Structural Interaction Fingerprints Research Articles

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models.

Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug-drug interactions. Among published in silico models for MATE inhibitors, a previous model obtained a ROC-AUC value of 0.78 using high throughput percentage inhibition data [J. Med. Chem. 2013, 56(3), 781-795] which we aimed to improve upon here using a combined fingerprint and physics-based approach. To this end, we collected 225 publicly available compounds with pIC50 values against MATE1. Subsequently, on the one hand, we performed a physics-based approach using an Alpha-Fold protein structure, from which we obtained MM-GB/SA scores for those compounds. On the other hand, we built Random Forest (RF) and message passing neural network models using extended-connectivity fingerprints with radius 4 (ECFP4) and chemical structures as graphs, respectively, which also included MM-GB/SA scores as input variables. In a five-fold cross-validation with a separate test set, we found that the best predictivity for the hold-out test was observed in the RF model (including ECFP4 and MM-GB/SA data) with an ROC-AUC of 0.833 ± 0.036; while that of the MM-GB/SA regression model was 0.742. However, the MM-GB/SA model did not show a dependency of the performance on the particular chemical space being predicted. Additionally, via structural interaction fingerprint analysis, we identified interacting residues with inhibitor as identical for those with noninhibitors, including substrates, such as Gln49, Trp274, Tyr277, Tyr299, Ile303, and Tyr306. The similar binding modes are consistent with the observed similar IC50 value inhibitor when using different substrates experimentally, and practically, this can release the experimental scientists from bothering of selecting substrates for MATE1. Hence, we were able to build highly predictive classification models for MATE1 inhibitory activity with both ECFP4 and MM-GB/SA score as input features, which is fit-for-purpose for use in the drug discovery process.

Strategic development and validation of Isoquinoline-Derived inhibitors targeting ROCK1 kinase

Rho-associated coiled-coil containing kinases (ROCKs) have emerged as pivotal downstream effectors of small guanosine triphosphatases. In recent years, isoquinoline derivatives have garnered substantial attention as potent inhibitors of ROCK1 (RKIs). To delve deeper into their pharmacological properties and to identify potential inhibitors, we conducted an in-depth exploration of a series of RKIs derived from isoquinoline derivatives. This exploration encompassed a multifaceted approach, combining three-dimensional quantitative structure–activity relationship modeling, induced fit docking, and virtual screening. Our comparative molecular field analysis model (q2 = 0.609, R2 = 0.991, ONC = 6, and r2pred = 0.973) and the most robust comparative molecular similarity indices analysis model (q2 = 0.712, R2 = 0.998, ONC = 7, and r2pred = 0.89) exhibited commendable predictive accuracy, bolstered by robust validation parameters. To gain deeper insights into the mechanism of action of isoquinoline-based RKIs, we harnessed precise docking techniques, including induced fit docking, structural interaction fingerprints, and binding pose metadynamics simulations. Employing the PRRR_2 pharmacophore, we meticulously screened Enamine's HLL-460 compound library, unearthing thousands of isoquinoline compounds that aligned with the desired profile. Subsequently, we identified three promising compounds (DS01, DS02, and DS03) through Glide docking, each assessed at varying precision levels. These compounds underwent comprehensive evaluation for their drug-like properties, leveraging QikProp and SwissADME, ultimately unveiling their potential as novel and efficacious RKIs. Furthermore, bolstered by molecular dynamics simulations and validated through activity assays, the bioactivity of the screened molecules DS02, and DS03 was corroborated. DS02 and DS03, previously unreported for their activity against ROCK1, delivered exceptionally promising results. This comprehensive study not only elucidates validated strategies for drug development but also imparts invaluable insights into the design of potent RKIs.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Multitargeted Molecular Dynamic Understanding of Butoxypheser Against SARS-CoV-2: An in Silico Study

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was initially reported in the Wuhan province of China, spread throughout the world, and caused massive destruction in the form of a global pandemic that started back in 2020. SARS-CoV-2 is an RNA virus with various proteins like main protease, spike protein, NSP15 endoribonuclease, RNA-dependent RNA polymerase, and papain-like protease targeted to screen and find the novel drug candidate that can potentially work against the virus. Previous studies have reported multiple drugs after screening and validation against a single target and reported multiple medications. Nevertheless, many drugs are being used to date but do not have enough potential to work against SARS-CoV-2 and curb the spread and death rate. In this study, with the hypothesis of 1 drug and multiple targets, we have taken 5 main target proteins and screened the Asinex's complete BioDesign library (1,70,269 compounds) and identified N-{2-[(2S)-2-Amino-3-methylbutoxy]-6-propylbenzoyl}-L-phenylalanyl-L-serine (Butoxypheser) as multitarget inhibitor against SARS-CoV-2. Also, Butoxypheser has shown excellent docking scores, hydrogen bonding, and other bonding configurations like van der Waals force and water bridges. The stability and interaction pattern of the compound was validated with structural interaction fingerprints (SIFts) and molecular dynamics (MD) simulation. The Butoxypheser has performed flawlessly throughout the study, and the same results were used to compare the compound's activity against multiple targets. After a thorough theoretical comparative analysis, Butoxypheser can be treated as a multitargeted inhibitor candidate against SARS-CoV-2. Further, this study needs to be validated experimentally before human use.

FingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

2D SIFt: a matrix of ligand-receptor interactions

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.

In silico study to predict potential precursors of human dipeptidyl peptidase-IV inhibitors from hazelnut

Chinese hazelnut was chosen to become a probable precursor of biological active peptides via computer simulations in this article. There were a large number of bioactive peptides in Chinese hazelnut sequences according to analytical results from the BIOPEP database. The most prominent of these was the inhibitory peptide for dipeptidyl peptidase-IV (DPP-IV; EC 3.4.14.5), which can be used to treat type 2 diabetes, so the theoretical method to obtain DPP-IV inhibitory peptides by hydrolysis with a single or combination of enzymes was studied. Cytotoxicity analysis performed by ToxinPred showed that all of the DPP-IV inhibitory peptides generated from protein hydrolysis were not cytotoxic. Structural interaction fingerprint analysis revealed that Asp663 and Phe357 may be important residues for ligand binding. In order to further understand the inhibitory mechanism of peptide, VR with lowest half maximum inhibitory concentration (IC50) and IPI (inhibitors have been reported) were selected as ligand of DPP-IV to perform steered molecular dynamics simulations and PMF calculations. The results showed that P1 is the preferred (un)binding tunnel for the inhibitors obtained. Our findings help in the development of new DPP-IV inhibitors which were derived from common food. Communicated by Ramaswamy H. Sarma

A molecular dynamics simulations study of the ionic liquid effect on the β-glucosidase active site interactions with a flavonoid glycoside

Since the ionic liquid (IL) media is an appropriate environment for the enzymatic extraction of glycosides from natural resources, a detailed molecular understanding of the effect of IL concentration on enzyme function, with a focus on enzyme-substrate interactions, is critical. The best docking pose of Quercetin-4′-O-beta-D-glucopyranoside (Q-4′) in the active site of the β-glucosidase (BGL) enzyme was determined using molecular docking methods. Since flavonoid glycosides and enzymes are soluble in imidazolium-based ILs, the aqueous 1-butyl-3-methylimidazolium chloride ([BMIM][Cl]) was used as a solvent in this study. The interactive residues of the BGL active site, as well as the types of interactions, in BGL–Q-4′ complex were then discovered using structural interaction fingerprints (SIFt) calculations. After that, the molecular perspective of the ionic liquid (IL) concentration effect on this complex and its interactions were simulated using 100 ns all-atom MD simulations. According to SIFt changes, changing the IL concentration influenced the overall interactions in the BGL–Q-4′ complex and involved residues. The RMSF findings indicated that the maximum flexibility of the interactive residues was observed in the 50% IL solution, however, flexibility changes were not high enough to alter enzyme activity. More analysis showed that solvent effects influenced hydrogen bond (HB) interactions with the substrate in both active site subsites; glycone (subsite -1) and aglycone subsites. Aromatic interactions followed a similar trend, suggesting that changes in IL concentration affected them. The solvent effects had a greater impact on the interactions with the aglycone part in both cases, which was attributed to aglycone subsite's greater solvent accessibility. Furthermore, it was found that IL concentration had a negative impact on the enzymatic hydrolysis process by influencing the formation of HB with Glu193 as well as the nucleophilic attack of Glu402. Finally, SDF findings indicated that an accumulation of IL molecules at the active site entrance could reduce enzyme activity by reducing the active site's accessibility to the substrate. By providing a molecular perspective on the BGL-substrate complex behavior in the IL system, our findings lead to a deeper understanding of the flavonoid glycoside hydrolysis process in these useful solvents.

In-silico identification of potential inhibitors targeting the DNA binding domain of estrogen receptor α for the treatment of hormone therapy-resistant breast cancer

Estrogen receptor α (ERα) plays a critical role in breast cancer (BC) development. The standard therapeutic strategies for ERα- positive (ERα+) BC consist of impairing ERα signalling pathway by either estrogen competitors blocking its interaction with the ligand binding domain (LBD) or agents inhibiting the production of estrogen. These strategies are limited by many factors that lead to constitutive activation of ERα and consequently, resistance to treatment. Targeting the DNA binding domain (DBD) of ERα instead of its LBD with small-molecule inhibitors could be an alternative to impair ERα’s signalling pathway. For this purpose, we conducted a structure based virtual screening of DrugBank against the crystal structure of ERα-DBD (PDB ID: 1HCQ) using the Glide module in standard precision (SP) and extra precision (XP) mode of docking. Molecules with XP Gscore less than −8 kcal/mol were selected and visually inspected to keep only the reasonable docking poses. Subsequently, these molecules were clustered using structural interaction fingerprints analysis and the complexes of the top ranked molecules of each cluster based on XP Gscore were subjected to 200 ns molecular dynamics simulations followed by MM-GBSA binding free energy calculation for the last 100 ns of each complex. In this study, we identified three molecules from DrugBank namely DB03450, DB02593 and DB08001 showing significant stability and strong interaction with the key amino acids during MD simulation suggesting a potential inhibition of the target. These molecules could be used as promising lead compounds to impair the ERα signalisation in hormone therapy-resistant breast cancer. Communicated by Ramaswamy H. Sarma

Structural insights into serotonin receptor ligands polypharmacology

Identifying desired interactions with a target receptor is often the first step when designing new active compounds. However, attention should also be focused on contacts with other proteins that result in either selective or polypharmacological compounds. Here, the search for the structural determinants of selectivity between selected serotonin receptor subtypes was carried out. Special attention was focused on 5-HT7R and the cross-interactions between its ligands and the 5-HT1AR, 5-HT1BR, 5-HT2AR, 5-HT2BR, and 5-HT6R subtypes. Selective and non-selective compounds for each pair of 5-HT7/5-HTx receptors were docked to the respective 5-HTR homology models and 5-HT1B/5-HT2BR crystal structures. The contacts present in the ligand-receptor complexes obtained by docking were characterized by the structural interaction fingerprint and statistically analyzed in terms of their frequency. The results allowed for the identification of amino acids that discriminate between selective and non-selective compounds for each 5-HT7/5-HTx receptor pair, which was further compared with available mutagenesis data. Interaction pattern characteristics for compounds with particular activity profiles can constitute the basis for the coherent selectivity theory within a considered set of proteins, supporting the ongoing development of new ligands targeting these receptors. The in silico results were used to analyze prospective virtual screening results towards the 5-HT7 receptor in which compounds of different chemotypes to known 5-HT7R ligands, with micromolar level activities were identified. The findings in this study not only confirm the legitimacy of the approach but also constitute a great starting point for further studies on 5-HT7R ligands selectivity.

Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis

BackgroundNon-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib.ResultsTo decode the drug sensitivity/selectivity of EGFR mutants, it is important to analyze the interaction between EGFR mutants and EGFR inhibitors. In this paper, the 30 most common EGFR mutants were selected and the technique of protein-ligand interaction fingerprint (IFP) was applied to analyze and compare the binding modes of EGFR mutant-gefitinib/erlotinib complexes. Molecular dynamics simulations were employed to obtain the dynamic trajectory and a matrix of IFPs for each EGFR mutant-inhibitor complex. Multilinear Principal Component Analysis (MPCA) was applied for dimensionality reduction and feature selection. The selected features were further analyzed for use as a drug sensitivity predictor. The results showed that the accuracy of prediction of drug sensitivity was very high for both gefitinib and erlotinib. Targeted Projection Pursuit (TPP) was used to show that the data points can be easily separated based on their sensitivities to gefetinib/erlotinib.ConclusionsWe can conclude that the IFP features of EGFR mutant-TKI complexes and the MPCA-based tensor object feature extraction are useful to predict the drug sensitivity of EGFR mutants. The findings provide new insights for studying and predicting drug resistance/sensitivity of EGFR mutations in NSCLC and can be beneficial to the design of future targeted therapies and innovative drug discovery.

Development of Postprocessing Method of Protein-Ligand Docking using Interaction Fingerprint

Protein-ligand docking is an important method in Structure-based Drug Discovery [1]. Although many programs have been developed for docking [2], the accuracy is still insufficient due to the difficulty in the scoring function [3]. Interaction fingerprint is one of the solutions, which generate fingerprints of ligands using the interactions between the ligand and the protein. Interaction fingerprints use the information of known compounds so that compounds that have similar interaction to the known active ligands are expected to find through the virtual screening. However, existing interaction fingerprints such as SIFt [4] and SPLIF [5] only assess the existence or the distance of the interactions and do not consider the strength correctly. In this study, we made a new scoring function of protein-ligand docking called SIEVE-Score (Similarity of Interaction Energy VEctor-Score), which can consider the strength of each interaction explicitly. SIEVE-Score is calculated based on the similarity of the interaction energy vector, which is the list of interaction energy between the ligand and each residue of the protein. We also evaluate the accuracy of virtual screening using SIEVE-Score after the docking by Glide [6].[1] Chiba, S., et al. Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Scientific reports 5:17209, 2015.[2] Elizabeth Y., Jessica H., and Paul A. R., Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. Journal of Molecular Recognition, 28(10):581–604, 2015.[3] Yan L., Li H., Zhihai L., and Renxiao W., Comparative assessment of scoring functions on an updated benchmark: 2. evaluation methods and general results. Journal of Chemical Information and Modeling, 54(6):1717–1736, 2014.[4] Zhan D., Claudio C., and Juswinder S., Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. Journal of Medicinal Chemistry, 47(2):337–344, 2004.[5] Da C., and Kireev D., Structural protein–ligand interaction fingerprints (SPLIF) for structure- based virtual screening: Method and benchmark study. Journal of Chemical Information and Modeling, 54(9):2555–2561, 2014.[6] Richard A. F., et al., Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7):1739–1749, 2004.

Virtual High-Throughput Screening for Matrix Metalloproteinase Inhibitors.

Structure-based virtual screening (SBVS) is a common method for the fast identification of hit structures at the beginning of a medicinal chemistry program in drug discovery. The SBVS, described in this manuscript, is focused on finding small molecule hits that can be further utilized as a starting point for the development of inhibitors of matrix metalloproteinase 13 (MMP-13) via structure-based molecular design. We intended to identify a set of structurally diverse hits, which occupy all subsites (S1'-S3', S2, and S3) centering the zinc containing binding site of MMP-13, by the virtual screening of a chemical library comprising more than ten million commercially available compounds. In total, 23 compounds were found as potential MMP-13 inhibitors using Glide docking followed by the analysis of the structural interaction fingerprints (SIFt) of the docked structures.

Label-free piezoelectric biosensor for prognosis and diagnosis of Systemic Lupus Erythematosus

An autoantigen piezoelectric sensor to quantify specific circulating autoantibodies in human serum is developed. The sensor consisted on a quartz crystal microbalance with dissipation monitoring (QCM-D) where TRIM21 and TROVE2 autoantigens were covalently immobilized, allowing the selective determination of autoantibodies for diagnosis and prognosis of Systemic Lupus Erythematosus (SLE). The sensitivity of the biosensor, measured as IC50 value, was 1.51U/mL and 0.32U/mL, for anti-TRIM21 and anti-TROVE2 circulating autoantibodies, respectively. The sensor is also able to establish a structural interaction fingerprint pattern or profile of circulating autoantibodies, what allows scoring accurately SLE patients. Furthermore, a statistical association of global disease activity with TRIM21-TROVE2 interaction was found (n=130 lupic patient samples, p-value=0.0413). The performances of the biosensor were compared with standard ELISA and multiplex DVD-array high-throughput screening assays, corroborating the viability of piezoelectric biosensor as a cost-effective in vitro assay for the early detection, monitoring or treatment of rare diseases.

Structural Insights into the Molecular Design of HER2 Inhibitors

Background: The present study was aimed at designing some potential candidates as HER2 inhibitors used in breast cancer. Methods: An energy optimized pharmacophore (E-pharmacophore) model was developed and used to screen the molecular databases (such as ASINEX and NCI databases) against a six site (ADHRRR) hypothesis. The shape similarity of the retrieved hits was calculated and then filtered applying ADME and Lipinski’s filters. Further, these hits were docked into the crystal structure of HER2 protein (3W32) using Glide XP protocol to obtain the docking poses and XP gscores. The performance of the virtual screening (VS) methods was evaluated using Schrödinger’s decoy set of 1000 molecules. Ranking of the actives in the VS protocol was assessed by a variety of well-established methods including the average rank of actives, EF, ROC, BEDROC, AUAC, and the RIE. The retrieved hits were submitted to Canvas for generating binary fingerprints (dendritic) to identify structural diversity among the hits and clustered on the basis of Tanimoto coefficient using hierarchical clustering. Results: Seven structurally diverse clusters were selected applying above protocol, having XP gscores >-10, and fitness scores > 1, considering top scoring cluster representative from each cluster. The best scoring hit 355682-ASINEX was submitted to Combiglide to discover some better candidates with improved scores. Finally, structural interaction fingerprint (SIFT) analysis was employed to study the binding interaction, which showed H-bond interaction with Met793, Gln791 and Thr854 residues of HER2 protein. Conclusion: The applied methodology and the retrieved hits could be useful in the design of potent inhibitors of HER2 proteins, commonly found to be expressed in the breast cancer patients.

An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.

A computational procedure to search for selective ligands for structurally related protein targets was developed and verified for serotonergic 5-HT7/5-HT1A receptor ligands. Starting from a set of compounds with annotated activity at both targets (grouped into four classes according to their activity: selective toward each target, not-selective and not-selective but active) and with an additional set of decoys (prepared using DUD methodology), the SVM (Support Vector Machines) models were constructed using a selective subset as positive examples and four remaining classes as negative training examples. Based on these four component models, the consensus classifier was then constructed using a data fusion approach. The combination of two approaches of data representation (molecular fingerprints vs. structural interaction fingerprints), different training set sizes and selection of the best SVM component models for consensus model generation, were evaluated to determine the optimal settings for the developed algorithm. The results showed that consensus models with molecular fingerprints, a larger training set and the selection of component models based on MCC maximization provided the best predictive performance.

Structure-Based Virtual Screening of Commercially Available Compound Libraries.

Virtual screening (VS) is an efficient hit-finding tool. Its distinctive strength is that it allows one to screen compound libraries that are not available in the lab. Moreover, structure-based (SB) VS also enables an understanding of how the hit compounds bind the protein target, thus laying ground work for the rational hit-to-lead progression. SBVS requires a very limited experimental effort and is particularly well suited for academic labs and small biotech companies that, unlike pharmaceutical companies, do not have physical access to quality small-molecule libraries. Here, we describe SBVS of commercial compound libraries for Mer kinase inhibitors. The screening protocol relies on the docking algorithm Glide complemented by a post-docking filter based on structural protein-ligand interaction fingerprints (SPLIF).

KLIFS: a structural kinase-ligand interaction database.

Protein kinases play a crucial role in cell signaling and are important drug targets in several therapeutic areas. The KLIFS database contains detailed structural kinase-ligand interaction information derived from all (>2900) structures of catalytic domains of human and mouse protein kinases deposited in the Protein Data Bank in order to provide insights into the structural determinants of kinase-ligand binding and selectivity. The kinase structures have been processed in a consistent manner by systematically analyzing the structural features and molecular interaction fingerprints (IFPs) of a predefined set of 85 binding site residues with bound ligands. KLIFS has been completely rebuilt and extended (>65% more structures) since its first release as a data set, including: novel automated annotation methods for (i) the assessment of ligand-targeted subpockets and the analysis of (ii) DFG and (iii) αC-helix conformations; improved and automated protocols for (iv) the generation of sequence/structure alignments, (v) the curation of ligand atom and bond typing for accurate IFP analysis and (vi) weekly database updates. KLIFS is now accessible via a website (http://klifs.vu-compmedchem.nl) that provides a comprehensive visual presentation of different types of chemical, biological and structural chemogenomics data, and allows the user to easily access, compare, search and download the data.