Catalytic Reaction Mechanism of Glyoxalase II: A Quantum Mechanics/Molecular Mechanics Study

Lactate orchestrates metabolic hemodynamic adaptations through a unique combination of venocontraction, artery relaxation, and positive inotropy

AIM

H+ facilitates metabolic blood flow regulation while negatively impacting cardiac contractility. Cardiovascular consequences of conjugate bases accumulating alongside H+ remain unclear. Here, we evaluate the cardiovascular effects of nine prominent carboxylates-particularly lactate, 3-hydroxybutyrate, and butyrate-linked to metabolic and microbial activity.

METHODS

Comparing the actions of pH-adjusted Na-carboxylates to equiosmolar NaCl, we study arteries and veins isolated from healthy rats and humans with ischaemic heart disease, isolated perfused rat hearts, and rat cardiovascular function in vivo.

RESULTS

The tested carboxylates generally relax arteries and veins. L-lactate relaxes human and rat arteries up to 70% (EC50 = 10.1 mM) and rat brachial and mesenteric veins up to 30% of pre-contractions, yet stands out by augmenting contractions of rat femoral, saphenous, and lateral marginal veins and human internal thoracic and great saphenous veins up to 50%. D-lactate shows only minor actions. In isolated perfused hearts, 10 mM L-lactate increases coronary flow (17.1 ± 7.7%) and left ventricular developed pressure (10.1 ± 3.0%) without affecting heart rate. L-lactate infusion in rats-reaching 3.7 ± 0.3 mM in the circulation-increases left ventricular end-diastolic volume (11.3 ± 2.8%), stroke volume (22.6 ± 3.0%), cardiac output (23.4 ± 3.5%), and ejection fraction (10.6 ± 2.0%), and lowers systemic vascular resistance (34.1 ± 3.7%) without influencing blood pressure or heart rate. The ketone body 3-hydroxybutyrate causes lactate accumulation and elevates left ventricular end-diastolic volume in vivo.

CONCLUSION

Carboxylate metabolites generally relax arteries and veins. L-lactate relaxes arteries, lowering systemic vascular resistance, causes preferential venocontraction with increased ventricular diastolic filling, and elevates cardiac contractility and cardiac output. We propose that L-lactate optimizes cardiovascular function during metabolic disturbances.

Metal preference of glyoxalase II, a quantum mechanics/molecular mechanics study

The catalytic activity of the binuclear glyoxalase II (GlxII) enzyme is closely linked to the type and charge of metal ions in its active site. Using hybrid quantum mechanics/molecular mechanics (QM/MM) calculations, we investigated the reaction mechanism of human GlxII, which features two Zn(II) ions in its active site. By systematically replacing these Zn(II) ions with Fe(II), Fe(III), or Co(II), we evaluated the impact of metal substitutions on reaction energetics and active-site geometry. Our results reveal that the type and position of the metal ions are critical to the catalytic activity of GlxII. Substitution of the Zn(II) ion in the three-histidine site with Fe(II), Fe(III), or Co(II) significantly increased the activation barrier, indicating that these configurations are less favorable. In contrast, substituting Zn(II) in the two-histidine site with either Fe(II) or Co(II) resulted in a reduced activation barrier and produced geometries closely resembling those observed when both metal sites are occupied by Zn(II). Additionally, moving the metal ions from the QM to the MM region inhibited the reaction, highlighting their direct chemical involvement in catalysis beyond electrostatic stabilization. These results underscore that the metal ions chemically participate in the catalytic process beyond their electrostatic contributions. Collectively, our results provide insights into the structural and electronic factors governing GlxII catalysis, offering a theoretical framework to complement and refine experimental studies.

Mechanism of Nucleic Acid Phosphodiester Bond Cleavage by Human Endonuclease V: MD and QM/MM Calculations Reveal a Versatile Metal Dependence

Human endonuclease V (EndoV) catalytically removes deaminated nucleobases by cleaving the phosphodiester bond as part of RNA metabolism. Despite being implicated in several diseases (cancers, cardiovascular diseases, and neurological disorders) and potentially being a useful tool in biotechnology, details of the human EndoV catalytic pathway remain unclear due to limited experimental information beyond a crystal structure of the apoenzyme and select mutational data. Since a mechanistic understanding is critical for further deciphering the central roles and expanding applications of human EndoV in medicine and biotechnology, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations were used to unveil the atomistic details of the catalytic pathway. Due to controversies surrounding the number of metals required for nuclease activity, enzyme-substrate models with different numbers of active site metals and various metal-substrate binding configurations were built based on structural data for other nucleases. Subsequent MD simulations revealed the structure and stability of the human EndoV-substrate complex for a range of active site metal binding architectures. Four unique pathways were then characterized using QM/MM that vary in metal number (one versus two) and modes of substrate coordination [direct versus indirect (water-mediated)], with several mechanisms being fully consistent with experimental structural, kinetic, and mutational data for related nucleases, including members of the EndoV family. Beyond uncovering key roles for several active site amino acids (D240 and K155), our calculations highlight that while one metal is essential for human EndoV activity, the enzyme can benefit from using two metals due to the presence of two suitable metal binding sites. By directly comparing one- versus two-metal-mediated P-O bond cleavage reactions within the confines of the same active site, our work brings a fresh perspective to the "number of metals" controversy.

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.

Elucidation of the catalytic mechanism of a single-metal dependent homing endonuclease using QM and QM/MM approaches: the case study of I-PpoI

Homing endonucleases (HEs) are highly specific DNA cleaving enzymes, with I-PpoI having been suggested to use a single metal to accelerate phosphodiester bond cleavage. Although an I-PpoI mechanism has been proposed based on experimental structural data, no consensus has been reached regarding the roles of the metal or key active site amino acids. This study uses QM cluster and QM/MM calculations to provide atomic-level details of the I-PpoI catalytic mechanism. Minimal QM cluster and large-scale QM/MM models demonstrate that the experimentally-proposed pathway involving direct Mg2+ coordination to the substrate coupled with leaving group protonation through a metal-activated water is not feasible due to an inconducive I-PpoI active site alignment. Despite QM cluster models of varying size uncovering a pathway involving leaving group protonation by a metal-activated water, indirect (water-mediated) metal coordination to the substrate is required to afford this pathway, which renders this mechanism energetically infeasible. Instead, QM cluster models reveal that the preferred pathway involves direct Mg2+-O3' coordination to stabilize the charged substrate and assist leaving group departure, while H98 activates the water nucleophile. These calculations also underscore that both catalytic residues that directly interact with the substrate and secondary amino acids that position or stabilize these residues are required for efficient catalysis. QM/MM calculations on the solvated enzyme-DNA complex verify the preferred mechanism, which is fully consistent with experimental kinetic, structural, and mutational data. The fundamental understanding of the I-PpoI mechanism of action, gained from the present work can be used to further explore potential uses of this enzyme in biotechnology and medicine, and direct future computational investigations of other members of the understudied HE family.