Lifetime of actin-dependent protein nanoclusters

Protein nanoclusters (PNCs) are dynamic collections of a few proteins that spatially organize in nanometer length clusters. PNCs are one of the principal forms of spatial organization of membrane proteins and they have been shown or hypothesized to be important in various cellular processes, including cell signaling. PNCs show remarkable diversity in size, shape, and lifetime. In particular, the lifetime of PNCs can vary over a wide range of timescales. The diversity in size and shape can be explained by the interaction of the clustering proteins with the actin cytoskeleton or the lipid membrane, but very little is known about the processes that determine the lifetime of the nanoclusters. In this paper, using mathematical modelling of the cluster dynamics, we model the biophysical processes that determine the lifetime of actin-dependent PNCs. In particular, we investigated the role of actin aster fragmentation, which had been suggested to be a key determinant of the PNC lifetime, and found that it is important only for a small class of PNCs. A simple extension of our model allowed us to investigate the kinetics of protein-ligand interaction near PNCs. We found an anomalous increase in the lifetime of ligands near PNCs, which agrees remarkably well with experimental data on RAS-RAF kinetics. In particular, analysis of the RAS-RAF data through our model provides falsifiable predictions and novel hypotheses that will not only shed light on the role of RAS-RAF kinetics in various cancers, but also will be useful in studying membrane protein clustering in general.

Saponins as a Potential PFN2 Inhibitor: In silico Approach

Colorectal cancer (CRC) disease has a high mortality rate and has recently involved human profilin II (Pfn2), an actin-binding protein, as a promoter of its invasiveness and progression. This work evaluated the binding affinity of oleanolic acid saponin over Pfn2 and its structural stability. QM and MM techniques were applied to perform geometrical optimization and calculation of the reactive sites from oleanolic acid, whereas molecular docking and MD simulations for protein-ligand interaction under physiological conditions. Oleanolic acid saponin showed a high binding affinity to the Pfn2 PLPbinding site. Analysis of the protein-ligand structure suggests saponin as a molecule with high potential for developing new drugs against Pfn2 in colorectal cancer cells.

Modelling of the Citrus CCD4 Family Members: In Silico Analysis of Membrane Binding and Substrate Preference

Coloring is one of the most important characteristics in commercial flowers and fruits, generally due to the accumulation of carotenoid pigments. Enzymes of the CCD4 family in citrus intervene in the generation of β-citraurin, an apocarotenoid responsible for the reddish-orange color of mandarins. Citrus CCD4s enzymes could be capable of interacting with the thylakoid membrane inside chloroplasts. However, to date, this interaction has not been studied in detail. In this work, we present three new complete models of the CCD4 family members (CCD4a, CCD4b, and CCD4c), modeled with a lipid membrane. To identify the preference for substrates, typical carotenoids were inserted in the active site of the receptors and the protein–ligand interaction energy was evaluated. The results show a clear preference of CCD4s for xanthophylls over aliphatic carotenes. Our findings indicate the ability to penetrate the membrane and maintain a stable interaction through the N-terminal α-helical domain, spanning a contact surface of 2250 to 3250 Å2. The orientation and depth of penetration at the membrane surface suggest that CCD4s have the ability to extract carotenoids directly from the membrane through a tunnel consisting mainly of hydrophobic residues that extends up to the catalytic center of the enzyme.

Repurposing the Trypanosomatidic GSK Kinetobox for the Inhibition of Parasitic Pteridine and Dihydrofolate Reductases

Three open-source anti-kinetoplastid chemical boxes derived from a whole-cell phenotypic screening by GlaxoSmithKline (Tres Cantos Anti-Kinetoplastid Screening, TCAKS) were exploited for the discovery of a novel core structure inspiring new treatments of parasitic diseases targeting the trypansosmatidic pteridine reductase 1 (PTR1) and dihydrofolate reductase (DHFR) enzymes. In total, 592 compounds were tested through medium-throughput screening assays. A subset of 14 compounds successfully inhibited the enzyme activity in the low micromolar range of at least one of the enzymes from both Trypanosoma brucei and Lesihmania major parasites (pan-inhibitors), or from both PTR1 and DHFR-TS of the same parasite (dual inhibitors). Molecular docking studies of the protein–ligand interaction focused on new scaffolds not reproducing the well-known antifolate core clearly explaining the experimental data. TCMDC-143249, classified as a benzenesulfonamide derivative by the QikProp descriptor tool, showed selective inhibition of PTR1 and growth inhibition of the kinetoplastid parasites in the 5 μM range. In our work, we enlarged the biological profile of the GSK Kinetobox and identified new core structures inhibiting selectively PTR1, effective against the kinetoplastid infectious protozoans. In perspective, we foresee the development of selective PTR1 and DHFR inhibitors for studies of drug combinations.

Unsupervised Representation Learning for Proteochemometric Modeling

In silico protein–ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to create an accurate model of the protein–ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous studies in PCM modeling rely on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of related proteins. However, recent advances in representation learning have shown that unsupervised machine learning can be used to generate embeddings that outperform complex, human-engineered representations. Several different embedding methods for proteins and molecules have been developed based on various language-modeling methods. Here, we demonstrate the utility of these unsupervised representations and compare three protein embeddings and two compound embeddings in a fair manner. We evaluate performance on various splits of a benchmark dataset, as well as on an internal dataset of protein–ligand binding activities and find that unsupervised-learned representations significantly outperform handcrafted representations.

Docking, Binding Free Energy Estimation, and MD Simulation of Newly Designed CQ and HCQ Analogues Against the Spike-ACE2 Complex of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) virus has been spreading rapidly, and scientists are endeavouring to discover drugs for its efficacious treatment. Chloroquine phosphate, an old drug for treatment of malaria, has shown to have apparent efficacy and acceptable safety against COVID-19. As a part of Drug Discovery Hackathon-2020, in this study, the authors have tried making the derivatives of CQ and HCQ using MarvinSketch by ChemAxon. Molecular docking studies of these ligands were performed using Glide by Schrodinger, and ADME profiles were obtained by using QikProp. The obtained results after data analysis demonstrated that ligands HCQ_imidazoll, choloroquine_3c, HCQ_pyrrolC had good binding affinity and complied with all the ADME parameters. The molecular dynamic simulation of these ligands in complex with the 2019-nCoV RBD/ACE-2-B0AT1 complex PDB ID: 6M17 were carried out, and the parameters like RMSD, RMSF, and radius of gyration were observed to understand the fluctuations and protein-ligand interaction.