Insight into the kinetics and thermodynamics of the hydride transfer reactions between quinones and lumiflavin: a density functional theory study

Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique

A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it.

Scientific contribution

We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.

Writing a literature review as a class project in an upper-level undergraduate biochemistry course

A literature review is an important part of conducting academic research. Knowing how to conduct a literature search and write a high-quality literature review is a valuable skill. Herein, the authors describe the method of introducing a literature review writing exercise in an upper-level biochemistry course. Since 2020, authors have collaborated with numerous undergraduates writing literature reviews on topics in biochemistry that resulted in peer-reviewed publications. Authors believe that this unique idea of providing a course-based undergraduate research experience (CURE) to many undergraduates, especially those who otherwise do not receive collaborative research experience through traditional research paths, must be shared with other instructors.

Cyclic Changes in Active Site Polarization and Dynamics Drive the 'Ping-pong' Kinetics in NRH:Quinone Oxidoreductase 2: An Insight from QM/MM Simulations

Quinone reductases belong to the family of flavin-dependent oxidoreductases. With the redox active cofactor, flavin adenine dinucleotide, quinone reductases are known to utilize a 'ping-pong' kinetic mechanism during catalysis in which a hydride is bounced back and forth between flavin and its two substrates. However, the continuation of this catalytic cycle requires product displacement steps, where the product of one redox half-cycle is displaced by the substrate of the next half-cycle. Using improved hybrid quantum mechanical/molecular mechanical simulations, both the catalytic hydride transfer and the product displacement reactions were studied in NRH:quinone oxidoreductase 2. Initially, the self-consistent charge-density functional tight binding theory was used to describe flavin ring and the substrate atoms, while embedded in the molecular mechanically-treated solvated active site. Then, for each step of the catalytic cycle, a further improvement of energetics was made using density functional theory-based corrections. The present study showcases an integrated interplay of solvation, protonation, and protein matrix-induced polarization as the driving force behind the thermodynamic wheel of the 'ping-pong' kinetics. Reported here is the first-principles model of the 'ping-pong' kinetics that portrays how cyclic changes in the active site polarization and dynamics govern the oscillatory hydride transfer and product displacement in this enzyme.