogues was synthesized and obtained compounds were assessed for their ability to inhibit DNMT-dependent DNA methylation in vitro. The biological screening allowed the definition of a set of preliminary structure-activity relationships and the identification of compounds promising for further development. Among the synthesized compounds, L-glutamic acid derivatives 22, 23, and 24 showed the highest ability to prevent DNA methylation in a total cell lysate. Compound 22 inhibited DNMT1 and DNMT3A activity in a concentration-dependent manner in the micromolar range. In addition, compound 22 proved to be stable in human serum and it was thus selected as a starting point for further biological studies.

Developmental DNA methyltransferase inhibitors in the treatment of gynecologic cancers.

INTRODUCTION: DNA methylation has become an attractive target for the treatment of cancer. DNA methyltransferase inhibitors have proven useful for the treatment of myelodysplastic syndrome and are being evaluated in gynecological neoplasias. AREAS COVERED: We provide an overview of the current knowledge on DNA methylation and cancer and the role of DNA methylation in cervical, ovarian and endometrial carcinomas. The results of recent clinical trials with demethylating agents for cervical and ovarian cancer treatment are also discussed. EXPERT OPINION: There are few studies of DNA demethylating agents for cervical and ovarian cancer treatment; nevertheless, the results are promising. To accelerate these advances, there are at least two actions that can be simultaneously pursued. One is to greatly increase the number of small clinical exploratory trials with existing demethylating drugs and using methylome analyses to identify predictive factors for response and/or toxicity. The second is finding out epigenetic 'drivers' unique to gynecological cancers and their subtypes, and then proceed to clinical trials in a highly selected population of patients. It is expected that in the future, DNA demethylation could have a role in the treatment of gynecologic cancers.