2.1 Application of active learning model in the screening of potential SQLE inhibitors
Leveraging an active learning model, we have significantly enhanced the precision of virtual screening protocols for the identification of SQLE inhibitors, methodically detailed in Figure 1. The evolved iterations of the model, as visualized in Figure 1A-D, demonstrate an upward trajectory in predictive accuracy, with R² values advancing from 0.52 to a commendable 0.68. This increment evidences the model's augmented capacity to discern bona fide inhibitors amidst a complex chemical library.In Figure 1E, the spatial distribution of the shortlisted inhibitors within the SQLE binding pocket underscores the model's acuity in pinpointing molecules that exhibit structural compatibility at the active site. Figure 1F offers an incisive interaction fingerprint analysis, elucidating the nature of the contacts between the inhibitors and the critical amino acid residues of SQLE. This analysis is instrumental for the prospective structural refinement of these molecular entities.
Extending this analysis, as shown in Table 1, we note that the selected compounds uniformly present a state penalty of zero, indicative of their unerring alignment with the target enzyme's active site sans promiscuous off-target interactions. Their docking scores oscillate between -10.036 to -11.644, decisively besting the benchmarks set by known inhibitors and signifying a potent binding affinity. The ligand strain energy metrics remain comfortably beneath the 10 kcal/mol ceiling, suggesting an innate propensity for these compounds to adopt a conformationally relaxed pose within the enzyme's pocket. Moreover, the MMGBSA dG Bind values, deeply ensconced in the negative spectrum from -60.47 to -73.52 kcal/mol, are profoundly indicative of thermodynamically stable complexes with the enzyme, particularly for compound 18775 which exhibits the most negative binding energy. This stark contrast with the MMGBSA values of established inhibitors, spanning from -49.18 to -60.66 kcal/mol, positions the screened compounds as potentially superior contenders, endowed with substantial binding free energy favoring stable and potent inhibition.
In summation, the compounds emerging from this enhanced computational screening process, armed with formidable docking scores, minimal ligand strain energies, and profoundly negative MMGBSA dG Bind values, stand poised to redefine efficacy in SQLE inhibition. They represent not only a leap forward in virtual screening outcomes but also a promising stride towards the discovery of new therapeutic leads that could surpass the inhibitory capabilities of current pharmacological agents.
2.2 Analysis of compound interactions and scoring in BPMD simulations
Binding Pose Metadynamics (BPMD), an advanced protocol harnessing automated and enhanced sampling based on metadynamics principles, effectively facilitates the dynamic exploration of ligand binding poses. This innovative approach has shown proficiency in distinguishing the accurate binding pose of ligands from less precise alternatives often generated in conventional docking studies. Utilizing BPMD, we analyzed the conformational stability of thirteen compounds over ten simulations, each lasting 10 nanoseconds. The collective variable root mean square deviation (CV RMSD) profiles focused on compounds maintaining stability within a 2 Å fluctuation threshold. The associated scoring metrics - PoseScore, PersScore, and CompScore - played a crucial role in refining the selection process. A lower PoseScore indicates enhanced stability, while a higher PersScore suggests the sustained presence of hydrogen bonds. CompScore, integrating these factors, identifies complexes with the highest stability.
Within this framework, compounds CMNPD8663, CMNPD8904, CMNPD11566, and CMNPD18677 distinguished themselves with optimal stability profiles, as shown in Figure 2A. CMNPD8663, as illustrated in Figure 2B, primarily relies on hydrophobic interactions within the SQLE receptor's pocket. Despite the absence of specific hydrogen bonds, these interactions form the cornerstone of the compound's affinity. CMNPD8904, depicted in Figure 2C, exhibits a similar pattern of hydrophobic interactions, with potential Pi-Pi stacking with Tyr-195 contributing to enhanced binding specificity. CMNPD11566, shown in Figure 2D, displays a more intricate interaction profile, combining hydrophobic contacts and hydrogen bonds with Tyr-195 and Leu-416, indicating a refined interaction that augments both stability and specificity. Finally, CMNPD18677 (Figure 2E) presents a multifaceted binding mode, incorporating extensive hydrophobic interactions, a salt bridge with Tyr-195, and hydrogen bonds with Leu-416 and Thr-417, indicative of high affinity and selectivity.
These observations provide a comprehensive understanding of the diverse interactions between ligands and the SQLE receptor, emphasizing the complex interplay of hydrophobic forces, hydrogen bonding, and salt bridge formation. Specifically, CMNPD11566 and CMNPD18677 exhibit advanced binding properties, suggesting their potential as effective SQLE inhibitors.
2.3 Evaluation of physicochemical properties and drug-likeness criteria for selected compounds
ADMET analysis serves as a cornerstone in drug development, offering vital insights into how a compound interacts with the body. This encompasses absorption, distribution, metabolism, excretion, and toxicity—factors that collectively dictate a compound's success as a drug.
Within the context of physicochemical properties, depicted in Figure 3A-D, the compounds CMNPD8663, CMNPD8904, CMNPD11566, and CMNPD18677 showcase distinct profiles. CMNPD8663 and CMNPD8904 display moderate physicochemical attributes conducive to good permeability and distribution. CMNPD11566 presents a particularly advantageous profile, with a lower molecular weight and topological polar surface area hinting at superior oral bioavailability. CMNPD18677, with its broader property spread, suggests a capacity for more complex interactions within biological systems.
Transitioning to drug-likeness, Figure 3E summarizes the compliance of these compounds with established drug-likeness rules. While CMNPD8663 falls short on several parameters, CMNPD8904 is aligned with Lipinski's Rule, indicating its potential as an orally available drug. Notably, CMNPD11566 stands out, conforming to all the applied rules, including Lipinski, Pfizer, GSK, and the Golden Triangle, marking it as the most promising candidate in terms of drug-likeness.
Focusing on CMNPD11566, which emerges as the front-runner in drug-likeness, its ADMET characteristics are explored in Supplementary Table 1. Although the Caco-2 permeability data suggest some absorption challenges, the MDCK permeability values offer a counterbalance, indicating the possibility of effective passive diffusion. The high plasma protein binding percentage points to a significant bound fraction in circulation, with only a small percentage available for active pharmacological effect. Metabolically, the compound engages with a variety of Cytochrome P450 enzymes, a factor that requires careful monitoring due to the potential for drug-drug interactions.
The clearance and half-life data of CMNPD11566 imply a moderate rate of elimination and a brief systemic presence, respectively, influencing dosing intervals. Toxicity predictions, though varied, signal the need for careful consideration of the compound's safety profile, especially in areas of genotoxic carcinogenicity and skin sensitization.
In conclusion, while the physicochemical properties of CMNPD11566 provide a favorable starting point, and its adherence to drug-likeness rules is exemplary, the comprehensive ADMET profile underscores the need for a nuanced approach to its development. Thorough preclinical studies will be essential to navigate its complex ADMET landscape and fully elucidate its therapeutic potential.
2.4 Molecular Dynamics Simulation and Biophysical Analysis of CMNPD11566 and Its Derivative HCJ007 in Interaction with SQLE
Molecular dynamics simulations are essential in analyzing the structure-activity relationships of drug candidates, providing critical insights into their interactions with target proteins. In the 1000 ns molecular dynamics simulation analysis of CMNPD11566, as illustrated in Figures 4A-C, we identified several potential limitations. Figure 4A displays the RMSD trajectories of CMNPD11566, with fluctuations ranging between 1 to 3 Å. Although this indicates relatively stable interactions with the SQLE enzyme, there is still room for further optimization. Figure 4B shows that while CMNPD11566 has high-frequency interactions with residues like Tyr-195 and Thr-417, it also exhibits extremely low interaction frequencies with residues such as Met-196 and Ile-197. This suggests that the structure-activity relationship of CMNPD11566 with SQLE could potentially be compromised by its inherent instability. Further, Figure 4C reveals the reasons behind many low-frequency interaction sites of CMNPD11566; notably, its alkyl chain fails to form stable interactions with residues in the pocket.
Given the mobility of CMNPD11566's alkyl chain, which could lead to binding stability issues, we deemed structural modification necessary. By carefully analyzing the interaction patterns of CMNPD11566 and incorporating structural insights from known target inhibitors Naftifine and Butenafine, we designed a new compound, HCJ007. This approach aimed to address the identified binding issues of CMNPD11566 while preserving its effective interactions with key residues.
Subsequent simulations of HCJ007, as depicted in Figures 4D-F, showed significant improvements in its dynamic characteristics. Figure 4D reveals a more stable RMSD for HCJ007, indicating enhanced binding conformation stability. Figure 4E shows that HCJ007 has increased interaction frequencies with key residues, and significantly fewer low-frequency interaction sites. Figure 4F further illustrates the detailed interaction patterns of HCJ007, revealing stronger and more consistent binding interactions compared to CMNPD11566, suggesting that it may have more effective binding and therapeutic action.
In orchestrating the development of HCJ007 as a potent therapeutic against SQLE, a series of intricate biophysical analyses were pivotal, each unraveling a layer of interaction dynamics and molecular stability. Throughout its modification process, HCJ007 consistently adhered to the four key drug-likeness rules, and notably, it displayed no PAINS (Pan-Assay Interference Compounds) alerts, as detailed in Supplementary Table 2. These insights are instrumental in sculpting HCJ007's design, focusing on enhancing its specificity and efficacy.
Initially, Figure 5A reveals the evolving affinity between HCJ007 and SQLE through the lens of MMGBSA binding free energy. The trend of deepening negative values, averaging at -124.48 kcal/mol and bottoming out at -151.82 kcal/mol, illustrates an intensifying molecular interaction. This trend is not a mere data trajectory but underscores HCJ007's potential as a high-affinity inhibitor, robustly engaging with SQLE.Building upon this overarching energy landscape, Figure 5B delves into the molecular intricacies, mapping the contribution of individual residues. The marked negative contributions of residues like Asn-193 and Asn-382 transcend being mere numerical values; they are pivotal in stabilizing the HCJ007-SQLE complex, guiding the precision in drug design and optimization.
Transitioning to the atomic scale, Figure 5C offers insights into HCJ007's internal dynamics. Here, the RMSF increment starting from atom 16 suggests a deliberate retention of flexibility within the alkyl chain, essential for maintaining an adaptable binding conformation.This narrative of internal mobility and conformational adaptability is further complemented by Figure 5D's torsion angle analysis. The minimal fluctuations in torsion angles lend credence to the structural integrity of the alkyl chain, ensuring its functional alignment during the binding interaction.
Segueing into the realm of molecular stability and interface dynamics, Figures 5E and 5F together illustrate a cohesive story. Figure 5E showcases the consistent pattern of intra-molecular hydrogen bonding, a hallmark of molecular stability and functional integrity. The steadiness punctuated by transient spikes mirrors the compound's ability to dynamically respond to structural shifts within the complex, indicating a resilient yet adaptable binding profile. Complementing this, Figure 5F shifts the focus to the broader interface properties like SASA, MolSA, and PSA. Their concurrent trends, subtly modulating the hydrophobic-hydrophilic balance, narrate an intricate tale of the molecular interface. These forces are not merely coexisting; they are intricately intertwined, dictating the binding dynamics and stability of the molecular complex.
In totality, these analytical vignettes paint a composite picture of HCJ007 as a meticulously tuned compound for efficacy. Each atomic and molecular interaction within HCJ007 is choreographed to ensure synergistic engagement with SQLE, setting the stage for HCJ007's evolution as a promising therapeutic agent.