Journal of Chemical Information and Modeling 最新文献

Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture richer protein and drug features, leading to improved DTA prediction performance. However, existing methods often neglect to incorporate valuable protein cavity information, a key aspect of protein physical chemistry. This study addresses this gap by proposing a novel topology-enhanced GNN for DTA prediction that integrates protein pocket data. Additionally, we optimize training and message-passing strategies to enhance the model's feature representation capabilities. Our model's effectiveness is validated on the Davis and KIBA data sets, demonstrating its ability to capture the intricate interplay between drugs and targets. The source code is publicly available on https://github.com/ZZDXgangwu/DTA.

The Gibbs energy of binding (absolute binding free energy, ABFE) of a drug to proteins in the body determines the drug's affinity to its molecular target and its selectivity. ABFE is challenging to measure, and experimental values are not available for many proteins together with potential drugs and other molecules that bind them. Accurate means of calculating such values are, therefore, highly in demand. Realizing that toxicity and side effects are closely related to off-target binding, here we calculate the ABFE of two drugs, each to multiple proteins, in order to examine whether it is possible to carry out such calculations and achieve the required accuracy. The methods that were used were free energy perturbation with replica exchange molecular dynamics (FEP/REMD) and density functional theory (DFT) with a cluster approach and a simplified model. DFT calculations were supplemented with energy decomposition analysis (EDA). The accuracy of each method is discussed, and suggestions are made for the approach toward better ABFE calculations.

The Gibbs energy of binding (absolute binding free energy, ABFE) of a drug to proteins in the body determines the drug’s affinity to its molecular target and its selectivity. ABFE is challenging to measure, and experimental values are not available for many proteins together with potential drugs and other molecules that bind them. Accurate means of calculating such values are, therefore, highly in demand. Realizing that toxicity and side effects are closely related to off-target binding, here we calculate the ABFE of two drugs, each to multiple proteins, in order to examine whether it is possible to carry out such calculations and achieve the required accuracy. The methods that were used were free energy perturbation with replica exchange molecular dynamics (FEP/REMD) and density functional theory (DFT) with a cluster approach and a simplified model. DFT calculations were supplemented with energy decomposition analysis (EDA). The accuracy of each method is discussed, and suggestions are made for the approach toward better ABFE calculations.

Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture richer protein and drug features, leading to improved DTA prediction performance. However, existing methods often neglect to incorporate valuable protein cavity information, a key aspect of protein physical chemistry. This study addresses this gap by proposing a novel topology-enhanced GNN for DTA prediction that integrates protein pocket data. Additionally, we optimize training and message-passing strategies to enhance the model’s feature representation capabilities. Our model’s effectiveness is validated on the Davis and KIBA data sets, demonstrating its ability to capture the intricate interplay between drugs and targets. The source code is publicly available on https://github.com/ZZDXgangwu/DTA.

Computational predictions of biomolecular structure via artificial intelligence (AI) based approaches, as exemplified by AlphaFold software, have the potential to model of all life's biomolecules. We performed oligonucleotide structure prediction and gauged the accuracy of the AI-generated models via their agreement with experimental solution-state observables. We find parts of these models in good agreement with experimental data, and others falling short of the ground truth. The latter include internal or capping loops, noncanonical base pairings, and regions involving conformational flexibility, all essential for RNA folding, interactions, and function. We estimate root-mean-square (r.m.s.) errors in predicted nucleotide bond vector orientations ranging between 7° and 30°, with higher accuracies for simpler architectures of individual canonically paired helical stems. These mixed results highlight the necessity of experimental validation of AI-based oligonucleotide model predictions and their current tendency to mimic the training data set rather than reproduce the underlying reality.

Computational predictions of biomolecular structure via artificial intelligence (AI) based approaches, as exemplified by AlphaFold software, have the potential to model of all life’s biomolecules. We performed oligonucleotide structure prediction and gauged the accuracy of the AI-generated models via their agreement with experimental solution-state observables. We find parts of these models in good agreement with experimental data, and others falling short of the ground truth. The latter include internal or capping loops, noncanonical base pairings, and regions involving conformational flexibility, all essential for RNA folding, interactions, and function. We estimate root-mean-square (r.m.s.) errors in predicted nucleotide bond vector orientations ranging between 7° and 30°, with higher accuracies for simpler architectures of individual canonically paired helical stems. These mixed results highlight the necessity of experimental validation of AI-based oligonucleotide model predictions and their current tendency to mimic the training data set rather than reproduce the underlying reality.

All-atom molecular dynamics (MD) simulations offer crucial insights into biomolecular dynamics, but inherent time scale constraints often limit their effectiveness. Advanced sampling techniques help overcome these limitations, enabling predictions of deeply rugged folding free energy surfaces (FES) of RNA at atomistic resolution. The Multithermal-Multiumbrella On-the-Fly Probability Enhanced Sampling (MM-OPES) method, which combines temperature and collective variables (CVs) to accelerate sampling, has shown promise and cost-effectiveness. However, the applications have so far been limited to simpler RNA systems, such as stem-loops. In this study, we optimized the MM-OPES method to explore the FES of an H-type RNA pseudoknot, a more complex fundamental RNA folding unit. Through systematic exploration of CV combinations and temperature ranges, we identified an optimal strategy for both sampling and analysis. Our findings demonstrate that treating the native-like contacts in two stems as independent CVs and using a temperature range of 300-480 K provides the most effective sampling, while projections onto native Watson-Crick-type hydrogen bond CVs yield the best resolution FES prediction. Additionally, our sampling scheme also revealed various folding/unfolding pathways. This study provides practical insights and detailed decision-making strategies for adopting the MM-OPES method, facilitating its application to complex RNA structures at atomistic resolution.

Dedicated water channels are involved in the facilitated diffusion of water molecules across cell membranes in plants. Transporter proteins are also known to transport water molecules along with substrates; however, the molecular mechanism of water permeation is not well understood in plant transporters. Here, we show that plant sugar transporters from the SWEET (sugar will eventually be exported transporter) family act as water-conducting carrier proteins via a variety of passive and active mechanisms that allow the diffusion of water molecules from one side of the membrane to the other. This study provides a molecular perspective on how plant membrane transporters act as water carrier proteins, a topic that has not been extensively explored in the literature. Water permeation in membrane transporters could occur via four distinct mechanisms, which form our hypothesis for water transport in SWEETs. These hypotheses are tested using molecular dynamics simulations of the outward-facing, occluded, and inward-facing states of AtSWEET1 to identify the water permeation pathways and the flux associated with them. The hydrophobic gates at the center of the transport tunnel act as barriers that restrict water permeation. We have performed in silico single and double mutations of the hydrophobic gate residues to examine the changes in water conductivity. Surprisingly, the double mutant allows water permeation to the intracellular half of the membrane and forms a continuous water channel. These computational results are validated by experimentally examining the transport of hydrogen peroxide molecules by the AtSWEET family of transporters. We have also shown that the transport of hydrogen peroxide follows a mechanism similar to that of water transport in AtSWEET1. Finally, we conclude that similar water-conduction states are also present in other SWEETs due to the high degree of sequence and structural conservation exhibited by this transporter family.

Dedicated water channels are involved in the facilitated diffusion of water molecules across cell membranes in plants. Transporter proteins are also known to transport water molecules along with substrates; however, the molecular mechanism of water permeation is not well understood in plant transporters. Here, we show that plant sugar transporters from the SWEET (sugar will eventually be exported transporter) family act as water-conducting carrier proteins via a variety of passive and active mechanisms that allow the diffusion of water molecules from one side of the membrane to the other. This study provides a molecular perspective on how plant membrane transporters act as water carrier proteins, a topic that has not been extensively explored in the literature. Water permeation in membrane transporters could occur via four distinct mechanisms, which form our hypothesis for water transport in SWEETs. These hypotheses are tested using molecular dynamics simulations of the outward-facing, occluded, and inward-facing states of AtSWEET1 to identify the water permeation pathways and the flux associated with them. The hydrophobic gates at the center of the transport tunnel act as barriers that restrict water permeation. We have performed in silico single and double mutations of the hydrophobic gate residues to examine the changes in water conductivity. Surprisingly, the double mutant allows water permeation to the intracellular half of the membrane and forms a continuous water channel. These computational results are validated by experimentally examining the transport of hydrogen peroxide molecules by the AtSWEET family of transporters. We have also shown that the transport of hydrogen peroxide follows a mechanism similar to that of water transport in AtSWEET1. Finally, we conclude that similar water-conduction states are also present in other SWEETs due to the high degree of sequence and structural conservation exhibited by this transporter family.

All-atom molecular dynamics (MD) simulations offer crucial insights into biomolecular dynamics, but inherent time scale constraints often limit their effectiveness. Advanced sampling techniques help overcome these limitations, enabling predictions of deeply rugged folding free energy surfaces (FES) of RNA at atomistic resolution. The Multithermal-Multiumbrella On-the-Fly Probability Enhanced Sampling (MM-OPES) method, which combines temperature and collective variables (CVs) to accelerate sampling, has shown promise and cost-effectiveness. However, the applications have so far been limited to simpler RNA systems, such as stem-loops. In this study, we optimized the MM-OPES method to explore the FES of an H-type RNA pseudoknot, a more complex fundamental RNA folding unit. Through systematic exploration of CV combinations and temperature ranges, we identified an optimal strategy for both sampling and analysis. Our findings demonstrate that treating the native-like contacts in two stems as independent CVs and using a temperature range of 300–480 K provides the most effective sampling, while projections onto native Watson–Crick-type hydrogen bond CVs yield the best resolution FES prediction. Additionally, our sampling scheme also revealed various folding/unfolding pathways. This study provides practical insights and detailed decision-making strategies for adopting the MM-OPES method, facilitating its application to complex RNA structures at atomistic resolution.