Quantifying Global Origin-Diagnostic Features and Patterns in Biotic and Abiotic Acyclic Lipids for Life Detection.

Lipids are a geologically robust class of organics ubiquitous to life as we know it. Lipid-like soluble organics are synthesized abiotically and have been identified in carbonaceous meteorites and on Mars. Ascertaining the origin of lipids on Mars would be a profound astrobiological achievement. We enumerate origin-diagnostic features and patterns in two acyclic lipid classes, fatty acids (i.e., carboxylic acids) and acyclic hydrocarbons, by collecting and analyzing molecular data reported in over 1500 samples from previously published studies of terrestrial and meteoritic organics. We identify 27 combined (15 for fatty acids, 12 for acyclic hydrocarbons) molecular patterns and structural features that can aid in distinguishing biotic from abiotic synthesis. Principal component analysis (PCA) demonstrates that multivariate analyses of molecular features (16 for fatty acids, 14 for acyclic hydrocarbons) can potentially indicate sample origin. Terrestrial lipids are dominated by longer straight-chain molecules (C4-C34 fatty acids, C14-C46 acyclic hydrocarbons), with predominance for specific branched and unsaturated isomers. Lipid-like meteoritic soluble organics are shorter, with random configurations. Organic solvent-extraction techniques are most commonly reported, motivating the design of our novel instrument, the Extractor for Chemical Analysis of Lipid Biomarkers in Regolith (ExCALiBR), which extracts lipids while preserving origin-diagnostic features that can indicate biogenicity.

Molecular analysis of the type III interferon complex and its applications in protein engineering.

Type III interferons (IFNλs) are cytokines with critical roles in the immune system and are attractive therapeutic candidates due to their tissue-specific activity. Despite entering several clinical trials, results have demonstrated limited efficacy and potency, partially attributed to low-affinity protein-protein interactions (PPIs) responsible for receptor complex formation. Subsequently, structural studies of the native IFNλ signaling complexes remain inaccessible. While protein engineering can overcome affinity limitations, tools to investigate low-affinity systems like these remain limited. To provide insights into previous efforts to strengthen the PPIs within this complex, we perform a molecular analysis of the extracellular ternary complexes of IFNλ3 using both computational and experimental approaches. We first use molecular simulations and modeling to quantify differences in PPIs and residue strain fluctuations, generate detailed free energy landscapes, and reveal structural differences between an engineered, high-affinity complex, and a model of the wild-type, low-affinity complex. This analysis illuminates distinct behaviors of these ligands, yielding mechanistic insights into IFNλ complex formation. We then apply these computational techniques in protein engineering and design by utilizing simulation data to identify hotspots of interaction to rationally engineer the native cytokine-receptor complex for increased stability. These simulations are then validated by experimental techniques, showing that a single mutation at a computationally predicted site of interaction between the two receptors increases PPIs and improves complex formation for all IFNλs. This study highlights the power of molecular dynamics simulations for protein engineering and design as applied to the IFNλ family but also presents a potential tool for analysis and engineering of other systems with low-affinity PPIs.

Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity.

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib's mechanism of Mpro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1-M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

Denoising Autoencoder Trained on Simulation-Derived Structures for Noise Reduction in Chromatin Scanning Transmission Electron Microscopy.

Scanning transmission electron microscopy tomography with ChromEM staining (ChromSTEM), has allowed for the three-dimensional study of genome organization. By leveraging convolutional neural networks and molecular dynamics simulations, we have developed a denoising autoencoder (DAE) capable of postprocessing experimental ChromSTEM images to provide nucleosome-level resolution. Our DAE is trained on synthetic images generated from simulations of the chromatin fiber using the 1-cylinder per nucleosome (1CPN) model of chromatin. We find that our DAE is capable of removing noise commonly found in high-angle annular dark field (HAADF) STEM experiments and is able to learn structural features driven by the physics of chromatin folding. The DAE outperforms other well-known denoising algorithms without degradation of structural features and permits the resolution of α-tetrahedron tetranucleosome motifs that induce local chromatin compaction and mediate DNA accessibility. Notably, we find no evidence for the 30 nm fiber, which has been suggested to serve as the higher-order structure of the chromatin fiber. This approach provides high-resolution STEM images that allow for the resolution of single nucleosomes and organized domains within chromatin dense regions comprising of folding motifs that modulate the accessibility of DNA to external biological machinery.

Toward wide-spectrum antivirals against coronaviruses: Molecular characterization of SARS-CoV-2 NSP13 helicase inhibitors.

To date, effective therapeutic treatments that confer strong attenuation against coronaviruses (CoVs) remain elusive. Among potential drug targets, the helicase of CoVs is attractive due to its sequence conservation and indispensability. We rely on atomistic molecular dynamics simulations to explore the structural coordination and dynamics associated with the SARS-CoV-2 Nsp13 apo enzyme, as well as their complexes with natural ligands. A complex communication network is revealed among the five domains of Nsp13, which is differentially activated because of the presence of the ligands, as shown by shear strain analysis, principal components analysis, dynamical cross-correlation matrix analysis, and water transport analysis. The binding free energy and the corresponding mechanism of action are presented for three small molecules that were shown to be efficient inhibitors of the previous SARS-CoV Nsp13 enzyme. Together, our findings provide critical fresh insights for rational design of broad-spectrum antivirals against CoVs.

Tetranucleosome Interactions Drive Chromatin Folding.

The multiscale organizational structure of chromatin in eukaryotic cells is instrumental to DNA transcription, replication, and repair. At mesoscopic length scales, nucleosomes pack in a manner that serves to regulate gene expression through condensation and expansion of the genome. The particular structures that arise and their respective thermodynamic stabilities, however, have yet to be fully resolved. In this study, we combine molecular modeling using the 1CPN mesoscale model of chromatin with nonlinear manifold learning to identify and characterize the structure and free energy of metastable states of short chromatin segments comprising between 4- and 16-nucleosomes. Our results reveal the formation of two previously characterized tetranucleosomal conformations, the "α-tetrahedron" and the "ß-rhombus", which have been suggested to play an important role in the accessibility of DNA and, respectively, induce local chromatin compaction or elongation. The spontaneous formation of these motifs is potentially responsible for the slow nucleosome dynamics observed in experimental studies. Increases of the nucleosome repeat length are accompanied by more pronounced structural irregularity and flexibility and, ultimately, a dynamic liquid-like behavior that allows for frequent structural reorganization. Our findings indicate that tetranucleosome motifs are intrinsically stable structural states, driven by local internucleosomal interactions, and support a mechanistic picture of chromatin packing, dynamics, and accessibility that is strongly influenced by emergent local mesoscale structure.

Modeling the Binding Mechanism of Remdesivir, Favilavir, and Ribavirin to SARS-CoV-2 RNA-Dependent RNA Polymerase.

Recent efforts to repurpose drugs to combat COVID-19 have identified Remdesivir as a candidate. It acts on the RNA-dependent, RNA polymerase (RdRp) of the SARS-CoV-2 virus, a protein complex responsible for mediating replication of the virus's genome. However, its exact action mechanism, and that of other nucleotide analogue inhibitors, is not known. In this study, we examine at the molecular level the interaction of this drug and that of similar nucleotide analogue inhibitors, ribavirin and favilavir, by relying on atomistic molecular simulations and advanced sampling. By analyzing the binding free energies of these different drugs, it is found that all of them bind strongly at the active site. Surprisingly, however, ribavirin and favilavir do not bind the nucleotide on the complementary strand as effectively and seem to act by a different mechanism than remdesivir. Remdesivir exhibits similar binding interactions to the natural base adenine. Moreover, by analyzing remdesivir at downstream positions of the RNA, we also find that, consistent with a "delayed" termination mechanism, additional nucleotides can be incorporated after remdesivir is added, and its highly polar 1'-cyano group induces a set of conformational changes that can affect the normal RdRp complex function. By analyzing the fluctuations of residues that are altered by remdesivir binding, and comparing them to those induced by lethal point mutations, we find a possible secondary mechanism in which remdesivir destabilizes the protein complex and its interactions with the RNA strands.

Molecular characterization of ebselen binding activity to SARS-CoV-2 main protease.

There is an urgent need to repurpose drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recent computational-experimental screenings have identified several existing drugs that could serve as effective inhibitors of the virus' main protease, Mpro, which is involved in gene expression and replication. Among these, ebselen (2-phenyl-1,2-benzoselenazol-3-one) appears to be particularly promising. Here, we examine, at a molecular level, the potential of ebselen to decrease Mpro activity. We find that it exhibits a distinct affinity for the catalytic region. Our results reveal a higher-affinity, previously unknown binding site localized between the II and III domains of the protein. A detailed strain analysis indicates that, on such a site, ebselen exerts a pronounced allosteric effect that regulates catalytic site access through surface-loop interactions, thereby inducing a reconfiguration of water hotspots. Together, these findings highlight the promise of ebselen as a repurposed drug against SARS-CoV-2.

Overcoming the Heuristic Nature of k-Means Clustering: Identification and Characterization of Binding Modes from Simulations of Molecular Recognition Complexes.

The accurate and reproducible detection and description of thermodynamic states in computational data is a nontrivial problem, particularly when the number of states is unknown a priori and for large, flexible chemical systems and complexes. To this end, we report a novel clustering protocol that combines high-resolution structural representation, brute-force repeat clustering, and optimization of clustering statistics to reproducibly identify the number of clusters present in a data set (k) for simulated ensembles of butyrylcholinesterase in complex with two previously studied organophosphate inhibitors. Each structure within our simulated ensembles was depicted as a high-dimensionality vector with components defined by specific protein-inhibitor contacts at the chemical group level and the magnitudes of these components defined by their respective extents of pair-wise atomic contact, thus allowing for algorithmic differentiation between varying degrees of interaction. These surface-weighted interaction fingerprints were tabulated for each of over 1 million structures from more than 100 µs of all-atom molecular dynamics simulation per complex and used as the input for repetitive k-means clustering. Minimization of cluster population variance and range afforded accurate and reproducible identification of k, thereby allowing for the characterization of discrete binding modes from molecular simulation data in the form of contact tables that concisely encapsulate the observed intermolecular contact motifs. While the protocol presented herein to determine k and achieve non-heuristic clustering is demonstrated on data from massive atomistic simulation, our approach is generalizable to other data types and clustering algorithms, and is tractable with limited computational resources.

Understanding the enzyme-ligand complex: insights from all-atom simulations of butyrylcholinesterase inhibition.

All-atom molecular dynamics simulations of butyrylcholinesterase (BChE) sans inhibitor and in complex with each of 15 dialkyl phenyl phosphate derivatives were conducted to characterize inhibitor binding modes and strengths. Each system was sampled on the 250 ns timescale in explicit ionic solvent, for a total of over 4 µs of simulation time. A K-means algorithm was used to cluster the resulting structures into distinct binding modes, which were further characterized based on atomic-level contacts between inhibitor chemical groups and active site residues. Comparison of experimentally observed inhibition constants (KI) with the resulting contact tables provides structural explanations for relative binding coefficients and highlights several notable interaction motifs. These include ubiquitous contact between glycines in the oxyanion hole and the inhibitor phosphate group; a sterically driven binding preference for positional isomers that extend aromaticity; a stereochemical binding preference for choline-containing inhibitors, which mimic natural BChE substrates; and the mechanically induced opening of the omega loop region to fully expose the active site gorge in the presence of choline-containing inhibitors. Taken together, these observations can greatly inform future design of BChE inhibitors, and the approach reported herein is generalizable to other enzyme-inhibitor systems and similar complexes that depend on non-covalent molecular recognition.Communicated by Ramaswamy H. Sarma.

Ensemble Molecular Dynamics of a Protein-Ligand Complex: Residual Inhibitor Entropy Enhances Drug Potency in Butyrylcholinesterase.

Butyrylcholinesterase is a key enzyme that catalyzes the hydrolysis of the neurotransmitter acetylcholine and shows an increased activity in patients suffering from Alzheimer's disease (AD), making this enzyme a primary target in treating AD. Central to this problem, and to similar scenarios involving biomolecular recognition, is our understanding of the nature of the protein-ligand complex. The butyrylcholinesterase enzyme was studied via all-atom, explicit solvent, ensemble molecular dynamics simulations sans inhibitor and in the presence of three dialkyl phenyl phosphate inhibitors of known potency to a cumulative sampling of over 40 µs. Following the relaxation of these ensembles to conformational equilibria, binding modes for each inhibitor were identified. While classical models, which assume significant reduction in protein and ligand conformational entropies, continue to be favored in contemporary studies, our observations contradict those assumptions: bound ligands occupy many conformational states, thereby stabilizing the complex, while also promoting protein flexibility.

Synthesis, biochemical evaluation, and molecular modeling studies of aryl and arylalkyl di-n-butyl phosphates, effective butyrylcholinesterase inhibitors.

A series of dialkyl aryl phosphates and dialkyl arylalkyl phosphates were synthesized. Their inhibitory activities were evaluated against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). The di-n-butyl phosphate series consistently displayed selective inhibition of BChE over AChE. The most potent inhibitors of butyrylcholinesterase were di-n-butyl-3,5-dimethylphenyl phosphate (4b) [KI=1.0±0.4µM] and di-n-butyl 2-naphthyl phosphate (5b) [KI=1.9±0.4µM]. Molecular modeling was used to uncover three subsites within the active site gorge that accommodate the three substituents attached to the phosphate group. Phosphates 4b and 5b were found to bind to these three subsites in analogous fashion with the aromatic groups in both analogs being accommodated by the "lower region," while the lone pairs on the PO oxygen atoms were oriented towards the oxyanion hole. In contrast, di-n-butyl-3,4-dimethylphenyl phosphate (4a) [KI=9±1µM], an isomer of 4b, was found to orient its aromatic group in the "upper left region" subsite as placement of this group in the "lower region" resulted in significant steric hindrance by a ridge-like region in this subsite. Future studies will be designed to exploit these features in an effort to develop inhibitors of higher inhibitory strength against butyrylcholinesterase.

Discrepancias entre el diagnóstico Clínico y Anatomo-Patológico en el Hospital Escuela Universitario De Honduras / Discrepancies between the Clinical and Anatomy-Pathological diagnosis at the Hospital Escuela Universitario in Honduras

La calidad diagnóstica es el resultado de integrar el conocimiento médico y reconocimiento de los errores clínicos, se alcanza únicamente con la identificación de las causas de muerte; es la correlación clínico patológica la herramienta principal para dicha acción. El objetivo general de la investigación fue determinar la discrepancia clínico-patológica y su relación con otras variables en las autopsias realizadas en la institución. Se revisaron 159 protocolos de autopsia del período comprendido entre enero 2012 y junio 2016, elaborados por el Servicio de Patología del Hospital Escuela Universitario de Tegucigalpa, Honduras. Se excluyeron 36 por no cumplir los criterios de inclusión. Se utilizaron la CIE-10 y la clasificación de Goldman et al. para clasificar las patologías y establecer las discrepancias diagnósticas, respectivamente. El sexo predominante fue el femenino (2,96:1), la edad media fue de 38 años; prevalecieron los diagnósticos de embarazo/parto/puerperio y enfermedades infecciosas y parasitarias. Concluimos que en 46% de los casos existe discrepancia diagnóstica y la glomerulonefritis fue la principal causa de error, seguida de bronconeumonía. Se recomienda estandarizar el protocolo de autopsias y promover sesiones clínico-patológicas periódicas e integrales.

Diagnostic quality is the result of the integration of medical knowledge and recognition of clinical error, achieved only by identifying the cause of death; clinical pathological correlation is the primary tool for this action. The overall objective of this research was to determine clinical pathological discrepancy and its relationship with other variables within the autopsies performed at the institution. 159 autopsy protocols, elaborated by the Department of Pathology of Hospital Escuela Universitario in Tegucigalpa, Honduras, from January 2012 to June 2016, were reviewed. 36 were excluded for not meeting the inclusion criteria. ICD-10 and Goldman et al. modified by Battle criteria were used to classify diseases and establish diagnostic discrepancies, respectively. The majority of patients were female (2.96:1), the mean age was 38 years old; diagnoses of pregnancy/birth/puerperium and infectious and parasitic diseases prevailed. We conclude that diagnostic discrepancies exist in 46% of all cases and glomerulonephritis was the leading cause of error, followed by bronchopneumonia. It is recommended that autopsy protocols be standardized, and integrative clinical pathological sessions are promoted and integral.