SARS-CoV-2 has caused the largest pandemic of the twenty-first century , threatening the life and economy of all countries in the world. The identification of novel therapies and vaccines that can mitigate or control this global health threat is among the most important challenges facing biomedical sciences. To construct a long-term strategy to fight both SARS-CoV-2 and other possible future threats from coronaviruses, it is critical to understand the molecular mechanisms underlying the virus action. The viral entry and associated infectivity stems from the formation of the SARS-CoV-2 spike protein complex with angiotensin-converting enzyme 2 (ACE2). The detection of putative allosteric sites on the viral spike protein molecule can be used to elucidate the molecular pathways that can be targeted with allosteric drugs to weaken the spike-ACE2 interaction and, thus, reduce viral infectivity. In this study, we present the results of the application of different computational methods aimed at detecting allosteric sites on the SARS-CoV-2 spike protein. The adopted tools consisted of the protein contact networks (PCNs), SEPAS (Affinity by Flexibility), and perturbation response scanning (PRS) based on elastic network modes. All of these methods were applied to the ACE2 complex with both the SARS-CoV2 and SARS-CoV spike proteins. All of the adopted analyses converged toward a specific region (allosteric modulation region [AMR]), present in both complexes and predicted to act as an allosteric site modulating the binding of the spike protein with ACE2. Preliminary results on hepcidin (a molecule with strong structural and sequence with AMR) indicated an inhibitory effect on the binding affinity of the spike protein toward the ACE2 protein.

A highly infectious coronavirus, SARS-CoV-2, has spread in many countries. This virus recognizes its receptor, angiotensin-converting enzyme 2 (ACE2), using the receptor binding domain of its spike protein subunit S1. Many missense mutations are reported in various human populations for the ACE2 gene. In the current study, we predict the affinity of many ACE2 variants for binding to S1 protein using different computational approaches. The dissociation process of S1 from some variants of ACE2 is studied in the current work by molecular dynamics approaches. We study the relation between structural dynamics of ACE2 in closed and open states and its affinity for S1 protein of SARS-CoV-2.

Ca(2+) is an essential second messenger, playing a fundamental role in maintaining cell viability and neuronal activity. Two specific endoplasmic reticulum calcium channels, ryanodine receptors (RyRs) and inositol 1,4,5-trisphosphate receptors (IP3Rs) play an important role in Ca(2+) regulation. In the present study, we provided a 3D structure of RyR and IP3R by homology modeling, and we predicted their interactions with a known neuroprotective compound, 3-thiomethyl-5,6-(dimethoxyphenyl)-1,2,4-triazine (TDMT), as well as two inhibitors, dantrolene and 2-aminoethoxydiphenyl borate (2-APB). Interestingly, we found that dantrolene and 2-APB can bind to the IP3-binding domain of IP3R and RyR, while TDMT may directly block both channels by interacting with the putative resident domains in the pore. Cell culture experiments showed that these compounds could protect PC12 cells against H2O2-induced apoptosis and activate autophagic pathways. Collectively, our computational (in silico) and cell culture studies suggest that RyR and IP3R are novel and promising targets to be used against neurodegenerative diseases.

Alzheimer, a neurodegenerative disease, and a large variety of pathologic conditions are associated with a form of protein aggregation known as amyloid fibrils. Since fibrils and prefibrillar intermediates are cytotoxic, numerous attempts have been made to inhibit fibrillation process as a therapeutic strategy. Peptides, surfactants and aromatic small molecules have been used as fibrillation inhibitors. Here we studied the effects of paclitaxel, a polyphenol with a high tendency for interaction with proteins, on fibrillation of insulin as a model protein. The effects of paclitaxel on insulin fibrillation were determined by Thioflavin T fluorescence, Congo red absorbance, circular dichroism and atomic force microscopy. These studies indicated that paclitaxel considerably hindered nucleation, and therefore, fibrillation of insulin in a dose-dependant manner. The isothermal titration calorimetry studies showed that the interaction between paclitaxel and insulin was spontaneous. In addition, the van der Waal's interactions and hydrogen bonds were prominent forces contributing to this interaction. Computational results using molecular dynamic simulations and docking studies revealed that paclitaxel diminished the polarity of insulin dimer and electrostatic interactions by increasing the hydrophobicity of its dimer state. Furthermore, paclitaxel reduced disrupting effects of insulin fibrils on PC12 cell's neurite outgrowth and complexity, and enhanced their survival.

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Knowledge about the structure and stability of protein–protein interactions is vital to decipher the behavior of protein systems. Prediction of protein complexes’ stability is an interesting topic in the field of structural biology. There are some promising published computational approaches that predict the affinity between subunits of protein dimers using 3D structures of both subunits. In the current study, we classify protein complexes with experimentally measured affinities into distinct classes with different mean affinities. By predicting the mechanical stiffness of the protein binding patch (PBP) region on a single subunit, we successfully predict the assigned affinity class of the PBP in the classification step. Now to predict the experimentally measured affinity between protein monomers in solution, we just need the 3D structure of the suggested PBP on one subunit of the proposed dimer. We designed the SEPAS software and have made the software freely available for academic non-commercial research purposes at “”. SEPAS predicts the stability of the intended dimer in a classwise manner by utilizing the computed mechanical stiffness of the introduced binding site on one subunit with the minimum accuracy of 0.72.

α-Lactalbumin α-La), together with oleic acid can be converted to a complex, which kills tumor cells selectively. Cytotoxic α-La -oleic acid and α-La -linoleic acid complexes were generated by adding fatty acid to camel holo α-La at 60 ° C (referred to as La-OA-60 and La-LA-60 state, respectively). Structural properties of these complexes were studied and compared to the camel α-La. The experimental results show that linoleic acid induces α-La partial unfolding but oleic acid does not change the protein structure significantly. Also the stability of La-OA-60 and La-LA-60 toward thermal denaturation was measured. The order of temperature at the transition midpoint is as follows: La-LA-60 < La-OA-60 < α-La. La-OA-60 complex inhibited tubulin polymerization in vitro. Although the structures of La-OA-60 and La-LA-60 were different, these two complexes had similar cytotoxic effect to DU145 human prostate cancer cells. Samples of La-OA-60 that have been renatured after denaturation lost the specific biological activity toward tumor cells.

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