Integrative pharmacogenomics to infer large-scale drug taxonomy

Mapping Intimacies ◽

10.1101/046219 ◽

2016 ◽

Cited By ~ 2

Author(s):

Nehme El-Hachem ◽

Deena M.A. Gendoo ◽

Laleh Soltan Ghoraie ◽

Zhaleh Safikhani ◽

Petr Smirnov ◽

...

Keyword(s):

Drug Targets ◽

Large Scale ◽

Prior Information ◽

Structural Information ◽

Drug Efficacy ◽

Basic Drug ◽

Flexible Tool ◽

Experimental Drugs ◽

New Compounds ◽

Novel Drug

ABSTRACTIdentification of drug targets and mechanism of action (MoA) for new and uncharacterlzed drugs is important for optimization of drug efficacy. Current MoA prediction approaches largely rely on prior information including side effects, therapeutic indication and/or chemo-informatics. Such information is not transferable or applicable for newly identified, previously uncharacterlzed small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary towards development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose a new integrative computational pharmacogenomlc approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that relies only on basic drug characteristics towards elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. We demonstrate that the DNF taxonomy succeeds in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. We highlight how the scalability of DNF facilitates identification of key drug relationships across different drug categories, and poses as a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on the compound MoA and potential insights for drug repurposlng.

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Computational Methods for the Prediction of GPCRs Coupling Selectivity

Handbook of Research on Systems Biology Applications in Medicine ◽

10.4018/978-1-60566-076-9.ch009 ◽

2009 ◽

pp. 167-181

Author(s):

Nikolaos G. Sgourakis ◽

Pantelis G. Bagos ◽

Stavros J. Hamodrakas

Keyword(s):

G Proteins ◽

Drug Targets ◽

Large Scale ◽

Single Receptor ◽

Pharmacological Significance ◽

Experimental Works ◽

Orphan Gpcrs ◽

Coupling Specificity ◽

Novel Drug ◽

Genome Projects

GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of recent genome projects, there is a wealth of information at the sequence level that lacks any functional annotation. These receptors, often quoted as orphan GPCRs, could potentially lead to novel drug targets. However, typical experiments that aim at elucidating their function are hampered by the lack of knowledge on their selective coupling partners at the interior of the cell, the G-proteins. Up-to-date, computational efforts to predict properties of GPCRs have been focused mainly on the ligand-binding specificity, while the aspect of coupling has been less studied. Here, we present the main motivations, drawbacks, and results from the application of bioinformatics techniques to predict the coupling specificity of GPCRs to G-proteins, and discuss the application of the most successful methods in both experimental works that focus on a single receptor and large-scale genome annotation studies.

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Advances in Development of New Treatment for Leishmaniasis

BioMed Research International ◽

10.1155/2015/815023 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 66

Author(s):

Juliana Perrone Bezerra de Menezes ◽

Carlos Eduardo Sampaio Guedes ◽

Antônio Luis de Oliveira Almeida Petersen ◽

Deborah Bittencourt Mothé Fraga ◽

Patrícia Sampaio Tavares Veras

Keyword(s):

High Throughput ◽

Drug Targets ◽

Scientific Community ◽

Large Scale ◽

High Throughput Analysis ◽

Large Scale Analysis ◽

Parasite Biology ◽

New Treatment ◽

Novel Drug ◽

Chemotherapeutic Interventions

Leishmaniasis is a neglected infectious disease caused by several different species of protozoan parasites of the genusLeishmania. Current strategies to control this disease are mainly based on chemotherapy. Despite being available for the last 70 years, leishmanial chemotherapy has lack of efficiency, since its route of administration is difficult and it can cause serious side effects, which results in the emergence of resistant cases. The medical-scientific community is facing difficulties to overcome these problems with new suitable and efficient drugs, as well as the identification of new drug targets. The availability of the complete genome sequence ofLeishmaniahas given the scientific community the possibility of large-scale analysis, which may lead to better understanding of parasite biology and consequent identification of novel drug targets. In this review we focus on how high-throughput analysis is helping us and other groups to identify novel targets for chemotherapeutic interventions. We further discuss recent data produced by our group regarding the use of the high-throughput techniques and how this helped us to identify and assess the potential of new identified targets.

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Flexibility and mobility of SARS-CoV-2-related protein structures

Scientific Reports ◽

10.1038/s41598-021-82849-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rudolf A. Römer ◽

Navodya S. Römer ◽

A. Katrine Wallis

Keyword(s):

In Silico ◽

Drug Targets ◽

Large Scale ◽

Structural Information ◽

Protein Structures ◽

Antiviral Drug ◽

Docking Studies ◽

Related Protein

AbstractThe worldwide CoVid-19 pandemic has led to an unprecedented push across the whole of the scientific community to develop a potent antiviral drug and vaccine as soon as possible. Existing academic, governmental and industrial institutions and companies have engaged in large-scale screening of existing drugs, in vitro, in vivo and in silico. Here, we are using in silico modelling of possible SARS-CoV-2 drug targets, as deposited on the Protein Databank (PDB), and ascertain their dynamics, flexibility and rigidity. For example, for the SARS-CoV-2 spike protein—using its complete homo-trimer configuration with 2905 residues—our method identifies a large-scale opening and closing of the S1 subunit through movement of the S$${}^\text{B}$$ B domain. We compute the full structural information of this process, allowing for docking studies with possible drug structures. In a dedicated database, we present similarly detailed results for the further, nearly 300, thus far resolved SARS-CoV-2-related protein structures in the PDB.

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Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

Neuropsychopharmacology ◽

10.1038/s41386-021-01023-4 ◽

2021 ◽

Author(s):

Max Lam ◽

Chia-Yen Chen ◽

Tian Ge ◽

Yan Xia ◽

David W. Hill ◽

...

Keyword(s):

Drug Targets ◽

Large Scale ◽

Association Studies ◽

Drug Repurposing ◽

Genome Wide Association Studies ◽

Data Set ◽

Nootropic Drug ◽

Gene Sets ◽

Genome Wide ◽

Novel Drug

AbstractBroad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify “druggable” targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

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Large-Scale Functional Genomics Screen to Identify Modulators of Human β-Cell Insulin Secretion

Biomedicines ◽

10.3390/biomedicines10010103 ◽

2022 ◽

Vol 10 (1) ◽

pp. 103

Author(s):

Iwona Szczerbinska ◽

Annamaria Tessitore ◽

Lena Kristina Hansson ◽

Asmita Agrawal ◽

Alejandro Ragel Lopez ◽

...

Keyword(s):

Insulin Secretion ◽

Candidate Genes ◽

Drug Targets ◽

Large Scale ◽

Cell Mass ◽

Β Cell ◽

Cellular Mechanisms ◽

Ghrelin Receptor ◽

Novel Drug

Type 2 diabetes (T2D) is a chronic metabolic disorder affecting almost half a billion people worldwide. Impaired function of pancreatic β-cells is both a hallmark of T2D and an underlying factor in the pathophysiology of the disease. Understanding the cellular mechanisms regulating appropriate insulin secretion has been of long-standing interest in the scientific and clinical communities. To identify novel genes regulating insulin secretion we developed a robust arrayed siRNA screen measuring basal, glucose-stimulated, and augmented insulin secretion by EndoC-βH1 cells, a human β-cell line, in a 384-well plate format. We screened 521 candidate genes selected by text mining for relevance to T2D biology and identified 23 positive and 68 negative regulators of insulin secretion. Among these, we validated ghrelin receptor (GHSR), and two genes implicated in endoplasmic reticulum stress, ATF4 and HSPA5. Thus, we have demonstrated the feasibility of using EndoC-βH1 cells for large-scale siRNA screening to identify candidate genes regulating β-cell insulin secretion as potential novel drug targets. Furthermore, this screening format can be adapted to other disease-relevant functional endpoints to enable large-scale screening for targets regulating cellular mechanisms contributing to the progressive loss of functional β-cell mass occurring in T2D.

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Drug Target Interplay: A Network-based Analysis of Human Diseases and the Drug Targets

Current Topics in Medicinal Chemistry ◽

10.2174/1568026618666180719160922 ◽

2018 ◽

Vol 18 (13) ◽

pp. 1053-1061 ◽

Cited By ~ 2

Author(s):

Bhushan Jain ◽

Utkarsh Raj ◽

Pritish Kumar Varadwaj

Keyword(s):

Drug Target ◽

Drug Targets ◽

Disease Gene ◽

Search Space ◽

Disease Genes ◽

Data Generation ◽

Human Interactome ◽

Experimental Drugs ◽

Novel Drug ◽

Disease Specific

Screening and identifying a disease-specific novel drug target is the first step towards a rational drug designing approach. Due to the advent of high throughput data generation techniques, the protein search space has now exceeded 24,500 human protein coding genes, which encodes approximately 1804proteins. This work aims at mining out the relationship between target proteins, drugs, and diseases genes through a network-based systems biology approach. A network of all FDA approved drugs, along with their targets were utilized to construct the proposed Drug Target (DT) network. Further, the experimental drugs were mapped into the DT network to infer the functional relationship by utilizing the respective network attributes. Similar to the DT network, a network of disease genes was created through OMIM Gene Map and Morbid Map, to link the binary associations of disorder-disease genes. In the proposed model of Human Interactome Network, shortest path length between the target protein and disease gene was used to infer the correlation between ‘Drug Targets’ and ‘Disease-Gene’. This network-based study will help researchers to analyze, infer and identify disease-specific novel drug targets through harnessing the graph theory based network attributes.

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GPCRs: Lipid-Dependent Membrane Receptors That Act as Drug Targets

Advances in Biology ◽

10.1155/2014/143023 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 55

Author(s):

Amitabha Chattopadhyay

Keyword(s):

Drug Targets ◽

Membrane Lipids ◽

Structural Information ◽

Membrane Receptors ◽

Future Research ◽

Membrane Cholesterol ◽

Drug Candidates ◽

Basic Biology ◽

Novel Drug

G protein-coupled receptors (GPCRs) are the largest class of molecules involved in signal transduction across cell membranes and represent major targets in the development of novel drug candidates in all clinical areas. Although there have been some recent leads, structural information on GPCRs is relatively rare due to the difficulty associated with crystallization. A specific reason for this is the intrinsic flexibility displayed by GPCRs, which is necessary for their functional diversity. Since GPCRs are integral membrane proteins, interaction of membrane lipids with them constitutes an important area of research in GPCR biology. In particular, membrane cholesterol has been reported to have a modulatory role in the function of a number of GPCRs. The role of membrane cholesterol in GPCR function is discussed with specific example of the serotonin1A receptor. Recent results show that GPCRs are characterized with structural motifs that preferentially associate with cholesterol. An emerging and important concept is oligomerization of GPCRs and its role in GPCR function and signaling. The role of membrane cholesterol in GPCR oligomerization is highlighted. Future research in GPCR biology would offer novel insight in basic biology and provide new avenues for drug discovery.

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Flexibility and mobility of SARS-CoV-2-related protein structures

10.1101/2020.07.12.199364 ◽

2020 ◽

Author(s):

Rudolf A. Römer ◽

Navodya S. Römer ◽

A. Katrine Wallis

Keyword(s):

In Silico ◽

Drug Targets ◽

Large Scale ◽

Structural Information ◽

Protein Structures ◽

Antiviral Drug ◽

Docking Studies ◽

Related Protein

ABSTRACTThe worldwide CoVid-19 pandemic has led to an unprecedented push across the whole of the scientific community to develop a potent antiviral drug and vaccine as soon as possible. Existing academic, governmental and industrial institutions and companies have engaged in large-scale screening of existing drugs, in vitro, in vivo and in silico. Here, we are using in silico modelling of SARS-CoV-2 drug targets, i.e. SARS-CoV-2 protein structures as deposited on the Protein Databank (PDB). We study their flexibility, rigidity and mobility, an important first step in trying to ascertain their dynamics for further drug-related docking studies. We are using a recent protein flexibility modelling approach, combining protein structural rigidity with possible motion consistent with chemical bonds and sterics. For example, for the SARS-CoV-2 spike protein in the open configuration, our method identifies a possible further opening and closing of the S1 subunit through movement of SB domain. With full structural information of this process available, docking studies with possible drug structures are then possible in silico. In our study, we present full results for the more than 200 thus far published SARS-CoV-2-related protein structures in the PDB.

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