Understanding structure-based dynamic interactions of antihypertensive peptides extracted from food sources

Understanding the basis of thermostability for enzyme "Nanoluc" towards designing industry-competent engineered variants

As a leading contender in the study of luminescence, nanoluciferase has recently attracted attention and proven effective in a wide variety of research areas. Although numerous attempts have been made to improve activity, there has yet to be a thorough exploration of further possibilities to improve thermostability. In this study, protein engineering in tandem with molecular dynamics simulation at various temperatures (300 K, 400 K, 450 K and 500 K) was used to improve our understanding of nanoluciferase dynamics and identification of factors that could significantly enhance the thermostability. Based on these, three novel mutations have been narrowed down, which were hypothesised to improve thermostability. Root mean square deviation and root mean square fluctuation studies confirmed higher stability of mutant at high temperature. Solvent-accessible surface area and protein unfolding studies revealed a decreased tendency of mutant to unfold at higher temperatures. Further free energy landscape and principal component analysis was adapted to get deeper insights into the thermodynamic and structural behavior of these proteins at elevated temperature. Thus, this study provides a deeper insight into the dynamic factors for thermostability and introduces a novel, enhanced nanoluciferase candidate with potential use in industry.Communicated by Ramaswamy H. Sarma.

Development of nano-delivery systems for loaded bioactive compounds: using molecular dynamics simulations

Over the past decade, a remarkable surge in the development of functional nano-delivery systems loaded with bioactive compounds for healthcare has been witnessed. Notably, the demanding requirements of high solubility, prolonged circulation, high tissue penetration capability, and strong targeting ability of nanocarriers have posed interdisciplinary research challenges to the community. While extensive experimental studies have been conducted to understand the construction of nano-delivery systems and their metabolic behavior in vivo, less is known about these molecular mechanisms and kinetic pathways during their metabolic process in vivo, and lacking effective means for high-throughput screening. Molecular dynamics (MD) simulation techniques provide a reliable tool for investigating the design of nano-delivery carriers encapsulating these functional ingredients, elucidating the synthesis, translocation, and delivery of nanocarriers. This review introduces the basic MD principles, discusses how to apply MD simulation to design nanocarriers, evaluates the ability of nanocarriers to adhere to or cross gastrointestinal mucosa, and regulates plasma proteins in vivo. Moreover, we presented the critical role of MD simulation in developing delivery systems for precise nutrition and prospects for the future. This review aims to provide insights into the implications of MD simulation techniques for designing and optimizing nano-delivery systems in the healthcare food industry.

Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine

The recent elevation of cases infected from novel COVID-19 has placed the human life in trepidation mode, especially for those suffering from comorbidities. Most of the studies in the last few months have undeniably raised concerns for hypertensive patients that face greater risk of fatality from COVID-19. Furthermore, one of the recent WHO reports has estimated a total of 1.13 billion people are at a risk of hypertension of which two-thirds live in low and middle income countries. The gradual escalation of the hypertension problem andthe sudden rise of COVID-19 cases have placed an increasingly higher number of human lives at risk in low and middle income countries. To lower the risk of hypertension, most physicians recommend drugs that have angiotensin-converting enzyme (ACE) inhibitors. However, prolonged use of such drugs is not recommended due to metabolic risks and the increase in the expression of ACE-II which could facilitate COVID-19 infection. In contrast, the intake of optimal macronutrients is one of the possible alternatives to naturally control hypertension. In the present study, a nontrivial feature selection and machine learning algorithm is adopted to intelligently predict the food-derived antihypertensive peptide. The proposed idea of the paper lies in reducing the computational power while retaining the performance of the support vector machine (SVM) by estimating the dominant pattern in the features space through feature filtering. The proposed feature filtering algorithm has reported a trade-off performance by reducing the chances of Type I error, which is desirable when recommending a dietary food to patients suffering from hypertension. The maximum achievable accuracy of the best performing SVM models through feature selection are 86.17% and 85.61%, respectively.

Synthesis of Dihydrobenzofuro[3,2-b]chromenes as a potential 3CLpro inhibitors of SARS-CoV-2: A molecular docking and dynamics simulation study

The recent emergence of pandemic of coronavirus (COVID‐19) caused by SARS‐CoV‐2 has raised significant global health concerns. More importantly, there is no specific therapeutics currently available to combat against this deadly infection. The enzyme 3‐chymotrypsin‐like cysteine protease (3CLpro) is known to be essential for viral life cycle as it controls the coronavirus replication. 3CLpro could be a potential drug target as established before in the case of severe acute respiratory syndrome coronavirus (SARS‐CoV) and Middle East respiratory syndrome coronavirus (MERS‐CoV). In the current study, we wanted to explore the potential of fused flavonoids as 3CLpro inhibitors. Fused flavonoids (5a,10a‐dihydro‐11H‐benzofuro[3,2‐b]chromene) are unexplored for their potential bioactivities due to their low natural occurrences. Their synthetic congeners are also rare due to unavailability of general synthetic methodology. Here we designed a simple strategy to synthesize 5a,10a‐dihydro‐11H‐benzofuro[3,2‐b]chromene skeleton and it's four novel derivatives. Our structural bioinformatics study clearly shows excellent potential of the synthesized compounds in comparison to experimentally validated inhibitor N3. Moreover, in‐silico ADMET study displays excellent druggability and extremely low level of toxicity of the synthesized molecules. Further, for better understanding, the molecular dynamic approach was implemented to study the change in dynamicity after the compounds bind to the protein. A detailed investigation through clustering analysis and distance calculation gave us sound comprehensive data about their molecular interaction. In summary, we anticipate that the currently synthesized molecules could not only be a potential set of inhibitors against 3CLpro but also the insights acquired from the current study would be instrumental in further developing novel natural flavonoid based anti‐COVID therapeutic spectrums.

Anti-hypertensive Peptide Predictor: A Machine Learning-Empowered Web Server for Prediction of Food-Derived Peptides with Potential Angiotensin-Converting Enzyme-I Inhibitory Activity

Angiotensin converting enzyme-I (ACE-I) is a key therapeutic target of the renin-angiotensin-aldosterone system (RAAS), the central pathway of blood pressure regulation. Food-derived peptides with ACE-I inhibitory activities are receiving significant research attention. However, identification of ACE-I inhibitory peptides from different food proteins is a labor-intensive, lengthy, and expensive process. For successful identification of potential ACE-I inhibitory peptides from food sources, a machine learning and structural bioinformatics-based web server has been developed and reported in this study. The web server can take input in the FASTA format or through UniProt ID to perform the in silico gastrointestinal digestion and then screen the resulting peptides for ACE-I inhibitory activity. This unique platform provides elaborated structural and functional features of the active peptides and their interaction with ACE-I. Thus, it can potentially enhance the efficacy and reduce the time and cost in identifying and characterizing novel ACE-I inhibitory peptides from food proteins. URL: http://hazralab.iitr.ac.in/ahpp/index.php.

Biophysical insight into the interaction of levocabastine with human serum albumin: spectroscopy and molecular docking approach

Interaction of levocabastine with human serum albumin (HSA) is investigated by applying fluorescence spectroscopy, circular dichroism spectroscopy and molecular docking methods. Levocabastine is an important drug in treatment of allergy and currently a target drug for drug repurposing to treat other diseases like vernal keratoconjuctivitis. Fluorescence quenching data revealed that levocabastine bind weakly to protein with binding constant in the order of 10³ M^-1. Förster resonance energy transfer results indicated the binding distance of 2.28 nm for levocabastine. Synchronous fluorescence result suggest slight blue shift for tryptophan upon levocabastine binding, binding of levocabastine impelled rise in α-helical structure in protein, while there are minimal changes in tertiary structure in protein. Moreover, docking results indicate levocabastine binds to pocket near to the drug site-I in HSA via hydrogen bonding and hydrophobic interactions. Understanding the interaction of levocabastine with HSA is significant for the advancement of therapeutic and diagnostic strategies for optimal treatment results.Communicated by Ramaswamy H. Sarma.