The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.
- MeSH
- Alzheimerova nemoc * diagnóza farmakoterapie metabolismus MeSH
- klinické zkoušky jako téma MeSH
- lidé MeSH
- neurozobrazování metody MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.
- MeSH
- biotechnologie * MeSH
- internet * MeSH
- rozpustnost MeSH
- výpočetní biologie MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
Comparative evolutionary genomics has revealed that novel protein coding genes can emerge randomly from non-coding DNA. While most of the myriad of transcripts which continuously emerge vanish rapidly, some attain regulatory regions, become translated and survive. More surprisingly, sequence properties of de novo proteins are almost indistinguishable from randomly obtained sequences, yet de novo proteins may gain functions and integrate into eukaryotic cellular networks quite easily. We here discuss current knowledge on de novo proteins, their structures, functions and evolution. Since the existence of de novo proteins seems at odds with decade-long attempts to construct proteins with novel structures and functions from scratch, we suggest that a better understanding of de novo protein evolution may fuel new strategies for protein design.
- MeSH
- genomika MeSH
- molekulární evoluce * MeSH
- proteiny * genetika MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
The three-dimensional (3D) organization of chromatin plays a crucial role in the regulation of gene expression. Chromatin conformation is strongly affected by the composition, structural features and dynamic properties of the nucleosome, which in turn determine the nature and geometry of interactions that can occur between neighboring nucleosomes. Understanding how chromatin is spatially organized above the nucleosome level is thus essential for understanding how gene regulation is achieved. Towards this end, great effort has been made to understand how an array of nucleosomes folds into a regular chromatin fiber. This review summarizes new insights into the 3D structure of the chromatin fiber that were made possible by recent advances in cryo-electron microscopy.
Conventional wisdom has it that proteins fold and assemble into definite structures, and that this defines their function. Glycosaminoglycans (GAGs) are different. In most cases the structures they form have a low degree of order, even when interacting with proteins. Here, we discuss how physical features common to all GAGs-hydrophilicity, charge, linearity and semi-flexibility-underpin the overall properties of GAG-rich matrices. By integrating soft matter physics concepts (e.g. polymer brushes and phase separation) with our molecular understanding of GAG-protein interactions, we can better comprehend how GAG-rich matrices assemble, what their properties are, and how they function. Taking perineuronal nets (PNNs)-a GAG-rich matrix enveloping neurons-as a relevant example, we propose that microphase separation determines the holey PNN anatomy that is pivotal to PNN functions.
- MeSH
- extracelulární matrix - proteiny metabolismus MeSH
- extracelulární matrix metabolismus MeSH
- glykosaminoglykany chemie metabolismus MeSH
- hydrofobní a hydrofilní interakce MeSH
- neurony metabolismus MeSH
- vazba proteinů MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
