Drug Target Proteins Research Articles

Investigation of plant metabolites as potential inhibitors of Acinetobacter baumannii: An In-Silico approach

The gram-negative bacteria Acinetobacter baumannii is responsible for a broad spectrum of dangerous nosocomial infections. Acinetobacter baumannii is easily transmitted into the body via mechanical ventilators, open wounds, and intravascular catheters. Bloodstream infections and ventilator-associated pneumonia caused by A. baumannii have the greatest mortality rates. Due to its multidrug resistance (MDR), broad drug resistance, and pan-drug resistance phenotypes, A. baumannii is now recognized as a particularly challenging pathogen to control. In order to develop novel therapeutics for the existing condition, an in-silico approach was used. Based on the review of the literature, five drug target proteins including OmpA (Peptidoglycan-binding domain), CarO, DcaP, OmpW, and PBP were identified to be vital for the pathogen's survival, infection to the host, and multidrug resistance. A total of 20 potential plant metabolites with antibacterial properties were docked against these proteins. Among them, Corilagin, (+)-Lyoniresinol-3 alpha-O-beta-d-glucopyranoside, Epsilon-Viniferin, and Epigallocatechin gallate (EGCG) showed superior binding energy compared to the reference drug carbapenem. Corilagin and -(+)-Lyoniresinol-3 alpha-O-beta-d-glucopyranoside showed the best binding energy with DcaP protein having a docking score of about −261.74, and −238.19 respectively. Those two best protein-ligand complexes were assessed for MD simulation where the average RMSD value of 3.5 A° for Dcap-Corilagin and 3.0 A° for Dcap-(+)-Lyoniresinol-3 alpha-O-beta-d-glucopyranoside indicated their excellent stability. In-silico ADME analysis showed all four metabolites had a good estimated solubility (ESOL) between −3.56 and −6.32, a Log P range of 1.87–2.71 and they responded negatively in Blood-Brain Barrier, and CYP inhibition. Toxicity study showed that the expected new drugs, particularly Corilagin, are completely non-carcinogenic, non-toxic, and safe to use as therapeutic treatments against Acinetobacter baumannii. Therefore, all the predicted four metabolites could be used as a medication against Acinetobacter baumannii. As a result of the promising outcomes from our present study, we strongly recommend more in vivo analysis for experimental confirmation.

Mutual-DTI: A mutual interaction feature-based neural network for drug-target protein interaction prediction.

The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years, many sequence-based DTI prediction methods have been proposed, and introducing attention mechanisms has improved their forecasting performance. However, these methods have some shortcomings. For example, inappropriate dataset partitioning during data preprocessing can lead to overly optimistic prediction results. Additionally, only single non-covalent intermolecular interactions are considered in the DTI simulation, ignoring the complex interactions between their internal atoms and amino acids. In this paper, we propose a network model called Mutual-DTI that predicts DTI based on the interaction properties of sequences and a Transformer model. We use multi-head attention to extract the long-distance interdependent features of the sequence and introduce a module to extract the sequence's mutual interaction features in mining complex reaction processes of atoms and amino acids. We evaluate the experiments on two benchmark datasets, and the results show that Mutual-DTI outperforms the latest baseline significantly. In addition, we conduct ablation experiments on a label-inversion dataset that is split more rigorously. The results show that there is a significant improvement in the evaluation metrics after introducing the extracted sequence interaction feature module. This suggests that Mutual-DTI may contribute to modern medical drug development research. The experimental results show the effectiveness of our approach. The code for Mutual-DTI can be downloaded from https://github.com/a610lab/Mutual-DTI.

GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity.

Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug. However, existing deep-learning DTA prediction methods do not consider the interactions between drug substructures and protein sub-sequences. This work proposes GraphATT-DTA, a DTA prediction model that constructs the essential regions for determining interaction affinity between compounds and proteins, modeled with an attention mechanism for interpretability. We make the model consider the local-to-global interactions with the attention mechanism between compound and protein. As a result, GraphATT-DTA shows an improved prediction of DTA performance and interpretability compared with state-of-the-art models. The model is trained and evaluated with the Davis dataset, the human kinase dataset; an external evaluation is achieved with the independently proposed human kinase dataset from the BindingDB dataset.

Analysis of copper-induced protein precipitation across the E. coli proteome.

Metal cations have been exploited for their precipitation properties in a wide variety of studies, ranging from differentiating proteins from serum and blood to identifying the protein targets of drugs. Despite widespread recognition of this phenomenon, the mechanisms of metal-induced protein aggregation have not been fully elucidated. Recent studies have suggested that copper's (Cu) ability to induce protein aggregation may be a main contributor to Cu-induced cell death. Here, we provide the first proteome-wide analysis of the relative sensitivities of proteins across the Escherichia coli proteome to Cu-induced aggregation. We utilize a metal-induced protein precipitation (MiPP) methodology that relies on quantitative bottom-up proteomics to define the metal concentration-dependent precipitation properties of proteins on a proteomic scale. Our results establish that Cu far surpasses other metals in promoting protein aggregation and that the protein aggregation is reversible upon metal chelation. The bulk of the Cu bound in the protein aggregates is Cu1+, regardless of the Cu2+ source. Analysis of our MiPP data allows us to investigate underlying biophysical characteristics that determine a protein's sensitivity to Cu-induced aggregation, which is independent of the relative concentration of protein in the lysate. Overall, this analysis provides new insights into the mechanism behind Cu cytotoxicity, as well as metal cation-induced protein aggregation.

Inhibition of mitochondrial translocase SLC25A5 and histone deacetylation is an effective combination therapy in neuroblastoma.

The mitochondrion is a gatekeeper of apoptotic processes, and mediates drug resistance to several chemotherapy agents used to treat cancer. Neuroblastoma is a common solid cancer in young children with poor clinical outcomes following conventional chemotherapy. We sought druggable mitochondrial protein targets in neuroblastoma cells. Among mitochondria-associated gene targets, we found that high expression of the mitochondrial adenine nucleotide translocase 2 (SLC25A5/ANT2), was a strong predictor of poor neuroblastoma patient prognosis and contributed to a more malignant phenotype in pre-clinical models. Inhibiting this transporter with PENAO reduced cell viability in a panel of neuroblastoma cell lines in a TP53-status-dependant manner. We identified the histone deacetylase inhibitor, suberanilohydroxamic acid (SAHA), as the most effective drug in clinical use against mutant TP53 neuroblastoma cells. SAHA and PENAO synergistically reduced cell viability, and induced apoptosis, in neuroblastoma cells independent of TP53-status. The SAHA and PENAO drug combination significantly delayed tumour progression in pre-clinical neuroblastoma mouse models, suggesting that these clinically advanced inhibitors may be effective in treating the disease.

D-Peptide and D-Protein Technology: Recent Advances, Challenges, and Opportunities.

Total chemical protein synthesis provides access to entire D-protein enantiomers enabling unique applications in molecular biology, structural biology, and bioactive compound discovery. Key enzymes involved in the central dogma of molecular biology have been prepared in their D-enantiomeric forms facilitating the development of mirror-image life. Crystallization of a racemic mixture of L- and D-protein enantiomers provides access to high-resolution X-ray structures of polypeptides. Additionally, D-enantiomers of protein drug targets can be used in mirror-image phage display allowing discovery of non-proteolytic D-peptide ligands as lead candidates. This review discusses the unique applications of D-proteins including the synthetic challenges and opportunities.

Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.

Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a target protein, pharmacokinetic endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates. However, designing efficient selective inhibitors with releasing activities against various homologs of a target protein remains a difficult issue. In this study, we developed a de novo structure generator based on reinforcement learning that is capable of simultaneously optimizing multiobjective problems. Our structure generator successfully proposed selective inhibitors for tyrosine kinases while optimizing 18 objectives consisting of inhibitory activities against 9 tyrosine kinases, 3 pharmacokinetics endpoints, and 6 other important properties. These results show that our structure generator and optimization strategy for selective inhibitors will contribute to the further development of practical structure generators for drug designs.

Experimental and DFT studies of 2-methyl-quinoxaline and its silver (I) complex: Non-covalent interaction analysis, antimicrobial activity and molecular docking study

• Structural and spectroscopic analysis of 2-Methyl-quinoxaline (2-MQ) and its silver nitrate complex. • Investigation of electronic, optical and thermodynamic properties. • Topological analysis (AIM) of the 2-MQ and [Ag(2-MQ)NO 3 ]. • Biological activity and molecular docking studies. The current study describes the characterization and vibrational spectra (elemental analysis, FT-IR, 1 H NMR and UV–Visible absorption) of 2-Methyl-quinoxaline (2-MQ) and synthesized [Ag(2-Methyl-quinoxaline)(NO 3 )]. The experimental investigation is supported by theoretical calculations at the DFT level. The frontier molecular orbitals (HOMO and LUMO), the global reactivity descriptors, MEP, and thermodynamic analysis are also computed to investigate the reactivity of the ligand and the complex. The intermolecular interactions in the title compound were analyzed by topological AIM and RDG approaches. The chemical structure of the ligand and its Ag(I) complex were elucidated by the ELF analysis. In addition, a molecular docking study was implemented to look into the studied compounds for their antibacterial activity. According to the molecular docking study used to evaluate the inhibitory effect on target proteins for antimicrobial drugs, the metal complex has a greater binding affinity than the free ligand. These compounds might thus be effective antibacterial candidates. Thus, antimicrobial studies have been performed with 2-MQ and Ag(I) complex against various Gram-positive and Gram-negative bacteria. The 2-MQ proved to be the most active compound in this study and showed the highest antimicrobial activity against S. aureus ATCC 29213, B. cereus 709 Roma, V. anguillarum ATCC 43312 and C. albicans ATCC 90028. Antimicrobial activity against Ag(I) complex , L. monocytogenes, V. anguillarum, A. hydrophila, E. aerogenes, S. dysenteria, B. cereus and C. albicans was determined.

DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing

The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein–ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein–ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein–ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein–ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.

The Pharmacorank Search Tool for the Retrieval of Prioritized Protein Drug Targets and Drug Repositioning Candidates According to Selected Diseases.

We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease's treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer's disease. Via the tool's URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access.

ICAN: Interpretable cross-attention network for identifying drug and target protein interactions.

Drug–target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.

Computer-Based Identification of Potential Druggable Targets in Multidrug-Resistant Acinetobacter baumannii: A Combined In Silico, In Vitro and In Vivo Study.

The remarkable rise in antimicrobial resistance is alarming for Acinetobacter baumannii, which necessitates effective strategies for the discovery of promising anti-acinetobacter agents. We used a subtractive proteomics approach to identify unique protein drug targets. Shortlisted targets passed through subtractive channels, including essentiality, non-homology to the human proteome, druggability, sub-cellular localization prediction and conservation. Sixty-eight drug targets were shortlisted; among these, glutamine synthetase, dihydrodipicolinate reductase, UDP-N-acetylglucosamine acyltransferase, aspartate 1-decarboxylase and bifunctional UDP-N-acetylglucosamine diphosphorylase/glucosamine-1-phosphate N-acetyltransferase were evaluated in vitro by determining the minimum inhibitory concentration (MIC) of candidate ligands, citric acid, dipicolinic acid, D-tartaric acid, malonic acid and 2-(N-morpholino)ethanesulfonic acid (MES), respectively, which ranged from 325 to 1500 μg/mL except for MES (25 mg/mL). The candidate ligands, citric acid, D-tartaric acid and malonic acid, showed good binding energy scores to their targets upon applying molecular docking, in addition to a significant reduction in A. baumannii microbial load in the wound infection mouse model. These ligands also exhibited good tolerability to human skin fibroblast. The significant increase in the MIC of malonic acid in β-alanine and pantothenate-supplemented media confirmed its selective inhibition to aspartate 1-decarboxylase. In conclusion, three out of sixty-eight potential A. baumannii drug targets were effectively inhibited in vitro and in vivo by promising ligands.

Solvent-Induced Protein Precipitation for Drug Target Discovery.

Traditional chemical proteomics approaches for screening drug targets usually require the immobilization/modification of the drug molecules to pull down the interacting proteins. The solvent-induced protein precipitation (SIP) approach provides an alternative way to study drug-protein interaction by using complex cell lysate directly without modifying a compound of interest. It relies on the fact that the ligand-bound proteins have higher resistance to solvent-induced precipitation. This chapter describes the protocol for identifying drug-target protein interactions by performing unbiased SIP with total cell lysate using a mass spectrometry-based proteomic strategy.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Network Pharmacology and Molecular Docking Approach to Reveal the Immunotherapeutic Mechanism of Cuscutae Semen in Treating Thin Endometrium.

Objective Thin endometrium is considered as a leading cause of infertility, recurrent pregnancy loss, and repeated implantation failure. The seed of Cuscutae Semen (CS) has been used to prevent aging and improve sexual function in Traditional Chinese Medicine. However, the pharmacological mechanism of CS in preventing and treating thin endometrium remains to be elucidated. Methods Three public databases, TCMSP, GeneCards, and OMIM, were searched to collect the main active compounds and putative molecules of CS, as well as the targets of thin endometrium, respectively. The CS and thin endometrium common targets were subject to protein-protein interaction (PPI) analysis followed by functional enrichment analysis. The best binding mode of CS compounds and common target proteins was evaluated by molecular docking and analysis in the AutoDockTools. Results In total, 11 main active compounds, 102 drug target proteins, and 70 CS and thin endometrium common targets were identified. There were 68 nodes with 722 edges in the PPI network; HIF1A, MYC, ESR1, and EGFR were the top 4 targets. After functional enrichment analysis, it was revealed that the therapeutic effects of active compounds of CS on thin endometrium were achieved through cellular response to chemical stress, transcription regulator, DNA-binding transcription factor binding, chemical carcinogenesis-receptor activation, lipid, and atherosclerosis. The molecular docking analysis revealed that the 3 active compounds of CS, quercetin, matrine, and isorhamnetin, have good binding ability with their targets, HIF1A, MYC, ESR1, and EGFR. Conclusion Our study uncovers the main active compounds in CS and their corresponding targets related to thin endometrium which explains the pharmacological mechanism underlying therapeutic effects of CS on thin endometrium.

Compound Interaction Screen on a Photoactivatable Cellulose Membrane (CISCM) Identifies Drug Targets.

Identifying the protein targets of drugs is an important but tedious process. Existing proteomic approaches enable unbiased target identification but lack the throughput needed to screen larger compound libraries. Here, we present a compound interaction screen on a photoactivatable cellulose membrane (CISCM) that enables target identification of several drugs in parallel. To this end, we use diazirine-based undirected photoaffinity labeling (PAL) to immobilize compounds on cellulose membranes. Functionalized membranes are then incubated with protein extract and specific targets are identified via quantitative affinity purification and mass spectrometry. CISCM reliably identifies known targets of natural products in less than three hours of analysis time per compound. In summary, we show that combining undirected photoimmobilization of compounds on cellulose with quantitative interaction proteomics provides an efficient means to identify the targets of natural products.

Network pharmacology study of Yishen capsules in the treatment of diabetic nephropathy.

ObjectiveIn this study, we used network pharmacology to explore the possible therapeutic mechanism underlying the treatment of diabetic nephropathy with Yishen capsules.MethodsThe active chemical constituents of Yishen capsules were acquired using the Traditional Chinese Medicine Systems Pharmacology platform and the Encyclopedia of Traditional Chinese Medicine. Component target proteins were then searched and screened in the BATMAN database. Target proteins were cross-validated using the Comparative Toxicogenomics Database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the target proteins were performed. Then, protein–protein interaction (PPI) analysis was performed using the STRING database. Finally, a pharmacological network was constructed to show the component-target-pathway relationships. Molecular docking was used to analyse the interaction between drug components and target proteins.ResultsIn total, 285 active chemical components were found, including 85 intersection targets against DN. In the pharmacological network, 5 key herbs (A. membranaceus, A. sinensis, E. ferox, A. orientale, and R. rosea) and their corresponding 12 key components (beta-sitosterol, beta-carotene, stigmasterol, alisol B, mairin, quercetin, caffeic acid, 1-monolinolein, kaempferol, jaranol, formononetin, and calycosin) were screened. Furthermore, the 12 key components were related to 24 target protein nodes (e.g., AGT, AKT1, AKT2, BCL2, NFKB1, and SIRT1) and enriched in 24 pathway nodes (such as the NF-kappa B, AGE-RAGE, toll-like receptor, and relaxin signaling pathways). Molecular docking revealed that hydrogen bond was formed between drug components and target proteins.ConclusionIn conclusion, the active constituents of Yishen capsules modulate targets or signaling pathways in DN pathogenesis.

Antiprotozoal Activity of Thymoquinone (2-Isopropyl-5-methyl-1,4-benzoquinone) for the Treatment of Leishmania major-Induced Leishmaniasis: In Silico and In Vitro Studies.

Leishmaniasis, a neglected tropical parasitic disease (NTPD), is caused by various Leishmania species. It transmits through the bites of the sandfly. The parasite is evolving resistance to commonly prescribed antileishmanial drugs; thus, there is an urgent need to discover novel antileishmanial drugs to combat drug-resistant leishmaniasis. Thymoquinone (2-isopropyl-5-methyl-1,4-benzoquinone; TQ), a primary pharmacologically active ingredient of Nigella sativa (black seed) essential oil, has been reported to possess significant antiparasitic activity. Therefore, the present study was designed to investigate the in vitro and in silico antileishmanial activity of TQ against various infectious stages of Leishmania major (L. major), i.e., promastigotes and amastigotes, and its cytotoxicity against mice macrophages. In silico molecular dockings of TQ were also performed with multiple selected target proteins of L. major, and the most preferred antileishmanial drug target protein was subjected to in silico molecular dynamics (MD) simulation. The in vitro antileishmanial activity of TQ revealed that the half-maximal effective concentration (EC50), half-maximal cytotoxic concentration (CC50), and selectivity index (SI) values for promastigotes are 2.62 ± 0.12 μM, 29.54 ± 0.07 μM, and 11.27, while for the amastigotes, they are 17.52 ± 0.15 μM, 29.54 ± 0.07 μM, and 1.69, respectively. The molecular docking studies revealed that squalene monooxygenase is the most preferred antileishmanial drug target protein for TQ, whereas triosephosphate isomerase is the least preferred. The MD simulation revealed that TQ remained stable in the binding pocket throughout the simulation. Additionally, the binding energy calculations using Molecular Mechanics Generalized-Born Surface Area (MMGBSA) indicated that TQ is a moderate binder. Thus, the current study shows that TQ is a promising antileishmanial drug candidate that could be used to treat existing drug-resistant leishmaniasis.

TwoStepCisMR: A Novel Method and R Package for Attenuating Bias in cis-Mendelian Randomization Analyses.

Mendelian randomisation (MR) is an increasingly popular method for strengthening causal inference in epidemiological studies. cis-MR in particular uses genetic variants in the gene region of a drug target protein as an instrumental variable to provide quasi-experimental evidence for on-target drug effects. A limitation of this framework is when the genetic variant is correlated to another variant that also effects the outcome of interest (confounding through linkage disequilibrium). Methods for correcting this bias, such as multivariable MR, struggle in a cis setting because of the high correlation among genetic variants. Here, through simulation experiments and an applied example considering the effect of interleukin 6 receptor signaling on coronary artery disease risk, we present an alternative method for attenuating bias that does not suffer from this problem. As our method uses both MR and the product and difference method for mediation analysis, our proposal inherits all assumptions of these methods. We have additionally developed an R package, TwoStepCisMR, to facilitate the implementation of the method.

Mitochondrially targeted proximity biotinylation and proteomic analysis in Plasmodium falciparum.

Despite ongoing efforts to control malaria infection, progress in lowering the number of deaths and infections appears to have stalled. The continued high incidence of malaria infection and mortality is in part due to emergence of parasites resistant to frontline antimalarials. This highlights the need for continued identification of novel protein drug targets. Mitochondrial functions in Plasmodium falciparum, the deadliest species of human malaria parasite, are targets of validated antimalarials including atovaquone and proguanil (Malarone). Thus, there has been great interest in identifying other essential mitochondrial proteins as candidates for novel drug targets. Garnering an increased understanding of the proteomic landscape inside the P. falciparum mitochondrion will also allow us to learn about the basic biology housed within this unique organelle. We employed a proximity biotinylation technique and mass spectrometry to identify novel P. falciparum proteins putatively targeted to the mitochondrion. We fused the leader sequence of a mitochondrially targeted chaperone, Hsp60, to the promiscuous biotin ligase TurboID. Through these experiments, we generated a list of 122 “putative mitochondrial” proteins. To verify whether these proteins were indeed mitochondrial, we chose five candidate proteins of interest for localization studies using ectopic expression and tagging of each full-length protein. This allowed us to localize four candidate proteins of unknown function to the mitochondrion, three of which have previously been assessed to be essential. We suggest that phenotypic characterization of these and other proteins from this list of 122 could be fruitful in understanding the basic mitochondrial biology of these parasites and aid antimalarial drug discovery efforts.