Structural Interaction Fingerprints Research Articles

Multiple conformational states in retrospective virtual screening - homology models vs. crystal structures: beta-2 adrenergic receptor case study.

BackgroundDistinguishing active from inactive compounds is one of the crucial problems of molecular docking, especially in the context of virtual screening experiments. The randomization of poses and the natural flexibility of the protein make this discrimination even harder. Some of the recent approaches to post-docking analysis use an ensemble of receptor models to mimic this naturally occurring conformational diversity. However, the optimal number of receptor conformations is yet to be determined.In this study, we compare the results of a retrospective screening of beta-2 adrenergic receptor ligands performed on both the ensemble of receptor conformations extracted from ten available crystal structures and an equal number of homology models. Additional analysis was also performed for homology models with up to 20 receptor conformations considered.ResultsThe docking results were encoded into the Structural Interaction Fingerprints and were automatically analyzed by support vector machine. The use of homology models in such virtual screening application was proved to be superior in comparison to crystal structures. Additionally, increasing the number of receptor conformational states led to enhanced effectiveness of active vs. inactive compounds discrimination.ConclusionsFor virtual screening purposes, the use of homology models was found to be most beneficial, even in the presence of crystallographic data regarding the conformational space of the receptor. The results also showed that increasing the number of receptors considered improves the effectiveness of identifying active compounds by machine learning methods.Graphical abstractComparison of machine learning results obtained for various number of beta-2 AR homology models and crystal structures.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0062-x) contains supplementary material, which is available to authorized users.

Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods--A Case Study of Serotonin Receptors 5-HT(6) and 5-HT(7).

Molecular docking, despite its undeniable usefulness in computer-aided drug design protocols and the increasing sophistication of tools used in the prediction of ligand-protein interaction energies, is still connected with a problem of effective results analysis. In this study, a novel protocol for the automatic evaluation of numerous docking results is presented, being a combination of Structural Interaction Fingerprints and Spectrophores descriptors, machine-learning techniques, and multi-step results analysis. Such an approach takes into consideration the performance of a particular learning algorithm (five machine learning methods were applied), the performance of the docking algorithm itself, the variety of conformations returned from the docking experiment, and the receptor structure (homology models were constructed on five different templates). Evaluation using compounds active toward 5-HT6 and 5-HT7 receptors, as well as additional analysis carried out for beta-2 adrenergic receptor ligands, proved that the methodology is a viable tool for supporting virtual screening protocols, enabling proper discrimination between active and inactive compounds.

Biased Agonism at Opioid Receptors: Insights from Analysis of Structural Interaction Fingerprints

Biased agonism, that is the ligand-dependent selectivity for specific signaling pathways in a G protein coupled receptor (GPCR), is an emerging, promising strategy for the development of safer drugs. In the case of the kappa opioid receptor (KOPr), its preferential activation of G-protein versus β-arrestin has been proposed to provide a more direct route to discovering non-addictive opioid therapeutics with reduced side effects. In fact, the KOPr-mediated dysphoria that usually accompanies the beneficial analgesic effect of KOPr agonists has recently been attributed to the activation of the p38 mitogen-activated protein kinase pathway, which is believed to follow arrestin recruitment to the activated KOPr. This observation suggests that KOPr agonists that selectively activate the G protein, but do not recruit arrestin, may be more effective analgesics since they would not exhibit the adverse effects triggered by the arrestin pathway.Based on the above, understanding the structural and chemical determinants of biased agonism at the KOPr is highly desirable as it can guide the discovery/design of improved therapeutics. Here, we employ flexible docking of a set of recently characterized functionally selective KOPr ligands to establish a predictive model for G protein-biased agonism at this receptor based on characteristics of modes of interaction between the ligand and the KOPr. We apply the resulting classifier to a large set of established KOPr agonists to assess their ability to preferentially promote G protein coupling, arrestin recruitment, or both.

Characterization of the PH1704 protease from Pyrococcus horikoshii OT3 and the critical functions of Tyr120.

The PH1704 protease from hyperthermophilic archaean Pyrococcus horikoshii OT3 is a member of DJ-1/ThiJ/PfpI superfamily with diverse functional subclasses. The recombinant PH1704 was efficiently purified and was systematically characterized by a combination of substrate specificity analysis, steady-state kinetics study and molecular docking research. The homogeneous protease was obtained as a presumed dodecamer with molecular weight of ∼240 kDa. Iodoacetamide strongly inhibited the peptidase activity, confirming that Cys100 is a nucleophilic residue. The recombinant protein was identified as both an aminopeptidase and an endopeptidase. Experimental data showed that L-R-amc was the best substrate of PH1704. Structural interaction fingerprint analysis (SIFt) indicated the binding pose of PH1704 and showed that Tyr120 is important in substrate binding. Kinetic parameters K cat and K cat /K m of the Y120P mutant with L-R-amc was about 7 and 7.8 times higher than that of the wild type (WT). For the endopeptidase Y120P with AAFR-amc, K cat and K cat /K m is 10- and 21- fold higher than that of WT. Experimental data indicate the important functions of Tyr120: involvement in enzyme activity to form a hydrogen bond with Cys100 and as an entrance gate of the substrate with Lys43. The results of this study can be used to investigate the DJ-1/ThiJ/PfpI superfamily.

Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study.

Accurate and affordable assessment of ligand–protein affinity for structure-based virtual screening (SB-VS) is a standing challenge. Hence, empirical postdocking filters making use of various types of structure–activity information may prove useful. Here, we introduce one such filter based upon three-dimensional structural protein–ligand interaction fingerprints (SPLIF). SPLIF permits quantitative assessment of whether a docking pose interacts with the protein target similarly to a known ligand and rescues active compounds penalized by poor initial docking scores. An extensive benchmark study on 10 diverse data sets selected from the DUD-E database has been performed in order to evaluate the absolute and relative efficiency of this method. SPLIF demonstrated an overall better performance than relevant standard methods.

Structure–activity relationships and molecular modeling studies of novel arylpiperazinylalkyl 2-benzoxazolones and 2-benzothiazolones as 5-HT7 and 5-HT1A receptor ligands

A novel series of arylpiperazinylalkyl 2-benzoxazolones and 2-benzothiazolones 18–38 was designed, synthesized and tested to evaluate their affinity for the 5-HT7 and 5-HT1A receptors. Compounds with a 2-benzothiazolone nucleus generally had affinity values higher than the corresponding 2-benzoxazolone compounds. In particular, derivatives possessing a six or seven carbon chain linker between 2-benzothiazolone and arylpiperazine had Ki values in the subnanomolar range for the 5-HT1A receptor and in the low nanomolar range for the 5-HT7 receptor, indicating that they may be interesting dual ligands. Molecular modeling studies revealed different docking poses for the investigated compounds in homology models of 5-HT1A and 5-HT7 receptors, which explained their experimentally determined affinities and general low selectivity. Additionally, structural interaction fingerprints analysis identified the important amino acid residues for the specific interactions of long-chain arylpiperazines within the binding pockets of both serotonin receptors.

Screening of phytochemicals against protease activated receptor 1 (PAR1), a promising target for cancer

Context: Drug resistance and drug-associated toxicity are the primary causes for withdrawal of many drugs, although patient recovery is satisfactory in many instances. Interestingly, the use of phytochemicals in the treatment of cancer as an alternative to synthetic drugs comes with a host of advantages; minimum side effects, good human absorption and low toxicity to normal cells. Protease activated receptor 1 (PAR1) has been established as a promising target in many diseases including various cancers. Strong evidences suggest its role in metastasis also. Objective: There are no natural compounds known to inhibit its activity, so we aimed to identify phytochemicals with antagonist activity against PAR1. Methods: We screened phytochemicals from Naturally Occurring Plant-based Anticancer Compound-Activity-Target database (NPACT, http://crdd.osdd.net/raghava/npact/) against PAR1 using virtual screening workflow of Schrödinger software. It analyzes pharmaceutically relevant properties using Qikprop and calculates binding energy using Glide at three accuracy levels (high-throughput virtual screening, standard precision and extra precision). Results and conclusion: Our study led to the identification of phytochemicals, which showed interaction with at least one experimentally determined active site residue of PAR1, showed no violations to Lipinski's rule of five along with predicted high human absorption. Furthermore, structural interaction fingerprint analysis indicated that the residues H255, D256, E260, S344, V257, L258, L262, Y337 and S344 may play an important role in the hydrogen bond interactions of the phytochemicals screened. Of these residues, H255 and L258 residues were experimentally proved to be important for antagonist binding. The residues Y183, L237, L258, L262, F271, L332, L333, Y337, L340, A349, Y350, A352, and Y353 showed maximum hydrophobic interactions with the phytochemicals screened. The results of this work suggest that phytochemicals Reissantins D, 24,25-dihydro-27-desoxywithaferin A, Isoguaiacin, 20-hydroxy-12-deoxyphorbol angelate, etc. could be potential antagonist of PAR1. However, further experimental studies are necessary to validate their antagonistic activity against PAR1.

Biological Activity of Sporolides A and B from Salinispora tropica: in silico Target Prediction Using Ligand‐Based Pharmacophore Mapping and in vitro activity Validation on HIV‐1 Reverse Transcriptase

Sporolides A and B are novel polycyclic macrolides from the obligate marine actinomycetes, Salinispora tropica. The unique and novel structure of sporolides makes them interesting candidates for targeting diverse biological activities. Biological target prediction of sporolides was carried out using ligand-based pharmacophore screening against known inhibitors and drugs. Validation of pharmacophore screening was carried out for the identified hits. New biological targets predicted for sporolides using this method were HIV-1 reverse transcriptase, adenosine A3 receptor, endothelin receptor ET-A, oxytocin receptor, voltage-gated L-type calcium channel α-1C subunit/calcium channel α/Δ subunit 1. Drug-likeness properties were predicted for the selected compounds using QikProp module. Sporolides A and B showed maximum docking score with HIV-1 reverse transcriptase. Structural interaction fingerprints analysis indicated similar binding pattern of the sporolides with the HIV-1 reverse transcriptase. Sporolide B exhibited good inhibitory activity against HIV-1 reverse transcriptase in in vitro fluorescent assay.

An application of machine learning methods to structural interaction fingerprints—a case study of kinase inhibitors

In this Letter, we present a novel methodology of searching for biologically active compounds, which is based on the combination of docking experiments and analysis of the results by machine learning methods. The study was performed for 5 different protein kinases, and several sets of compounds (active, inactive and assumed inactives) were docked into their targets. The resulting ligand–protein complexes were represented by the means of structural interaction fingerprints profiles (SIFts profiles) that constituted an input for ML methods. The developed protocol was found to be superior to the combination of classification algorithms with the standard fingerprint MACCSFP.

New Strategy for Receptor-Based Pharmacophore Query Construction: A Case Study for 5-HT7 Receptor Ligands

In this paper, a new approach for generating receptor-based 3D pharmacophore models for rapid in silico virtual screening is presented. The method combines information from docking poses of known ligands of different structures and further ligand-receptor complexes analyses using structural interaction fingerprints (SIFts). Next, the best linear combination of three-, four-, and five-feature pharmacophores in terms of selected performance parameter (i.e., recall, F-score, and MCC) is constructed. The resultant queries showed significantly better VS performance and new scaffold recognition when compared with the known ligand- and receptor-based pharmacophore models. The approach was developed and validated on 5-HT₇ receptor homology models created on available crystal structure templates. The efficiency of the obtained linear combinations exhibited only a minor dependence on the template selection.

Application of Structural Interaction Fingerpints (SIFts) into post-docking analysis - insight into activity and selectivity

Cheminformatic methods, such as Virtual Screening, constitute a vital part of modern drug design process. This technique enables not only viable prediction of physicochemical properties of the molecules, but also effective database mining, being particularly useful tool in search for ligands of desired activity. Successful performance in case of single target drugs, implies a potential to extend its capabilities to compounds bearing desired activity towards multiple receptors. In this research, we present application of Structural Interaction Fingerprints (SIFts) [1] combined with Machine Learning (ML) as a method to select single- and multi-target ligands from the docking results. A handful of protein kinases pairs was designated as targets. Collection of pseudo selective compounds, with various activity profiles, was acquired from ChEMBL database. Furthermore, for each target a set of ligands of various activity was aggregated. Decoy structures were random ligands from ZINC database. The compounds were docked into respective proteins, and SIFts were calculated for each protein-ligand complex. Training sets used in ML experiments consisted of cluster centroids of active and inactive ligands, whereas test sets were composed of remaining compounds, that returned docking poses. The key aim of this study is to develop a viable method to filter the docking results, so that the compounds meeting desired activity profile are selected.

Protein binding site analysis by means of structural interaction fingerprint patterns

We introduce a new approach to the known concept of interaction profiles, based on Structural Interaction Fingerprints (SIFt), for precise and rapid binding site description. A set of scripts for batch generation and analysis of SIFt were prepared, and the implementation is computationally efficient and supports parallelization. It is based on a 9-digit binary interaction pattern that describes physical ligand-protein interactions in structures and models of ligand-protein complexes. The tool performs analysis and identifies binding site residues (crucial and auxiliary) and classifies interactions according to type (hydrophobic, aromatic, charge, polar, side chain, and backbone). It is convenient and easy to use, and gives manageable output data for both, interpretation and further processing. In the presented Letter, SIFts are applied to analyze binding sites in models of antagonist-5-HT7 receptor complexes and structures of cyclin dependent kinase 2-ligand complexes.

Pharmacophore Modeling Methods in Focused Library Selection – Applications in the Context of a New Classification Scheme

A pharmacophore is a model which represents the key physico-chemical interactions that mediate biological activity. There is a long history of using pharmacophore modeling methods to select subsets of compounds, focused towards a specific target of interest. This paper will review existing computational methods for deriving and comparing pharmacophore models. We outline a new classification of pharmacophore methods based on the abstraction of the underlying chemical interactions which embody a pharmacophore, and the methods available to quantitatively compare them. Within the context of this classification, example studies, using specific pharmacophore modeling methods for focused library selection, will be discussed.

Rapid binding site analysis by means of structural interaction fingerprint patterns – an implication to GPCR-targeted CADD

One of the most troublesome stages of Computer Aided Drug Design (CADD) process is analyzing huge amount of data provided by docking studies. Simple scoring functions alone can provide only shallow information about ligand-receptor interactions, since they do not distinguish neither residues nor single atoms. Very often a visual inspection is the only way to determine a binding mode. Here, we introduce an implementation of interaction profiles [1] based on Structural Interaction Fingerprints (SIFt) [2], which allow precise and rapid binding site description. SIFts employed in our modelling agenda form a nine-digit binary pattern for each amino-acid in the receptor, covering all major types of interactions. Computed over an ensemble of ligands and/or receptor conformations, averaged real number SIFt elegantly depicts overall preferences towards particular interactions. This enables construction of a compact scheme of crucial forces involved in ligand-receptor complex formation, which then facilitate design of a binding mode hypothesis and description of preferred ligand positions within the active site. An application is presented basing on a model of an allosteric domain of mGluR4 receptor [3] – a challenging computational task, due to untrivial evolutionary relationships for homology model construction purposes, and shortage of experimental data for hypotheses validation.

CREDO: A Protein–Ligand Interaction Database for Drug Discovery

Harnessing data from the growing number of protein-ligand complexes in the Protein Data Bank is an important task in drug discovery. In order to benefit from the abundance of three-dimensional structures, structural data must be integrated with sequence as well as chemical data and the protein-small molecule interactions characterized structurally at the inter-atomic level. In this study, we present CREDO, a new publicly available database of protein-ligand interactions, which represents contacts as structural interaction fingerprints, implements novel features and is completely scriptable through its application programming interface. Features of CREDO include implementation of molecular shape descriptors with ultrafast shape recognition, fragmentation of ligands in the Protein Data Bank, sequence-to-structure mapping and the identification of approved drugs. Selected analyses of these key features are presented to highlight a range of potential applications of CREDO. The CREDO dataset has been released into the public domain together with the application programming interface under a Creative Commons license at http://www-cryst.bioc.cam.ac.uk/credo. We believe that the free availability and numerous features of CREDO database will be useful not only for commercial but also for academia-driven drug discovery programmes.

A Kinase‐focused Compound Collection: Compilation and Screening Strategy

Lead identification by high-throughput screening of large compound libraries has been supplemented with virtual screening and focused compound libraries. To complement existing approaches for lead identification at Biogen Idec, a kinase-focused compound collection was designed, developed and validated. Two strategies were adopted to populate the compound collection: a ligand shape-based virtual screening and a receptor-based approach (structural interaction fingerprint). Compounds selected with the two approaches were cherry-picked from an existing high-throughput screening compound library, ordered from suppliers and supplemented with specific medicinal compounds from internal programs. Promising hits and leads have been generated from the kinase-focused compound collection against multiple kinase targets. The principle of the collection design and screening strategy was validated and the use of the kinase-focused compound collection for lead identification has been added to existing strategies.

Knowledge-Based Interaction Fingerprint Scoring: A Simple Method for Improving the Effectiveness of Fast Scoring Functions

A new method for the postprocessing of docking outputs has been developed, based on encoding putative 3D binding modes (docking solutions) as ligand-protein interactions into simple bit strings, a method analogous to the structural interaction fingerprint. Instead of employing traditional scoring functions, the method uses a series of new, knowledge-based scores derived from the similarity of the bit strings for each docking solution to that of a known reference binding mode. A GOLD docking study was carried out using the Bissantz estrogen receptor antagonist set along with the new scoring method. Superior recovery rates, with up to 2-fold enrichments, were observed when the new knowledge-based scoring was compared to the GOLD fitness score. In addition, top ranking sets of molecules (actives and potential actives or decoys) were structurally diverse with low molecular weights and structural complexities. Principal component analysis and clustering of the fingerprints permits the easy separation of active from inactive binding modes and the visualization of diverse binding modes.

Structural Interaction Fingerprints: A New Approach to Organizing, Mining, Analyzing, and Designing Protein–Small Molecule Complexes

The combination of advances in structure-based drug design efforts in the pharmaceutical industry in parallel with structural genomics initiatives in the public domain has led to an explosion in the number of structures of protein-small molecule complexes structures. This information has critical importance to both the understanding of the structural basis for molecular recognition in biological systems and the design of better drugs. A significant challenge exists in managing this vast amount of data and fully leveraging it. Here, we review our work to develop a simple, fast way to store, organize, mine, and analyze large numbers of protein-small molecule complexes. We illustrate the utility of the approach to the management of inhibitor complexes from the protein kinase family. Finally, we describe our recent efforts in applying this method to the design of target-focused chemical libraries.

Knowledge-Based Design of Target-Focused Libraries Using Protein−Ligand Interaction Constraints

Here we present a new strategy for designing and filtering potentially massive combinatorial libraries using structural information of a binding site. We have developed a variation of the structural interaction fingerprint (SIFt) named r-SIFt, which incorporates the binding interactions of variable fragments in a combinatorial library. This method takes into account the 3D structure of the active site of the target molecule and translates desirable ligand-target binding interactions into library filtering constraints. We show using the MAP kinase p38 as a test case that we can efficiently analyze and classify compounds on the basis of their abilities to interact with the target in the desired binding mode. On the basis of these classifications, decision tree models were generated using the molecular descriptors of the compounds as predictor variables. Our results suggest that r-SIFt coupled with the classification models should be a valuable tool for structure-based focusing of combinatorial chemical libraries.

Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions.

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze approximately 90 known X-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.