High Population Frequency of GNRHR p.Q106R in Malta: An Evaluation of Fertility and Hormone Profiles in Heterozygotes.

Context: The gonadotropin-releasing hormone receptor variant GNRHR p.Q106R (rs104893836) in homozygosity, compound heterozygosity, or single heterozygosity is often reported as the causative variant in idiopathic hypogonadotropic hypogonadism (IHH) patients with GnRH deficiency. Genotyping of a Maltese newborn cord-blood collection yielded a minor allele frequency (MAF) 10 times higher (MAF = 0.029; n = 493) than that of the global population (MAF = 0.003). Objective: To determine whether GNRHR p.Q106R in heterozygosity influences profiles of endogenous hormones belonging to the hypothalamic-pituitary axis and the onset of puberty and fertility in adult men (n = 739) and women (n = 239). Design Setting and Participants: Analysis of questionnaire data relating to puberty and fertility, genotyping of the GNRHR p.Q106R variant, and hormone profiling of a highly phenotyped Maltese adult cohort from the Maltese Acute Myocardial Infarction Study. Main Outcome and Results: Out of 978 adults, 43 GNRHR p.Q106R heterozygotes (26 men and 17 women) were identified. Hormone levels and fertility for all heterozygotes are within normal parameters except for TSH, which was lower in men 50 years or older. Conclusion: Hormone data and baseline fertility characteristics of GNRHR p.Q106R heterozygotes are comparable to those of homozygous wild-type individuals who have no reproductive problems. The heterozygous genotype alone does not impair the levels of investigated gonadotropins and sex steroid hormones or affect fertility. GNRHR p.Q106R heterozygotes who exhibit IHH characteristics must have at least another variant, probably in a different IHH gene, that drives pathogenicity. We also conclude that GNRHR p.Q106R is likely a founder variant due to its overrepresentation and prevalence in the island population of Malta.

Exploring the crop epigenome: a comparison of DNA methylation profiling techniques.

Epigenetic modifications play a vital role in the preservation of genome integrity and in the regulation of gene expression. DNA methylation, one of the key mechanisms of epigenetic control, impacts growth, development, stress response and adaptability of all organisms, including plants. The detection of DNA methylation marks is crucial for understanding the mechanisms underlying these processes and for developing strategies to improve productivity and stress resistance of crop plants. There are different methods for detecting plant DNA methylation, such as bisulfite sequencing, methylation-sensitive amplified polymorphism, genome-wide DNA methylation analysis, methylated DNA immunoprecipitation sequencing, reduced representation bisulfite sequencing, MS and immuno-based techniques. These profiling approaches vary in many aspects, including DNA input, resolution, genomic region coverage, and bioinformatics analysis. Selecting an appropriate methylation screening approach requires an understanding of all these techniques. This review provides an overview of DNA methylation profiling methods in crop plants, along with comparisons of the efficacy of these techniques between model and crop plants. The strengths and limitations of each methodological approach are outlined, and the importance of considering both technical and biological factors are highlighted. Additionally, methods for modulating DNA methylation in model and crop species are presented. Overall, this review will assist scientists in making informed decisions when selecting an appropriate DNA methylation profiling method.

Few-Shot Learning for Low-Data Drug Discovery.

The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of conventional machine learning techniques to this problem domain. This work explores few-shot machine learning for hit discovery and lead optimization. We build on the state-of-the-art and introduce two new metric-based meta-learning techniques, Prototypical and Relation Networks, to this problem domain. We also explore using different embeddings, namely, extended-connectivity fingerprints (ECFP) and embeddings generated through graph convolutional networks (GCN), as inputs to neural networks for classification. This study shows that learned embeddings through GCNs consistently perform better than extended-connectivity fingerprints for toxicity and LBVS experiments. We conclude that the effectiveness of few-shot learning is highly dependent on the nature of the data. Few-shot learning models struggle to perform consistently on MUV and DUD-E data, in which the active compounds are structurally distinct. However, on Tox21 data, the few-shot models perform well, and we find that Prototypical Networks outperform the state-of-the-art, which is based on the Matching Networks architecture. Additionally, training these networks is substantially faster (up to 190%) and therefore takes a fraction of the time to train for comparable, or better, results.

Machine Learning Using Neural Networks for Metabolomic Pathway Analyses.

Elucidating the mechanisms of metabolic pathways helps us understand the cascade of enzyme-catalyzed reactions that lead to the conversion of substances into final products. This has implications for predicting how newly synthesized compounds will affect a person's metabolism and, hence, the development of novel treatments to improve one's health. The study of metabolomic pathways, together with protein engineering, may also aid in the extraction, at a scale, of natural products to be used as drugs and drug precursors. Several approaches have been used to correlate protein annotations to metabolic pathways in order to derive pathways directly related to specific organisms. These could range from association rule-mining techniques to machine learning methods such as decision trees, naïve Bayes, logistic regression, and ensemble methods.In this chapter, we will be reviewing the use of machine learning for metabolic pathway analyses, with a step-by-step focus on the use of deep learning to predict the association of compounds (metabolites) to their respective metabolomic pathway classes. This prediction could help explain interactions of small molecules in organisms. Inspired by the work of Baranwal et al. (2019), we demonstrate how to build and train a deep learning neural network model to perform a multi-label prediction. We considered two different types of fingerprints as features (inputs to the model). The output of the model is the set of metabolic pathway classes (from the KEGG dataset) in which the input molecule participates. We will walk through the various steps of this process, including data collection, feature engineering, model selection, training, and evaluation. This model-building and evaluation process may be easily transferred to other domains of interest. All the source code used in this chapter is made publicly available at https://github.com/jp-um/machine_learning_for_metabolomic_pathway_analyses .

Possible Role of Circulating Bone Marrow Mesenchymal Progenitors in Modulating Inflammation and Promoting Wound Repair.

Circulating bone marrow mesenchymal progenitors (BMMPs) are known to be potent antigen-presenting cells that migrate to damaged tissue to secrete cytokines and growth factors. An altered or dysregulated inflammatory cascade leads to a poor healing outcome. A skin model developed in our previous study was used to observe the immuno-modulatory properties of circulating BMMP cells in inflammatory chronic wounds in a scenario of low skin perfusion. BMMPs were analysed exclusively and in conjunction with recombinant tumour necrosis factor alpha (TNFα) and recombinant hepatocyte growth factor (HGF) supplementation. We analysed the expression levels of interleukin-8 (IL-8) and ecto-5'-nucleotidase (CD73), together with protein levels for IL-8, stem cell factor (SCF), and fibroblast growth factor 1 (FGF-1). The successfully isolated BMMPs were positive for both hemopoietic and mesenchymal markers and showed the ability to differentiate into adipocytes, chondrocytes, and osteocytes. Significant differences were found in IL-8 and CD73 expressions and IL-8 and SCF concentrations, for all conditions studied over the three time points taken into consideration. Our data suggests that BMMPs may modulate the inflammatory response by regulating IL-8 and CD73 and influencing IL-8 and SCF protein secretions. In conclusion, we suggest that BMMPs play a role in wound repair and that their induced application might be suitable for scenarios with a low skin perfusion.

Dwarna: a blockchain solution for dynamic consent in biobanking.

Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within the context of biobanking, it gives individuals access to information and control to determine how and where their biospecimens and data should be used. We present Dwarna-a web portal for 'dynamic consent' that acts as a hub connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the general public. The portal stores research partners' consent in a blockchain to create an immutable audit trail of research partners' consent changes. Dwarna's structure also presents a solution to the European Union's General Data Protection Regulation's right to erasure-a right that is seemingly incompatible with the blockchain model. Dwarna's transparent structure increases trustworthiness in the biobanking process by giving research partners more control over which research studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen and associated data are destroyed.

Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening.

We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein-ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein-ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, "information-rich" mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity's performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein-ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives-and thus their lowest-energy conformations-have been optimized for conformational complementarity with their cognate binding sites.

Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening.

Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional information about molecular shape, as well as other properties, into a small set of numeric descriptors. These can be used to efficiently compute a measure of similarity between pairs of molecules using a simple inverse Manhattan Distance metric. In this study we explore the use of suitable Machine Learning techniques that can be trained using USR descriptors, so as to improve the similarity detection of potential new leads. We use molecules from the Directory for Useful Decoys-Enhanced to construct machine learning models based on three different algorithms: Gaussian Mixture Models (GMMs), Isolation Forests and Artificial Neural Networks (ANNs). We train models based on full molecule conformer models, as well as the Lowest Energy Conformations (LECs) only. We also investigate the performance of our models when trained on smaller datasets so as to model virtual screening scenarios when only a small number of actives are known a priori. Our results indicate significant performance gains over a state of the art USR-derived method, ElectroShape 5D, with GMMs obtaining a mean performance up to 430% better than that of ElectroShape 5D in terms of Enrichment Factor with a maximum improvement of up to 940%. Additionally, we demonstrate that our models are capable of maintaining their performance, in terms of enrichment factor, within 10% of the mean as the size of the training dataset is successively reduced. Furthermore, we also demonstrate that running times for retrospective screening using the machine learning models we selected are faster than standard USR, on average by a factor of 10, including the time required for training. Our results show that machine learning techniques can significantly improve the virtual screening performance and efficiency of the USR family of methods.

Are the physicochemical properties of antibacterial compounds really different from other drugs?

BACKGROUND: It is now widely recognized that there is an urgent need for new antibacterial drugs, with novel mechanisms of action, to combat the rise of multi-drug resistant bacteria. However, few new compounds are reaching the market. Antibacterial drug discovery projects often succeed in identifying potent molecules in biochemical assays but have been beset by difficulties in obtaining antibacterial activity. A commonly held view, based on analysis of marketed antibacterial compounds, is that antibacterial drugs possess very different physicochemical properties to other drugs, and that this profile is required for antibacterial activity. RESULTS: We have re-examined this issue by performing a cheminformatics analysis of the literature data available in the ChEMBL database. The physicochemical properties of compounds with a recorded activity in an antibacterial assay were calculated and compared to two other datasets extracted from ChEMBL, marketed antibacterials and drugs marketed for other therapeutic indications. The chemical class of the compounds and Gram-negative/Gram-positive profile were also investigated. This analysis shows that compounds with antibacterial activity have physicochemical property profiles very similar to other drug classes. CONCLUSIONS: The observation that many current antibacterial drugs lie in regions of physicochemical property space far from conventional small molecule therapeutics is correct. However, the inference that a compound must lie in one of these "outlier" regions in order to possess antibacterial activity is not supported by our analysis. Graphical abstract.

Crowdsourcing yields a new standard for kinks in protein helices.

Kinks are functionally important structural features found in the α-helices of proteins. Structurally, they are points at which a helix abruptly changes direction. Current kink definition and identification methods often disagree with one another. Here we describe a crowdsourcing approach to obtain a reliable gold standard set of kinks. Using an online interface, we collected more than 10,000 classifications of 300 helices into straight, curved, or kinked categories. We found that participants were better at discriminating between straight and not-straight helices than between kinked and curved helices. Surprisingly, more obvious kinks were not necessarily identified as more localized within the helix. We present a set of 252 helices where more than 50% of the participants agree on a classification. This set can be used as a reliable gold standard to develop, train, and compare computational methods. An interactive visualization of the results is available online at http://opig.stats.ox.ac.uk/webapps/ahah/php/experiment_results.php .

Fragment-based modeling of membrane protein loops: successes, failures, and prospects for the future.

Membrane proteins (MPs) have become a major focus in structure prediction, due to their medical importance. There is, however, a lack of fast and reliable methods that specialize in the modeling of MP loops. Often methods designed for soluble proteins (SPs) are applied directly to MPs. In this article, we investigate the validity of such an approach in the realm of fragment-based methods. We also examined the differences in membrane and soluble protein loops that might affect accuracy. We test our ability to predict soluble and MP loops with the previously published method FREAD. We show that it is possible to predict accurately the structure of MP loops using a database of MP fragments (0.5-1 Å median root-mean-square deviation). The presence of homologous proteins in the database helps prediction accuracy. However, even when homologues are removed better results are still achieved using fragments of MPs (0.8-1.6 Å) rather than SPs (1-4 Å) to model MP loops. We find that many fragments of SPs have shapes similar to their MP counterparts but have very different sequences; however, they do not appear to differ in their substitution patterns. Our findings may allow further improvements to fragment-based loop modeling algorithms for MPs. The current version of our proof-of-concept loop modeling protocol produces high-accuracy loop models for MPs and is available as a web server at http://medeller.info/fread.

The emerging role of cloud computing in molecular modelling.

There is a growing recognition of the importance of cloud computing for large-scale and data-intensive applications. The distinguishing features of cloud computing and their relationship to other distributed computing paradigms are described, as are the strengths and weaknesses of the approach. We review the use made to date of cloud computing for molecular modelling projects and the availability of front ends for molecular modelling applications. Although the use of cloud computing technologies for molecular modelling is still in its infancy, we demonstrate its potential by presenting several case studies. Rapid growth can be expected as more applications become available and costs continue to fall; cloud computing can make a major contribution not just in terms of the availability of on-demand computing power, but could also spur innovation in the development of novel approaches that utilize that capacity in more effective ways.

Memoir: template-based structure prediction for membrane proteins.

Membrane proteins are estimated to be the targets of 50% of drugs that are currently in development, yet we have few membrane protein crystal structures. As a result, for a membrane protein of interest, the much-needed structural information usually comes from a homology model. Current homology modelling software is optimized for globular proteins, and ignores the constraints that the membrane is known to place on protein structure. Our Memoir server produces homology models using alignment and coordinate generation software that has been designed specifically for transmembrane proteins. Memoir is easy to use, with the only inputs being a structural template and the sequence that is to be modelled. We provide a video tutorial and a guide to assessing model quality. Supporting data aid manual refinement of the models. These data include a set of alternative conformations for each modelled loop, and a multiple sequence alignment that incorporates the query and template. Memoir works with both α-helical and ß-barrel types of membrane proteins and is freely available at http://opig.stats.ox.ac.uk/webapps/memoir.

Plasmodium subtilisin-like protease 1 (SUB1): insights into the active-site structure, specificity and function of a pan-malaria drug target.

Release of the malaria merozoite from its host erythrocyte (egress) and invasion of a fresh cell are crucial steps in the life cycle of the malaria pathogen. Subtilisin-like protease 1 (SUB1) is a parasite serine protease implicated in both processes. In the most dangerous human malarial species, Plasmodium falciparum, SUB1 has previously been shown to have several parasite-derived substrates, proteolytic cleavage of which is important both for egress and maturation of the merozoite surface to enable invasion. Here we have used molecular modelling, existing knowledge of SUB1 substrates, and recombinant expression and characterisation of additional Plasmodium SUB1 orthologues, to examine the active site architecture and substrate specificity of P. falciparum SUB1 and its orthologues from the two other major human malaria pathogens Plasmodium vivax and Plasmodium knowlesi, as well as from the rodent malaria species, Plasmodium berghei. Our results reveal a number of unusual features of the SUB1 substrate binding cleft, including a requirement to interact with both prime and non-prime side residues of the substrate recognition motif. Cleavage of conserved parasite substrates is mediated by SUB1 in all parasite species examined, and the importance of this is supported by evidence for species-specific co-evolution of protease and substrates. Two peptidyl alpha-ketoamides based on an authentic PfSUB1 substrate inhibit all SUB1 orthologues examined, with inhibitory potency enhanced by the presence of a carboxyl moiety designed to introduce prime side interactions with the protease. Our findings demonstrate that it should be possible to develop 'pan-reactive' drug-like compounds that inhibit SUB1 in all three major human malaria pathogens, enabling production of broad-spectrum antimalarial drugs targeting SUB1.

Freely available conformer generation methods: how good are they?

Conformer generation has important implications in cheminformatics, particularly in computational drug discovery where the quality of conformer generation software may affect the outcome of a virtual screening exercise. We examine the performance of four freely available small molecule conformer generation tools (Balloon, Confab, Frog2, and RDKit) alongside a commercial tool (MOE). The aim of this study is 3-fold: (i) to identify which tools most accurately reproduce experimentally determined structures; (ii) to examine the diversity of the generated conformational set; and (iii) to benchmark the computational time expended. These aspects were tested using a set of 708 drug-like molecules assembled from the OMEGA validation set and the Astex Diverse Set. These molecules have varying physicochemical properties and at least one known X-ray crystal structure. We found that RDKit and Confab are statistically better than other methods at generating low rmsd conformers to the known structure. RDKit is particularly suited for less flexible molecules while Confab, with its systematic approach, is able to generate conformers which are geometrically closer to the experimentally determined structure for molecules with a large number of rotatable bonds (≥10). In our tests RDKit also resulted as the second fastest method after Frog2. In order to enhance the performance of RDKit, we developed a postprocessing algorithm to build a diverse and representative set of conformers which also contains a close conformer to the known structure. Our analysis indicates that, with postprocessing, RDKit is a valid free alternative to commercial, proprietary software.