Protein ligand
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PLIP: fully automated protein–ligand interaction profiler Open
The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we …
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Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods Open
Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in livi…
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<i>K</i><sub>DEEP</sub>: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks Open
Accurately predicting protein-ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-lea…
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A systematic analysis of atomic protein–ligand interactions in the PDB Open
We compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins representing 750 873 protein–ligand atomic interactions, and analyzed the frequency, geometry and the impact of each interaction type. The most freq…
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P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure Open
P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does …
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Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations Open
Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation …
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ProteinsPlus: interactive analysis of protein–ligand binding interfaces Open
Due to the increasing amount of publicly available protein structures searching, enriching and investigating these data still poses a challenging task. The ProteinsPlus web service (https://proteins.plus) offers a broad range of tools addr…
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Exploring the computational methods for protein-ligand binding site prediction Open
Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has…
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Protein–ligand binding with the coarse-grained Martini model Open
The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an …
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Molecular recognition of ternary complexes: a new dimension in the structure-guided design of chemical degraders Open
Molecular glues and bivalent inducers of protein degradation (also known as PROTACs) represent a fascinating new modality in pharmacotherapeutics: the potential to knockdown previously thought ‘undruggable’ targets at sub-stoichiometric co…
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New machine learning and physics-based scoring functions for drug discovery Open
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across di…
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On the Frustration to Predict Binding Affinities from Protein–Ligand Structures with Deep Neural Networks Open
Accurate prediction of binding affinities from protein-ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks describing both the ligand and the protein in…
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Advances in Molecular Dynamics Simulations and Enhanced Sampling Methods for the Study of Protein Systems Open
Molecular dynamics (MD) simulation is a rigorous theoretical tool that when used efficiently could provide reliable answers to questions pertaining to the structure-function relationship of proteins. Data collated from protein dynamics can…
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Development of a protein–ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions Open
Motivation Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despi…
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Protein–protein interaction prediction with deep learning: A comprehensive review Open
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein …
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Water in protein hydration and ligand recognition Open
This review describes selected basics of water in biomolecular recognition. We focus on a qualitative understanding of the most important physical aspects, how these change in magnitude between bulk water and protein environment, and how t…
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Isothermal Analysis of ThermoFluor Data can readily provide Quantitative Binding Affinities Open
Differential scanning fluorimetry (DSF), also known as ThermoFluor or Thermal Shift Assay, has become a commonly-used approach for detecting protein-ligand interactions, particularly in the context of fragment screening. Upon binding to a …
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Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review Open
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods fo…
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Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets Open
Predicting protein-ligand interactions using artificial intelligence (AI) models has attracted great interest in recent years. However, data-driven AI models unequivocally suffer from a lack of sufficiently large and unbiased datasets. Her…
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A defined structural unit enables de novo design of small-molecule–binding proteins Open
A new tool in the protein design toolbox Protein design can compute protein folds from first principles. However, designing new proteins that are functional remains challenging, in part because designing binding interactions requires simul…
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Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning Open
Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular in…
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Do Halogen–Hydrogen Bond Donor Interactions Dominate the Favorable Contribution of Halogens to Ligand–Protein Binding? Open
Halogens are present in a significant number of drugs, contributing favorably to ligand-protein binding. Currently, the contribution of halogens, most notably chlorine and bromine, is largely attributed to halogen bonds involving favorable…
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The Structural Biology of Galectin-Ligand Recognition: Current Advances in Modeling Tools, Protein Engineering, and Inhibitor Design Open
Galectins (formerly known as "S-type lectins") are a subfamily of soluble proteins that typically bind β-galactoside carbohydrates with high specificity. They are present in many forms of life, from nematodes and fungi to animals, where th…
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Accurate Estimation of Ligand Binding Affinity Changes upon Protein Mutation Open
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 prima…
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DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity Open
Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are severa…
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AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks Open
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep le…
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Molecular Docking: From Lock and Key to Combination Lock Open
Accurate modeling of protein ligand binding is an important step in structure-based drug design, is a useful starting point for finding new lead compounds or drug candidates. The 'Lock and Key' concept of protein-ligand binding has dominat…
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Atomic Convolutional Networks for Predicting Protein-Ligand Binding\n Affinity Open
Empirical scoring functions based on either molecular force fields or\ncheminformatics descriptors are widely used, in conjunction with molecular\ndocking, during the early stages of drug discovery to predict potency and\nbinding affinity …
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Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity Open
Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of …
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graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes Open
In this work, we present graph-convolutional neural networks for the prediction of binding constants of protein-ligand complexes. We derived the model using multi task learning, where the target variables are the dissociation constant (K d…