Ligand Binding Site Prediction Research Articles

PUResNetV2.0: a deep learning model leveraging sparse representation for improved ligand binding site prediction

Accurate ligand binding site prediction (LBSP) within proteins is essential for drug discovery. We developed ProteinUNetResNetV2.0 (PUResNetV2.0), leveraging sparse representation of protein structures to improve LBSP accuracy. Our training dataset included protein complexes from 4729 protein families. Evaluations on benchmark datasets showed that PUResNetV2.0 achieved an 85.4% Distance Center Atom (DCA) success rate and a 74.7% F1 Score on the Holo801 dataset, outperforming existing methods. However, its performance in specific cases, such as RNA, DNA, peptide-like ligand, and ion binding site prediction, was limited due to constraints in our training data. Our findings underscore the potential of sparse representation in LBSP, especially for oligomeric structures, suggesting PUResNetV2.0 as a promising tool for computational drug discovery.

A Comprehensive <i>in Silico</i> Analysis of the Functional and Structural Consequences of the Deleterious Missense Nonsynonymous SNPs in Human <i>GABRA6</i> Gene

Epilepsy, a prevalent neurological disorder, affects more than 50 million individuals worldwide and is characterized by recurring seizures. Nonsynonymous single nucleotide polymorphisms (nsSNPs) found within coding regions of epilepsy-related genes are believed to have significant impacts on protein function. This is due to their tendency to cause mutations in the encoded amino acids, which can subsequently lead to pathogenic alterations in protein structure and function. Consequently, nsSNPs have the potential to serve as diagnostic markers for epilepsy and other neuropsychiatric conditions. The primary objective of this study is to evaluate the harmful effects of missense nsSNP mutations on the <em>GABRA6</em> gene. The <em>GABRA6</em> gene encodes the alpha-6 subunit of the GABA<sub>A</sub> receptor, and previous research showed one case substitution mutation in the GABRA6 gene is associated with childhood absence epilepsy (CAE) and atonic seizures. To achieve this, we employed various computational tools, including SIFT, PolyPhen-2, PROVEAN, Condel, SNPs & GO, PMut, SNAP2, MutPred2, and SNPeffect4.0, for predicting missense nsSNPs. Additionally, we used I-Mutant3.0 and MUpro to analyze protein stability, ConSurf to assess evolutionary conservation, FTSite and COACH to predict ligand binding sites, SOPMA and PSIPRED to analyze protein secondary structures, project HOPE to predict structural changes, and I-TASSER to model the 3D structure. Furthermore, structural validation was conducted using the PROCHECK and ERRAT servers. At the same time, molecular dynamics simulations were performed using GROMACS to gain a better understanding of the effects of mutations on protein structure. Among the 451 missense nsSNPs identified within the <em>GABRA6</em> gene, three were found to have pathogenic effects on the structure and function of the protein, potentially, there may be a contribution to the development of seizures or other neuropsychiatric disorders. Notably, two of these missense nsSNPs (<em>W87S</em> and <em>W112R</em>) were located within the ligand-binding domain, while the third (<em>C310R</em>) was situated in the transmembrane domain. It is crucial to acknowledge that despite their predicted pathogenicity, these variants are currently classified as of uncertain significance in clinical and genomic databases worldwide due to the lack of correlation with epilepsy in empirical studies. Without experimental data to validate these predictions, caution is warranted in interpreting the findings.

A computational discovery of hexokinase 2 inhibitors from Newbouldia laevis for Hepatocellular carcinoma (HCC) treatment

Newbouldia laevis, also known as the African Border Tree or Fever Tree, is a deciduous tree native to West Africa. The plant is valued for its medicinal properties and is used in traditional medicine for its antimicrobial and anti-inflammatory effects. N. laevis, is a storage tank of phytochemicals with huge health benefits and performances globally for the treatment and management of numerous disease conditions. Limited research exists on the usage of N. laevis for hepatocellular carcinoma (HCC) treatment. This study aims to explore the inhibitory activities of phytochemicals from N. laevis against the hexokinase 2 protein, a target in hepatocarcinoma (HCC) treatment. This study presents a unique in silico approach that includes ligand binding site prediction, molecular docking, molecular dynamics simulation, and Molecular Mechanics Poisson–Boltzmann Surface Area (MM/PBSA) methods. A total of 35 phytochemicals with available 3D structures were identified through literature mining. The literature review highlighted the significance of hexokinase 2 protein as a target inhibitor in the treatment of hepatocellular carcinoma (HCC). Molecular docking experiment with the all the identified phytochemicals and hexokinase 2 revealed that all the identified phytochemicals had potential inhibitory activities against the target protein. Moreover, chrysarobin, apigenin and ursolic acid were the best inhibitors with lowest binding energy of −8.9 kcal/mol, −8.7 kcal/mol, and −8.5 kcal/mol, respectively. The docking experiment was validated by comparing the binding affinities with known reference drug Cabozantinib-S-malate (−8.3 kcal/mol). Further, molecular dynamics studies of complexes with the best docking scores and reference drug complexes were described in detail here. The results of 100 ns modeling (RMSD, RMSF, Rg and SASA) show extraordinary stability during the establishment of complexes with apigenin and ursolic acid, as well as favorable binding energy, which was determined theoretically by means of the MM/PBSA method, thereby increase the probability of their acting as promising and likely hexokinase 2 inhibitors. Therefore, the study predicted that apigenin and ursolic acid could be used as a potential inhibitor/antagonist for the hexokinase 2 enzyme.

Bioinformatics analysis and molecular dynamics simulations of azoreductases (AzrBmH2) from Bacillus megaterium H2 for the decolorization of commercial dyes

The present study aimed to investigate the decolorization of various commercial dyes by azoreductases (AzrBmH21, AzrBmH22/3, and AzrBmH24/5) through bioinformatics means, comprising molecular docking, molecular dynamics simulation, and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA). Therefore, four commercial dyes, namely acid orange 7, cresol red, methylene blue, and malachite green, were selected as potential targets for degradation by the above said azoreductases derived from Bacillus megaterium H2. The prediction of ligand binding or catalytic sites for AzrBmH21, AzrBmH22/3, and AzrBmH24/5 were performed using a machine learning algorithm based on the Prank Web and DeepSite chemoinformatic tool. This analysis revealed that several amino acids of AzrBmH2 interacted with the tested dyes, indicating the presence of distinct ligand-binding sites for AzrBmH2-dye complexes. Likewise, the binding affinity for AzrBmH21, AzrBmH22/3, and AzrBmH24/5 ranged from − 9.4 to − 5.5 kcal/mol, − 9.2 to − 5.4 kcal/mol, and − 9.0 to − 5.4 kcal/mol, respectively, with each complex stabilized at a minimum of 0–5 hydrogen bonds. MD simulations revealed stable AzrBmH2-dye complexes with RMSD and RMSF values ranging from 0.15 to 0.42 nm and 0.05 to 0.48 nm, respectively, with Rg values between 1.75 and 1.88 nm. MM-PBSA calculations indicated that the AzrBmH2–dye complexes, except for AzrBmH2–malachite green, exhibited the lowest binding energy (− 191.05 ± 7.08 to 314.19 ± 6.88 kcal/mol), with prevalent hydrophobic interactions (− 268.25 ± 12.25 to − 418.92 ± 29.45 kcal/mol) through van der Waals forces. Therefore, this study was able to highlight the potential role of enzymes, specifically azoreductases from Bacillus megaterium H2, in predicting the decolorization of commercial dyes. These findings could contribute to our understanding of the azoreductases’ mechanisms in bioremediation and for biotechnological applications.

ANN based prediction of ligand binding sites outside deep cavities to facilitate drug designing

The ever-changing environmental conditions and pollution are the prime reasons for the onset of several emerging and re-merging diseases. This demands the faster designing of new drugs to curb the deadly diseases in less waiting time to cure the animals and humans. Drug molecules interact with only protein surface on specific locations termed as ligand binding sites (LBS). Therefore, the knowledge of LBS is required for rational drug designing. Existing geometrical LBS prediction methods rely on search of cavities based on the fact that 83% of the LBS found in deep cavities, however, these methods usually fail where LBS localize outside deep cavities. To overcome this challenge, the present work provides an artificial neural network (ANN) based method to predict LBS outside deep cavities in animal proteins including human to facilitate drug designing. In the present work a feed-forward backpropagation neural network was trained by utilizing 38 structural, atomic, physiochemical, and evolutionary discriminant features of LBS and non-LBS residues localized in the extracted roughest patch on protein surface. The performance of this ANN based prediction method was found 76% better for those proteins where cavity subspace (extracted by MetaPocket 2.0, a consensus method) failed to predict LBS due to their localization outside the deep cavities. The prediction of LBS outside deep cavities will facilitate in drug designing for the proteins where it is not possible due to lack of LBS information as the geometrical LBS prediction methods rely on extraction of deep cavities.

A structure-based drug design approach for the identification of antiviral compounds targeting the chikungunya virus RdRp protein

There are an estimated 3 million Chikungunya virus (CHIKV) infections per year, but no vaccines or specific pharmaceutical treatments are available. RNA-dependent RNA polymerase (RdRp) of CHIKV, encoded by nonstructural protein 4 (nsP4), was targeted to identify potential antiviral compounds in this study. A 3D model of the nsP4 was generated by homology modeling technique and Pockdrug-Server was used to predict ligand binding sites. The pharmacophore hypothesis was developed followed by virtual screening. 300 most potential ligands were selected from a library of 3000 compounds, after filtering for the molecules satisfying Lipinski's Rule of Five. Using molecular docking followed by ADMET analysis, 27 leads were then selected. We used MD simulations of the three best compounds that bind to the catalytic, palm, and thumb domains to find out how the ligand-protein interactions change over time. RMSD, RMSF, radius of gyration, free energy landscapes, and principal component analyses revealed that complexes nsP4-ZINC-12820763 and nsP4-ZINC-33280972 were more stable. Simulated trajectories were further used to compute MMGBSA binding energies of PubChem-135638918 (-22.33 kcal/mol), ZINC-12820763 (-50.19 kcal/mol) and ZINC-33280972 (-32.69 kcal/mol) bound to catalytic, palm and thumb domains, respectively. HOMO and LUMO of potential molecules were also investigated using density functional theory computations. With the support of computational simulations, three compounds were shown to have the potential to inhibit RdRp of CHIKV and need further evaluation using in-vitro and in-vivo analysis.

Identification of Potential Modulators of a Pathogenic G Protein-Gated Inwardly Rectifying K+ Channel 4 Mutant: In Silico Investigation in the Context of Drug Discovery for Hypertension.

Genetic abnormalities have been associated with primary aldosteronism, a major cause of secondary hypertension. This includes mutations in the KCNJ5 gene, which encodes G protein-gated inwardly rectifying K+ channel 4 (GIRK4). For example, the substitution of glycine with glutamic acid gives rise to the pathogenic GIRK4G151E mutation, which alters channel selectivity, making it more permeable to Na+ and Ca2+. While tertiapin and tertiapin-Q are well-known peptide inhibitors of the GIRK4WT channel, clinically, there is a need for the development of selective modulators of mutated channels, including GIRK4G151E. Using in silico methods, including homology modeling, protein-peptide docking, ligand-binding site prediction, and molecular docking, we aimed to explore potential modulators of GIRK4WT and GIRK4G151E. Firstly, protein-peptide docking was performed to characterize the binding site of tertiapin and its derivative to the GIRK4 channels. In accordance with previous studies, the peptide inhibitors preferentially bind to the GIRK4WT channel selectivity filter compared to GIRK4G151E. A ligand-binding site analysis was subsequently performed, resulting in the identification of two potential regions of interest: the central cavity and G-loop gate. Utilizing curated chemical libraries, we screened over 700 small molecules against the central cavity of the GIRK4 channels. Flavonoids, including luteolin-7-O-rutinoside and rutin, and the macrolides rapamycin and troleandomycin bound strongly to the GIRK4 channels. Similarly, xanthophylls, particularly luteoxanthin, bound to the central cavity with a strong preference towards the mutated GIRK4G151E channel compared to GIRK4WT. Overall, our findings suggest potential lead compounds for further investigation, particularly luteoxanthin, that may selectively modulate GIRK4 channels.

Multi-cavity molecular descriptor interconnections: Enhanced protocol for prediction of serum albumin drug binding

The role of human serum albumin (HSA) in the transport of molecules predicates its involvement in the determination of drug distribution and metabolism. Optimization of ADME properties are analogous to HSA binding thus this is imperative to the drug discovery process. Currently, various in silico predictive tools exist to complement the drug discovery process, however, the prediction of possible ligand-binding sites on HSA has posed several challenges. Herein, we present a strong and deeper-than-surface case for the prediction of HSA-ligand binding sites using multi-cavity molecular descriptors by exploiting all experimentally available and crystallized HSA-bound drugs. Unlike previously proposed models found in literature, we established an in-depth correlation between the physicochemical properties of available crystallized HSA-bound drugs and different HSA binding site characteristics to precisely predict the binding sites of investigational molecules. Molecular descriptors such as the number of hydrogen bond donors (nHD), number of heteroatoms (nHet), topological polar surface area (TPSA), molecular weight (MW), and distribution coefficient (LogD) were correlated against HSA binding site characteristics, including hydrophobicity, hydrophilicity, enclosure, exposure, contact, site volume, and donor/acceptor ratio. Molecular descriptors nHD, TPSA, LogD, nHet, and MW were found to possess the most inherent capacities providing baseline information for the prediction of serum albumin binding site. We believe that these associations may form the bedrock for establishing a solid correlation between the physicochemical properties and Albumin binding site architecture. Information presented in this report would serve as critical in provisions of rational drug designing as well as drug delivery, bioavailability, and pharmacokinetics.

Pharmacology of Berberine and Its Metabolites, Is It the Natures Ozempic or Imatinib?

Background: Berberine, a naturally occurring alkaloid, is widely explored for several health benefits, including weight management and metabolic disorders. The major pharmacological action of berberine is reported to be by activation of AMP-activated protein kinase, while its other clinical outcomes are devoid of clear mechanism of action/s. Hence in this study a detailed pharmacology of berberine and its two major metabolites (berberrubine, and jatrorrhizine) in humans was evaluated using well established Insilco tools. Materials and Methods: The targets of berberine and its metabolites were identified in SwissTargetPrediction server and their affinity was assed using AutoDock vina 1.2.0. The binding pockets of the highest ligand receptor combinations was assessed using the PrankWeb: Ligand Binding Site Prediction tool. Results: Kinases, enzymes and family A GPCR’s were identified as the top three target category of berberine and its metabolites. ROCK2, PIK3CD, KCNMA1, CSF1R, and KIT were observed to be the high affinity targets of berberine and its metabolites with affinity values of <4 uM. The affinity of berberine and its metabolites against all AMPKs and lipid/glucose regulator targets (LDLR, DDP4 and PCSK9) were >10 uM. The IC50 value of berberine and its metabolites against ROCK2 was the least (<1 uM), while their other high affinity targets (PIK3CD, KCNMA1, CSF1R and KIT) showed IC50 values <5 uM. Conclusion: The diverse range of protein targets and the observed novel high affinity targets (ROCK2, PIK3CD, KCNMA1, CSF1R and KIT) offer valuable insights into the potential mechanisms of action and therapeutic effects of berberine and its metabolites in various disease conditions, which warrants validation in suitable efficacy analysis studies.

Pseq2Sites: Enhancing protein sequence-based ligand binding-site prediction accuracy via the deep convolutional network and attention mechanism

Protein-ligand interactions play an essential role in many biological processes, and prior knowledge of ligand binding sites is necessary for successful drug design. Many 3D structure- and sequence-based methods have been proposed for identifying ligand binding sites. The 3D structure-based methods typically achieve better binding site prediction than the sequence-based methods. However, as deep-learning techniques that can extract structural information from large-scale sequence data have been developed, the performance gap between 3D structure- and sequence-based methods is narrowing. Nonetheless, there remains room for improvement in sequence-based prediction. We propose Pseq2Sites, a sequence-based deep-learning model for predicting ligand binding sites. Pseq2Sites comprises a 1D convolutional neural network that extracts local features from the protein sequence, and a position-based attention mechanism that captures long-distance dependencies between binding residues. To verify the effectiveness of the proposed method, we compared it with other state-of-the-art methods using three public datasets: COACH420, HOLO4K, and CSAR-NRC HiQ. Utilizing solely protein sequence information, Pseq2Sites outperformed 3D structure-based state-of-the-art methods on external test datasets; within the COACH420 dataset, Pseq2Sites remarkably identified 97% of the binding pockets (at a significance level δ = 0.5), which was 27% higher than the second highest-performing model. Pseq2Sites also achieved outstanding binding site prediction, even for proteins with low similarity to the training dataset. Our code is available at https://github.com/Blue1993/Pseq2Sites.

High-throughput virtual screening and molecular dynamics simulation reveals NPC170742 a novel chalconoid compound as a potential inhibitor of D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase in Helicobacter pylori.

Helicobacter pylori is a gram negative spiral shaped bacteria that causes peptic ulcer and gastric cancer. It Is the sixth most prevalent cancer in the world and the third leading cause of cancer death. The increase in reported cases of H. pylori resistance to the drugs and antibiotics shows the need for the development of new and efficient drugs against the pathogen. In the present study, D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase (GmhB), an enzyme involved in the biosynthesis of lipopolysaccharides that encourages bacterial adherence, self-aggregation and identifying the host cells was modelled and the active sites were predicted through POCASA which is an automated ligand binding site prediction server. Natural product activity and species source (NPASS) is a database of 96,481 natural compounds that were subjected to virtual screening workflow that includes Qikprop, Lipinski rule, filtering out reactive functional groups followed by high throughput virtual screening and the top 10 compounds were selected for further induced fit docking along with the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate. The compound NPC170742 (Alpha, Beta, 3,4,5,2',4',6'-Octahydroxy dihydrochalcone) showed higher affinity than the substrate, and both the substrate D-glycero-β-D-manno-heptose 1,7-bisphosphate and the compound NPC170742 were subjected to molecular dynamics simulation. The results exposed the compound NPC170742 could be a potential lead compound against the enzyme D-glycero-D-manno-heptose-1,7-bisphosphate 7-phosphatase of H. pylori.Communicated by Ramaswamy H. Sarma.

ZPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15.

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions. This module enables the extraction of protein-ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic-level protein-ligand feature encoding and fusion to output refined ligand poses and ligand per-atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF-2016 protein-ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein-ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side-chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein-ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

Faltwerk: a library for spatial exploratory data analysis of protein structures.

Proteins are fundamental building blocks of life and are investigated in a broad range of scientific fields, especially in the context of recent progress using in silico structure prediction models and the surge of resulting protein structures in public databases. However, exploratory data analysis of these proteins can be slow because of the need for several methods, ranging from geometric and spatial analysis to visualization. The Python library faltwerk provides an integrated toolkit to perform explorative work with rapid feedback. This toolkit includes support for protein complexes, spatial analysis (point density or spatial autocorrelation), ligand binding site prediction and an intuitive visualization interface based on the grammar of graphics. faltwerk is distributed under the permissive BSD-3 open source license. Source code and documentation, including an extensive common-use case tutorial, can be found at github.com/phiweger/faltwerk; binaries are available from the pypi repository.

A multilayer dynamic perturbation analysis method for predicting ligand–protein interactions

BackgroundLigand–protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets.ResultsIn this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods.ConclusionsMDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa.

A structural biology community assessment of AlphaFold2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.

RiboXYZ: a comprehensive database for visualizing and analyzing ribosome structures.

Recent advances in Cryo-EM led to a surge of ribosome structures deposited over the past years, including structures from different species, conformational states, or bound with different ligands. Yet, multiple conflicts of nomenclature make the identification and comparison of structures and ortholog components challenging. We present RiboXYZ (available at https://ribosome.xyz), a database that provides organized access to ribosome structures, with several tools for visualisation and study. The database is up-to-date with the Protein Data Bank (PDB) but provides a standardized nomenclature that allows for searching and comparing ribosomal components (proteins, RNA, ligands) across all the available structures. In addition to structured and simplified access to the data, the application has several specialized visualization tools, including the identification and prediction of ligand binding sites, and 3D superimposition of ribosomal components. Overall, RiboXYZ provides a useful toolkit that complements the PDB database, by implementing the current conventions and providing a set of auxiliary tools that have been developed explicitly for analyzing ribosome structures. This toolkit can be easily accessed by both experts and non-experts in structural biology so that they can search, visualize and compare structures, with various potential applications in molecular biology, evolution, and biochemistry.

HProteome-BSite: predicted binding sites and ligands in human 3D proteome.

Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.

In Silico Domain Structural Model Analysis of Coronavirus ORF1ab Polyprotein

The world today is battling with a coronavirus infection that is considered a global pandemic. Coronavirus infection is mainly attribute to the varying technique of the replication and release of different genomic components of the virus. The present study aims to establish the physical and chemical features, as well as the basic structural and functional properties of Coronavirus ORF1ab domain. A molecular approach was adopt in this study using the Swiss Model and Phyre2 server whereas the prediction of the active ligand binding sites was done using Phyre2. The analysis of the structure of the protein showed that it has good structural and heat stability, as well as better hydrophilic features and acidic in nature. Based on the Homology modeling, only two binding active sites were noted with catalytic function being mediated by Zn2+ as the metallic heterogeneous ligand for binding sites prediction. The proteins mostly exhibited helical secondary configurations. This study can help in predicting and understanding the role of this domain protein in active coronavirus infection.

Prediction of ligand binding sites using improved blind docking method with a Machine Learning-Based scoring function

The identification of the ligand binding sites (LBS) in proteins is of great significance for the elucidation of protein structure and function. Different methods have been published to predict protein–ligand binding sites efficiently. However, most of the current prediction methods only focus on the characteristics of proteins without considering the interactions between proteins and their different ligands, which often leads to frustrating results when the structure or function of the protein is complex or diverse. Therefore, an improved blind docking method with a machine learning-based scoring function is proposed in this paper for the LBS prediction. The blind docking method is used to search binding pockets and an artificial neural network is constructed to analyze binding features, which makes the proposed method possible to distinguish true binding sites from other possible pockets. Two cases of LBS prediction are presented to show the excellent performance of the proposed method. This paper aims to provide new ideas for the prediction of interactions between proteins and small molecules, which can further guide the research of structure-based drug discovery.

PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures.

Knowledge of protein-ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https://prankweb.cz/) for fast and accurate LBS prediction. Here, we present significant enhancements to PrankWeb. First, a new, more accurate evolutionary conservation estimation pipeline based on the UniRef50 sequence database and the HMMER3 package is introduced. Second, PrankWeb now allows users to enter UniProt ID to carry out LBS predictions in situations where no experimental structure is available by utilizing the AlphaFold model database. Additionally, a range of minor improvements has been implemented. These include the ability to deploy PrankWeb and P2Rank as Docker containers, support for the mmCIF file format, improved public REST API access, or the ability to batch download the LBS predictions for the whole PDB archive and parts of the AlphaFold database.