Molecular mechanism of α-synuclein aggregation on lipid membranes revealed.

The central hallmark of Parkinson's disease pathology is the aggregation of the α-synuclein protein, which, in its healthy form, is associated with lipid membranes. Purified monomeric α-synuclein is relatively stable in vitro, but its aggregation can be triggered by the presence of lipid vesicles. Despite this central importance of lipids in the context of α-synuclein aggregation, their detailed mechanistic role in this process has not been established to date. Here, we use chemical kinetics to develop a mechanistic model that is able to globally describe the aggregation behaviour of α-synuclein in the presence of DMPS lipid vesicles, across a range of lipid and protein concentrations. Through the application of our kinetic model to experimental data, we find that the reaction is a co-aggregation process involving both protein and lipids and that lipids promote aggregation as much by enabling fibril elongation as by enabling their initial formation. Moreover, we find that the primary nucleation of lipid-protein co-aggregates takes place not on the surface of lipid vesicles in bulk solution but at the air-water and/or plate interfaces, where lipids and proteins are likely adsorbed. Our model forms the basis for mechanistic insights, also in other lipid-protein co-aggregation systems, which will be crucial in the rational design of drugs that inhibit aggregate formation and act at the key points in the α-synuclein aggregation cascade.

Polypharmacological in Silico Bioactivity Profiling and Experimental Validation Uncovers Sedative-Hypnotic Effects of Approved and Experimental Drugs in Rat.

In this work, we describe the computational ("in silico") mode-of-action analysis of CNS-active drugs, which is taking both multiple simultaneous hypotheses as well as sets of protein targets for each mode-of-action into account, and which was followed by successful prospective in vitro and in vivo validation. Using sleep-related phenotypic readouts describing both efficacy and side effects for 491 compounds tested in rat, we defined an "optimal" (desirable) sleeping pattern. Compounds were subjected to in silico target prediction (which was experimentally confirmed for 21 out of 28 cases), followed by the utilization of decision trees for deriving polypharmacological bioactivity profiles. We demonstrated that predicted bioactivities improved classification performance compared to using only structural information. Moreover, DrugBank molecules were processed via the same pipeline, and compounds in many cases not annotated as sedative-hypnotic (alcaftadine, benzatropine, palonosetron, ecopipam, cyproheptadine, sertindole, and clopenthixol) were prospectively validated in vivo. Alcaftadine, ecopipam cyproheptadine, and clopenthixol were found to promote sleep as predicted, benzatropine showed only a small increase in NREM sleep, whereas sertindole promoted wakefulness. To our knowledge, the sedative-hypnotic effects of alcaftadine and ecopipam have not been previously discussed in the literature. The method described extends previous single-target, single-mode-of-action models and is applicable across disease areas.

Deep sequencing of 10,000 human genomes.

We report on the sequencing of 10,545 human genomes at 30×-40× coverage with an emphasis on quality metrics and novel variant and sequence discovery. We find that 84% of an individual human genome can be sequenced confidently. This high-confidence region includes 91.5% of exon sequence and 95.2% of known pathogenic variant positions. We present the distribution of over 150 million single-nucleotide variants in the coding and noncoding genome. Each newly sequenced genome contributes an average of 8,579 novel variants. In addition, each genome carries on average 0.7 Mb of sequence that is not found in the main build of the hg38 reference genome. The density of this catalog of variation allowed us to construct high-resolution profiles that define genomic sites that are highly intolerant of genetic variation. These results indicate that the data generated by deep genome sequencing is of the quality necessary for clinical use.

Comparing global and local likelihood score thresholds in multiclass laplacian-modified Naive Bayes protein target prediction.

The increase of publicly available bioactivity data has led to the extensive development and usage of in silico bioactivity prediction algorithms. A particularly popular approach for such analyses is the multiclass Naïve Bayes, whose output is commonly processed by applying empirically-derived likelihood score thresholds. In this work, we describe a systematic way for deriving score cut-offs on a per-protein target basis and compare their performance with global thresholds on a large scale using both 5-fold cross-validation (ChEMBL 14, 189k ligand-protein pairs over 477 protein targets) and external validation (WOMBAT, 63k pairs, 421 targets). The individual protein target cut-offs derived were compared to global cut-offs ranging from -10 to 40 in score bouts of 2.5. The results indicate that individual thresholds had equal or better performance in all comparisons with global thresholds, ranging from 95% of protein targets to 57.96%. It is shown that local thresholds behave differently for particular families of targets (CYPs, GPCRs, Kinases and TFs). Furthermore, we demonstrate the discrepancy in performance when we move away from the training dataset chemical space, using Tanimoto similarity as a metric (from 0 to 1 in steps of 0.2). Finally, the individual protein score cut-offs derived for the in silico bioactivity application used in this work are released, as well as the reproducible and transferable KNIME workflows used to carry out the analysis.

Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis.

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts.

BACKGROUND: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. RESULTS: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. CONCLUSION: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.

Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application to Xenopus laevis Phenotypic Readouts.

The simultaneous increase of computational power and the availability of chemical and biological data have contributed to the recent popularity of in silico bioactivity prediction algorithms. Such methods are commonly used to infer the 'Mechanism of Action' of small molecules and they can also be employed in cases where full bioactivity profiles have not been established experimentally. However, protein target predictions by themselves do not necessarily capture information about the effect of a compound on a biological system, and hence merging their output with a systems biology approach can help to better understand the complex network modulation which leads to a particular phenotype. In this work, we review approaches and applications of target prediction, as well as their shortcomings, and demonstrate two extensions of this concept which are exemplified using phenotypic readouts from a chemical genetic screen in Xenopus laevis. In particular, the experimental observations are linked to their predicted bioactivity profiles. Predicted targets are annotated with pathways, which lead to further biological insight. Moreover, we subject the prediction to further machine learning algorithms, namely decision trees, to capture the differential pharmacology of ligand-target interactions in biological systems. Both methodologies hence provide new insight into understanding the Mechanism of Action of compound activities from phenotypic screens.

Using buriedness to improve discrimination between actives and inactives in docking.

A continuing problem in protein-ligand docking is the correct relative ranking of active molecules versus inactives. Using the ChemScore scoring function as implemented in the GOLD docking software, we have investigated the effect of scaling hydrogen bond, metal-ligand, and lipophilic interactions based on the buriedness of the interaction. Buriedness was measured using the receptor density, the number of protein heavy atoms within 8.0 A. Terms in the scaling functions were optimized using negative data, represented by docked poses of inactive molecules. The objective function was the mean rank of the scores of the active poses in the Astex Diverse Set (Hartshorn et al. J. Med. Chem., 2007, 50, 726) with respect to the docked poses of 99 inactives. The final four-parameter model gave a substantial improvement in the average rank from 18.6 to 12.5. Similar results were obtained for an independent test set. Receptor density scaling is available as an option in the recent GOLD release.

The use of protein-ligand interaction fingerprints in docking.

The use of structure-based virtual screening to predict small-molecule binding in a target active site is an increasingly popular approach in drug discovery programs. As the number of structures of protein-ligand complexes in public and proprietary databases grow, it is important to incorporate prior structural knowledge of ligand binding into virtual screening experiments. The structural interaction fingerprint (SIFt) approach aims to capture a 1D representation of the interactions between ligand and protein either in complexes of known structure or in docked poses. This review describes recent developments in the use of the SIFt in rescoring docked ligand poses in virtual screening.

Diverse, high-quality test set for the validation of protein-ligand docking performance.

A procedure for analyzing and classifying publicly available crystal structures has been developed. It has been used to identify high-resolution protein-ligand complexes that can be assessed by reconstructing the electron density for the ligand using the deposited structure factors. The complexes have been clustered according to the protein sequences, and clusters have been discarded if they do not represent proteins thought to be of direct interest to the pharmaceutical or agrochemical industry. Rules have been used to exclude complexes containing non-drug-like ligands. One complex from each cluster has been selected where a structure of sufficient quality was available. The final Astex diverse set contains 85 diverse, relevant protein-ligand complexes, which have been prepared in a format suitable for docking and are to be made freely available to the entire research community (http://www.ccdc.cam.ac.uk). The performance of the docking program GOLD against the new set is assessed using a variety of protocols. Relatively unbiased protocols give success rates of approximately 80% for redocking into native structures, but it is possible to get success rates of over 90% with some protocols.

Structural analysis of DNA-PKcs: modelling of the repeat units and insights into the detailed molecular architecture.

DNA-dependent protein kinase (DNA-PK) is part of the eukaryotic DNA double strand break repair pathway and as such is crucial for maintenance of genomic stability, as well as for V(D)J (variable-diversity-joining) recombination. The catalytic subunit of DNA-PK (DNA-PKcs) belongs to the phosphatidylinositol-3 (PI-3) kinase-like kinase (PIKK) superfamily and is comprised of approximately 4100 amino acids. We have used a novel repeat detection method to analyse this enormous protein and have identified two different types of helical repeat motifs in the N-terminal region of the sequence, as well as other previously unreported features in this repeat region. A comparison with the ATMs, ATRs, and TORs show that the features identified are likely to be conserved throughout the PIKK superfamily. Homology modelling of parts of the DNA-PKcs sequence has been undertaken and we have been able to fit the models to previously obtained electron microscopy data. This work provides an insight into the overall architecture of the DNA-PKcs protein and identifies regions of interest for further experimental studies.

Identification of DNA-PK in the arthropods. Evidence for the ancient ancestry of vertebrate non-homologous end-joining.

Cellular life depends upon the preservation and transmission of genetic material. Double stranded DNA breaks (DSBs) cause catastrophic gene loss in cell division and must be promptly and accurately repaired. In eukaryotes DSBs may be repaired by either non-homologous end-joining (NHEJ), single strand annealing or homologous recombination (HR). Vertebrate NHEJ has been shown to depend upon the DNA-dependent protein kinase (DNA-PK) consisting of the phosphatidylinositol 3 (PI 3)-kinase like (PIKK) catalytic sub-unit (DNA-PKcs) and the DNA targeting factor Ku. Our analysis of recently completed genomes found several novel PIKKs in Anopheles gambiae and Drosophila melanogaster including a novel mosquito DNA-PKcs orthologue, the first non-vertebrate DNA-PKcs described to date. We also detected a DNA-PKcs fragment in the high quality EST set of Apis mellifera ligustica (honey bee) suggesting that DNA-PK is a far older and more important eukaryotic complex than previously thought.