Publications by authors named "S Durdagi"

is implicated in a range of conditions, including autism spectrum disorder, intellectual disability, seizures, autosomal recessive nonsyndromic intellectual disability, heterotaxy, and ciliary dysfunction. In order to understand the molecular mechanisms underlying these conditions, we focused on the structural and dynamic activity consequences of mutations within this gene. In this study, whole exome sequencing identified the c.

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Tryptamine, a monoamine alkaloid with an indole ring structure, is derived from the decarboxylation of the amino acid tryptophan, which is present in fungi, plants, and animals. Tryptamine analogues hold significant therapeutic potential due to their broad pharmacological activities, including roles as neurotransmitters and potential therapeutic agents for various diseases. Structural modifications of tryptamine enhance receptor selectivity and metabolic stability, improving therapeutic efficacy.

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The development of HS-donating derivatives of non-steroidal anti-inflammatory drugs (NSAIDs) is considered important to reduce or overcome their gastrointestinal side effects. Sulforaphane, one of the most extensively studied isothiocyanates (ITCs), effectively releases HS at a slow rate. Thus, we rationally designed, synthesized, and characterized new ITC derivatives (I1-3 and I1a-e) inspired by the natural compound sulforaphane.

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CCR5 is a class A GPCR and serves as one of the coreceptors facilitating HIV-1 entry into host cells. This receptor has vital roles in the immune system and is involved in the pathogenesis of different diseases. Various studies were conducted to understand its activation mechanism, including structural studies in which inactive and active states of the receptor were determined in complex with various binding partners.

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Article Synopsis
  • The human immunodeficiency virus (HIV) poses a serious global health issue due to its ability to quickly mutate and develop resistance to drugs.
  • Recent research highlights the success of machine learning (ML) and deep learning (DL) in predicting which HIV strains will resist FDA-approved treatments, but expanding these models to consider both viral isolates and drug characteristics remains a challenge.
  • A new framework called the drug-isolate-fold change (DIF) model has been proposed, utilizing advanced modeling techniques and pre-trained graph neural networks to improve predictions on drug resistance, achieving notable accuracy and demonstrating the value of incorporating detailed molecular representations.
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