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.

• MeSH
• Protein Conformation MeSH
• Quantum Theory * MeSH
• Ligands MeSH
• Proteins * chemistry   metabolism   MeSH
• Molecular Dynamics Simulation * MeSH
• Thermodynamics MeSH
• Protein Binding MeSH
• Publication type
• Journal Article MeSH
• Comparative Study MeSH
• Names of Substances
• Ligands MeSH
• Proteins * MeSH

Proton transfer reactions are among the most common chemical transformations and are central to enzymatic catalysis and bioenergetic processes. Their mechanisms are often investigated using DFT or approximate quantum chemical methods, whose accuracy directly impacts the reliability of the simulations. Here, a comprehensive set of semiempirical molecular orbital and tight-binding DFT approaches, along with recently developed machine learning (ML) potentials, are benchmarked against high-level MP2 reference data for a curated set of proton transfer reactions representative of biochemical systems. Relative energies, geometries, and dipole moments are evaluated for isolated reactions. Microsolvated reactions are also simulated using a hybrid QM/MM partition. Traditional DFT methods offer high accuracy in general but show markedly larger deviations for proton transfers involving nitrogen-containing groups. Among approximate models, RM1, PM6, PM7, DFTB2-NH, DFTB3, and GFN2-xTB show reasonable accuracy across properties, though their performance varies by chemical group. The ML-corrected (Δ-learning) model PM6-ML improves accuracy for all properties and chemical groups and transfers well to QM/MM simulations. Conversely, standalone ML potentials perform poorly for most reactions. These results provide a basis for evaluating approximate methods and selecting potentials for proton transfer simulations in complex environments.

• Publication type
• Journal Article MeSH

The cation-independent mannose-6-phosphate/IGF2 receptor (CI-M6P/IGF2R) plays a crucial role in transporting lysosomal enzymes and other ligands. In this study, we designed and synthesized novel stable mannose-6-phosphate (M6P) derivatives to enhance their affinity for CI-M6P/IGF2R. To evaluate the binding potency, we employed a sensitive and cost-effective fluorescence polarization assay, enabling rapid quantification of receptor-ligand interactions in solution. The tested compounds included di-, tri-, and penta-M6P peptides along with various M6P-derived small molecules featuring phosphate isosteres or other functional modifications. Our findings indicate that ligands bearing multiple M6P moieties exhibit significantly higher receptor affinities than monomeric compounds and that phosphonate groups may serve as a more stable and potent alternative to native M6P. Computational modeling of ligand interactions with the CI-M6P/IGF2R domains further elucidated the binding mechanisms, offering new directions for the development of more effective ligands. This study advances the design of therapeutic strategies that leverage CI-M6P/IGF2R for targeted biomolecule delivery to lysosomes, thereby opening new possibilities for biomedical applications.

• Keywords
• IGF2, Mannose‐6‐phosphate, fluorescence polarization assay, ligand binding, receptor,
• MeSH
• Fluorescence Polarization MeSH
• Humans MeSH
• Ligands MeSH
• Lysosomes metabolism   MeSH
• Mannosephosphates * chemistry   metabolism   chemical synthesis   MeSH
• Drug Design MeSH
• Receptor, IGF Type 2 * metabolism   chemistry   MeSH
• Protein Binding MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• IGF2R protein, human MeSH Browser
• Ligands MeSH
• Mannosephosphates * MeSH
• mannose-6-phosphate MeSH Browser
• Receptor, IGF Type 2 * MeSH

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
• Insulin metabolism   chemistry   MeSH
• Protein Conformation MeSH
• Humans MeSH
• Receptor, Insulin * chemistry   metabolism   MeSH
• Molecular Dynamics Simulation * MeSH
• Thermodynamics MeSH
• Protein Binding MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Insulin MeSH
• Receptor, Insulin * 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
• Antifungal Agents * pharmacology   chemistry   chemical synthesis   pharmacokinetics   MeSH
• Cryptococcus neoformans * drug effects   enzymology   MeSH
• Protease Inhibitors * pharmacology   chemistry   chemical synthesis   pharmacokinetics   MeSH
• Humans MeSH
• Macrocyclic Compounds * pharmacology   chemistry   chemical synthesis   pharmacokinetics   MeSH
• Microbial Sensitivity Tests MeSH
• Structure-Activity Relationship MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Antifungal Agents * MeSH
• Protease Inhibitors * MeSH
• Macrocyclic Compounds * 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.

• Publication type
• Journal Article 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
• Galectins * metabolism   chemistry   antagonists & inhibitors   MeSH
• Quantum Theory * MeSH
• Ligands MeSH
• Molecular Docking Simulation MeSH
• Protein Binding MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Galectins * MeSH
• Ligands MeSH

Schistosomiasis, caused by a parasitic blood fluke of the genus Schistosoma, is a global health problem for which new chemotherapeutic options are needed. We explored the scaffold of gallinamide A, a natural peptidic metabolite of marine cyanobacteria that has previously been shown to inhibit cathepsin L-type proteases. We screened a library of 19 synthetic gallinamide A analogs and identified nanomolar inhibitors of the cathepsin B-type protease SmCB1, which is a drug target for the treatment of schistosomiasis mansoni. Against cultured S. mansoni schistosomula and adult worms, many of the gallinamides generated a range of deleterious phenotypic responses. Imaging with a fluorescent-activity-based probe derived from gallinamide A demonstrated that SmCB1 is the primary target for gallinamides in the parasite. Furthermore, we solved the high-resolution crystal structures of SmCB1 in complex with gallinamide A and its two analogs and describe the acrylamide covalent warhead and binding mode in the active site. Quantum chemical calculations evaluated the contribution of individual positions in the peptidomimetic scaffold to the inhibition of the target and demonstrated the importance of the P1' and P2 positions. Our study introduces gallinamides as a powerful chemotype that can be exploited for the development of novel antischistosomal chemotherapeutics.

• Keywords
• Schistosoma mansoni, acrylamide inhibitor, cathepsin B, cysteine protease, drug target, parasite,
• MeSH
• Cathepsin B * antagonists & inhibitors   metabolism   MeSH
• Crystallography, X-Ray MeSH
• Models, Molecular MeSH
• Schistosoma mansoni * enzymology   drug effects   MeSH
• Schistosomicides pharmacology   chemistry   MeSH
• Protein Binding MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Cathepsin B * MeSH
• Schistosomicides MeSH

Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization.

• MeSH
• Ligands MeSH
• Proteins * metabolism   MeSH
• Drug Design * MeSH
• Thermodynamics MeSH
• Protein Binding MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Ligands MeSH
• Proteins * MeSH

Cathepsin K (CatK) is a target for the treatment of osteoporosis, arthritis, and bone metastasis. Peptidomimetics with a cyanohydrazide warhead represent a new class of highly potent CatK inhibitors; however, their binding mechanism is unknown. We investigated two model cyanohydrazide inhibitors with differently positioned warheads: an azadipeptide nitrile Gü1303 and a 3-cyano-3-aza-β-amino acid Gü2602. Crystal structures of their covalent complexes were determined with mature CatK as well as a zymogen-like activation intermediate of CatK. Binding mode analysis, together with quantum chemical calculations, revealed that the extraordinary picomolar potency of Gü2602 is entropically favoured by its conformational flexibility at the nonprimed-primed subsites boundary. Furthermore, we demonstrated by live cell imaging that cyanohydrazides effectively target mature CatK in osteosarcoma cells. Cyanohydrazides also suppressed the maturation of CatK by inhibiting the autoactivation of the CatK zymogen. Our results provide structural insights for the rational design of cyanohydrazide inhibitors of CatK as potential drugs.

• Keywords
• Cathepsin K, azadipeptide nitrile, cyanohydrazide warhead, protease inhibitor, structure,
• MeSH
• Hydrazines chemistry   pharmacology   MeSH
• Protease Inhibitors chemistry   pharmacology   MeSH
• Cathepsin K antagonists & inhibitors   metabolism   MeSH
• Humans MeSH
• Models, Molecular MeSH
• Molecular Structure MeSH
• Tumor Cells, Cultured MeSH
• Nitriles chemistry   pharmacology   MeSH
• Dose-Response Relationship, Drug MeSH
• Structure-Activity Relationship MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• CTSK protein, human MeSH Browser
• Hydrazines MeSH
• Protease Inhibitors MeSH
• Cathepsin K MeSH
• Nitriles MeSH