Biomedical subjects

Emre Karakoc

Publications and source records attributed to Emre Karakoc.

3 recordsLinked to original sources

PubMed2025

Tau uptake by human neurons depends on receptor LRP1 and kinase LRRK2.

Extracellular release and uptake of pathogenic forms of the microtubule-associated protein tau contribute to the pathogenesis of several neurodegenerative diseases, including Alzheimer's disease. Defining the cellular mechanisms and pathways for tau entry to human neurons is essential to understanding tauopathy pathogenesis and enabling the rational design of disease-modifying therapeutics. Here, whole-genome, loss-of-function CRISPR screens in human iPSC-derived excitatory neurons, the major neuronal cell type affected in these diseases, provide insights into the different cellular pathways for uptake of extracellular monomeric and fibrillar tau. Monomeric and fibrillar tau are both taken up by human neurons by receptor-mediated endocytosis, but involve different routes of entry at the neuronal surface: the low-density lipoprotein LRP1 is the primary receptor for monomeric tau, but contributes less to fibrillar tau entry. Similarly, endocytosis of monomeric tau is dependent on the familial Parkinson's disease gene LRRK2, but not required for endocytosis of fibrillar tau. These findings implicate LRP1 and LRRK2 in the pathogenesis of tauopathies and Parkinson's disease, and identify LRRK2 as a potential therapeutic target for altering progression of these diseases.

Humans↗

PubMed2006

Distance based algorithms for small biomolecule classification and structural similarity search.

MOTIVATION: Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds wrt the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds. RESULTS: In this paper we focus on the k-nearest-neighbor search method, which, until recently was not considered for small molecule classification. The few recent applications of k-nn to compound classification focus on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance L(p). Here we show how to computationally design the optimal weighted Minkowski distance wL(p) for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. We then show how to construct pruning based k-nn search data structures for any wL(p) distance that minimizes similarity search time. The accuracy achieved by our classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, our classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, our classifier quantifies the level of bioactivity rather than returning a binary decision and thus is more informative than the ANN approach.

Algorithms↗

PubMedDate not supplied

Comparative QSAR- and fragments distribution analysis of drugs, druglikes, metabolic substances, and antimicrobial compounds.

A number of binary QSAR models have been developed using methods of artificial neural networks, k-nearest neighbors, linear discriminative analysis, and multiple linear regression and have been compared for their ability to recognize five types of chemical compounds that include conventional drugs, inactive druglikes, antimicrobial substituents, and bacterial and human metabolites. Thus, 20 binary classifiers have been created using a variety of 'inductive' and traditional 2D QSAR descriptors which allowed up to 99% accurate separation of the studied groups of activities. The comparison of the performance by four computational approaches demonstrated that the neural nets result in generally more accurate predictions, followed closely by k-nearest neighbors methods. It has also been demonstrated that complementation of 'inductive' descriptors with conventional QSAR parameters does not generally improve the quality of resulting solutions, conforming high predictive ability of 'inductive' variables. The conducted comparative QSAR analysis based on a novel linear optimization approach has helped to identify the extent of overlapping between the studied groups of compounds, such as cross-recognition of bacterial metabolites and antimicrobial compounds reflecting their immanent resemblance and similar origin. Human metabolites have been characterized as a very distinctive class of substances, separated from all other groups in the descriptors space and exhibiting different QSAR behavior. The analysis of unique structural fragments and substituents revealed inhomogeneous scale-free organization of human metabolites illustrating the fact that certain molecular scaffolds (such as sugars and nucleotides) may be strongly favored by natural evolution. The established scale-free organization of human metabolites has been contemplated as a factor of their unique positioning in the descriptors space and their distinctive QSAR properties. It is anticipated that the study may bring additional insight into QSAR determinants for conventional drugs, inactive chemicals, and metabolic substances and may help in rationalizing design and discovery of novel antimicrobials and human therapeutics with improved, metabolite-like properties.

Anti-Infective Agents↗