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

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

Pathogenic Candida albicans yeasts frequently cause infections in hospitals. Antifungal drugs lose effectiveness due to other Candida species and resistance. New medications are thus required. Secreted aspartic protease of C. parapsilosis (Sapp1p) is a promising target. We have thus solved the crystal structures of Sapp1p complexed to four peptidomimetic inhibitors. Three potent inhibitors (Ki: 0.1, 0.4, 6.6 nM) resembled pepstatin A (Ki: 0.3 nM), a general aspartic protease inhibitor, in terms of their interactions with Sapp1p. However, the weaker inhibitor (Ki: 14.6 nM) formed fewer nonpolar contacts with Sapp1p, similarly to the smaller HIV protease inhibitor ritonavir (Ki: 1.9 µM), which, moreover, formed fewer H-bonds. The analyses have revealed the structural determinants of the subnanomolar inhibition of C. parapsilosis aspartic protease. Because of the high similarity between Saps from different Candida species, these results can further be used for the design of potent and specific Sap inhibitor-based antimycotic drugs.

• Keywords
• Inhibitor, crystal structure, hydrogen bonds, noncovalent interactions, peptidomimetics,
• MeSH
• Aspartic Acid Endopeptidases antagonists & inhibitors   metabolism   MeSH
• Candida parapsilosis enzymology   MeSH
• Fungal Proteins antagonists & inhibitors   metabolism   MeSH
• Protease Inhibitors chemical synthesis   chemistry   pharmacology   MeSH
• Models, Molecular MeSH
• Molecular Structure MeSH
• Peptidomimetics chemical synthesis   chemistry   pharmacology   MeSH
• Dose-Response Relationship, Drug MeSH
• Structure-Activity Relationship MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Aspartic Acid Endopeptidases MeSH
• Fungal Proteins MeSH
• Protease Inhibitors MeSH
• Peptidomimetics MeSH
• SAPP1 protein, Candida parapsilosis MeSH Browser

Influenza A virus (IAV) encodes a polymerase composed of three subunits: PA, with endonuclease activity, PB1 with polymerase activity and PB2 with host RNA five-prime cap binding site. Their cooperation and stepwise activation include a process called cap-snatching, which is a crucial step in the IAV life cycle. Reproduction of IAV can be blocked by disrupting the interaction between the PB2 domain and the five-prime cap. An inhibitor of this interaction called pimodivir (VX-787) recently entered the third phase of clinical trial; however, several mutations in PB2 that cause resistance to pimodivir were observed. First major mutation, F404Y, causing resistance was identified during preclinical testing, next the mutation M431I was identified in patients during the second phase of clinical trials. The mutation H357N was identified during testing of IAV strains at Centers for Disease Control and Prevention. We set out to provide a structural and thermodynamic analysis of the interactions between cap-binding domain of PB2 wild-type and PB2 variants bearing these mutations and pimodivir. Here we present four crystal structures of PB2-WT, PB2-F404Y, PB2-M431I and PB2-H357N in complex with pimodivir. We have thermodynamically analysed all PB2 variants and proposed the effect of these mutations on thermodynamic parameters of these interactions and pimodivir resistance development. These data will contribute to understanding the effect of these missense mutations to the resistance development and help to design next generation inhibitors.

• Keywords
• VX-787, antivirals, influenza A polymerase, pimodivir, resistance,
• MeSH
• Crystallography, X-Ray MeSH
• Quantum Theory MeSH
• Models, Molecular MeSH
• Mutation genetics   MeSH
• Mutant Proteins metabolism   MeSH
• Protein Subunits antagonists & inhibitors   chemistry   metabolism   MeSH
• Protein Domains MeSH
• Pyridines chemistry   pharmacology   MeSH
• Pyrimidines chemistry   pharmacology   MeSH
• Pyrroles chemistry   pharmacology   MeSH
• RNA-Dependent RNA Polymerase antagonists & inhibitors   chemistry   metabolism   MeSH
• Thermodynamics MeSH
• Drug Resistance, Viral drug effects   MeSH
• Viral Proteins antagonists & inhibitors   chemistry   metabolism   MeSH
• Influenza A virus drug effects   enzymology   MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Mutant Proteins MeSH
• PB2 protein, Influenzavirus A MeSH Browser
• pimodivir MeSH Browser
• Protein Subunits MeSH
• Pyridines MeSH
• Pyrimidines MeSH
• Pyrroles MeSH
• RNA-Dependent RNA Polymerase MeSH
• Viral Proteins MeSH