Nejvíce citovaný článek - PubMed ID 38321025
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes
Single-structure scoring functions have been considered inferior to expensive ensemble free energy methods in predicting protein-ligand affinities. We are revisiting this dogma with the recently developed semiempirical quantum-mechanical (SQM)-based scoring function, SQM2.20, comparing its performance to the standard scoring functions on one hand and state-of-the-art molecular dynamics (MD)-based free-energy methods on the other hand. The comparison is conducted on a well-established Wang data set comprising eight protein targets with 200 ligands. The initial low correlation of SQM2.20 scores with the experimental binding affinities of R2 = 0.21 was improved to R2 = 0.47 by a systematic refinement of the input structures and omission of the ligand deformation energy. Consequently, SQM2.20 representing accurate single-structure scoring functions, exhibited an average performance comparable to that of MD-based methods (R2 = 0.52) and surpassed the performance of standard scoring functions (R2 = 0.26). The per-target analysis highlighted the pivotal role of high-quality input structures on the outcomes of single-structure methods. In the instances where such structures are available, SQM2.20 scoring has been shown to rival or even exceed MD-based methods in predicting protein-ligand binding affinities, while exhibiting significantly reduced computation time.
The quantitative characterization of residue contributions to protein-protein binding across extensive flexible interfaces poses a significant challenge for biophysical computations. It is attributable to the inherent imperfections in the experimental structures themselves, as well as to the lack of reliable computational tools for the evaluation of all types of noncovalent interactions. This study leverages recent advancements in semiempirical quantum-mechanical and implicit solvent approaches embodied in the PM6-D3H4S/COSMO2 method for the development of a hierarchical computational protocols encompassing molecular dynamics, fragmentation, and virtual glycine scan techniques for the investigation of flexible protein-protein interactions. As a model, the binding of insulin to its receptor is selected, a complex and dynamic process that has been extensively studied experimentally. The interaction energies calculated at the PM6-D3H4S/COSMO2 level in ten molecular dynamics snapshots did not correlate with molecular mechanics/generalized Born interaction energies because only the former method is able to describe nonadditive effects. This became evident by the examination of the energetics in small-model dimers featuring all the present types of noncovalent interactions with respect to DFT-D3 calculations. The virtual glycine scan has identified 15 hotspot residues on insulin and 15 on the insulin receptor, and their contributions have been quantified using PM6-D3H4S/COSMO2. The accuracy and credibility of the approach are further supported by the fact that all the insulin hotspots have previously been detected by biochemical and structural methods. The modular nature of the protocol has enabled the formulation of several variants, each tailored to specific accuracy and efficiency requirements. The developed computational strategy is firmly rooted in general biophysical chemistry and is thus offered as a general tool for the quantification of interactions across relevant flexible protein-protein interfaces.
- MeSH
- inzulin metabolismus chemie MeSH
- konformace proteinů MeSH
- lidé MeSH
- receptor inzulinu * chemie metabolismus MeSH
- simulace molekulární dynamiky * MeSH
- termodynamika MeSH
- vazba proteinů MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- inzulin MeSH
- receptor inzulinu * MeSH
Macrocyclic inhibitors have emerged as a privileged scaffold in medicinal chemistry, offering enhanced selectivity, stability, and pharmacokinetic profiles compared to their linear counterparts. Here, we describe a novel, on-resin macrocyclization strategy for the synthesis of potent inhibitors targeting the secreted protease Major Aspartyl Peptidase 1 in Cryptococcus neoformans, a pathogen responsible for life-threatening fungal infections. By employing diverse aliphatic linkers and statine-based transition-state mimics, we constructed a focused library of 624 macrocyclic compounds. Screening identified several subnanomolar inhibitors with desirable pharmacokinetic and antifungal properties. Lead compound 25 exhibited a Ki of 180 pM, significant selectivity against host proteases, and potent antifungal activity in culture. The streamlined synthetic approach not only yielded drug-like macrocycles with potential in antifungal therapy but also provided insights into structure-activity relationships that can inform broader applications of macrocyclization in drug discovery.
- MeSH
- antifungální látky * farmakologie chemie chemická syntéza farmakokinetika MeSH
- Cryptococcus neoformans * účinky léků enzymologie MeSH
- inhibitory proteas * farmakologie chemie chemická syntéza farmakokinetika MeSH
- lidé MeSH
- makrocyklické sloučeniny * farmakologie chemie chemická syntéza farmakokinetika MeSH
- mikrobiální testy citlivosti MeSH
- vztahy mezi strukturou a aktivitou MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- antifungální látky * MeSH
- inhibitory proteas * MeSH
- makrocyklické sloučeniny * MeSH
Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction. The method demonstrates superior performance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML's accuracy and robustness. Its practical application is facilitated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml.
- Publikační typ
- časopisecké články MeSH
The use of quantum mechanical potentials in protein-ligand affinity prediction is becoming increasingly feasible with growing computational power. To move forward, validation of such potentials on real-world challenges is necessary. To this end, we have collated an extensive set of over a thousand galectin inhibitors with known affinities and docked them into galectin-3. The docked poses were then used to systematically evaluate several modern force fields and semiempirical quantum mechanical (SQM) methods up to the tight-binding level under consistent computational workflow. Implicit solvation models available with the tested methods were used to simulate solvation effects. Overall, the best methods in this study achieved a Pearson correlation of 0.7-0.8 between the computed and experimental affinities. There were differences between the tested methods in their ability to rank ligands across the entire ligand set as well as within subsets of structurally similar ligands. A major discrepancy was observed for a subset of ligands that bind to the protein via a halogen bond, which was clearly challenging for all the tested methods. The inclusion of an entropic term calculated by the rigid-rotor-harmonic-oscillator approximation at SQM level slightly worsened correlation with experiment but brought the calculated affinities closer to experimental values. We also found that the success of the prediction strongly depended on the solvation model. Furthermore, we provide an in-depth analysis of the individual energy terms and their effect on the overall prediction accuracy.
- MeSH
- galektiny * metabolismus chemie antagonisté a inhibitory MeSH
- kvantová teorie * MeSH
- ligandy MeSH
- simulace molekulového dockingu MeSH
- vazba proteinů MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- galektiny * MeSH
- ligandy MeSH
