Structural screening and molecular simulation identify potential ligands against the K700E hot spot variant and functional pockets of SF3B1 to modulate splicing in myelodysplastic syndrome.

Myelodysplastic syndrome (MDS), a blood disorder with ineffective hematopoiesis and risk of transformation to acute myeloid leukemia, is characterized by recurring cytogenetic and molecular alterations. By chromosome analysis, approximately 60% of patients, carry chromosome 5 and 7 alterations, trisomy of chromosome 8 and may also present with increasingly complex karyotypes, especially in higher grade MDS (MDS with refractory anemia and increased blasts type 1 and 2). Moreover, somatic pathogenic variants in genes associated with aberrant mRNA splicing are frequently mutated with SF3B1 the most frequently mutated. In the setting of SF3B1, the K700E hot-spot mutation is present in approximately 50% of cases. Since recent studies have highlighted modulation of functional dynamics in SF3B1 by mutant splicing factors, the objective of the study was to identify potential small molecule modulators against the frequently mutated RNA splicing factor SF3B1(K700E) and functional allosteric sites by using a molecular structure-based approach and a molecular dynamic simulation. To identify potential SF3B1 modulators, we collected a series of chemical compounds from the Zinc and Enamine database. An initial screen followed by further molecular analysis and simulation using the Schrödinger suite was performed. Parameters used to monitor the stability and binding of the protein-ligand complex included: RMSF, protein-ligand contacts, electrostatic, Van Der Waals forces and binding energies (MMGBSA). A 100-nanosecond simulation showed strong binding between selected compounds and key amino acid residues, including the mutation hot-spot K700E and functional allosteric amino acid residue R630. Ligand binding energies between compounds and key amino acid residues ranged from -50.67 to -58.04 kcal/mol. In brief, small molecule modulators show strong binding to SF3B1 suggesting these compounds may be used against cells harboring the K700E variant or to modulate splicing by targeting functional allosteric sites.

Machine learning prediction for COVID-19 disease severity at hospital admission.

IMPORTANCE: Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. OBJECTIVE: To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data. DESIGN, SETTING, AND PARTICIPANTS: We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest's feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods. MAIN OUTCOMES AND MEASURES: Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation. RESULTS: This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html . CONCLUSIONS AND RELEVANCE: In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19.

Identification of potential antiviral compounds against SARS-CoV-2 structural and non structural protein targets: A pharmacoinformatics study of the CAS COVID-19 dataset.

SARS-CoV-2 is a newly discovered virus which causes COVID-19 (coronavirus disease of 2019), initially documented as a human pathogen in 2019 in the city of Wuhan China, has now quickly spread across the globe with an urgency to develop effective treatments for the virus and emerging variants. Therefore, to identify potential therapeutics, an antiviral catalogue of compounds from the CAS registry, a division of the American Chemical Society was evaluated using a pharmacoinformatics approach. A total of 49,431 compounds were initially recovered. After a biological and chemical curation, only 23,575 remained. A machine learning approach was then used to identify potential compounds as inhibitors of SARS-CoV-2 based on a training dataset of molecular descriptors and fingerprints of known reported compounds to have favorable interactions with SARS-CoV-2. This approach identified 178 compounds, however, a molecular docking analysis revealed only 39 compounds with strong binding to active sites. Downstream molecular analysis of four of these compounds revealed various non-covalent interactions along with simultaneous modulation between ligand and protein active site pockets. The pharmacological profiles of these compounds showed potential drug-likeness properties. Our work provides a list of candidate anti-viral compounds that may be used as a guide for further investigation and therapeutic development against SARS-CoV-2.

Pyrophosphate Release in the Protein HIV Reverse Transcriptase.

Enzymatic reactions usually occur in several steps: a step of substrate binding to the surface of the protein, a step of protein reorganization around the substrate and conduction of a chemical reaction, and a step of product release. The release of inorganic phosphate-PPi-from the matrix of the protein HIV reverse transcriptase is investigated computationally. Atomically detailed simulations with explicit solvent are analyzed to obtain the free energy profile, mean first passage time, and detailed molecular mechanisms of PPi escape. A challenge for the computations is of time scales. The experimental time scale of the process of interest is in milliseconds, and straightforward molecular dynamics simulations are in sub-microseconds. To overcome the time scale gap, we use the algorithm of Milestoning along a reaction coordinate to compute the overall free energy profile and rate. The methods of locally enhanced sampling and steered molecular dynamics determine plausible reaction coordinates. The observed molecular mechanism couples the transfer of the PPi to positively charged lysine side chains that are found on the exit pathway and to an exiting magnesium ion. In accord with experimental findings, the release rate is comparable to the chemical step, allowing for variations in substrate (DNA or RNA template) in which the release becomes rate determining.

Search for the global minimum structures of AlB3H(2n) (n = 0 - 6) clusters.

The global minimum structures of AlB3H2n (n = 0-6) clusters are determined using the stochastic search method at the B3LYP/6-31G level of theory. These initially specified geometries are recalculated using B3LYP and CCSD(T) methods using the 6-311++G(**) basis set. The structural and electronic properties of the two lowest-lying isomers are presented. The structural parameters obtained for aluminum borohydride are compared with the experimental and theoretical results. The H2 fragmentation energies of the most stable isomers are investigated. Chemical bonding analyses for the global minimum of AlB3H2n (n = 0-6) clusters are performed using the adaptive natural density partitioning method.

Structural, antimicrobial and computational characterization of 1-benzoyl-3-(5-chloro-2-hydroxyphenyl)thiourea.

A new thiourea derivative, 1-benzoyl-3-(5-chloro-2-hydroxyphenyl)thiourea (bcht) has been synthesized from the reaction of 2-amino-4-chlorophenol with benzoyl isothiocyanate. The title compound has been characterized by elemental analyses, FT-IR, (13)C, (1)H NMR spectroscopy and the single crystal X-ray diffraction analysis. The structure of bcht derived from X-ray diffraction of a single crystal has been presented. The structural and spectroscopic data of the molecule in the ground state were calculated by using density functional method using 6-311++G(d,p) basis set. The complete assignments of all vibrational modes were performed on the basis of the total energy distributions (TED). Isotropic chemical shifts ((13)C NMR and (1)H NMR) were calculated using the gauge-invariant atomic orbital (GIAO) method. Theoretical calculations of bond parameters, harmonic vibration frequencies and nuclear magnetic resonance are in good agreement with experimental results. The UV absorption spectra of the compound that dissolved in ACN and MeOH were recorded. Bcht was also screened for antimicrobial activity against pathogenic bacteria and fungi.