Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.
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Sensors (Basel)
January 2025
Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, TX 77705, USA.
Automated ultrasonic testing (AUT) is a critical tool for infrastructure evaluation in industries such as oil and gas, and, while skilled operators manually analyze complex AUT data, artificial intelligence (AI)-based methods show promise for automating interpretation. However, improving the reliability and effectiveness of these methods remains a significant challenge. This study employs the Segment Anything Model (SAM), a vision foundation model, to design an AI-assisted tool for weld defect detection in real-world ultrasonic B-scan images.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computer Sciences, National University of Science and Technology Polithenica Bucharest, 313 Spl. Independenței, RO060042 Bucharest, Romania.
Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
The Institute of Cancer Research, London, United Kingdom.
Background: Deep learning (DL) has set new standards in cancer diagnosis, significantly enhancing the accuracy of automated classification of whole slide images (WSIs) derived from biopsied tissue samples. To enable DL models to process these large images, WSIs are typically divided into thousands of smaller tiles, each containing 10-50 cells. Multiple Instance Learning (MIL) is a commonly used approach, where WSIs are treated as bags comprising numerous tiles (instances) and only bag-level labels are provided during training.
View Article and Find Full Text PDFOptom Vis Sci
January 2025
Johnson & Johnson MedTech (Vision), Irvine, California.
Significance: Optimal meibography utilization and interpretation are hindered due to poor lid presentation, blurry images, or image artifacts and the challenges of applying clinical grading scales. These results, using the largest image dataset analyzed to date, demonstrate development of algorithms that provide standardized, real-time inference that addresses all of these limitations.
Purpose: This study aimed to develop and validate an algorithmic pipeline to automate and standardize meibomian gland absence assessment and interpretation.
Neuroinformatics
January 2025
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
This paper introduces the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source, Python-based pipeline that consumes MR images of the brain and produces anatomical segmentations, lesion segmentations, and human-interpretable imaging features describing the lesions in the brain. ALFE pipeline is modeled after the neuroradiology workflow and generates features that can be used by physicians for quantitative analysis of clinical brain MRIs and for machine learning applications. The pipeline uses a decoupled design which allows the user to customize the image processing, image registrations, and AI segmentation tools without the need to change the business logic of the pipeline.
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