AIMC Topic: Mutation

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Investigating the Nature of PRM:SH3 Interactions Using Artificial Intelligence and Molecular Dynamics.

Journal of chemical information and modeling
Understanding the binding interactions within protein-peptide complexes is crucial for elucidating key physicochemical phenomena in biological systems. Among the outcomes of these interactions, biomolecular condensates have recently emerged as vital ...

Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR-Cas9 off-target activity.

NAR genomics and bioinformatics
Despite advances in determining the factors influencing cleavage activity of a CRISPR-Cas9 single guide RNA (sgRNA) at an (off-)target DNA sequence, a comprehensive assessment of pertinent physico-chemical/structural descriptors is missing. In partic...

Multidimensional computational strategies enhance the thermostability of alpha-galactosidase.

International journal of biological macromolecules
Alpha-Galactosidase has significant industrial application value in food processing, animal nutrition and medical applications. Microbial-derived α-galactosidases predominate industrial implementation due to high productivity, yet their inherent ther...

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.

Lung cancer (Amsterdam, Netherlands)
BACKGROUND: Epidermal growth factor receptor (EGFR) mutations play a pivotal role in guiding targeted therapy for lung cancer, making their accurate detection essential for personalized treatment. Recently, artificial intelligence (AI) has emerged as...

An informed deep learning model of the Omicron wave and the impact of vaccination.

Computers in biology and medicine
The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, vaccine efficacy, and potential for reinfection is crucial. This study examines the dynamics of Omicr...

Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning.

IEEE journal of biomedical and health informatics
Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the ...

Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

Scientific reports
Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA as a cancer biomarker depends on the ability to accurately detect somatic variants associated with c...

Deep learning-guided design of dynamic proteins.

Science (New York, N.Y.)
Deep learning has advanced the design of static protein structures, but the controlled conformational changes that are hallmarks of natural signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep learning-gui...

Prognostic value of circadian rhythm-associated genes in breast cancer.

World journal of surgical oncology
OBJECTIVE: Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence indicates that genetic variations related to circadian rhythms, as well as the timing of therapeutic interventions, influence the response to radiation...

Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations.

Journal of chemical theory and computation
Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostatic coupling between the QM and MM subsystems. In this work, we proposed a reweighting ME (mechanic e...