Synthesis, Structural Characterization and Anticancer Activity of New 5-Trifluoromethyl-2-thioxo-thiazolo[4,5-d]pyrimidine Derivatives

Thiazolo[4,5-d]pyrimidine derivatives are considered potential therapeutic agents, particularly in the development of anticancer drugs. In this study, new 7-oxo-(2a-e), 7-chloro-(3a-e) and also three 7-amino-(4a-c) 5-trifluoromethyl-2-thioxo-thiazolo[4,5-d]pyrimidine derivatives have been synthesized and evaluated for their potential anticancer activity. These derivatives were characterized by spectroscopic methods and elemental analysis, and the single-crystal X-ray diffraction was further performed to confirm a 3D structure for compounds 2e and 4b. The antiproliferative activity evaluation of twelve new compounds was carried out on a variety of cell lines including four human cancer (A375, C32, DU145, MCF-7/WT) and two normal cell lines (CHO-K1 and HaCaT). Four of them (2b, 3b, 4b and 4c) were selected by the National Cancer Institute and evaluated for their in vitro anticancer activity using the NCI-60 screening program. 7-Chloro-3-phenyl-5-(trifluoromethyl)[1,3]thiazolo[4,5-d]pyrimidine-2(3H)-thione (3b) proved to be the most active among the newly synthesized compounds.

Analysis of Integrin αIIb Subunit Dynamics Reveals Long-Range Effects of Missense Mutations on Calf Domains

Integrin αIIbβ3, a glycoprotein complex expressed at the platelet surface, is involved in platelet aggregation and contributes to primary haemostasis. Several integrin αIIbβ3 polymorphisms prevent the aggregation that causes haemorrhagic syndromes, such as Glanzmann thrombasthenia (GT). Access to 3D structure allows understanding the structural effects of polymorphisms related to GT. In a previous analysis using Molecular Dynamics (MD) simulations of αIIb Calf-1 domain structure, it was observed that GT associated with single amino acid variation affects distant loops, but not the mutated position. In this study, experiments are extended to Calf-1, Thigh, and Calf-2 domains. Two loops in Calf-2 are unstructured and therefore are modelled expertly using biophysical restraints. Surprisingly, MD revealed the presence of rigid zones in these loops. Detailed analysis with structural alphabet, the Proteins Blocks (PBs), allowed observing local changes in highly flexible regions. The variant P741R located at C-terminal of Calf-1 revealed that the Calf-2 presence did not affect the results obtained with isolated Calf-1 domain. Simulations for Calf- 1+ Calf-2, and Thigh + Calf-1 variant systems are designed to comprehend the impact of five single amino acid variations in these domains. Distant conformational changes are observed, thus highlighting the potential role of allostery in the structural basis of GT.

Fine mapping with epigenetic information and 3D structure

Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.

A database of calculated solution parameters for the recently released AlphaFold predicted protein structures

Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting "folding frenzy" has already produced predicted protein structure databases for the entire human and other organisms' proteomes. However, rapidly ascertaining a predicted structure's reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients (D0t(20,w),s0(20,w)) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US-SOMO) suite we have calculated from the AlphaFold structures the corresponding D0t(20,w), s0(20,w), [η], p(r) vs. r, and other parameters. Circular dichroism spectra were also computed. The resulting US-SOMO-AF database should aid in rapidly evaluating the consistency in solution of AlphaFold predicted protein structures.

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

The Aptamer Functionalized Nanocomposite Used for Prostate Cancer Diagnosis and Therapy

Prostate cancer is one of the major malignancies that threaten men’s health all over the world. Due to the lack of specific symptoms and signs in the early stage, as well as the limitations of existing detection methods, it is difficult to achieve early diagnosis for prostate cancer. As short single-stranded oligonucleotides (DNA or RNA) with specific 3D structure which can be produced using an in vitro selection process termed systematic evolution of ligands by exponential enrichment (SELEX), aptamers can specifically bind to the corresponding targets. They have become a class of novel targeting ligand for accurate diagnosis and effective treatment of cancer. Owing to distinctive physicochemical features, and some other special properties such as easy modifiability, good biocompatibility, being easily coupled with other ligands, nanomaterials are extensively used in biological medical field research. Enlighteningly, the combination of aptamers with nanomaterials, including metal nanoparticles, nanosilica, quantum dots, and carbon nanomaterials, can enhance the ability of nanomaterials to recognize tumor cells, which is beneficial to overcome the shortcomings such as low sensitivity in early detection and lack of specificity of traditional antineoplastic drugs, thus, clinically helpful to improve the early metaphase diagnosis rate, providing a technical guarantee for the “personalized treatment” strategy for prostate cancer. Herein, we mainly review the basic and applied research of aptamer functionalized nanocomposite in prostate cancer diagnosis and treatment, including biosensing, bioimaging, and cancer therapy, hoping to provide new ideas for prostate cancer diagnosis and treatment.

How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?

Spaceborne lidar sensors have potential to improve the accuracy of forest above-ground biomass (AGB) estimates by providing direct measurements of 3D structure of forests over large spatial scales. The ICESat-2 (Ice, Cloud and land Elevation Satellite 2), launched in 2018, provides a good coverage of the boreal forest zone and has been previously shown to provide good estimates of forest canopy height and AGB. However, spaceborne lidar data are affected by various conditions, such as presence of snow, solar noise, and in the case of ICESat-2, the power difference between the so-called strong and weak beams. The aim of this study was to explore the effects of these conditions on the performance of AGB modeling using ICESat-2 photon data in a boreal forest area. The framework of the study is multiphase modeling, where AGB field data and wall-to-wall airborne laser scanning (ALS) data are used to produce proxy ALS plots on ICESat-2 track positions. Models between the ALS-predicted AGB and the ICESat-2 photon data are then formulated and evaluated by subsets, such as only strong beam data captured in snowy conditions.Our results indicate that, if possible, strong beam night data from snowless conditions should be used in AGB estimation, because our models showed clearly smallest RMSE (27.0%) for this data subset. If more data are needed, we recommend using only strong beam data and constructing separate models for the different data subsets. In the order of increasing RMSE\%, the next best options were snow/night/strong (30.5%), snow/day/strong (33.6%), and snowless/day/strong (34.2%). Weak beam data from snowy night conditions could also be used if necessary (31.1%).

3D atomic-scale imaging of mixed Co-Fe spinel oxide nanoparticles during oxygen evolution reaction

The three-dimensional (3D) distribution of individual atoms on the surface of catalyst nanoparticles plays a vital role in their activity and stability. Optimising the performance of electrocatalysts requires atomic-scale information, but it is difficult to obtain. Here, we use atom probe tomography to elucidate the 3D structure of 10 nm sized Co2FeO4 and CoFe2O4 nanoparticles during oxygen evolution reaction (OER). We reveal nanoscale spinodal decomposition in pristine Co2FeO4. The interfaces of Co-rich and Fe-rich nanodomains of Co2FeO4 become trapping sites for hydroxyl groups, contributing to a higher OER activity compared to that of CoFe2O4. However, the activity of Co2FeO4 drops considerably due to concurrent irreversible transformation towards CoIVO2 and pronounced Fe dissolution. In contrast, there is negligible elemental redistribution for CoFe2O4 after OER, except for surface structural transformation towards (FeIII, CoIII)2O3. Overall, our study provides a unique 3D compositional distribution of mixed Co-Fe spinel oxides, which gives atomic-scale insights into active sites and the deactivation of electrocatalysts during OER.

Optimal Design of a BLDC Motor Considering Three-Dimensional Structures Using the Response Surface Methodology

In this paper, the optimal design of a brushless direct current motor with a three-dimensional (3D) structure using the response surface methodology (RSM) is presented. There were two optimization goals: reduction of the cogging torque and maintenance of the back electromotive force to prevent performance degradation. For motors with a 3D structure, a 3D finite element method analysis is essential, though it requires considerable computation time. Therefore, to reduce the optimal design time, the 3D structure was placed on the 2D plane. Thereafter, a 2D corrected model was applied to the RSM. For the validity of the technique, the analysis results of the initial 3D model, 2D model, and 2D corrected model were compared, and the results of the optimal design 3D model, 2D corrected model, and experiment were compared.