Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features.

Serine proteases, implicated in important physiological functions, have a high intra-family similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity. However, the availability of sequence and structure data has now made it possible to develop approaches to design pharmacological agents that can discriminate successfully between their related binding sites. In this study, we have quantified the relationship between 12,625 distinct protease inhibitors and their bioactivity against 67 targets of the serine protease family (20,213 data points) in an integrative manner, using proteochemometric modelling (PCM). The benchmarking of 21 different target descriptors motivated the usage of specific binding pocket amino acid descriptors, which helped in the identification of active site residues and selective compound chemotypes affecting compound affinity and selectivity. PCM models performed better than alternative approaches (models trained using exclusively compound descriptors on all available data, QSAR) employed for comparison with R(2)/RMSE values of 0.64 ± 0.23/0.66 ± 0.20 vs. 0.35 ± 0.27/1.05 ± 0.27 log units, respectively. Moreover, the interpretation of the PCM model singled out various chemical substructures responsible for bioactivity and selectivity towards particular proteases (thrombin, trypsin and coagulation factor 10) in agreement with the literature. For instance, absence of a tertiary sulphonamide was identified to be responsible for decreased selective activity (by on average 0.27 ± 0.65 pChEMBL units) on FA10. Among the binding pocket residues, the amino acids (arginine, leucine and tyrosine) at positions 35, 39, 60, 93, 140 and 207 were observed as key contributing residues for selective affinity on these three targets.Q.A. thanks the Islamic Development Bank and Cambridge Commonwealth Trust for Funding. O.M.L. is grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. G.v.W. thanks EMBL 90 (EIPOD) and Marie Curie (COFUND) for funding. A.B. thanks Unilever and the ERC (Starting Grant RC-2013-StG 336159 MIXTURE) for funding. ICC thanks the Institut Pasteur and the Pasteur-Paris International PhD programme for funding. TM thanks the Institut Pasteur for funding.This is the final version of the article. It first appeared from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C4IB00175

Analysis of Differential Efficacy and Affinity of GABAA (α1/α2) Selective Modulators.

Selective modulators of the γ-amino butyric acid (GABAA) family of receptors have the potential to treat a range of disease states related to cognition, pain, and anxiety. While the development of various α subunit-selective modulators is currently underway for the treatment of anxiety disorders, a mechanistic understanding of the correlation between their bioactivity and efficacy, based on ligand-target interactions, is currently still lacking. In order to alleviate this situation, in the current study we have analyzed, using ligand- and structure-based methods, a data set of 5440 GABAA modulators. The Spearman correlation (ρ) between binding activity and efficacy of compounds was calculated to be 0.008 and 0.31 against the α1 and α2 subunits of GABA receptor, respectively; in other words, the compounds had little diversity in structure and bioactivity, but they differed significantly in efficacy. Two compounds were selected as a case study for detailed interaction analysis due to the small difference in their structures and affinities (ΔpKi(comp1_α1 - comp2_α1) = 0.45 log units, ΔpKi(comp1_α2 - comp2_α2) = 0 log units) as compared to larger relative efficacies (ΔRE(comp1_α1 - comp2_α1) = 1.03, ΔRE(comp1_α2 - comp2_α2) = 0.21). Docking analysis suggested that His-101 is involved in a characteristic interaction of the α1 receptor with both compounds 1 and 2. Residues such as Phe-77, Thr-142, Asn-60, and Arg-144 of the γ chain of the α1γ2 complex also showed interactions with heterocyclic rings of both compounds 1 and 2, but these interactions were disturbed in the case of α2γ2 complex docking results. Binding pocket stability analysis based on molecular dynamics identified three substitutions in the loop C region of the α2 subunit, namely, G200E, I201T, and V202I, causing a reduction in the flexibility of α2 compared to α1. These amino acids in α2, as compared to α1, were also observed to decrease the vibrational and dihedral entropy and to increase the hydrogen bond content in α2 in the apo state. However, freezing of both α1 and α2 was observed in the ligand-bound state, with an increased number of internal hydrogen bonds and increased entropy. Therefore, we hypothesize that the amino acid differences in the loop C region of α2 are responsible for conformational changes in the protein structure compared to α1, as well as for the binding modes of compounds and hence their functional signaling

Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features

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Prediction of Structure of Human WNT-CRD (FZD) Complex for Computational Drug Repurposing

<div><p>The observed genetic alterations of various extracellular and intracellular WNT (<em>Wingless</em>, <em>Int-1</em> proto-oncogene) signaling components can result in an increase or decrease in gene expression, and hence can be obstructed proficiently. These genetics target sites may include the prevention of WNT-FZD (Frizzled) binding, destruction of <em>β-catenin</em> and formation of <em>Axin</em>, <em>APC</em> and <em>GSK-3β</em> complex. Hence, the localized targeting of these interacting partners can help in devising novel inhibitors against WNT signaling. Our present study is an extension of our previous work, in which we proposed the co-regulated expression pattern of the WNT gene cluster (WNT-1, WNT-6, WNT-10A and WNT-10B) in human breast carcinoma. We present here the computationally modeled three dimensional structure of human WNT-1 in complex with the FZD-1 CRD (Cysteine Rich Domain) receptor. The dimeric cysteine-rich domain was found to fit into the evolutionarily conserved U-shaped groove of WNT protein. The two ends of the U- shaped cleft contain N-terminal and C-terminal hydrophobic residues, thus providing a strong hydrophobic moiety for the frizzled receptor and serving as the largest binding pocket for WNT-FZD interaction. Detailed structural analysis of this cleft revealed a maximum atomic distance of ∼28 Å at the surface, narrowing down to ∼17 Å and again increasing up to ∼27 Å at the bottom. Altogether, structural prediction analysis of WNT proteins was performed to reveal newer details about post-translational modification sites and to map the novel pharmacophore models for potent WNT inhibitors.</p> </div

Superimposed 3D modeled structures of the (active conserved domain) WNT-1, -2B, -6, -10A and -10B.

<p>Highly conserved residues from 70–80 and 90 onwards are making the running backbone, whereas fluctuating regions are found among the residues ranging between 81–89 and 56–61. Each superimposed protein is shown in a different color (WNT-1: yellow, WNT-2B: magenta, WNT-6: blue, WNT-10A: red and WNT-10B: green).</p

List of grid and docking parameters used to perform docking experiment.

<p>List of grid and docking parameters used to perform docking experiment.</p

Multiple Sequence Alignment of 19 human WNTs paralogs.

<p>Signal peptide sequences are shown in red. Transmembrane helices of each WNT are shown in blue. The 23 conserved cysteines in all 19 WNT proteins are marked with yellow. These cysteine residues help in the formation of disulphide bridges and improve folding process. The palmitoylation site is also shown, displaying the conserved sequence for post-translational modification of WNTs. All WNTs have a common conserved family signature, as indicated. The signal peptide sequences were obtained from SignalP web server. TMHMM web server was used to determine helices inside, outside and between membranes. Motif enrichment analysis was performed using PRINTS, PRODOM, Blocks, Pfam and InterPro. The MSA was generated using ClustalX 2.0.</p

Predicted docked surfaces of CRD dimmer.

<p>The interacting residues are shown in different colors on the basis of their interactions. The residues involved in hydrogen bonding are shown in blue; residues connected to each other with disulphide bridge formation are shown in yellow, whereas the electro statically interacting residues are shown in orange red. This dimmer surface has the greatest interactions and has binding potential to bind with WNT ligands and hence could be targeted for inhibition. H-2 and H-4 helices and a loop region between these helices are involved in binding interactions.</p

WNT U-shaped binding pocket with two hydrophobic arms grasping Frizzled CRD.

<p>N-terminal binding Site 1 contains most hydrophobic amino acid residues including Leu-3, Ala-5, and Pro-8. Binding site 2 is present at C-terminal end. This binding site was found to be most conserved in all WNT proteins, ranging between amino acid residues 300–325. The opening of the U-groove is 28 Å wide. The groove then widens to 31 Å, before narrowing to 17 Å at its centre and finally widening again to 27 Å at the base.</p

Analysis of Differential Efficacy and Affinity of GABA_A (α1/α2) Selective Modulators

Selective modulators of the γ-amino butyric acid (GABA_A) family of receptors have the potential to treat a range of disease states related to cognition, pain, and anxiety. While the development of various α subunit-selective modulators is currently underway for the treatment of anxiety disorders, a mechanistic understanding of the correlation between their bioactivity and efficacy, based on ligand–target interactions, is currently still lacking. In order to alleviate this situation, in the current study we have analyzed, using ligand- and structure-based methods, a data set of 5440 GABA_A modulators. The Spearman correlation (ρ) between binding activity and efficacy of compounds was calculated to be 0.008 and 0.31 against the α1 and α2 subunits of GABA receptor, respectively; in other words, the compounds had little diversity in structure and bioactivity, but they differed significantly in efficacy. Two compounds were selected as a case study for detailed interaction analysis due to the small difference in their structures and affinities (Δp<i>K</i>_i_{(comp1_α1 – comp2_α1)} = 0.45 log units, Δp<i>K</i>_i_{(comp1_α2 – comp2_α2)} = 0 log units) as compared to larger relative efficacies (ΔRE_{(comp1_α1 – comp2_α1)} = 1.03, ΔRE_{(comp1_α2 – comp2_α2)} = 0.21). Docking analysis suggested that His-101 is involved in a characteristic interaction of the α1 receptor with both compounds <b>1</b> and <b>2</b>. Residues such as Phe-77, Thr-142, Asn-60, and Arg-144 of the γ chain of the α1γ2 complex also showed interactions with heterocyclic rings of both compounds <b>1</b> and <b>2</b>, but these interactions were disturbed in the case of α2γ2 complex docking results. Binding pocket stability analysis based on molecular dynamics identified three substitutions in the loop C region of the α2 subunit, namely, G200E, I201T, and V202I, causing a reduction in the flexibility of α2 compared to α1. These amino acids in α2, as compared to α1, were also observed to decrease the vibrational and dihedral entropy and to increase the hydrogen bond content in α2 in the apo state. However, freezing of both α1 and α2 was observed in the ligand-bound state, with an increased number of internal hydrogen bonds and increased entropy. Therefore, we hypothesize that the amino acid differences in the loop C region of α2 are responsible for conformational changes in the protein structure compared to α1, as well as for the binding modes of compounds and hence their functional signaling