scholarly journals Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery

2003 ◽  
Vol 50 (1) ◽  
pp. 167-181 ◽  
Author(s):  
Terry Roemer ◽  
Bo Jiang ◽  
John Davison ◽  
Troy Ketela ◽  
Karynn Veillette ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Drug Discovery ◽  
Large Scale ◽  
Essential Gene ◽  
Antifungal Drug ◽  
Gene Identification

scholarly journals A novel CRISPRi platform to study the role of essential genes in antifungal drug-resistant Candida albicans isolates

2021 ◽  
Vol 3 (12) ◽  
Author(s):  
Lauren Wensing ◽  
Rebecca Shapiro ◽  
Deeva Uthayakumar ◽  
Viola Halder ◽  
Jehoshua Sharma ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Transcriptional Repression ◽  
Drug Targets ◽  
Large Scale ◽  
Essential Gene ◽  
Essential Genes ◽  
Antifungal Drugs ◽  
Antifungal Drug ◽  
Drug Resistant ◽  
Fungal Organisms

With the emergence of antifungal resistant Candida albicans strains, the need for new antifungal drugs is critical in combating this fungal pathogen. Investigating essential genes in C. albicans is a vital step in characterizing putative antifungal drug targets. As some of these essential genes are conserved between fungal organisms, developed therapies targeting these genes have the potential to be broad range antifungals. In order to study these essential genes, classical genetic knockout or CRISPR-based approaches cannot be used as disrupting essential genes leads to lethality in the organism. Fortunately, a variation of the CRISPR system (CRISPR interference or CRISPRi) exists that enables precise transcriptional repression of the gene of interest without introducing genetic mutations. CRISPRi utilizes an endonuclease dead Cas9 protein that can be targeted to a precise location but lacks the ability to create a double-stranded break. The binding of the dCas9 protein to DNA prevents the binding of RNA polymerase to the promoter through steric hindrance thereby reducing expression. We recently published the novel use of this technology in C. albicans and are currently working on expanding this technology to large scale repression of essential genes. Through the construction of an essential gene CRISPRi-sgRNA library, we can begin to study the function of essential genes under different conditions and identify genes that are involved in critical processes such as drug tolerance in antifungal resistant background strains. These genes can ultimately be characterized as putative targets for novel antifungal drug development, or targeted as a means to sensitize drug-resistant strains to antifungal treatment.

Comprehensive Essential Gene Identification as a Platform for Novel Antiinfective Drug Discovery

2002 ◽  
Vol 8 (13) ◽  
pp. 1155-1172 ◽  
Author(s):  
R. Haselbeck ◽  
D. Wall ◽  
B. Jiang ◽  
T. Ketela ◽  
J. Zyskind ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Essential Gene ◽  
Gene Identification

Integrated antifungal drug discovery in Candida albicans

10.1038/85634 ◽  
2001 ◽  
Vol 19 (3) ◽  
pp. 212-213 ◽  
Author(s):  
Dominique Sanglard
Keyword(s):  
Candida Albicans ◽  
Drug Discovery ◽  
Antifungal Drug

Essential gene identification and drug target prioritization in Leishmania species

2014 ◽  
Vol 10 (5) ◽  
pp. 1184-1195 ◽  
Author(s):  
M. L. Stanly Paul ◽  
Amandeep Kaur ◽  
Ankit Geete ◽  
M. Elizabeth Sobhia
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Targets ◽  
Essential Gene ◽  
Leishmania Species ◽  
Gene Identification ◽  
Specific Drug

New stage specific drug targets for contemporary drug discovery for leishmaniasis.

scholarly journals Large-Scale Biochemical Profiling of the Candida albicans Biofilm Matrix: New Compositional, Structural, and Functional Insights

mBio ◽  
2014 ◽  
Vol 5 (5) ◽  
Author(s):  
Jose L. Lopez-Ribot
Keyword(s):  
Drug Resistance ◽  
Candida Albicans ◽  
Large Scale ◽  
Pathogenic Fungi ◽  
Antifungal Drug ◽  
Content Type ◽  
Antifungal Drug Resistance ◽  
Functional Aspects ◽  
Main Components ◽  
Biofilm Matrix

ABSTRACTAmong pathogenic fungi,Candida albicansis most frequently associated with biofilm formation, a lifestyle that is entirely different from the planktonic state. One of the distinguishing features of these biofilms is the presence of extracellular material, commonly referred to as the “biofilm matrix.” The fungal biofilm matrix embeds sessile cells within these communities and plays important structural and physiological functions, including antifungal drug resistance with important clinical repercussions. This matrix is mostly self-produced by the fungal cells themselves and is composed of different types of biopolymers. InC. albicans, the main components of the biofilm matrix are carbohydrates, proteins, lipids, and DNA, but many of them remain unidentified and/or poorly characterized. In their recent article, Zarnowski et al. [mBio 5(4):e01333-14, 2014, doi:10.1128/mBio.01333-14] used a variety of biochemical and state-of-the-art “omic” approaches (glycomics, proteomics, and lipidomics) to identify and characterize unique biopolymers present in theC. albicansbiofilm matrix. Besides generating a true “encyclopedic” catalog of individual moieties from each of the different macromolecular categories, results also provide important insights into structural and functional aspects of the fungal biofilm matrix, particularly the interaction between different components and the contribution of multiple matrix constituents to biofilm antifungal drug resistance.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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