Coronavirus disease 2019 (COVID-19) is a novel disease that had devastating effects on human lives and the country\'s economies worldwide. This disease shows similar parasitic traits, requiring the host\'s biomolecules for its survival and propagation. Spike glycoproteins severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 spike protein) located on the surface of the COVID-19 virus serve as a potential hotspot for antiviral drug development based on their structure. COVID-19 virus calls into action the chaperonin system that assists the attacker, hence favoring infection. To investigate the interaction that occurs between SARS-CoV-2 spike protein and human molecular chaperons (HSPA8 and sHSP27), a series of steps were carried out which included sequence attainment and analysis, followed by multiple sequence alignment, homology modeling, and protein-protein docking which we performed using Cluspro to predict the interactions between SARS-CoV-2 spike protein and human molecular chaperones of interest. Our findings depicted that SARS-CoV-2 spike protein consists of three distinct chains, chains A, B, and C, which interact forming hydrogen bonds, hydrophobic interactions, and electrostatic interactions with both human HSPA8 and HSP27 with -828.3 and -827.9 kcal/mol as binding energies for human HSPA8 and -1166.7 and -1165.9 kcal/mol for HSP27.

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Quantum dots (QDs) are semiconductor nanocrystals (2-10 nm) with unique optical and electronic properties due to quantum confinement effects. They offer high photostability, narrow emission spectra, broad absorption spectrum, and high quantum yields, making them versatile in various applications. Due to their highly reactive surfaces, QDs can conjugate with biomolecules while being used, produced, or unintentionally released into the environment. This systematic review delves into intricate relationship between QDs and proteins, examining their interactions that influence their physicochemical properties, enzymatic activity, ligand binding affinity, and stability. The research utilized electronic databases like PubMed, WOS, and Proquest, along with manual reviews from 2013 to 2023 using relevant keywords, to identify suitable literature. After screening titles and abstracts, only articles meeting inclusion criteria were selected for full text readings. This systematic review of 395 articles identifies 125 articles meeting the inclusion criteria, categorized into five overarching themes, encompassing various mechanisms of QDs and proteins interactions, including adsorption to covalent binding, contingent on physicochemical properties of QDs. Through a meticulous analysis of existing literature, it unravels intricate nature of interaction, significant influence on nanomaterials and biological entities, and potential for synergistic applications harnessing both specific and nonspecific interactions across various fields.

SARS-CoV-2 is an enveloped RNA virus that causes severe respiratory illness in humans and animals. It infects cells by binding the Spike protein to the host\'s angiotensin-converting enzyme 2 (ACE2). The bat is considered the natural host of the virus, and zoonotic transmission is a significant risk and can happen when humans come into close contact with infected animals. Therefore, understanding the interconnection between human, animal, and environmental health is important to prevent and control future coronavirus outbreaks. This work aimed to systematically review the literature to identify characteristics that make mammals suitable virus transmitters and raise the main computational methods used to evaluate SARS-CoV-2 in mammals. Based on this review, it was possible to identify the main factors related to transmissions mentioned in the literature, such as the expression of ACE2 and proximity to humans, in addition to identifying the computational methods used for its study, such as Machine Learning, Molecular Modeling, Computational Simulation, between others. The findings of the work contribute to the prevention and control of future outbreaks, provide information on transmission factors, and highlight the importance of advanced computational methods in the study of infectious diseases that allow a deeper understanding of transmission patterns and can help in the development of more effective control and intervention strategies.

In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.

One of the most often utilized methods for drug discovery is molecular docking. With docking, one may discover new therapeutically relevant molecules by targeting the molecule and predicting the target-ligand interactions as well as different conformation of ligand at various positions. The prediction signifies the effectiveness of the molecule or the developed molecule having different affinity with target. Drug discovery plays an important role in the development of a new drug molecule of different moiety attached to it, which leads us in the management of several diseases. In silico approach led us to identification of numerous diseases caused by virus, fungi, bacteria, protozoa, and other microorganisms that affect human health. By means of computational approach, we can categorize disease symptoms and use the drugs available for such types of warning signs. After the docking process, molecular dynamics computational technique helps in the simulation of the physical movement of atoms and molecules for a fixed period of time, giving a view of the dynamic evaluation of the system. This review is an attempt to illustrate the role of molecular docking in drug development.

Triazole scaffolds, a series of 5-membered heterocycles, are well known for their high efficacy, low toxicity, and superior pharmacokinetics. Alzheimer\'s disease (AD) is the first neurodegenerative disorder with complex pathological mechanisms. Triazole, as an aromatic group with three nitrogen atoms, forms polar and non-polar interactions with diverse key residues in the receptor-ligand binding procedure, and has been widely used in the molecular design in the development of anti-AD agents. Moreover, considering the simple synthesis approaches, triazole scaffolds are commonly used to link two pharmacodynamic groups in one chemical molecule, forming multi-target directed ligands (MTDLs). Furthermore, the click reaction between azide- and cyano-modified enzyme and ligand provides feasibility for the new modulator discovery, compound tissue distribution evaluation, enzyme localization, and pharmacological mechanism study, promoting the diagnosis of AD course.

The MedLine database contains 570 publications, including 71 randomized clinical trials and 6 meta-analyses on the rebamipide molecule in 2022. Indications for the use of rebamipide are gastric ulcer, chronic gastritis with hyperacidityin the acute stage, erosive gastritis, prevention of damage to the gastrointestinal mucosa while taking non-steroidal anti-inflammatory drugs, eradication of Helicobacter pylori. Currently trials are studying the efficacy and safety of the drug in gouty and rheumatoid arthritis, osteoarthritis, Sjögren\'s syndrome, bronchial asthma, vitiligo, atherosclerosis, diseases of the kidneys and liver; using in traumatology to accelerate bone regeneration; in ophthalmology to improve the regeneration of corneal epithelium; in oncology to reduce inflammatory changes in the oral mucosa after chemoradiotherapy. The review article is about the main pharmacokinetic and pharmacodynamic characteristics of rebamipide. A detailed understanding of pharmacodynamics and pharmacokinetics allows for individual selection of therapy based on the characteristics of the patient\'s body - gender, age, comorbidities; choose the optimal route of administration and dosing regimen; predict adverse effects and drug interactions; be determined with new clinical indications.
В 2022 г. в базе данных MedLine насчитывается 570 публикаций, включая 71 рандомизированное клиническое исследование и 6 метаанализов, о молекуле ребамипида. Показания к применению ребамипида – язвенная болезнь желудка, хронический гастрит с повышенной кислотообразующей функцией желудка в стадии обострения, эрозивный гастрит, профилактика повреждений слизистой оболочки желудочно-кишечного тракта на фоне приема нестероидных противовоспалительных лекарственных препаратов, эрадикация Helicobacter pylori. Изучаются эффективность и безопасность ребамипида при подагрическом и ревматоидном артритах, остеоартрите, синдроме Шегрена, бронхиальной астме, витилиго, атеросклерозе, заболеваниях почек и печени; в травматологии – для ускорения костной регенерации, в офтальмологии – для регенерации поврежденного эпителия роговицы, в онкологии – для уменьшения воспаления слизистой оболочки ротовой полости после химиолучевой терапии. В обзорной статье разбираются основные фармакокинетические и фармакодинамические характеристики ребамипида. Понимание фармакодинамики и фармакокинетики позволяет осуществлять индивидуальный подбор терапии, исходя из особенностей организма пациента (пол, возраст, сопутствующая патология); выбирать оптимальный путь введения и режим дозирования; прогнозировать нежелательные эффекты и лекарственные взаимодействия; определяться с новыми показаниями.

The techniques of X-ray protein crystallography, NMR and high-resolution cryo-electron microscopy have all been used to determine the high-resolution structure of proteins. The most-commonly used method, however, remains X-ray crystallography but it does rely heavily on the production of suitable crystals. Indeed, the production of diffraction quality crystals remains the rate-limiting step for most protein systems. This mini-review highlights the crystallisation trials that used existing and newly developed crystallisation methods on two muscle protein targets - the actin binding domain (ABD) of α-actinin and the C0-C1 domain of human cardiac myosin binding protein C (cMyBP-C). Furthermore, using heterogenous nucleating agents the crystallisation of the C1 domain of cMyBP-C was successfully achieved in house along with preliminary actin binding studies using electron microscopy and co-sedimentation assays .

Genetic variant(s) of concern (VoC) of SARS-CoV-2 have been emerging worldwide due to mutations in the gene encoding spike glycoprotein. We performed comprehensive analyses of spike protein mutations in the significant variant clade of SARS-CoV-2, using the data available on the Nextstrain server. We selected various mutations, namely, A222V, N439K, N501Y, L452R, Y453F, E484K, K417N, T478K, L981F, L212I, N856K, T547K, G496S, and Y369C for this study. These mutations were chosen based on their global entropic score, emergence, spread, transmission, and their location in the spike receptor binding domain (RBD). The relative abundance of these mutations was mapped with global mutation D614G as a reference. Our analyses suggest the rapid emergence of newer global mutations alongside D614G, as reported during the recent waves of COVID-19 in various parts of the world. These mutations could be instrumentally imperative for the transmission, infectivity, virulence, and host immune system\'s evasion of SARS-CoV-2. The probable impact of these mutations on vaccine effectiveness, antigenic diversity, antibody interactions, protein stability, RBD flexibility, and accessibility to human cell receptor ACE2 was studied in silico. Overall, the present study can help researchers to design the next generation of vaccines and biotherapeutics to combat COVID-19 infection.