Investigating dynamic and energetic determinants of protein nucleic acid recognition: analysis of the zinc finger zif268-DNA complexes

<p>Abstract</p> <p>Background</p> <p>Protein-DNA recognition underlies fundamental biological processes ranging from transcription to replication and modification. Herein, we present a computational study of the sequence modulation of internal dynamic properties and of intraprotein networks of aminoacid interactions that determine the stability and specificity of protein-DNA complexes.</p> <p>Results</p> <p>To this aim, we apply novel theoretical approaches to analyze the dynamics and energetics of biological systems starting from MD trajectories. As model system, we chose different sequences of Zinc Fingers (ZF) of the Zif268 family bound with different sequences of DNA. The complexes differ for their experimental stability properties, but share the same overall 3 D structure and do not undergo structural modifications during the simulations. The results of our analysis suggest that the energy landscape for DNA binding may be populated by dynamically different states, even in the absence of major conformational changes. Energetic couplings between residues change in response to protein and/or DNA sequence variations thus modulating the selectivity of recognition and the relative importance of different regions for binding.</p> <p>Conclusions</p> <p>The results show differences in the organization of the intra-protein energy-networks responsible for the stabilization of the protein conformations recognizing and binding DNA. These, in turn, are reflected into different modulation of the ZF's internal dynamics. The results also show a correlation between energetic and dynamic properties of the different proteins and their specificity/selectivity for DNA sequences. Finally, a dynamic and energetic model for the recognition of DNA by Zinc Fingers is proposed.</p

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

Design, characterization, and first-in-human study of the vascular actions of a novel biased apelin receptor agonist.

[Pyr(1)]apelin-13 is an endogenous vasodilator and inotrope but is downregulated in pulmonary hypertension and heart failure, making the apelin receptor an attractive therapeutic target. Agonists acting at the same G-protein-coupled receptor can be engineered to stabilize different conformational states and function as biased ligands, selectively stimulating either G-protein or β-arrestin pathways. We used molecular dynamics simulations of apelin/receptor interactions to design cyclic analogues and identified MM07 as a biased agonist. In β-arrestin and internalization assays (G-protein-independent), MM07 was 2 orders of magnitude less potent than [Pyr(1)]apelin-13. In a G-protein-dependent saphenous vein contraction assay, both peptides had comparable potency (pD2:[Pyr(1)]apelin-13 9.93±0.24; MM07 9.54±0.42) and maximum responses with a resulting bias for MM07 of ≈350- to 1300-fold for the G-protein pathway. In rats, systemic infusions of MM07 (10-100nmol) caused a dose-dependent increase in cardiac output that was significantly greater than the response to [Pyr(1)]apelin-13. Similarly, in human volunteers, MM07 produced a significant dose-dependent increase in forearm blood flow with a maximum dilatation double that is seen with [Pyr(1)]apelin-13. Additionally, repeated doses of MM07 produced reproducible increases in forearm blood flow. These responses are consistent with a more efficacious action of the biased agonist. In human hand vein, both peptides reversed an established norepinephrine constrictor response and significantly increased venous flow. Our results suggest that MM07 acting as a biased agonist at the apelin receptor can preferentially stimulate the G-protein pathway, which could translate to improved efficacy in the clinic by selectively stimulating vasodilatation and inotropic actions but avoiding activating detrimental β-arrestin-dependent pathways.We acknowledge the Wellcome Trust Programmes in Translational Medicines and Therapeutics (085686) and in Metabolic and Cardiovascular Disease (096822/Z/11/Z), the British Heart Foundation PG/09/050/27734, the Medical Research Council, the Pulmonary Hypertension Association, and the National Institute for Health Research Cambridge Biomedical Research Centre.This is the final published version. It first appeared at http://hyper.ahajournals.org/content/65/4/834.long

Lateral fenestrations in K+-channels explored using MD simulations

Potassium channels are of paramount physiological and pathological importance and therefore constitute significant drug targets. One of the keys to rationalize the way drugs modulate ion channels is to understand the ability of such small molecules to access their respective binding sites, from which they can exert an activating or inhibitory effect. Many computational studies have probed the energetics of ion permeation, and the mechanisms of voltage gating, but little is known about the role of fenestrations as possible mediators of drug entry in potassium channels. To explore the existence, structure, and conformational dynamics of transmembrane fenestrations accessible by drugs in potassium channels, molecular dynamics simulation trajectories were analyzed from three potassium channels: the open state voltage-gated channel Kv1.2, the G protein-gated inward rectifying channel GIRK2 (Kir3.2), and the human two-pore domain TWIK-1 (K2P1.1). The main results of this work were the identification of the sequence identity of four main lateral fenestrations of similar length and with bottleneck radius in the range of 0.9-2.4 Å for this set of potassium channels. It was found that the fenestrations in Kv1.2 and Kir3.2 remain closed to the passage of molecules larger than water. In contrast, in the TWIK-1 channel, both open and closed fenestrations are sampled throughout the simulation, with bottleneck radius shown to correlate with the random entry of lipid membrane molecules into the aperture of the fenestrations. Druggability scoring function analysis of the fenestration regions suggests that Kv and Kir channels studied are not druggable in practice due to steric constraining of the fenestration bottleneck. A high (&gt;50%) fenestration sequence identity was found in each potassium channel subfamily studied, Kv1, Kir3, and K2P1. Finally, the reported fenestration sequence of TWIK-1 compared favorably with another channel, K2P channel TREK-2, reported to possess open fenestrations, suggesting that K2P channels could be druggable via fenestrations, for which we reported atomistic detail of the fenestration region, including the flexible residues M260 and L264 that interact with POPC membrane in a concerted fashion with the aperture and closure of the fenestrations

Cosolvent-enhanced Sampling and Unbiased Identification of Cryptic Pockets Suitable for Structure-based Drug Design

Modulating protein activity with small molecules binding to cryptic pockets offers great opportunities to overcome hurdles in drug design. Cryptic sites are atypical binding sites in proteins that are closed in the absence of a stabilizing ligand and are thus inherently difficult to identify. Many studies have proposed methods to predict cryptic sites. However, a general approach to prospectively sample open conformations of these sites and to identify cryptic pockets in an unbiased manner suitable for structure-based drug design remains elusive. Here, we describe an all-atom, explicit cosolvent, molecular dynamics (MD) simulations-based workflow to sample the open states of cryptic sites and identify opened pockets, in a manner that does not require a priori knowledge about these sites. Furthermore, the workflow relies on a target-independent parameterization that only distinguishes between binding pockets for peptides or small-molecules. We validated our approach on a diverse test set of seven proteins with crystallographically determined cryptic sites. The known cryptic sites were found among the three highest-ranked predicted cryptic sites, and an open site conformation was sampled and selected for most of the systems. Crystallographic ligand poses were well reproduced by docking into these identified open conformations for five of the systems. When the fully open state could not be reproduced, we were still able to predict the location of the cryptic site, or identify other cryptic sites that could be retrospectively validated with knowledge of the protein target. These characteristics render our approach valuable for investigating novel protein targets without any prior information

Prediction of Cytochrome P450 Xenobiotic Metabolism: Tethered Docking and Reactivity Derived from Ligand Molecular Orbital Analysis

Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools

A combination of computational and experimental approaches identifies DNA sequence constraints associated with target site binding specificity of the transcription factor CSL.

Regulation of transcription is fundamental to development and physiology, and occurs through binding of transcription factors to specific DNA sequences in the genome. CSL (CBF1/Suppressor of Hairless/LAG-1), a core component of the Notch signaling pathway, is one such transcription factor that acts in concert with co-activators or co-repressors to control the activity of associated target genes. One fundamental question is how CSL can recognize and select among different DNA sequences available in vivo and whether variations between selected sequences can influence its function. We have therefore investigated CSL-DNA recognition using computational approaches to analyze the energetics of CSL bound to different DNAs and tested the in silico predictions with in vitro and in vivo assays. Our results reveal novel aspects of CSL binding that may help explain the range of binding observed in vivo. In addition, using molecular dynamics simulations, we show that domain-domain correlations within CSL differ significantly depending on the DNA sequence bound, suggesting that different DNA sequences may directly influence CSL function. Taken together, our results, based on computational chemistry approaches, provide valuable insights into transcription factor-DNA binding, in this particular case increasing our understanding of CSL-DNA interactions and how these may impact on its transcriptional control.BBSRC [BB/J008842/1 to S.J.B., B.A., Dr Steve Russell.]; National Institutes of Health (NIH) [CA178974 to R.A.K.] and a Leukemia and Lymphoma Society Scholar Award (to R.A.K.); Unilever (to R.T. and R.G.). China Scholarship Council Cambridge (to J.L.); BBSRC Studentship (to R.A.F.); NIH training [5T32ES007250 to A.N.C.]. Funding for open access charge: University RCUK Open Access Fund.This is the final published version, originally published by Oxford Journals and available at http://nar.oxfordjournals.org/content/early/2014/08/11/nar.gku730.abstract

DeltaDelta Neural Networks for Lead Optimization of Small Molecule Potency

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives

DeltaDelta neural networks for lead optimization of small molecule potency

Machine learning approach tailored for ranking congeneric series based on 3D-convolutional neural networks tested it on over 3246 ligands and 13 targets. The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives