Search Results (9)

Comparative studies using lysophosphatidic acid (LPA) and the synthetic agonist, oleoyl-methoxy glycerophosphothionate (OMPT), in cells expressing the LPA₃ receptor revealed differences in the action of these agents. The possibility that more than one recognition cavity might exist for these ligands in the LPA₃ receptor was considered. We performed agonist docking studies exploring the whole protein to obtain tridimensional details of the ligand–receptor interaction. Functional in cellulo experiments using mutants were also executed. Our work includes blind docking using the unrefined and refined proteins subjected to hot spot predictions. Distinct ligand protonation (charge −1 and −2) states were evaluated. One LPA recognition cavity is located near the lower surface of the receptor close to the cytoplasm (Lower Cavity). OMPT displayed an affinity for an additional identification cavity detected in the transmembrane and extracellular regions (Upper Cavity). Docking targeted to Trp102 favored binding of both ligands in the transmembrane domain near the extracellular areas (Upper Cavity), but the associating amino acids were not identical due to close sub-cavities. A receptor model was generated using AlphaFold3, which properly identified the transmembrane regions of the sequence and co-modeled the lipid environment accordingly. These two models independently generated (with and without the membrane) and adopted essentially the same conformation, validating the data obtained. A DeepSite analysis of the model predicted two main binding pockets, providing additional confidence in the predicted ligand-binding regions and support for the relevance of the docking-based interaction models. In addition, mutagenesis was performed of the amino acids of the two detected cavities. In the in cellulo studies, LPA action was much less affected by the distinct mutations than that of OMPT (which was almost abolished). Therefore, docking and functional data indicate the presence of distinct agonist binding cavities in the LPA₃ receptor. Full article

Background/Objectives: Accurately predicting protein–ligand binding affinity is essential in drug discovery for identifying effective compounds. While existing sequence-based machine learning models for binding affinity prediction have shown potential, they lack accuracy and robustness in pattern recognition, which limits their generalizability across diverse and novel binding complexes. To overcome these limitations, we developed GNNSeq, a novel hybrid machine learning model that integrates a Graph Neural Network (GNN) with Random Forest (RF) and XGBoost. Methods: GNNSeq predicts ligand binding affinity by extracting molecular characteristics and sequence patterns from protein and ligand sequences. The fully optimized GNNSeq model was trained and tested on subsets of the PDBbind dataset. The novelty of GNNSeq lies in its exclusive reliance on sequence features, a hybrid GNN framework, and an optimized kernel-based context-switching design. By relying exclusively on sequence features, GNNSeq eliminates the need for pre-docked complexes or high-quality structural data, allowing for accurate binding affinity predictions even when interaction-based or structural information is unavailable. The integration of GNN, XGBoost, and RF improves GNNSeq performance by hierarchical sequence learning, handling complex feature interactions, reducing variance, and forming a robust ensemble that improves predictions and mitigates overfitting. The GNNSeq unique kernel-based context switching scheme optimizes model efficiency and runtime, dynamically adjusts feature weighting between sequence and basic structural information, and improves predictive accuracy and model generalization. Results: In benchmarking, GNNSeq performed comparably to several existing sequence-based models and achieved a Pearson correlation coefficient (PCC) of 0.784 on the PDBbind v.2020 refined set and 0.84 on the PDBbind v.2016 core set. During external validation with the DUDE-Z v.2023.06.20 dataset, GNNSeq attained an average area under the curve (AUC) of 0.74, demonstrating its ability to distinguish active ligands from decoys across diverse ligand–receptor pairs. To further evaluate its performance, we combined GNNSeq with two additional specialized models that integrate structural and protein–ligand interaction features. When tested on a curated set of well-characterized drug–target complexes, the hybrid models achieved an average PCC of 0.89, with the top-performing model reaching a PCC of 0.97. GNNSeq was designed with a strong emphasis on computational efficiency, training on 5000+ complexes in 1 h and 32 min, with real-time affinity predictions for test complexes. Conclusions: GNNSeq provides an efficient and scalable approach for binding affinity prediction, offering improved accuracy and generalizability while enabling large-scale virtual screening and cost-effective hit identification. GNNSeq is publicly available in a server-based graphical user interface (GUI) format. Full article

CD44, a pivotal cell surface molecule, plays a crucial role in many cellular functions, including cell-cell interactions, adhesion, and migration. It serves as a receptor for hyaluronic acid and is involved in lymphocyte activation, recirculation, homing, and hematopoiesis. Moreover, CD44 is a commonly used cancer stem cell marker associated with tumor progression and metastasis. The development of CD44 aptamers that specifically target CD44 can be utilized to target CD44-positive cells, including cancer stem cells, and for drug delivery. Building on the primary sequences of our previously selected thioaptamers (TAs) and observed variations, we developed a bead-based X-aptamer (XA) library by conjugating drug-like ligands (X) to the 5-positions of certain uridines on a complete monothioate backbone. From this, we selected an XA with high affinity to the CD44 hyaluronic acid binding domain (HABD) from a large combinatorial X-aptamer library modified with N-acetyl-2,3-dehydro-2-deoxyneuraminic acid (ADDA). This XA demonstrated an enhanced binding affinity for the CD44 protein up to 23-fold. The selected CD44 X-aptamers (both amine form and ADDA form) also showed enhanced binding affinity to CD44-overexpressing human ovarian cancer IGROV cells. Secondary structure predictions of CD44 using MFold identified several binding motifs and smaller constructs of various stem-loop regions. Among our identified binding motifs, X-aptamer motif 3 and motif 5 showed enhanced binding affinity to CD44-overexpressing human ovarian cancer IGROV cells with ADDA form, compared to the binding affinities with amine form and scrambled sequence. The effect of ADDA as a binding affinity enhancer was not uniform within the aptamer, highlighting the importance of optimal ligand positioning. The incorporation of ADDA not only broadened the XA’s chemical diversity but also increased the binding surface area, offering enhanced specificity. Therefore, the strategic use of site-directed modifications allows for fine-tuning aptamer properties and offers a flexible, generalizable framework for developing high-performance aptamers that target a wide range of molecules. Full article

Lactopontin (LPN) is a highly phosphorylated O-glycosylated acidic protein closely associated with infant gut, brain, and immune development, and its recognition is urgent due to its rising application in fortified dairy products and infant formula. In this study, an ssDNA aptamer against LPN was obtained, among which two kinds of matrix-background-assisted systematic evolution of ligands via exponential enrichment (SELEX) approaches were performed and compared. The direct approach was to utilize the sample matrix as the mixing-incubation background between the ssDNA library and LPN that can theoretically increase screening pressure and simulate practical application scenarios. The indirect approach was to utilize a PBS buffer as a screening background and to include counter-screening steps that adopt the “sample matrix” as a whole as the counter-screening target. Their screening evolutions were monitored through qPCR assays from sequence diversity convergences of each sub-library based on the change in the proportion of hetero- and homo-duplexes from the dissociation curve and melting temperature, which were also verified from the sequence statistics of high-throughput sequencing. The common sequence of Seq.I1II3 from the two approaches was finally fished out as the aptamer through multiple analyses of combining the sequence frequency, secondary structures, homology, and binding assessments, which was demonstrated good specificity and low-nanomolar affinity by qPCR assay (K_D, 5.9 nM). In addition, molecular docking and a dynamics simulation were performed for their binding site prediction and affinity confirmation. This study provides a potential identifying element and a basis for accelerating the development of methods for LPN detection in dairy products. Full article

Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neural network) and the FFNN (fast forward neural network) hybrid neural network framework. The HNN-denovo uses protein pocket structure and protein–ligand interactions as input features. The HNN-affinity uses protein sequences and ligand features as input features. The HNN method combines the CNN and FCNN machine architecture for the protein structure or protein sequence and ligand descriptors. To train the model, the HNN methods used thousands of known protein–ligand binding affinity data retrieved from the PDBBind database. We also developed the Random Forest (RF), Gradient Boosting (GB), Decision Tree with AdaBoost (DT), and a consensus model. We compared the HNN results with models developed based on the RF, GB, and DT methods. We also independently compared the HNN method results with the literature reported deep learning protein–ligand binding affinity predictions made by the DLSCORE, KDEEP, and DeepAtom. The predictive performance of the HNN methods (max Pearson’s R achieved was 0.86) was consistently better than or comparable to the DLSCORE, KDEEP, and DeepAtom deep learning learning methods for both balanced and unbalanced data sets. The HNN-affinity can be applied for the protein–ligand affinity prediction even in the absence of protein structure information, as it considers the protein sequence as standalone feature in addition to the ligand descriptors. The HNN-denovo method can be efficiently implemented to the structure-based de novo drug design campaign. The HNN-affinity method can be used in conjunction with the deep learning molecular docking protocols as a standalone. Further, it can be combined with the conventional molecular docking methods as a multistep approach to rapidly screen billions of diverse compounds. The HNN method are highly scalable in the cloud ML platform. Full article

Genetic diversity and evolution of infectious bronchitis virus (IBV) are mainly impacted by mutations in the spike 1 (S1) gene. This study focused on whole genome sequencing of an IBV isolate (IBV/Ck/Can/2558004), which represents strains highly prevalent in Canadian commercial poultry, especially concerning features related to its S1 gene and protein sequences. Based on the phylogeny of the S1 gene, IBV/Ck/Can/2558004 belongs to the GI-17 lineage. According to S1 gene and protein pairwise alignment, IBV/Ck/Can/2558004 had 99.44–99.63% and 98.88–99.25% nucleotide (nt) and deduced amino acid (aa) identities, respectively, with five Canadian Delmarva (DMV/1639) IBVs isolated in 2019, and it also shared 96.63–97.69% and 94.78–97.20% nt and aa similarities with US DMV/1639 IBVs isolated in 2011 and 2019, respectively. Further homology analysis of aa sequences showed the existence of some aa substitutions in the hypervariable regions (HVRs) of the S1 protein of IBV/Ck/Can/2558004 compared to US DMV/1639 isolates; most of these variant aa residues have been subjected to positive selection pressure. Predictive analysis of potential N-glycosylation and phosphorylation motifs showed either loss or acquisition in the S1 glycoprotein of IBV/Ck/Can/2558004 compared to S1 of US DMV/1639 IBV. Furthermore, bioinformatic analysis showed some of the aa changes within the S1 protein of IBV/Ck/Can/2558004 have been predicted to impact the function and structure of the S1 protein, potentially leading to a lower binding affinity of the S1 protein to its relevant ligand (sialic acid). In conclusion, these findings revealed that the DMV/1639 IBV isolates are under continuous evolution among Canadian poultry. Full article

This study presented the first insights into the genomic and chemical profiles of B9, a specific Penicillium strain derived from sponges of the South China Sea that demonstrated the closest morphological and phylogenetic affinity to P. paxillin. Via the Illumina MiSeq sequencing platform, the draft genome was sequenced, along with structural assembly and functional annotation. There were 34 biosynthetic gene clusters (BGCs) predicted against the antiSMASH database, but only 4 gene clusters could be allocated to known BGCs (≥50% identities). Meanwhile, the comparison between B9 and P. paxillin ATCC 10480 demonstrated clear distinctions in morphology, which might be ascribed to the unique environmental adaptability of marine endosymbionts. In addition, two novel pyridinones, penicidihydropyridone A (2) and penicidihydropyridone B (3), were isolated from cultures of B9, and structurally characterized by nuclear magnetic resonance (NMR) and mass spectrometry (MS). The absolute configurations were confirmed by comparison of experimental and calculated electronic circular dichroism (ECD) curves. In addition, structure-based molecular docking indicated that both neo-pyridinones might block the programmed cell death protein 1(PD-1) pathway by competitively binding a programmed cell death 1 ligand 1(PD-L1) dimer. This was verified by the significant inhibition rates of the PD-1/L1 interaction. These indicated that Penicillium sp. B9 possessed a potential source of active secondary metabolites. Full article

The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions. Full article

Molecular modeling by means of docking and molecular dynamics (MD) has become an integral part of early drug discovery projects, enabling the screening and enrichment of large libraries of small molecules. In the past decades, special emphasis was drawn to nucleic acid (NA)-based molecules in the fields of therapy, diagnosis, and drug delivery. Research has increased dramatically with the advent of the SELEX (systematic evolution of ligands by exponential enrichment) technique, which results in single-stranded DNA or RNA sequences that bind with high affinity and specificity to their targets. Herein, we discuss the role and contribution of docking and MD to the development and optimization of new nucleic acid-based molecules. This review focuses on the different approaches currently available for molecular modeling applied to NA interaction with proteins. We discuss topics ranging from structure prediction to docking and MD, highlighting their main advantages and limitations and the influence of flexibility on their calculations. Full article