Alzheimer's disease (AD) is a progressively worsening type of brain disorder that damages the nerve cells. It is marked by the buildup of amyloid-β plaques outside the cells, tau neurofibrillary tangles inside the cells, and overall molecular-level dysfunction. The therapies currently available mainly cater to alleviating the symptoms, whereas the newly approved disease-modifying antibodies, such as lecanemab and donanemab, bring out only limited clinical improvements. Being complicated and involving many factors, AD requires sophisticated computer-based methods to combine different biological data and find suitable therapy targets. In this review, we discuss how artificial intelligence (AI)-powered multi-omics data integration can be a catalyst in discovering drug targets, identifying biomarkers, and stratifying patients for AD. By utilizing machine learning techniques like random forests, graph neural networks, and deep learning, AI-led multi-omics methods have helped uncover new therapeutic targets. Models that were built using federated learning across various institutions outperformed single-center models with a higher area under the curve score (0.84, 0.94 versus 0.76, 0.85). AI-guided patient stratification lessened the clinical trial's sample size needs by 40, 55% while still retaining 80, 90% statistical power. Multi-omics analyses further pointed out that it is the downstream molecular pathways, and not amyloid pathology alone, that are significantly involved in disease progression, thereby questioning the effectiveness of single-target anti-amyloid therapies and endorsing combination treatment strategies. AI and multi-omics data combination can be a game-changer in facilitating new target discovery, making clinical trial design more efficient, and ushering in precision medicine in AD.
T. Periyasamy, Nishu Sekar, Hariprasath Lakshmanan· Journal of Alzheimer's Disea...· 0 citations
Penicillins and cephalosporins are crucial in treating bacterial infections, but conventional chemical synthesis routes for their production are costly and environmentally burdensome. Enzymatic alternatives, such as penicillin G acylase (PGA), offer a greener path; however, naturally occurring forms of this enzyme often exhibit suboptimal thermodynamic stability and electrostatic complementarity under industrial synthesis conditions. In this study, we investigated the effects of a single point mutation, βThr68→βTyr68, in the extracellular PGA from Bacillus megaterium (BmPGA), with the primary objective of improving binding free energy and structural stability rather than maximising raw docking affinity. To characterise the mutation’s impact, we performed homology modelling, molecular docking, and 100-ns molecular dynamics (MD) simulations coupled with MM-PBSA binding free energy calculations. It is important to note that while molecular docking provides a rapid, pose-based approximation of binding geometry, it does not account for solvent effects, conformational entropy, or electrostatic solvation factors that are critical in enzyme substrate systems. Accordingly, MM-PBSA was prioritised as the more physically rigorous metric for evaluating binding thermodynamics. Docking scores indicated that the wild-type enzyme exhibited stronger pose-based affinity (–9.1 kcal/mol) compared to the mutant (–7.6 kcal/mol); however, this difference reflects altered binding geometry rather than reduced thermodynamic favourability. MM-PBSA analysis revealed that the mutant achieved a more negative binding free energy (–28.02 ± 30.51 kJ/mol), driven predominantly by enhanced electrostatic interactions, indicating superior thermodynamic stability of the enzyme–substrate complex. Both enzyme variants remained structurally stable throughout MD simulations, with the mutant performing comparably or better across RMSD, flexibility, solvent-accessible surface area, hydrogen bonding, free energy landscape, and principal component analyses. The mutant also exhibited stronger protein–protein interaction profiles. Taken together, these findings demonstrate that the βThr68→βTry68 substitution meaningfully enhances the thermodynamic and structural properties of BmPGA, establishing it as a promising candidate for more efficient and robust enzymatic production of β-lactam antibiotics.
Sam Peniel Richard, Prajval Ramesh, Asiya Azarudeen et al.· Network Modeling Analysis in...· 0 citations
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