Journal of Computer-Aided Molecular Design最新文献

The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py.

Graphical abstract

We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger’s force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree–Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol⁻¹), followed by B3LYP (0.69 kcal mol⁻¹) and the HF theories (0.81–1.0 kcal mol⁻¹). As regards the force fields, the lowest errors were observed for the Allinger’s force fields MM3-00 (1.28 kcal mol⁻¹), ΜΜ3-96 (1.40 kcal mol⁻¹) and the Halgren’s MMFF94 force field (1.30 kcal mol⁻¹) and then for the MM2-91 clones MMX (1.77 kcal mol⁻¹) and MM+ (2.01 kcal mol⁻¹) and MM4 (2.05 kcal mol⁻¹). The DREIDING (3.63 kcal mol⁻¹) and UFF (3.77 kcal mol⁻¹) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.

Graphical abstract

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

The treatment of various disorders of the central nervous system (CNS) is often impeded by the limited brain exposure of drugs, which is regulated by the human blood–brain barrier (BBB). The screening of lead compounds for CNS penetration is challenging due to the biochemical complexity of the BBB, while experimental determination of permeability is not feasible for all types of compounds. Here we present a novel method for rapid preclinical screening of libraries of compounds by utilizing advancements in computing hardware, with its foundation in transition-based counting of the flux. This method has been experimentally validated for in vitro permeabilities and provides atomic-level insights into transport mechanisms. Our approach only requires a single high-temperature simulation to rank a compound relative to a library, with a typical simulation time converging within 24 to 72 h. The method offers unbiased thermodynamic and kinetic information to interpret the passive transport of small-molecule drugs across the BBB.

Graphical abstract

Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10–20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.

Omicron derived lineages viz. BA.2, BA.3, BA.4 BA.5, BF.7 and XBBs show prominence with improved immune escape, transmissibility, infectivity, and pathogenicity in general. Sub-variants, XBB.1.5 and XBB.1.16 have shown rapid spread, with mutations embedded throughout the viral genome, including the spike protein. Changing atomic landscapes in spike contributes significantly to modulate host pathogen interactions and infections thereof. In the present work, we computationally analyzed the binding affinities of spike receptor binding domains (RBDs) of XBB.1.5 and XBB.1.16 towards human angiotensin-converting enzyme 2 (hACE2) compared to Omicron. We have employed simulations and binding energy estimation of molecular complexes of spike-hACE2 to assess the interplay of interaction pattern and effect of mutations if any in the binding mode of the RBDs of these novel mutants. We calculated the binding free energy (BFE) of the RBD of the Omicron, XBB.1.5 and XBB.1.16 spike protein to hACE2. We showed that XBB.1.5 and XBB.1.16 can bind to human cells more strongly than Omicron due to the increased charge of the RBD, which enhances the electrostatic interactions with negatively charged hACE2. The per-residue decompositions further show that the Asp339His, Asp405Asn and Asn460Lys mutations in the XBBs RBD play a crucial role in enhancing the electrostatic interactions, by acquiring positively charged residues, thereby influencing the formation/loss of interfacial bonds and thus strongly affecting the spike RBD-hACE2 binding affinity. Simulation results also indicate less interference of heterogeneous glycans of XBB.1.5 spike RBD towards binding to hACE2. Moreover, despite having less interaction at the three interfacial contacts between XBB S RBD and hACE2 compared to Omicron, variants XBB.1.5 and XBB.1.16 had higher total binding free energies (ΔG_bind) than Omicron due to the contribution of non-interfacial residues to the free energy, providing insight into the increased binding affinity of XBB1.5 and XBB.1.16. Furthermore, the presence of large positively charged surface patches in the XBBs act as drivers of electrostatic interactions, thus support the possibility of a higher binding affinity to hACE2.

Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.

In this article, we present PoseEdit, a new, interactive frontend of the popular pose visualization tool PoseView. PoseEdit automatically produces high-quality 2D diagrams of intermolecular interactions in 3D binding sites calculated from ligands in complex with protein, DNA, and RNA. The PoseView diagrams have been improved in several aspects, most notably in their interactivity. Thanks to the easy-to-use 2D editor of PoseEdit, the diagrams are extensively editable and extendible by the user, can be merged with other diagrams, and even be created from scratch. A large variety of graphical objects in the diagram can be moved, rotated, selected and highlighted, mirrored, removed, or even newly added. Furthermore, PoseEdit enables a synchronized 2D-3D view of macromolecule-ligand complexes simplifying the analysis of structural features and interactions. The representation of individual diagram objects regarding their visualized chemical properties, like stereochemistry, and general graphical styles, like the color of interactions, can additionally be edited. The primary objective of PoseEdit is to support scientists with an enhanced way to communicate ligand binding mode information through graphical 2D representations optimized with the scientist’s input in accordance with objective criteria and individual needs. PoseEdit is freely available on the ProteinsPlus web server (https://proteins.plus).

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

Owing to the emergence of antibiotic resistance, the polymyxin colistin has been recently revived to treat acute, multidrug-resistant Gram-negative bacterial infections. Positively charged colistin binds to negatively charged lipids and damages the outer membrane of Gram-negative bacteria. However, the MCR-1 protein, encoded by the mobile colistin resistance (mcr) gene, is involved in bacterial colistin resistance by catalysing phosphoethanolamine (PEA) transfer onto lipid A, neutralising its negative charge, and thereby reducing its interaction with colistin. Our preliminary results showed that treatment with a reference pyrazolone compound significantly reduced colistin minimal inhibitory concentrations in Escherichia coli expressing mcr-1 mediated colistin resistance (Hanpaibool et al. in ACS Omega, 2023). A docking-MD combination was used in an ensemble-based docking approach to identify further pyrazolone compounds as candidate MCR-1 inhibitors. Docking simulations revealed that 13/28 of the pyrazolone compounds tested are predicted to have lower binding free energies than the reference compound. Four of these were chosen for in vitro testing, with the results demonstrating that all the compounds tested could lower colistin MICs in an E. coli strain carrying the mcr-1 gene. Docking of pyrazolones into the MCR-1 active site reveals residues that are implicated in ligand–protein interactions, particularly E246, T285, H395, H466, and H478, which are located in the MCR-1 active site and which participate in interactions with MCR-1 in ≥ 8/10 of the lowest energy complexes. This study establishes pyrazolone-induced colistin susceptibility in E. coli carrying the mcr-1 gene, providing a method for the development of novel treatments against colistin-resistant bacteria.