Protein-ligand Binding Research Articles

CryptoBench: Cryptic protein-ligand binding sites dataset and benchmark.

Structure-based methods for detecting protein-ligand binding sites play a crucial role in various domains, from fundamental research to biomedical applications. However, current prediction methodologies often rely on holo (ligand-bound) protein conformations for training and evaluation, overlooking the significance of the apo (ligand-free) states. This oversight is particularly problematic in the case of cryptic binding sites (CBSs) where holo-based assessment yields unrealistic performance expectations. To advance the development in this domain, we introduce CryptoBench, a benchmark dataset tailored for training and evaluating novel CBS prediction methodologies. CryptoBench is constructed upon a large collection of apo-holo protein pairs, grouped by UniProtID, clustered by sequence identity, and filtered to contain only structures with substantial structural change in the binding site. CryptoBench comprises 1,107 structures with predefined cross-validation splits, making it the most extensive CBS dataset to date. To establish a performance baseline, we measured the predictive power of sequence- and structure-based CBS residue prediction methods using the benchmark. We selected PocketMiner as the state-of-the-art representative of the structure-based methods for CBS detection, and P2Rank, a widely-used structure-based method for general binding site prediction that is not specifically tailored for cryptic sites. For sequence-based approaches, we trained a neural network to classify binding residues using protein language model embeddings. Our sequence-based approach outperformed PocketMiner and P2Rank across key metrics, including AUC, AUPRC, MCC, and F1 scores. These results provide baseline benchmark results for future CBS and potentially also non-CBS prediction endeavors, leveraging CryptoBench as the foundational platform for further advancements in the field. The CryptoBench dataset, including the benchmark model, is available on Open Science Framework - https://osf.io/pz4a9/. The code and tutorial are available at the GitHub repository - https://github.com/skrhakv/CryptoBench/. Supplementary data are available at Bioinformatics online.

Consistent theoretical description of nuclear spin long-lived states decay under conditions of reversible ligand-protein binding.

Determining the stability constant of the complex formed by an organic ligand with a protein is the first stage in the screening of new drugs. Nuclear spin long-lived states, in particular the singlet state, can be used to study the reversible binding of ligands to proteins. In a complex with a protein, the spins of the ligand interact with the spins of the protein, the system of protein and ligand nuclei can relax by a dipole-dipole mechanism, and the lifetime of the singlet state is strongly reduced. In this theoretical study, a system of encounter theory equations with the condition of fast relaxation in free protein was solved to determine the lifetime of the LLS in the presence of protein. It was shown that in the limit of fast chemical exchange, the relaxation of the LLS of the ligand nuclei due to dipole interaction with the protein nuclei is reduced to relaxation by the mechanism of dipole interaction with one proton of the protein, which is located at some effective distance from the ligand nuclei. Numerical calculations were made to test the applicability of the approximations used to process the experimental lifetime dependencies on the ligand concentration and external field, and it was shown that these approximations coincide with the limit of fast exchange in strong and weak magnetic fields, but not in the medium field. An analytical expression for the lifetime of the singlet state of ligand nuclei in an arbitrary magnetic field in the absence of protein was obtained.

Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn

Background Breast cancer has become the most prevalent disease and its incidence has almost doubled in the Indian population. This increased burden demands new targeted therapies with novel compounds either synthetically produced or derived from indigenous plants, which could be a promising approach for the development of drugs. Euphorbia thymifolia L is a widely growing tropical herb that has been reported to have various ethnopharmacological properties. Although Euphorbia genus is reported to have anticancer properties, E. thymifolia is not reported to have anticancer properties to date. Therefore, the aim of the present study was to screen the phytoconstituents and identify the active compounds present in the methanolic extract of E. thymifolia (ME.ET) as ligands to inhibit human cancer cell lines with special reference to potential protein targets implicated in breast cancer using an In-silico approach. Methods ME.ET was subjected to GC-MS analysis to screen the phytoconstituents, and the identified compounds were docked with protein targets such as extracellular signal-regulated kinases (ERK1), a serine/threonine kinase-1(AKT1), human epidermal growth factor 2 (HER2), estrogen receptor (ER), maternal embryonic leucine zipper kinase (MELK), polo-like kinase-1(PLK1), and protein tyrosine kinase (PTK6). Compounds with good docking scores were further subjected to dynamic studies to understand the protein ligand binding stability, ligand pathway calculation, and molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculations using the Schrodinger suite. Results GC-MS analysis revealed the presence of 245 phytoconstituents, 219 of which were unique. When subjected to docking, these phytocompounds, namely 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino) ethyl 4-isopropylphenyl ester (SADPE), showed good docking scores. Molecular dynamics studies showed a high affinity and low binding energy for TTDB with HER2, ERK1, and SADPE with ER. Conclusions Hence, in this study, we identified two lead compounds in E.thymifolia linn. Further invitro and invivo anticancer studies can be performed to confirm these results and to understand the molecular mechanism by which they exhibit anticancer activity against breast cancer.

Parahydrogen Polarization in Reverse Micelles and Application to Sensing of Protein-Ligand Binding.

A medium containing reverse micelles supports non-hydrogenative parahydrogen induced polarization (nhPHIP) in the organic phase while solubilizing a protein in the aqueous phase. Strongly enhanced NMR signals from iridium hydride complexes report on a ligand, 4-amino-2-benzylaminopyrimidine, which crosses the phase boundary and interacts with the thiaminase protein TenA. The calculation of binding equilibria reveals a KD of 39.7 ± 8.9 μM for protein binding. The nanoscale separation of the two phases allows the separate optimization of the parahydrogen polarization and solubilization of a biological macromolecule. The reverse micelles may be used to study other biological questions using signal enhancement by parahydrogen polarization, such as enzyme reactions, protein-protein interactions, and protein binding epitopes.

Leveraging a Separation of States Method for Relative Binding Free Energy Calculations in Systems with Trapped Waters.

Methods for calculating the relative binding free energy (RBFE) between ligands to a target protein are gaining importance in the structure-based drug discovery domain, especially as methodological advances and automation improve accuracy and ease of use. In an RBFE calculation, the difference between the binding affinities of two ligands to a protein is calculated by transforming one ligand into another, in the protein-ligand complex, and in solvent. Alchemical binding free energy calculations are often used for such ligand transformations. Such calculations are not without challenges, however; for example, it can be challenging to handle interfacial waters when these play a crucial role in mediating protein-ligand binding. In some cases, the exchange of the interfacial waters with solvent water might be very infrequent in the course of typical molecular simulations, and such interfacial waters can be considered trapped on the simulation time scale. In these cases, RBFE calculation between two ligands, where one ligand binds with a trapped water while the other ligand displaces it, can result in inaccuracies if the surrounding water structure is not sampled adequately for both ligands. So far, a popular choice for treating the trapped waters in RBFE calculations is to combine free energy calculations with enhanced sampling methods that insert/delete waters in the binding site. Despite recent developments in the enhanced sampling methods, they can result in hysteresis in the RBFE estimate, depending on whether the simulations were started with or without the trapped waters. In this study, we introduce an alternative method, separation of states, to calculate the RBFE between ligand pairs where the ligands bind to the protein with different numbers/positions of trapped waters. The separation of states approach treats the sampling of the trapped waters separately from the free energy calculation of the ligand transformation. In our method, a trapped water in protein's binding site is decoupled from the system first, and the cavity created by its decoupling is stabilized. We then grow a larger ligand into this cavity- a ligand that is known to displace the trapped water. In this study, we show that our method results in precise and accurate estimates of RBFEs for ligand pairs involving the rearrangement of trapped water via RBFE calculations for five such ligand pairs. We have optimized our simulation protocol to be suited for large distributed computational resources and have automated our RBFE calculation workflow.

Anti-diabetic drug pioglitazone reduces Islet amyloid aggregation overload in the Drosophila neuronal cells.

Amyloid-proteinopathy is observed in type 2 diabetes, where Islet amyloid polypeptide is secreted atypically and impedes cellular homeostasis. The thiazolidinediones family is reported to influence amyloid-beta aggregations. However, research on drug-based stimulation of insulin signaling to alleviate Islet amyloid aggregations is lacking. To understand the impact of pioglitazone on islet amyloid aggregation, we conductedan in vivo and in silico analysis. For in vivo analysis, we generated a transgenic Drosophila harboring the preproform of human Islet amyloid polypeptide(IAPP) that can be ectopically expressed in a spatio-temporal manner. We show that the unprocessed form of IAPP also has the propensity to form aggregates and cause degeneration. Pioglitazone feeding effectively reduces the burden of Islet amyloid aggregations in the larval brain. In silico analysis shows that there is a higher protein-ligand binding energy for IAPP with pioglitazone than amyloid-beta. These results suggests that pioglitazone might be repurposed as a drug to cure islet amyloidogenesis.

A Generally Applicable Method for Disentangling the Effect of Individual Noncovalent Interactions on the Binding Energy.

We introduce the fragment-pairwise Local Energy Decomposition (fp-LED) scheme for precise quantification of individual interactions contributing to the binding energy of arbitrary chemical entities, such as protein-ligand binding energies, lattice energies of molecular crystals, or association energies of large biomolecular assemblies. Using fp-LED, we can assess whether the contribution to the binding energy arising from noncovalent interactions between pairs of molecular fragments in any chemical system is attractive or repulsive, and accurately quantify its magnitude at the coupled cluster level - commonly considered as the "gold standard" of computational chemistry. Such insights are crucial for advancing molecular and material design strategies in fields like catalysis and therapeutic development. Illustrative applications across diverse fields demonstrate the versatility and accuracy of this theoretical framework, promising profound implications for fundamental understanding and practical applications.

Design, In silico Screening, Synthesis, Characterisation and DFT-based Electronic Properties of Dihydropyridine-based Molecule as L-type Calcium Channel Blocker.

People of all nationalities and social classes are now affected by the growing issue of hypertension. Over time, there has been a consistent rise in the fatality rate. A range of therapeutic compounds, on the other hand, are often used to handle hypertension. The objectives of this study are first to design potential antihypertensive drugs based on the DHP scaffold, secondly, to analyse drug-likeness properties of the ligands and investigate their molecular mechanisms of binding to the model protein Cav1.2 and finally to synthesise the best ligand. Due to the lack of 3D structures for human Cav1.2, the protein structure was modelled using a homology modelling approach. A protein-ligand complex's strength and binding interaction were investigated using molecular docking and molecular dynamics techniques. DFT-based electronic properties of the ligand were calculated using the M06-2X/ def2- TZVP level of theory. The SwissADME website was used to study the ADMET properties. In this study, a series of DHP compounds (19 compounds) were properly designed to act as calcium channel blockers. Among these compounds, compound 16 showed excellent binding scores (-11.6 kcal/mol). This compound was synthesised with good yield and characterised. To assess the structural features of the synthesised molecule quantum chemical calculations were performed. Based on molecular docking, molecular dynamics simulations, and drug-likeness properties of compound 16 can be used as a potential calcium channel blocker.

MM-DRPNet: A Multimodal Dynamic Radial Partitioning Network for Enhanced Protein–Ligand Binding Affinity Prediction

Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face substantial limitations in prediction accuracy. Moreover, the prevalent methodologies often overlook critical three-dimensional (3D) structural information, thereby constraining their practical utility in computer-aided drug design (CADD). Here we present MM-DRPNet, a multimodal deep learning framework that enhances binding affinity prediction by integrating protein-ligand structural information with interaction features and physicochemical properties. The core innovation lies in our dynamic radial partitioning (DRP) algorithm, which adaptively segments 3D space based on complex-specific interaction patterns, surpassing traditional fixed partitioning methods in capturing spatial interactions. MM-DRPNet further incorporates molecular topological features to comprehensively model both structural and spatial relationships. Extensive evaluations on benchmark datasets demonstrate that MM-DRPNet significantly outperforms state-of-the-art methods across multiple metrics, with ablation studies confirming the substantial contribution of each architectural component. Source code for MM-DRPNet is freely available for download at https://github.com/Bigrock-dd/MMDRPv1.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels of generalization capability and interpretability. Intuitively, the inductive bias of an ML-based model for PLA prediction should fit in with biological mechanisms relevant for binding to achieve good predictions with meaningful reasons. To this end, we propose an interaction-based inductive bias to restrict neural networks to functions relevant for binding with two assumptions: 1) A protein-ligand complex can be naturally expressed as a heterogeneous graph with covalent and non-covalent interactions; 2) The predicted PLA is the sum of pairwise atom-atom affinities determined by non-covalent interactions. The interaction-based inductive bias is embodied by an explainable heterogeneous interaction graph neural network (EHIGN) for explicitly modeling pairwise atom-atom interactions to predict PLA from 3D structures. Extensive experiments demonstrate that EHIGN achieves better generalization capability than other state-of-the-art ML-based baselines in PLA prediction and structure-based virtual screening. More importantly, comprehensive analyses of distance-affinity, pose-affinity, and substructure-affinity relations suggest that the interaction-based inductive bias can guide the model to learn atomic interactions that are consistent with physical reality. As a case study to demonstrate practical usefulness, our method is tested for predicting the efficacy of Nirmatrelvir against SARS-CoV-2 variants. EHIGN successfully recognizes the changes in the efficacy of Nirmatrelvir for different SARS-CoV-2 variants with meaningful reasons.

Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands.

In the binding process between proteins and ligand molecules, water molecules play a pivotal role by forming hydrogen bonds that enable proteins and ligand molecules to bind more strongly. However, current methodologies for predicting binding affinity overlook the importance of water molecules. Therefore, we developed a model called GraphWater-Net, specifically designed for predicting protein-ligand binding affinity, by incorporating water molecules. GraphWater-Net employs topological structures to represent protein atoms, ligand atoms and water molecules, and their interactions. Leveraging the Graphormer network, the model extracts interaction features between nodes within the topology, alongside the interaction features of edges and nodes. Subsequently, it generates embeddings with attention weights, inputs them into a Softmax function for regression prediction, and ultimately outputs the predicted binding affinity value. Experimental results on the Comparative Assessment of Scoring Functions (CASF) 2016 test set show that the introduction of water molecules into the complex significantly improves the prediction performance of the proposed model for protein and ligand binding affinity. Specifically, the Pearson correlation coefficient (Rp) exceeds that of current state-of-the-art methods by a margin of 0.022 to 0.129. By integrating water molecules, GraphWater-Net has the potential to facilitate the rational design of protein-ligand interactions and aid in drug discovery.

Effect of Water Networks On Ligand Binding: Computational Predictions vs Experiments.

Rational drug design focuses on the explanation and prediction of complex formation between therapeutic targets and small-molecule ligands. As a third and often overlooked interacting partner, water molecules play a critical role in the thermodynamics of protein-ligand binding, impacting both the entropy and enthalpy components of the binding free energy and by extension, on-target affinity and bioactivity. The community has realized the importance of binding site waters, as evidenced by the number of computational tools to predict the structure and thermodynamics of their networks. However, quantitative experimental characterization of relevant protein-ligand-water systems, and consequently the validation of these modeling methods, remains challenging. Here, we investigated the impact of solvent exchange from light (H2O) to heavy water (D2O) to provide complete thermodynamic profiling of these ternary systems. Utilizing the solvent isotope effects, we gain a deeper understanding of the energetic contributions of various components. Specifically, we conducted isothermal titration calorimetry experiments on trypsin with a series of p-substituted benzamidines, as well as carbonic anhydrase II (CAII) with a series of aromatic sulfonamides. Significant differences in binding enthalpies found between light vs heavy water indicate a substantial role of the binding site water network in protein-ligand binding. Next, we challenged two conceptually distinct modeling methods, the grid-based WaterFLAP and the molecular dynamics-based MobyWat, by predicting and scoring relevant water networks. The predicted water positions accurately reproduce those in available high-resolution X-ray and neutron diffraction structures of the relevant protein-ligand complexes. Estimated energetic contributions of the identified water networks were corroborated by the experimental thermodynamics data. Besides providing a direct validation for the predictive power of these methods, our findings confirmed the importance of considering binding site water networks in computational ligand design.

Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction.

The novel orthosteric agonist M1 muscarinic acetylcholine receptor reveals anti-Alzheimer’s disease activity

Cholinergic treatments with an emphasis on M1 muscarinic acetylcholine receptor (mAChR) agonists as potential modulating agents are a new approach in Alzheimer’s disease (AD) therapy. In previous research, we designed and characterized novel thiazolidine-2,4-dione (TZD)-derived compounds that possess anti-AD properties and enhance the expression of mAChRM1 in rats. This study evaluated a novel orthosteric agonist of mAChRM1 from related pathways that has shown promising anti-Alzheimer’s disease activity. PC12 cells were exposed to various concentrations of TZ4M before they were exposed to scopolamine (3 µM). Immunocytochemistry and western blot analyses revealed that TZ4M increased the expression of mAChRM1 in differentiated cells induced by scopolamine-treated PC12 cells. The results showed that TZ4M (3 and 5 µM) markedly upregulated PKC and ChAT protein expression, and the cells were significantly protected against increased ROS levels followed by neuronal cell loss, as evidenced by the MTT assay. TUNEL staining indicated that TZ4M impeded the shaping of apoptotic bodies. Analysis of the amino acid sequences of the ligand–protein binding site indicated that TZ4M is bound to the orthosteric site (acetylcholine site). This study revealed that TZ4M, a derivative of TZD, effectively protects against scopolamine-induced damage. TZ4M, a novel mACRM1 orthosteric agonist, is promising for treating AD.

CPIScore: A Deep Learning Approach for Rapid Scoring and Interpretation of Protein-Ligand Binding Interactions.

Protein-ligand binding affinity prediction is a crucial and challenging task in the field of drug discovery. However, traditional simulation-based computational approaches are often prohibitively time-consuming, limiting their practical utility. In this study, we introduce a novel deep learning method, CPIScore, which leverages the capabilities of Transformer and Graph Convolutional Networks (GCN) to enhance the prediction of protein-ligand binding affinity. CPIScore utilizes the Transformer architecture to capture comprehensive global contexts of protein and ligand sequences, while the GCN component effectively extracts local features from small molecular graphs. Our results demonstrate that CPIScore surpasses both traditional machine learning and other deep learning models in accuracy, achieving a Pearson's r of 0.74 on our test set. Furthermore, CPIScore has been validated across multiple targets, proving its ability to discern inhibitors from a diverse compound library with high enrichment rates. Notably, when applied to a generated focused library of compounds, CPIScore successfully identified six potent small-molecule inhibitors of ATR, which were tested experimentally and four small molecules exhibited inhibitory activity below ten nanomoles. These results highlight CPIScore's potential to significantly streamline and enhance the efficiency of drug discovery processes.

Bayesian Approach for Computing Free Energy on Perturbation Graphs with Cycles.

A common approach for computing free energy differences among multiple states is to build a perturbation graph connecting the states and compute free energy differences on all edges of the graph. Such perturbation graphs are often designed to have cycles. Because free energy is a function of states, the free energy around any cycle is zero, which we refer to as the cycle consistency condition. Since the cycle consistency condition relates free energy differences on the edges of a cycle, it could be used to improve the accuracy of free energy estimates. Here, we propose a Bayesian method called the coupled Bayesian multistate Bennett acceptance ratio (CBayesMBAR) that can properly couple the calculations of free energy differences on the edges of cycles in a principled way. We apply the CBayesMBAR to compute free energy differences among harmonic oscillators and relative protein-ligand binding free energies. In both cases, the CBayesMBAR provides more accurate results compared to methods that do not consider the cycle consistency condition. Additionally, it outperforms the cycle closure correction method that also uses cycle consistency conditions.

Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands. Research studies have shown that our fusion model, PLA-STGCNnet, outperforms individual algorithms in accurately predicting binding affinity. The advantage of a fusion model is the ability to fully combine the advantages of multiple different models and improve overall performance by combining their features and outputs. Our fusion model shows satisfactory performance on different data sets, which proves its generalization ability and stability. The fusion-based model showed good performance in protein-ligand affinity prediction, and we successfully applied the model to drug screening. Our research underscores the promise of fusion spatial-temporal graph neural networks in addressing complex challenges in protein-ligand affinity prediction. The Python scripts for implementing various model components are accessible at https://github.com/ligaili01/PLA-STGCN.

Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn

Background Breast cancer has become the most prevalent disease and its incidence has almost doubled in the Indian population. This increased burden demands new targeted therapies with novel compounds either synthetically produced or derived from indigenous plants, which could be a promising approach for the development of drugs. Euphorbia thymifolia L is a widely growing tropical herb that has been reported to have various ethnopharmacological properties. Although Euphorbia genus is reported to have anticancer properties, E. thymifolia is not reported to have anticancer properties to date. Therefore, the aim of the present study was to screen the phytoconstituents and identify the active compounds present in the methanolic extract of E. thymifolia (ME.ET) as ligands to inhibit human cancer cell lines with special reference to potential protein targets implicated in breast cancer using an In-silico approach. Methods ME.ET was subjected to GC-MS analysis to screen the phytoconstituents, and the identified compounds were docked with protein targets such as extracellular signal-regulated kinases (ERK1), a serine/threonine kinase-1(AKT1), human epidermal growth factor 2 (HER2), estrogen receptor (ER), maternal embryonic leucine zipper kinase (MELK), polo-like kinase-1(PLK1), and protein tyrosine kinase (PTK6). Compounds with good docking scores were further subjected to dynamic studies to understand the protein ligand binding stability, ligand pathway calculation, and molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculations using the Schrodinger suite. Results GC-MS analysis revealed the presence of 245 phytoconstituents, 219 of which were unique. When subjected to docking, these phytocompounds, namely 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino) ethyl 4-isopropylphenyl ester (SADPE), showed good docking scores. Molecular dynamics studies showed a high affinity and low binding energy for TTDB with HER2, ERK1, and SADPE with ER. Conclusions Hence, in this study, we identified two lead compounds in E.thymifolia linn. Further invitro and invivo anticancer studies can be performed to confirm these results and to understand the molecular mechanism by which they exhibit anticancer activity against breast cancer.

Comparative evaluation of methods for the prediction of protein–ligand binding sites

The accurate identification of protein–ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank, PRANK and fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of our new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein–ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein–ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein–ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by PRANK and DeepPocket display the highest recall (60%) whilst IF-SitePred presents the lowest recall (39%). We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Finally, we propose top-N+2 recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark.Scientific contributionsThis study conducts the largest benchmark of ligand binding site prediction methods to date, comparing 13 original methods and 15 variants using 10 informative metrics. The LIGYSIS dataset is introduced, which aggregates biologically relevant protein–ligand interfaces across multiple structures of the same protein. The study highlights the detrimental effect of redundant binding site prediction and demonstrates significant improvement in recall and precision through stronger scoring schemes. Finally, top-N+2 recall is proposed as a universal benchmark metric for ligand binding site prediction, with a recommendation for open-source sharing of both methods and benchmarks.