Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design

Targeting SARS-CoV2 spike glycoprotein: molecular insights into phytocompounds binding interactions - in-silico molecular docking

This study utilized small molecular characterization and docking study to evaluate the binding affinity of seven antiviral phytocompounds with the SARS CoV-2 variants (SARS-CoV-2 Spike Glycoprotein, SARS-CoV-2 Spike Protein Variant in 1-RBD, Alpha Variant SARS-CoV2- Spike Protein). The results revealed that five of seven compounds, possesses excellent drug lead property reveled through in-silico ADMET analysis. In addition, six of seven except D-Glucosamine, exhibited excellent binding affinity. Six ligands possess significant binding affinity towards SARS-CoV-2 variants 6VXX, 7LWV and 7R13, which is certainly greater than Remdesivir. Fagaronine found to be the best drug candidate against SARS-CoV-2 variants, It was found that -7.4, -5.6 and -6.3 is the docking score respectively. Aranotin, Beta aescin, Gliotoxin, and Fagaronine formed hydrogen bonds with specific amino acids and exhibited significant binding interactions. These findings suggest that these phytocompounds could be promising candidates for developing antiviral therapies against SARS-CoV-2. Moreover, the study underscores the importance of molecular docking in understanding protein-ligand interactions and its role in drug discovery. The documented pharmacological properties of these compounds in the literature further support their potential therapeutic relevance in various diseases.

Colloidal Nanoparticles of a β-Cyclodextrin/L-Tryptophan Inclusion Complex for Use as Pickering Emulsion Stabilizers

Inclusion complexes of β-cyclodextrin (β-CD) and tryptophan (Trp) were synthesized using an antisolvent approach, and fully characterized. Scanning electron microscope images proved the formation of the β-CD/Trp NPs and the powder X-ray diffraction pattern indicated the formation of a crystalline channel-like structure for the β-CD/Trp nanoparticles (NPs). The NPs of a β-CD/Trp inclusion complex were used as a natural stabilizer at the oil/water interface of a Pickering emulsion. Pickering emulsions with an oil to water ratio of 1:1 (v:v) were obtained under high-speed homogenization and different mass ratios of the β-CD to Trp (1:0, 1:0.1, 1:0.25, 1:0.5, 1:1), and at different pH levels (3, 5, 7, 9). At pH 9, when the β-CD:Trp mass ratio was 1:0.1, the β-CD/Trp NPs were hydrophilic, and the oil-in-water Pickering emulsions stabilized by those nanoparticles showed the highest storage stability: 180 days at room temperature. In contrast, when the emulsions were prepared at pH 5 with the weight ratios of either 1:0.1 or 1:1, β-CD:Trp, the nanoparticles were hydrophobic and could be used to stabilize water-in-oil Pickering emulsions.

Investigating anti-inflammatory actions of marine algal compound against lipoxygenase concentrating on therapeutic applications through computational approach

Inflammation is the preliminary response given to any possible harmful stimuli including infections, injury or stress by immune system where neutrophils and macrophages gets activated and produces mediators, such as nitric oxide and cytokines that serves as biomarkers of inflammation. Lipoxygenases are enzymes that peroxidises lipids and are involved in the pathogenesis of several diseases including inflammatory diseases. These are oxidative enzymes comprising a non-heme iron atom in active site and are convoluted in inflammatory reactions. Fucoidan is sulphated polysaccharide that has numerous pharmacological implications. Implications of fucoidan on inflammatory diseases are still an objective of rigorous research. Therefore, this study focusses on investigating lipoxygenase inhibitory activities of fucoidan. The mechanism of lipoxygenase inhibitory activities of fucoidan was studied via molecular docking and molecular dynamics simulations. The docking score produced by the binding of the fucoidan to the lipoxygenase was - 6.69 kcal/mol whereas, the docking score in case of Aspirin and Zileuton were -5.8 kcal/mol and -7.0 kcal/mol and it was found that fucoidan makes hydrogen bonds with lipoxygenase protein through polar amino acid glutamine at GLN 514. The results obtained from molecular dynamics simulations proposed the development of a stable complex between fucoidan and lipoxygenase due to the establishment of favourable interactions with amino acid residues and indicated efficient results when compared with Aspirin and Zileuton. This study suggested that fucoidan had anti-inflammatory potentials and thus can be used as a promising drug candidate against inflammation.Communicated by Ramaswamy H. Sarma.

A comprehensive survey on protein-ligand binding site prediction

Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.

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.

Unraveling the Role of Flavor Structure and Physicochemical Properties in the Binding Phenomenon with Commercial Food Protein Isolates

Food protein-flavor binding influences flavor release and perception. The complexity of the binding phenomenon lies in the flavor and protein properties. Thus, molecular interactions between commercial whey- or plant-based protein isolates (PI) such as pea, soy, and lupin, with carbonyl and alcohol flavor compounds were assessed by static headspace (HS) GC-MS. HS results showed that not only the displacement of the carbonyl group from the inner part of the flavor structure toward the edge promoted binding up to 52.76% ± 4.65 but also the flavor's degree of unsaturation. Similarly, thermal treatment led to a slight increase in hexanal-protein binding because of possible protein conformational changes. Protein's residual fat (<1%) seemed insufficient to promote significant flavor binding to PI. Despite the complexity of commercial food protein isolates, the results displayed that binding is predominantly influenced by the flavor structure and physicochemical properties, with the protein source and residual fat playing a secondary role.

Mechanism of Nardostachyos Radix et Rhizoma-Salidroside in the treatment of premature ventricular beats based on network pharmacology and molecular docking

To analyse the mechanism of Nardostachyos Radix et Rhizoma-Salidroside in the treatment of Premature Ventricular Brats by using network pharmacology and molecular docking and to provide the basis for developing the use of experimental and clinical traditional Chinese medicine. The chemical compositions of Nardostachyos Radix et Rhizoma and Salidroside were determined, and their related targets were predicted. The disease-related targets were obtained by searching the common disease databases Genecards, OMIM, Drugbank and DisGeNET, and the intersection between the predicted targets and the disease targets was determined. Then using the STRING database to set up the protein‒protein interactions (PPIs) network between Nardostachyos Radix et Rhizoma-Salidroside and the common targets of PVB. An "herb-ingredient-target" network was constructed and analyzed by Cytoscape3.7.2 software. Using the metascape database to analysis the predicted therapeutic targets based on the GO and KEGG. Finally, molecular docking technology was used toconfirm the capacity of the primary active ingredients of the 2 herbs to bind to central targets using the online CB-Dock2 database. 41 active components of Nardostachyos Radix et Rhizoma-Salidroside were detected, with 420 potential targets of action, with a total of 1688 PVB targets, and the top 10 core targets of herb-disease degree values were AKT1, TNF, GAPDH, SRC, PPARG, EGFR, PTGS2, ESR1, MMP9, and STAT3. KEGG analysis indicated that its mechanism may be related to the calcium signalling pathway, cancer signalling pathway, AGE-RAGE signalling pathway and other pathways. Molecular docking suggested that main of the active ingredients of the Nardostachyos Radix et Rhizoma-Salidroside pairs were well bound to the core targets. Based on novel network pharmacology and molecular docking validation research methods, we revealed for the first time the potential mechanism of Nardostachyos Radix et Rhizoma-Salidroside in PVB therapy.

Bioindustrial applications of thermostable Endoglucanase purified from Trichoderma viride towards the conversion of agrowastes to value-added products

Importance of biocatalytic reactions and biotransformations mediated by fungal enzymes has increased tremendously in various industries. Endoglucanase obtained from Trichoderma viride has been utilized for bioconversion of agrowastes; wheat straw (WS) and corn stover (CS) as biomass into citric acid and single cell protein (SCP) as value-added products. The enzyme was purified to apparent homogeneity with Mr:44.67 kDa; purification-fold, yield, specific activity to be 19.5-, 29.2%, and 150.4 Units.mg-1, respectively, with thermostability up to 70 °C. The enzyme showed a novel N-terminal peptide and its computational analysis revealed a conserved 'SG' amino acid sequence alike microbial cellulases. The experimental results have shown the potential of endoglucanase for the conversion of agrowastes; wheat straw (WS) and corn stover (CS) into citric acid, maximum yield (KgM-3) found in submerged (WS:50;CS:45) fermentation process. Single-cell protein (SCP) production in WS (68 KgM-3) hydrolysate was superior to both CS hydrolysate (60 KgM-3) and YEPD (standard medium) (58 KgM-3).

In Silico Identification of 1-DTP Inhibitors of Corynebacterium diphtheriae Using Phytochemicals from Andrographis paniculata

A number of phytochemicals have been identified as promising drug molecules against a variety of diseases using an in-silico approach. The current research uses this approach to identify the phyto-derived drugs from Andrographis paniculata (Burm. f.) Wall. ex Nees (AP) for the treatment of diphtheria. In the present study, 18 bioactive molecules from Andrographis paniculata (obtained from the PubChem database) were docked against the diphtheria toxin using the AutoDock vina tool. Visualization of the top four molecules with the best dockscore, namely bisandrographolide (-10.4), andrographiside (-9.5), isoandrographolide (-9.4), and neoandrographolide (-9.1), helps gain a better understanding of the molecular interactions. Further screening using molecular dynamics simulation studies led to the identification of bisandrographolide and andrographiside as hit compounds. Investigation of pharmacokinetic properties, mainly ADMET, along with Lipinski's rule and binding affinity considerations, narrowed down the search for a potent drug to bisandrographolide, which was the only molecule to be negative for AMES toxicity. Thus, further modification of this compound followed by in vitro and in vivo studies can be used to examine itseffectiveness against diphtheria.

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science

BACKGROUND

Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines.

RESULTS

pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook.

CONCLUSIONS

We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Molecular Recognition and Self-Organization in Life Phenomena Studied by a Statistical Mechanics of Molecular Liquids, the RISM/3D-RISM Theory

There are two molecular processes that are essential for living bodies to maintain their life: the molecular recognition, and the self-organization or self-assembly. Binding of a substrate by an enzyme is an example of the molecular recognition, while the protein folding is a good example of the self-organization process. The two processes are further governed by the other two physicochemical processes: solvation and the structural fluctuation. In the present article, the studies concerning the two molecular processes carried out by Hirata and his coworkers, based on the statistical mechanics of molecular liquids or the RISM/3D-RISM theory, are reviewed.

In silico evaluation of drugs used in treatment of oral lichen planus

BACKGROUND

Oral lichen planus (OLP) is a chronic T cell-mediated, immunological, mucocutaneous disease with a number of genes and inflammatory mediators implicated in its pathogenesis. Heart shock protein 70 and the proinflammatory mediator TNFα have been predominantly involved in the etiopathogenesis of oral lichen planus.

METHODS

In this study, the action of 27 commonly used drugs for treating OLP at HSP70 and TNFα were evaluated by molecular docking using Maestro Schrodinger version 10.1. X-ray crystallographic structures of the target proteins, that is, Heat Shock Protein 70 (PDB Code: 6FDT) and tumor necrosis factor alpha-1 (PDB Code: 1TNF) were obtained from Protein Data Bank (PDB). The structures of the ligands (27 drugs) were obtained from PubChem in.sdf format. Using Ligprep, pre-processing of the ligands was done. Extra-precision docking was performed with the prepared protein and the ligands.

RESULTS

With respect to HSP70, the highest dock score (-4.768) and glide score (-4.818) were seen with hydroxychloroquine (HCQ), followed by epigallocatechin gallate (green tea), methotrexate, and curcumin. The highest dock (-9.525) and glide score (-9.584) in TNFα were seen in with epigallocatechin gallate, followed by HCQ, dapsone, and methotrexate.

CONCLUSION

The results of the study tend to explain the clinical use of HCQ in recalcitrant and severe cases, as well as the anti-inflammatory property of epigallocatechin gallate. The results of the study open ventures for exploring the in silico behavior of drugs for effective pathological management.

Exploring the computational methods for protein-ligand binding site prediction

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 great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

CB-Dock: a web server for cavity detection-guided protein-ligand blind docking

As the number of elucidated protein structures is rapidly increasing, the growing data call for methods to efficiently exploit the structural information for biological and pharmaceutical purposes. Given the three-dimensional (3D) structure of a protein and a ligand, predicting their binding sites and affinity are a key task for computer-aided drug discovery. To address this task, a variety of docking tools have been developed. Most of them focus on docking in the preset binding sites given by users. To automatically predict binding modes without information about binding sites, we developed a user-friendly blind docking web server, named CB-Dock, which predicts binding sites of a given protein and calculates the centers and sizes with a novel curvature-based cavity detection approach, and performs docking with a popular docking program, Autodock Vina. This method was carefully optimized and achieved ~70% success rate for the top-ranking poses whose root mean square deviation (RMSD) were within 2 Å from the X-ray pose, which outperformed the state-of-the-art blind docking tools in our benchmark tests. CB-Dock offers an interactive 3D visualization of results, and is freely available at http://cao.labshare.cn/cb-dock/.

In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors

The Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a segmented negative single-stranded RNA virus (-ssRNA) which causes severe hemorrhagic fever in humans with a mortality rate of ~50%. To date, no vaccine has been approved. Treatment is limited to supportive care with few investigational drugs in practice. Previous studies have identified viral RNA dependent RNA Polymerase (RdRp) as a potential drug target due to its significant role in viral replication and transcription. Since no crystal structure is available yet, we report the structural elucidation of CCHFV-RdRp by in-depth homology modeling. Even with low sequence identity, the generated model suggests a similar overall structure as previously reported RdRps. More specifically, the model suggests the presence of structural/functional conserved RdRp motifs for polymerase function, the configuration of uniform spatial arrangement of core RdRp sub-domains, and predicted positively charged entry/exit tunnels, as seen in sNSV polymerases. Extensive pharmacophore modeling based on per-residue energy contribution with investigational drugs allowed the concise mapping of pharmacophoric features and identified potential hits. The combination of pharmacophoric features with interaction energy analysis revealed functionally important residues in the conserved motifs together with in silico predicted common inhibitory binding modes with highly potent reference compounds.

Development of a Microemulsion Formulation for Antimicrobial SecA Inhibitors

In our previous study, we have identified five antimicrobial small molecules via structure based design, which inhibit SecA of Candidatus Liberibacter asiaticus (Las). SecA is a critical protein translocase ATPase subunit and is involved in pre-protein translocation across and integration into the cellular membrane in bacteria. In this study, eleven compounds were identified using similarity search method based on the five lead SecA inhibitors identified previously. The identified SecA inhibitors have poor aqueous solubility. Thus a microemulsion master mix (MMX) was developed to address the solubility issue and for application of the antimicrobials. MMX consists of N-methyl-2-pyrrolidone and dimethyl sulfoxide as solvent and co-solvent, as well as polyoxyethylated castor oil, polyalkylene glycol, and polyoxyethylene tridecyl ether phosphate as surfactants. MMX has significantly improved the solubility of SecA inhibitors and has no or little phytotoxic effects at concentrations less than 5.0% (v/v). The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the SecA inhibitors and streptomycin against eight bacteria including Agrobacterium tumefaciens, Liberibacter crescens, Rhizobium etli, Bradyrhizobium japonicum, Mesorhizobium loti, and Sinorhizobium meliloti phylogenetically related to Las were determined using the broth microdilution method. MIC and MBC results showed that the 16 SecA inhibitors have antibacterial activities comparable to that of streptomycin. Overall, we have identified 11 potent SecA inhibitors using similarity search method. We have developed a microemulsion formulation for SecA inhibitors which improved the antimicrobial activities of SecA inhibitors.

Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers

We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties.

Computational allosteric ligand binding site identification on Ras proteins

A number of computational techniques have been proposed to expedite the process of allosteric ligand binding site identification in inherently flexible and hence challenging drug targets. Some of these techniques have been instrumental in the discovery of allosteric ligand binding sites on Ras proteins, a group of elusive anticancer drug targets. This review provides an overview of these techniques and their application to Ras proteins. A summary of molecular docking and binding site identification is provided first, followed by a more detailed discussion of two specific techniques for binding site identification in ensembles of Ras conformations generated by molecular simulations.

Focusing on probe-modified peptides: a quick and effective method for target identification

BTC-ABPP was developed by converting the reactants of a click conjugation from proteins (biochemistry) to peptides (chemistry) to identify the modified peptides.

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets

Background

Computational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated.

Methods

In this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by eFindSite.

Results

A systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins.

Conclusions

This fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at www.brylinski.org/content/docking-box-size.

Graphical Abstract

Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features

BACKGROUND

Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking - how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results.

RESULTS

We have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocket points and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are finally merged into the resulting pocket score to be used for prioritization of the putative pockets. With the used of multiple datasets the experimental results demonstrate that the application of our method as a post-processing step greatly increases the quality of the prediction of Fpocket and ConCavity, two state of the art protein-ligand binding site prediction algorithms.

CONCLUSIONS

The positive experimental results show that our method can be used to improve the success rate, validity and applicability of existing protein-ligand binding site prediction tools. The method was implemented as a stand-alone program that currently contains support for Fpocket and Concavity out of the box, but is easily extendible to support other tools. PRANK is made freely available at http://siret.ms.mff.cuni.cz/prank.

A simple method for finding a protein's ligand-binding pockets

BACKGROUND

This paper provides a simple and rapid method for a protein-clustering strategy. The basic idea implemented here is to use computational geometry methods to predict and characterize ligand-binding pockets of a given protein structure. In addition to geometrical characteristics of the protein structure, we consider some simple biochemical properties that help recognize the best candidates for pockets in a protein's active site.

RESULTS

Our results are shown to produce good agreement with known empirical results.

CONCLUSIONS

The method presented in this paper is a low-cost rapid computational method that could be used to classify proteins and other biomolecules, and furthermore could be useful in reducing the cost and time of drug discovery.

A pharmacophore-based virtual screening approach for the discovery of Trypanosoma cruzi GAPDH inhibitors

BACKGROUND

Chagas disease is a major cause of morbidity and death for millions of people in Latin America. The drugs currently available exhibit poor efficacy and severe side effects. Therefore, there is an urgent need for new, safe and effective drugs against Chagas disease. The vital dependence on glycolysis as energy source makes the glycolytic enzymes of Trypanosoma cruzi, the causative agent of Chagas disease, attractive targets for drug design. In this work, glyceraldehyde-3-phosphate dehydrogenase from T. cruzi (TcGAPDH) was employed as molecular target for the discovery of new inhibitors as hits.

RESULTS

Integrated protein-based pharmacophore and structure-based virtual screening approaches resulted in the identification of three hits from three chemical classes with moderate inhibitory activity against TcGAPDH. The inhibitors showed IC50 values in the high micromolar range.

CONCLUSION

The new chemotypes are attractive molecules for future medicinal chemistry efforts aimed at developing new lead compounds for Chagas disease.

MP:PD--a data base of internal packing densities, internal packing defects and internal waters of helical membrane proteins

The membrane protein packing database (MP:PD) (http://proteinformatics.charite.de/mppd) is a database of helical membrane proteins featuring internal atomic packing densities, cavities and waters. Membrane proteins are not tightly packed but contain a considerable number of internal cavities that differ in volume, polarity and solvent accessibility as well as in their filling with internal water. Internal cavities are supposed to be regions of high physical compressibility. By serving as mobile hydrogen bonding donors or acceptors, internal waters likely facilitate transition between different functional states. Despite these distinct functional roles, internal cavities of helical membrane proteins are not well characterized, mainly because most internal waters are not resolved by crystal structure analysis. Here we combined various computational biophysical techniques to characterize internal cavities, reassign positions of internal waters and calculate internal packing densities of all available helical membrane protein structures and stored them in MP:PD. The database can be searched using keywords and entries can be downloaded. Each entry can be visualized in Provi, a Jmol-based protein viewer that provides an integrated display of low energy waters alongside membrane planes, internal packing density, hydrophobic cavities and hydrogen bonds.

Balancing target flexibility and target denaturation in computational fragment-based inhibitor discovery

Accounting for target flexibility and selecting "hot spots" most likely to be able to bind an inhibitor continue to be challenges in the field of structure-based drug design, especially in the case of protein-protein interactions. Computational fragment-based approaches using molecular dynamics (MD) simulations are a promising emerging technology having the potential to address both of these challenges. However, the optimal MD conditions permitting sufficient target flexibility while also avoiding fragment-induced target denaturation remain ambiguous. Using one such technology (Site Identification by Ligand Competitive Saturation, SILCS), conditions were identified to either prevent denaturation or identify and exclude trajectories in which subtle but important denaturation was occurring. The target system used was the well-characterized protein cytokine IL-2, which is involved in a protein-protein interface and, in its unliganded crystallographic form, lacks surface pockets that can serve as small-molecule binding sites. Nonetheless, small-molecule inhibitors have previously been discovered that bind to two "cryptic" binding sites that emerge only in the presence of ligand binding, highlighting the important role of IL-2 flexibility. Using the above conditions, SILCS with hydrophobic fragments was able to identify both sites based on favorable fragment binding while avoiding IL-2 denaturation. An important additional finding was that acetonitrile, a water-miscible fragment, fails to identify either site yet can induce target denaturation, highlighting the importance of fragment choice.

From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling

Protein structure-based pharmacophore (SBP) models derive the molecular features a ligand must contain to be biologically active by conversion of protein properties to reciprocal ligand space. SBPs improve molecular understanding of ligand–protein interactions and can be used as valuable tools for hit and lead optimization, compound library design, and target hopping.

[Development and validation of programs for ligand-binding-pocket search]

Searching for the ligand-binding pockets of proteins plays an important role in structure-based drug design (SBDD), which is based on knowledge of the three-dimensional structures of target proteins. In SBDD, small molecules that can interact with the target protein are designed. SBDD methods require the identification of ligand-binding pockets, in which ligand molecules interact with protein atoms. The computer programs for the detection of ligand-binding pockets are categorized into two types: one is programs using only geometric properties; and the other is programs using the physicochemical properties of proteins as well as geometry. This paper describes the development and evaluation of a program for ligand-binding pocket search. The program HBOP (Hydropho Bicity On a Protein) searches for ligand-binding pockets using hydrophobic potentials derived from experimentally determined functions. This is based on the fact that hydrophobicity plays a significant role in protein-ligand binding. The results of evaluation indicate that programs using physicochemical properties can discover actual ligand-binding pockets more efficiently than those using only geometric properties.

Using active site mapping and receptor-based pharmacophore tools: prelude to docking and de novo/fragment-based ligand design

Understanding the three-dimensional aspects of drug-receptor interactions and their specificity at the molecular level has become a focal point in modern drug discovery. Herein, we describe a set of methods by which the binding site on a protein can be located and mapped and the protein-ligand intermolecular interactions can be studied in the context of drug discovery. The methodology we describe is based on the empirical Hydropathic INTeraction (HINT) force field. Applications of the novel cavity detection algorithm, VICE, are demonstrated in delineating the binding pockets. The binding site environment is mapped using hydropathic "complementary map." The two binding sites are compared by calculating their 3D differences and the intermolecular interactions between a bound ligand and protein was further studied by HINT intermolecular maps. We illustrate the applications of these different types of HINT maps through an example from the development of selective COX-2 inhibitors.

MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets

MOTIVATION

Identification of ligand binding pockets on proteins is crucial for the characterization of protein functions. It provides valuable information for protein-ligand docking and rational engineering of small molecules that regulate protein functions. A major number of current prediction algorithms of ligand binding pockets are based on cubic grid representation of proteins and, thus, the results are often protein orientation dependent.

RESULTS

We present the MSPocket program for detecting pockets on the solvent excluded surface of proteins. The core algorithm of the MSPocket approach does not use any cubic grid system to represent proteins and is therefore independent of protein orientations. We demonstrate that MSPocket is able to achieve an accuracy of 75% in predicting ligand binding pockets on a test dataset used for evaluating several existing methods. The accuracy is 92% if the top three predictions are considered. Comparison to one of the recently published best performing methods shows that MSPocket reaches similar performance with the additional feature of being protein orientation independent. Interestingly, some of the predictions are different, meaning that the two methods can be considered complementary and combined to achieve better prediction accuracy. MSPocket also provides a graphical user interface for interactive investigation of the predicted ligand binding pockets. In addition, we show that overlap criterion is a better strategy for the evaluation of predicted ligand binding pockets than the single point distance criterion.

AVAILABILITY

The MSPocket source code can be downloaded from http://appserver.biotec.tu-dresden.de/MSPocket/. MSPocket is also available as a PyMOL plugin with a graphical user interface.

Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins

Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Ligand-binding site prediction of proteins based on known fragment-fragment interactions

Motivation: The identification of putative ligand-binding sites on proteins is important for the prediction of protein function. Knowledge-based approaches using structure databases have become interesting, because of the recent increase in structural information. Approaches using binding motif information are particularly effective. However, they can only be applied to well-known ligands that frequently appear in the structure databases.

Results: We have developed a new method for predicting the binding sites of chemically diverse ligands, by using information about the interactions between fragments. The selection of the fragment size is important. If the fragments are too small, then the patterns derived from the binding motifs cannot be used, since they are many-body interactions, while using larger fragments limits the application to well-known ligands. In our method, we used the main and side chains for proteins, and three successive atoms for ligands, as fragments. After superposition of the fragments, our method builds the conformations of ligands and predicts the binding sites. As a result, our method could accurately predict the binding sites of chemically diverse ligands, even though the Protein Data Bank currently contains a large number of nucleotides. Moreover, a further evaluation for the unbound forms of proteins revealed that our building up procedure was robust to conformational changes induced by ligand binding.

Availability: Our method, named ‘BUMBLE’, is available at http://bumble.hgc.jp/

Contact:kasahara@cb.k.u-tokyo.ac.jp

Supplementary information:Supplementary Material is available at Bioinformatics online.

On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins

Most function prediction methods that identify cognate ligands from binding site analyses work on the assumption of molecular complementarity. These approaches build on the conjectured complementarity of geometrical and physicochemical properties between ligands and binding sites so that similar binding sites will bind similar ligands. We found that this assumption does not generally hold for protein-ligand interactions and observed that it is not the chemical composition of ligand molecules that dictates the complementarity between protein and ligand molecules, but that the ligand's share within the functional mechanism of a protein determines the degree of complementarity. Here, we present for a set of cognate ligands a descriptive analysis and comparison of the physicochemical properties that each ligand experiences in various nonhomologous binding pockets. The comparisons in each ligand set reveal large variations in their experienced physicochemical properties, suggesting that the same ligand can bind to distinct physicochemical environments. In some protein ligand complexes, the variation was found to correlate with the electrochemical characteristic of ligand molecules, whereas in others it was disclosed as a prerequisite for the biochemical function of the protein. To achieve binding, proteins were observed to engage in subtle balancing acts between electrostatic and hydrophobic interactions to generate stabilizing free energies of binding. For the presented analysis, a new method for scoring hydrophobicity from molecular environments was developed showing high correlations with experimental determined desolvation energies. The presented results highlight the complexities of molecular recognition and underline the challenges of computational structural biology in developing methods to detect these important subtleties.

A novel and efficient tool for locating and characterizing protein cavities and binding sites

Systematic investigation of a protein and its binding site characteristics are crucial for designing small molecules that modulate protein functions. However, fundamental uncertainties in binding site interactions and insufficient knowledge of the properties of even well-defined binding pockets can make it difficult to design optimal drugs. Herein, we report the development and implementation of a cavity detection algorithm built with HINT toolkit functions that we are naming Vectorial Identification of Cavity Extents (VICE). This very efficient algorithm is based on geometric criteria applied to simple integer grid maps. In testing, we carried out a systematic investigation on a very diverse data set of proteins and protein-protein/protein-polynucleotide complexes for locating and characterizing the indentations, cavities, pockets, grooves, channels, and surface regions. Additionally, we evaluated a curated data set of unbound proteins for which a ligand-bound protein structures are also known; here the VICE algorithm located the actual ligand in the largest cavity in 83% of the cases and in one of the three largest in 90% of the cases. An interactive front-end provides a quick and simple procedure for locating, displaying and manipulating cavities in these structures. Information describing the cavity, including its volume and surface area metrics, and lists of atoms, residues, and/or chains lining the binding pocket, can be easily obtained and analyzed. For example, the relative cross-sectional surface area (to total surface area) of cavity openings in well-enclosed cavities is 0.06 +/- 0.04 and in surface clefts or crevices is 0.25 +/- 0.09. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

Form follows function: shape analysis of protein cavities for receptor-based drug design

Identification of potential ligand-binding pockets is an initial step in receptor-based drug design. While many geometric or energy-based binding-site prediction methods characterize the size and shape of protein cavities, few of them offer an estimate of the pocket's ability to bind small drug-like molecules. Here, we present a shape-based technique to examine binding-site druggability from the crystal structure of a given protein target. The method includes the PocketPicker algorithm to determine putative binding-site volumes for ligand-interaction. Pocket shape descriptors were calculated for both known ligand binding sites and empty pockets and were then subjected to self-organizing map clustering. Descriptors were calculated for structures derived from a database of representative drug-protein complexes with experimentally determined binding affinities to characterize the "druggable pocketome". The new method provides a means for selecting drug targets and potential ligand-binding pockets based on structural considerations and addresses orphan binding sites.

Lessons for fragment library design: analysis of output from multiple screening campaigns

Over the past 8 years, we have developed, refined and applied a fragment based discovery approach to a range of protein targets. Here we report computational analyses of various aspects of our fragment library and the results obtained for fragment screening. We reinforce the finding of others that the experimentally observed hit rate for screening fragments can be related to a computationally defined druggability index for the target. In general, the physicochemical properties of the fragment hits display the same profile as the library, as is expected for a truly diverse library which probes the relevant chemical space. An analysis of the fragment hits against various protein classes has shown that the physicochemical properties of the fragments are complementary to the properties of the target binding site. The effectiveness of some fragments appears to be achieved by an appropriate mix of pharmacophore features and enhanced aromaticity, with hydrophobic interactions playing an important role. The analysis emphasizes that it is possible to identify small fragments that are specific for different binding sites. To conclude, we discuss how the results could inform further development and improvement of our fragment library.