Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity.
Ontology highlight
ABSTRACT:
SUBMITTER: Papaneophytou CP
PROVIDER: S-EPMC4137368 | biostudies-literature | 2014 Aug
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.
| S-EPMC2877349 | biostudies-literature
Project description:Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.
| S-EPMC5910815 | biostudies-literature
Project description:Accurate prediction of the binding affinities of small-molecule ligands to their biological targets is fundamental for structure-based drug design but remains a very challenging task. In this paper, we have performed computational studies to predict the binding models of 31 small-molecule Smac (the second mitochondria-derived activator of caspase) mimetics to their target, the XIAP (X-linked inhibitor of apoptosis) protein, and their binding affinities. Our results showed that computational docking was able to reliably predict the binding models, as confirmed by experimentally determined crystal structures of some Smac mimetics complexed with XIAP. However, all the computational methods we have tested, including an empirical scoring function, two knowledge-based scoring functions, and MM-GBSA (molecular mechanics and generalized Born surface area), yield poor to modest prediction for binding affinities. The linear correlation coefficient (r(2)) value between the predicted affinities and the experimentally determined affinities was found to be between 0.21 and 0.36. Inclusion of ensemble protein-ligand conformations obtained from molecular dynamic simulations did not significantly improve the prediction. However, major improvement was achieved when the free-energy change for ligands between their free- and bound-states, or "ligand-reorganization free energy", was included in the MM-GBSA calculation, and the r(2) value increased from 0.36 to 0.66. The prediction was validated using 10 additional Smac mimetics designed and evaluated by an independent group. This study demonstrates that ligand reorganization free energy plays an important role in the overall binding free energy between Smac mimetics and XIAP. This term should be evaluated for other ligand-protein systems and included in the development of new scoring functions. To our best knowledge, this is the first computational study to demonstrate the importance of ligand reorganization free energy for the prediction of protein-ligand binding free energy.
| S-EPMC2773700 | biostudies-literature
Project description:Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson's correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of [Formula: see text]-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, [Formula: see text]-AEScore has an RMSE of 1.32 pK units and a Pearson's correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the docking and screening power of the underlying classical scoring function.
| S-EPMC8364054 | biostudies-literature
Project description:We investigate transport properties of model polyelectrolyte systems at physiological ionic strength (0.154 M). Covering a broad range of flow length scales-from diffusion of molecular probes to macroscopic viscous flow-we establish a single, continuous function describing the scale dependent viscosity of high-salt polyelectrolyte solutions. The data are consistent with the model developed previously for electrically neutral polymers in a good solvent. The presented approach merges the power-law scaling concepts of de Gennes with the idea of exponential length scale dependence of effective viscosity in complex liquids. The result is a simple and applicable description of transport properties of high-salt polyelectrolyte solutions at all length scales, valid for motion of single molecules as well as macroscopic flow of the complex liquid.
| S-EPMC4990340 | biostudies-literature
Project description:The quantification of the number of targets in biological systems is an important parameter to assess the suitability of surface markers as targets for drugs, drug delivery and medical imaging. Likewise, quantifying the interaction with the target in terms of affinity and binding kinetics is essential during drug development. Commonly used approaches to quantify membrane antigens on live cells are based on manual saturation techniques that are labour-intensive, require careful calibration of the generated signal and do not quantify the binding rates. Here, we present how measuring interactions in real-time on live cells and tissue under ligand depletion conditions can be used to simultaneously quantify the kinetic binding parameters as well as the number of available binding sites in a biological system. Suitable assay design was explored with simulated data and feasibility of the method verified with experimental data for exemplary low molecular weight peptide and antibody radiotracers as well as fluorescent antibodies. In addition to revealing the number of accessible target sites and improving the accuracy of binding kinetics and affinities, the presented method does not require knowledge about the absolute signal generated per ligand molecule. This enables a simplified workflow for use with both radioligands and fluorescent binders.
| S-EPMC10282064 | biostudies-literature
Project description:The design of proteins with novel ligand-binding functions holds great potential for application in biomedicine and biotechnology. However, our ability to engineer ligand-binding proteins is still limited, and current approaches rely primarily on experimentation. Computation could reduce the cost of the development process and would allow rigorous testing of our understanding of the principles governing molecular recognition. While computational methods have proven successful in the early stages of the discovery process, optimization approaches that can quantitatively predict ligand affinity changes upon protein mutation are still lacking. Here, we assess the ability of free energy calculations based on first-principles statistical mechanics, as well as the latest Rosetta protocols, to quantitatively predict such affinity changes on a challenging set of 134 mutations. After evaluating different protocols with computational efficiency in mind, we investigate the performance of different force fields. We show that both the free energy calculations and Rosetta are able to quantitatively predict changes in ligand binding affinity upon protein mutations, yet the best predictions are the result of combining the estimates of both methods. These closely match the experimentally determined ??G values, with a root-mean-square error of 1.2 kcal/mol for the full benchmark set and of 0.8 kcal/mol for a subset of protein systems providing the most reproducible results. The currently achievable accuracy offers the prospect of being able to employ computation for the optimization of ligand-binding proteins as well as the prediction of drug resistance.
| S-EPMC6311686 | biostudies-literature
Project description:Binding affinity prediction (BAP) using protein-ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein-ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.
| S-EPMC8637032 | biostudies-literature
Project description:Hydrogen (H)-bonds potentiate diverse cellular functions by facilitating molecular interactions. The mechanism and the extent to which H-bonds regulate molecular interactions are a largely unresolved problem in biology because the H-bonding process continuously competes with bulk water. This interference may significantly alter our understanding of molecular function, for example, in the elucidation of the origin of enzymatic catalytic power. We advance this concept by showing that H-bonds regulate molecular interactions via a hitherto unappreciated donor-acceptor pairing mechanism that minimizes competition with water. On the basis of theoretical and experimental correlations between H-bond pairings and their effects on ligand binding affinity, we demonstrate that H-bonds enhance receptor-ligand interactions when both the donor and acceptor have either significantly stronger or significantly weaker H-bonding capabilities than the hydrogen and oxygen atoms in water. By contrast, mixed strong-weak H-bond pairings decrease ligand binding affinity due to interference with bulk water, offering mechanistic insight into why indiscriminate strengthening of receptor-ligand H-bonds correlates poorly with experimental binding affinity. Further support for the H-bond pairing principle is provided by the discovery and optimization of lead compounds targeting dietary melamine and Clostridium difficile toxins, which are not realized by traditional drug design methods. Synergistic H-bond pairings have therefore evolved in the natural design of high-affinity binding and provide a new conceptual framework to evaluate the H-bonding process in biological systems. Our findings may also guide wider applications of competing H-bond pairings in lead compound design and in determining the origin of enzymatic catalytic power.
| S-EPMC4820369 | biostudies-literature
Project description:Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.
| S-EPMC10777193 | biostudies-literature