Biology subjects

Atas, H.

Publications and source records attributed to Atas, H..

5 recordsLinked to original sources

bioRxiv2023

Changes in social behaviour with alterations of MAPK3 and KCTD13/CUL3 pathways in two new outbred rat models for the 16p11.2 syndromes with autism spectrum disorders.

Copy number variations (CNVs) of the human 16p11.2 locus are associated with several developmental/neurocognitive syndromes. Particularly, deletion and duplication of this genetic interval are found in patients with autism spectrum disorders, intellectual disability and other psychiatric traits. The high gene density associated with the region and the strong phenotypic variability of incomplete penetrance, make the study of the 16p11.2 syndromes extremely complex. To systematically study the effect of 16p11.2 CNVs and identify candidate genes and molecular mechanisms involved in the pathophysiology, mouse models were generated previously and showed learning and memory, and to some extent social deficits. To go further in understanding the social deficits caused by 16p11.2 syndromes, we engineered deletion and duplication of the homologous region to the human 16p11.2 genetic interval in two rat outbred strains, Sprague Dawley (SD) and Long Evans (LE). The 16p11.2 rat models displayed convergent defects in social behaviour and only a few cognitive defects. Interestingly major pathways affecting MAPK3 and CUL3 were found altered in the rat 16p11.2 models with additional changes in males compared to females. Altogether, the consequences of the 16p11.2 genetic region dosage on social behaviour are now found in three different species: humans, mice and rats. In addition, the rat models pointed to sexual dimorphism, with lower severity of phenotypes in rat females compared to male mutants. This phenomenon is also observed in humans. We are convinced that the two rat models will be key to further investigating social behaviour and understanding the brain mechanisms and specific brain regions that are key to controlling social behaviour.

animal behavior and cognition↗

bioRxiv2022

How to Best Represent Proteins in Machine Learning-based Prediction of Drug/Compound-Target Interactions

The identification of drug/compound-target interactions (DTIs) constitutes the basis of drug discovery, for which computational predictive approaches have been applied. As a relatively new data-driven paradigm, proteochemometric (PCM) modeling utilizes both protein and compound properties as a pair at the input level and processes them via statistical/machine learning. The representation of input samples (i.e., proteins and their ligands) in the form of quantitative feature vectors is crucial for the extraction of interaction-related properties during the artificial learning and subsequent prediction of DTIs. Lately, the representation learning approach, in which input samples are automatically featurized via training and applying a machine/deep learning model, has been utilized in biomedical sciences. In this study, we performed a comprehensive investigation of different computational approaches/techniques for protein featurization (including both conventional approaches and the novel learned embeddings), data preparation and exploration, machine learning-based modeling, and performance evaluation with the aim of achieving better data representations and more successful learning in DTI prediction. For this, we first constructed realistic and challenging benchmark datasets on small, medium, and large scales to be used as reliable gold standards for specific DTI modeling tasks. We developed and applied a network analysis-based splitting strategy to divide datasets into structurally different training and test folds. Using these datasets together with various featurization methods, we trained and tested DTI prediction models and evaluated their performance from different angles. Our main findings can be summarized under 3 items: (i) random splitting of datasets into train and test folds lead to near-complete data memorization and produce highly over-optimistic results, as a result, it should be avoided, (ii) learned protein sequence embeddings work well in DTI prediction and offer high potential, even though no information related to protein structures, interactions or biochemical properties is utilized during their generation, and (iii) PCM models tend to learn from compound features and leave out protein features, mostly due to the natural bias in DTI data, indicating the requirement for new and unbiased datasets. We hope this study will aid researchers in designing robust and high-performing data-driven DTI prediction systems that have real-world translational value in drug discovery.

bioinformatics↗

bioRxiv2021

Protein Domain-Based Prediction of Compound-Target Interactions and Experimental Validation on LIM Kinases

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network- based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins structure/function, and bias in system training datasets. Here, we propose a new computational method "DRUIDom" to predict bio- interactions between drug candidate compounds and target proteins by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying physical or functional interactions. As such, other proteins containing the mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including the ones mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting compound-target pairs ([~]2.9M data points), and used as training data for calculating parameters of compound- domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of [~]5 million new compound-protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound-domain relationships. The datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom. Author SummaryDrug development comprises several interlinked steps from designing drug candidate molecules to running clinical trials, with the aim to bring a new drug to market. A critical yet costly and labor-intensive stage is drug discovery, in which drug candidate molecules that specifically interact with the intended biomolecular target (mostly proteins) are identified. Lately, data-centric computational methods have been proposed to aid experimental procedures in drug discovery. These methods have the ability to rapidly assess large molecule libraries and reduce the time and cost of the process; however, most of them suffer from problems related to producing reliable biologically relevant results, preventing them from gaining real-world usage. Here, we have developed a new method called DRUIDom to predict unknown interactions between drugs/drug candidate compounds and biological targets by utilizing the modular structure of proteins. For this, we identify the domains, i.e., the evolutionary and functional building blocks of proteins, where these potential drug compounds can bind, and utilize this information along with protein domain annotations to predict new drug targets. We have tested the biological relevance of DRUIDom on selected proteins that play critical roles in the progression of numerous types of cancer. Cell-based experimental results indicated that predicted inhibitors are effective even on drug-resistant cancer cells. Our results suggest that DRUIDom produces novel and biologically relevant results that can be directly used in the early steps of the drug discovery process.

bioinformatics↗

bioRxiv2020

Evaluation of Methods for Protein Representation Learning: A Quantitative Analysis

Data-centric approaches have been utilized to develop predictive methods for elucidating uncharacterized aspects of proteins such as their functions, biophysical properties, subcellular locations and interactions. However, studies indicate that the performance of these methods should be further improved to effectively solve complex problems in biomedicine and biotechnology. A data representation method can be defined as an algorithm that calculates numerical feature vectors for samples in a dataset, to be later used in quantitative modelling tasks. Data representation learning methods do this by training and using a model that employs statistical and machine/deep learning algorithms. These novel methods mostly take inspiration from the data-driven language models that have yielded ground-breaking improvements in the field of natural language processing. Lately, these learned data representations have been applied to the field of protein informatics and have displayed highly promising results in terms of extracting complex traits of proteins regarding sequence-structure-function relations. In this study, we conducted a detailed investigation over protein representation learning methods, by first categorizing and explaining each approach, and then conducting benchmark analyses on; (i) inferring semantic similarities between proteins, (ii) predicting ontology-based protein functions, and (iii) classifying drug target protein families. We examine the advantages and disadvantages of each representation approach over the benchmark results. Finally, we discuss current challenges and suggest future directions. We believe the conclusions of this study will help researchers in applying machine/deep learning-based representation techniques on protein data for various types of predictive tasks. Furthermore, we hope it will demonstrate the potential of machine learning-based data representations for protein science and inspire the development of novel methods/tools to be utilized in the fields of biomedicine and biotechnology.

bioinformatics↗

bioRxiv2020

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

bioinformatics↗