Journal of Chemical Information and Modeling 最新文献

Cardiotoxicity refers to the inhibitory effects of drugs on cardiac ion channels. Accurate prediction of cardiotoxicity is crucial yet challenging, as it directly impacts the evaluation of cardiac drug efficacy and safety. Numerous methods have been developed to predict cardiotoxicity, yet their performance remains limited. A key limitation is that these methods often rely solely on single-modal data, making multimodal data integration challenging. As a result, we present a multimodal method integrating molecular SMILES, structure, and fingerprint to enhance cardiotoxicity prediction. First, we designed a fusion layer to unify representations from different modalities. During training, the model maximizes intramodal similarity for the same molecule while minimizing intermolecular similarity, ensuring consistent cross-modal representations. This study evaluates the inhibitory effects of candidate drugs on voltage-gated potassium (hERG), sodium (Nav1.5), and calcium (Cav1.2) channels. Experimental results demonstrate that the proposed model significantly outperforms existing state-of-the-art methods in cardiotoxicity prediction. We anticipate that this model will contribute significantly to the development and safety evaluation of cardiac drugs, reducing cardiotoxicity-related risks.

A total of 1311 homodimers were collected and analyzed in three different categories to highlight the impact of lipid environment and secondary structure type: 422 cytoplasmic α-helix, 411 cytoplasmic β-strand, and 478 membrane complexes. Structural features of the interface connecting two monomers were investigated and compared to those of the non-interface surface. Every residue on the surface of each monomer was explored based on four attributes: solvent-accessible surface area (SASA), protrusion index (C_x), surface planarity, and surface roughness. SASA and C_x distribution profiles clearly distinguished the interface from the surface in all categories, where the rim of the interface displayed higher SASA and C_x values than the rest of the surface. Surface residues in membrane complexes protruded less than cytoplasmic ones due to the hydrophobic environment, and consequently, the difference between surface and interface residues became less noticeable in that category. Cytoplasmic β-strand complexes displayed markedly lower SASA at the interface core than at the surface. The major distinction between the surface and interface was achieved through surface roughness, which displayed significantly higher values for the interface than the surface, especially in cytoplasmic complexes. Clearly, a surface which is relatively rugged favors the association of two monomers through multiple van der Waals interactions and hydrogen-bond formations. Another structural descriptor with strong distinguishing ability was surface planarity, which was higher at the interface than at the non-interface surface. Surface flatness would eventually facilitate the interconnectedness of an interface with a network of residue pairs bridging two complementary surfaces. Analysis of contact pairs revealed that hydrophobic pairs have the highest frequency of occurrence in the lipid environment of membrane complexes. However, despite the scarcity of polar residues at the interface, the likelihood of observing a contact between polar residues was markedly higher than that of hydrophobic ones.

The energetic basis for the enhanced PAM (protospacer adjacent motif) readability in engineered Cas9-NG (a variant of Cas9 from Streptococcus pyogenes (SpCas9)) with seven mutations: (R1335V, E1219F, D1135V, L1111R, T1337R, G1218R, and A1322R) remains a fundamental unsolved problem. Utilizing the X-ray structure of the precatalytic complex (SpCas9:sgRNA:dsDNA) as a template, we calculated the changes in PAM (TGG, TGA, TGT, or TGC) binding affinity (ΔΔG) associated with each of the seven mutations in SpCas9 through rigorous alchemical simulations (sampling ∼ 53 μs). The underlying thermodynamics (ΔΔG) accounts for the experimentally observed differences in DNA cleavage activity between SpCas9 and Cas9-NG across various DNA substrates. The interaction energies between SpCas9 and DNA are significantly influenced by the type and location of the amino acid mutations. Notably, the R1335V mutation disfavors DNA binding by disrupting critical interactions with the PAM. However, the destabilizing effect of the R1335V mutation is mitigated by four advantageous mutations (E1219F, D1135V, L1111R, and T1337R), which primarily introduce nonbase-specific interactions and enhance PAM readability. The hydrophobic substitutions (E1219F and D1135V) are particularly impactful, as they exclude solvent from the PAM binding pocket, strengthening electrostatic interactions in the low dielectric medium and increasing the stability of the noncognate PAM complexes by ∼2-5 kcal/mol. Additionally, L1111R and T1337R facilitate DNA binding by forming direct electrostatic contacts. In contrast, the charge mutations G1218R and A1322R do not effectively promote interactions with the negatively charged DNA, clearly demonstrating that the location of mutations is crucial in shaping these interaction energetics. We demonstrated that stabilization of the Cas9-NG: noncognate PAM complexes enables broader PAM recognition. This is primarily achieved through two mechanisms: (1) the establishment of new nonbase-specific interactions between the protein and nucleotides and (2) the enhancement of electrostatic interactions within a relatively dry and hydrophobic pocket. The findings revealed that mutation-induced desolvation can improve the recognition of noncognate PAMs, paving the way for the rational and innovative design of SpCas9 mutants.

Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interactions. MLPs continue to advance and have had profound impact in applications that include drug discovery, enzyme catalysis, and materials design. The current landscape of MLP software presents challenges due to the limited interoperability between packages, which can lead to inconsistent benchmarking practices and necessitates separate interfaces with molecular dynamics (MD) software. To address these issues, we present DeePMD-GNN, a plugin for the DeePMD-kit framework that extends its capabilities to support external graph neural network (GNN) potentials.DeePMD-GNN enables the seamless integration of popular GNN-based models, such as NequIP and MACE, within the DeePMD-kit ecosystem. Furthermore, the new software infrastructure allows GNN models to be used within combined quantum mechanical/molecular mechanical (QM/MM) applications using the range corrected ΔMLP formalism.We demonstrate the application of DeePMD-GNN by performing benchmark calculations of NequIP, MACE, and DPA-2 models developed under consistent training conditions to ensure fair comparison.

Stacking interactions contribute significantly to the interaction of small molecules with RNA, and harnessing the power of these interactions will likely prove important in the development of RNA-targeting inhibitors. To this end, we present a comprehensive computational analysis of stacking interactions between a set of 54 druglike heterocycles and the natural nucleobases. We first show that heterocycle choice can tune the strength of stacking interactions with nucleobases over a large range and that heterocycles favor stacked geometries that cluster around a discrete set of stacking loci characteristic of each nucleobase. Symmetry-adapted perturbation theory results indicate that the strengths of these interactions are modulated primarily by electrostatic and dispersion effects. Based on this, we present a multivariate predictive model of the maximum strength of stacking interactions between a given heterocycle and nucleobase that depends on molecular descriptors derived from the electrostatic potential. These descriptors can be readily computed using density functional theory or predicted directly from atom connectivity (e.g., SMILES). This model is used to predict the maximum possible stacking interactions of a set of 1854 druglike heterocycles with the natural nucleobases. Finally, we show that trivial modifications of standard (fixed-charge) molecular mechanics force fields reduce errors in predicted stacking interaction energies from around 2 kcal/mol to below 1 kcal/mol, providing a pragmatic means of predicting more reliable stacking interaction energies using existing computational workflows. We also analyze the stacking interactions between ribocil and a bacterial riboswitch, showing that two of the three aromatic heterocyclic components engage in near-optimal stacking interactions with binding site nucleobases.

A total of 1311 homodimers were collected and analyzed in three different categories to highlight the impact of lipid environment and secondary structure type: 422 cytoplasmic α-helix, 411 cytoplasmic β-strand, and 478 membrane complexes. Structural features of the interface connecting two monomers were investigated and compared to those of the non-interface surface. Every residue on the surface of each monomer was explored based on four attributes: solvent-accessible surface area (SASA), protrusion index (C_x), surface planarity, and surface roughness. SASA and C_x distribution profiles clearly distinguished the interface from the surface in all categories, where the rim of the interface displayed higher SASA and C_x values than the rest of the surface. Surface residues in membrane complexes protruded less than cytoplasmic ones due to the hydrophobic environment, and consequently, the difference between surface and interface residues became less noticeable in that category. Cytoplasmic β-strand complexes displayed markedly lower SASA at the interface core than at the surface. The major distinction between the surface and interface was achieved through surface roughness, which displayed significantly higher values for the interface than the surface, especially in cytoplasmic complexes. Clearly, a surface which is relatively rugged favors the association of two monomers through multiple van der Waals interactions and hydrogen-bond formations. Another structural descriptor with strong distinguishing ability was surface planarity, which was higher at the interface than at the non-interface surface. Surface flatness would eventually facilitate the interconnectedness of an interface with a network of residue pairs bridging two complementary surfaces. Analysis of contact pairs revealed that hydrophobic pairs have the highest frequency of occurrence in the lipid environment of membrane complexes. However, despite the scarcity of polar residues at the interface, the likelihood of observing a contact between polar residues was markedly higher than that of hydrophobic ones.

As molecular modeling gains ever more prominence in understanding cellular processes, high quality visualization of models and dynamics has never been more important. Naturally, much molecular visualization software is written to enable the visualization of atomic level details in structures. While necessary, this means that visualization of increasingly popular coarse-grained (CG) models remains a challenge. Here, we present a Python package, MartiniGlass, that facilitates the visualization of systems simulated with the widely used CG Martini force field using the popular visualization package VMD. MartiniGlass rapidly processes molecular topologies and accounts for important topological features at CG resolution, such as secondary structure restraints, preparing them for easy visualization of simulated trajectories.

We present the Alchemical Transfer with Coordinate Swapping (ATS) method to enable the calculation of the relative binding free energies between large congeneric ligands and single-point mutant peptides to protein receptors with the Alchemical Transfer Method (ATM) framework. Similarly to ATM, the new method implements the alchemical transformation as a coordinate transformation and works with any unmodified force fields and standard chemical topologies. Unlike ATM, which transfers whole ligands in and out of the receptor binding site, ATS limits the magnitude of the alchemical perturbation by transferring only the portion of the molecules that differ between the bound and unbound ligands. The common region of the two ligands, which can be arbitrarily large, is unchanged and does not contribute to the magnitude and statistical fluctuations of the perturbation energy. Internally, the coordinates of the atoms of the common regions are swapped to maintain the integrity of the covalent bonding data structures of the OpenMM molecular dynamics engine. The work successfully validates the method on protein–ligand and protein-peptide RBFE benchmarks. This advance paves the road for the application of the relative binding free energy Alchemical Transfer Method protocol to study the effect of protein and nucleic acid mutations on the binding affinity and specificity of macromolecular complexes.

Brunner syndrome is a rare genetic disorder characterized by impulsive aggressiveness and intellectual disability, which is linked to impaired function of the monoamine oxidase A (MAO-A) enzyme. Patients with specific point mutations in the MAOA gene have been reported to exhibit these symptoms, along with notably elevated serotonin levels, which suggest a decreased catalytic performance of the mutated MAO-A enzymes. In this study, we present multiscale molecular simulations focusing on the rate-limiting step of MAO-A-catalyzed serotonin degradation for the C266F and V244I variants that are reportedly associated with pathologies characteristic of the Brunner syndrome. We found that the C266F mutation causes an approximately 18,000-fold slowdown of enzymatic function, which is equivalent to a MAOA gene knockout. For the V244I mutant, a somewhat smaller, yet still significant 300-fold slowdown has been estimated. Furthermore, we conducted a comprehensive comparison of the impact of enzyme electrostatics on the catalytic function of the wild-type (WT) MAO-A and both aforementioned mutants (C266F and V244I), as well as on the E446K mutant investigated in one of our earlier studies. The results have shown that the mutation induces a noteworthy change in electrostatic interactions between the reacting moiety and its enzymatic surroundings, leading to a decreased catalytic performance in all of the considered MAO-A variants. An analysis of mutation effects supported by geometry comparison of mutants and the wild-type enzyme at a residue level suggests that a principal driving force behind the altered catalytic performance of the mutants is subtle structural changes scattered along the entire enzyme. These shifts in geometry also affect domains most relevant to catalysis, where structural offsets of few tenths of an Å can significantly change contribution to the barrier of the involved residues. These results are in full agreement with the reasoning derived from clinical observations and biochemical data. Our research represents a step forward in the attempts of using fundamental principles of chemical physics in order to explain genetically driven pathologies. In addition, our results support the view that the catalytic function of enzymes is crucially driven by electrostatic interactions.

In silico drug prioritization may be a promising and time-saving strategy to identify potential drugs, standing as a faster and more cost-effective approach than de novo approaches. In recent years, artificial intelligence has greatly evolved the drug development process. Here, we present a novel computational framework for drug prioritization, labyrinth, designed to simulate human knowledge retrieval and inference to identify potential drug candidates for each disease. With the integration of up-to-date clinical trials, literature co-occurrences, drug-target interactions, and disease similarities, our framework achieves over 90% predictive accuracy across clinical trial phases and strong alignment with clinical practice in TCGA cohorts. We have demonstrated effectiveness across 20 different disease categories with robust ROC-AUC metrics and the balance between predictive accuracy and model interpretability. We further demonstrate its effectiveness at both the population and the individual levels. This study not only demonstrates the capacity for its drug prioritization but underscores the importance of aligning computational models with intuitive human reasoning. We have wrapped the core function into an R package named labyrinth, which is freely available on GitHub under the GPL-v2 license (https://github.com/hanjunwei-lab/labyrinth).