BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities

BindingDB () is a publicly accessible database currently containing ∼20 000 experimentally determined binding affinities of protein–ligand complexes, for 110 protein targets including isoforms and mutational variants, and ∼11 000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs

Binding MOAD, a high-quality protein–ligand database

Binding MOAD (Mother of All Databases) is a database of 9836 protein–ligand crystal structures. All biologically relevant ligands are annotated, and experimental binding-affinity data is reported when available. Binding MOAD has almost doubled in size since it was originally introduced in 2004, demonstrating steady growth with each annual update. Several technologies, such as natural language processing, help drive this constant expansion. Along with increasing data, Binding MOAD has improved usability. The website now showcases a faster, more featured viewer to examine the protein–ligand structures. Ligands have additional chemical data, allowing for cheminformatics mining. Lastly, logins are no longer necessary, and Binding MOAD is freely available to all at http://www.BindingMOAD.org

ZIKA VIRUS SERENE PROTEASE COMPLEX (NS2B-NS3) INHIBITION BY 2-AMINO-5-{[(1Z)-AMINO({[(Z)-BENZOYL]IMINO})METHYL]AMINO}-N-(5-AMINO-7-{[CARBAMOYL(PHENYL)METHYL]AMINO}-6-OXOHEPTYL)PENTANAMIDE, IN SILICO STUDIES

Objective: The present in silico study is taken to report 2-amino-5-{[(1Z) -amino ({[(Z) -benzoyl] imino}) methyl] amino} -N-(5-amino-7-{[carbamoyl (phenyl) methyl] amino} -6-oxoheptyl) pentanamide as Zika virus (ZIKV) NS2B-NS3 protease inhibitor.Methods: In silico studies performed on online docking servers. NS2B-NS3 serine protease from ZIKV with PDB ID: 5GJ4 a hydrolase with total structure weight of 102878.54 is selected as the target. Docking server is used for carrying out docking calculations. Lamarckian genetic algorithm and the Solis and Wets local search methods are used for performing docking simulations. Free energy calculations, hydrogen bond (HB) formation, polar and hydrophobic interactions and HB plot are studied in this study.Results: Binding pocket is found on a serine protease NS2B chain A. Binding site predictions propose NKK as the suitable ligand for binding, which has structure closely related to the proposed ligand2-amino-5-{[(1Z) -amino ({[(Z) -benzoyl] imino}) methyl] amino} -N-(5-amino-7-{[carbamoyl (phenyl) methyl] amino} -6-oxoheptyl) pentanamide. Free energy of binding is - 4.08 kcal/Mol and inhibition constant (Ki) is very less 1.02 mm. The ligand binds to chain A of NS2B and chain B of NS3 serine protease. The legend is bound to serine protease complex through strong HB, formed between THR 60 (A) and N6 of ligand, GLU62 (A) and N8 of ligand, ARG 55 (A) and N3 of ligand and ASN108 (B) and N7 of ligand apart from polar and hydrophobic interactions.Conclusion: Docking studies performed establishes the proposed ligand2-amino-5-{[(1Z)-amino ({[(Z)-benzoyl] imino}) methyl] amino} -N-(5- amino-7-{[carbamoyl (phenyl) methyl] amino}-6-oxoheptyl) pentanamide as a molecule which can be used for the inhibition of ZIKV NS2B-NS3 serine protease. Â

Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Structural Evidence and Strategies for Finding Small Molecule Modulators

(1) Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose descriptors explaining the nature of different protein-protein complexes are desirable. In this work, we introduce Epic Protein Interface Classification (EPIC) as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines (SVM), C4.5 Decision Trees, K Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms (GA) to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, we represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors (ACVs), DrugScore pair potential vectors (DPV) and SFCscore descriptor vectors (SDV). We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (2) Since protein-protein interactions play pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost interest. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere the complex formation of proteins. On the other hand, there a several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, e.g. as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligand accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable targets. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules. (3) AffinDB is a database of affinity data for structurally resolved protein-ligand complexes from the PDB. It is freely accessible at http://www.agklebe.de/affinity. Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein-ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code, and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH-value of the measurement, ligand molecular weight, and publication data (author, journal, year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design

Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Structural Evidence and Strategies for Finding Small Molecule Modulators

(1) Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose descriptors explaining the nature of different protein-protein complexes are desirable. In this work, we introduce Epic Protein Interface Classification (EPIC) as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines (SVM), C4.5 Decision Trees, K Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms (GA) to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, we represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors (ACVs), DrugScore pair potential vectors (DPV) and SFCscore descriptor vectors (SDV). We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (2) Since protein-protein interactions play pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost interest. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere the complex formation of proteins. On the other hand, there a several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, e.g. as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligand accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable targets. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules. (3) AffinDB is a database of affinity data for structurally resolved protein-ligand complexes from the PDB. It is freely accessible at http://www.agklebe.de/affinity. Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein-ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code, and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH-value of the measurement, ligand molecular weight, and publication data (author, journal, year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design

Integration and analysis of large scale data in chemical biology

much lower molecular weight than macromolecules like proteins or DNA. Small molecules are grouped into different families according to their physico-chemical or functional properties, and they can be either natural (like lipids) or synthetic (like drugs). Only a staggeringly low fraction of the small molecule universe has been characterize, and very little is known about it. For instance, we know that lipids can play the role of scaffolding and energy storage compounds, and that they differently compose biological membranes. However, we don’t know if it influences some biological functions, including protein recruitment to membranes and cellular transport. Chemical biology aims at utilizing chemicals in order to explore biological systems. Advances in synthesizing big chemical libraries as well as in high-throughput screenings have led to technologies capable of studying protein-lipid interactions at large scale and in physiological conditions. Therefore, answering such questions has become possible, but it presents many new computational challenges. For instance, establishing methods capable of automatically classifying interactions as binding or non-binding requiring a minimal interaction with human experts. Making use of unsupervised clustering methods to identify clusters of lipids and proteins exhibiting similar patterns and linking them to similar biological functions. To tackle these challenges, I have developed a computational pipeline performing a technical and functional analysis on the readouts produced by the high-throughput technology LiMA. Applied to a screen focusing on 94 proteins and 122 lipid combinations yielding more than 10,000 interactions, I have demonstrated that cooperativity was a key mechanism for membrane recruitment and that it could be applied to most PH domains. Furthermore, I have identified a conserved motif conferring PH domains the ability to be recruited to organellar membranes and which is linked to cellular transport functions. Two amino acids of this motif are found mutated in some human cancer, and we predicted and confirmed that these mutations could induce discrete changes in binding affinities in vitro and protein mis-localization in vivo. These results represent milestones in the field of protein-lipid interactions. While we are progressing toward a global understanding of protein-lipid interactions, data on the bioactivities of small molecules is accumulating at a tremendous speed. In vitro data on interactions with targets are complemented by other molecular and phenotypic readouts, such as gene expression profiles or toxicity readouts. The diversity of screening technologies accompanied by big efforts to collect the resulting data in public databases have created unprecedented opportunities for chemo-informatics work to integrate these data and make new inferences. For instance, is the protein target profile of a drug correlated with a given phenotype? Can we predict the side effects of a drug based on its toxicology readouts? In this context, I have developed CART: a computational platform with which we address major chemo-informatics challenges to answer such questions. CART integrates many resources covering molecular and phenotypical readouts, and annotates sets of chemical names with these integrated resources. CART includes state-of-the-art full-text search engine technologies in order to match chemical names at a very high speed and accuracy. Importantly, CART is a scalable resource that can cope with the increasing number of new chemical annotation resources, and therefore, constitutes a major contribution to chemical biology