AIMC Topic: Evolution, Molecular

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Evolutionary accumulation modeling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance.

mBio
Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of anti...

Computationally designed proteins mimic antibody immune evasion in viral evolution.

Immunity
Recurrent waves of viral infection necessitate vaccines and therapeutics that remain effective against emerging viruses. Our ability to evaluate interventions is currently limited to assessments against past or circulating variants, which likely diff...

MEGA-GPT: Artificial Intelligence Guidance and Building Analytical Protocols Using MEGA Software.

Molecular biology and evolution
Over the past three decades, the Molecular Evolutionary Genetics Analysis (MEGA) software has evolved into a powerful tool with an ever-expanding suite of functionalities. Yet, despite its user-friendly design and widespread adoption by researchers a...

Evolution of gene regulatory networks in insects.

Current opinion in insect science
Changes in gene regulatory networks (GRNs) underlying the evolution of traits have been intensively studied, with insects providing excellent model cases. In studies using Drosophila, butterflies, and other insects, several well-known cases have show...

Methylomes Reveal Recent Evolutionary Changes in Populations of Two Plant Species.

Genome biology and evolution
Plant DNA methylation changes occur hundreds to thousands of times faster than DNA mutations and can be transmitted transgenerationally, making them useful for studying population-scale patterns in clonal or selfing species. However, a state-of-the-a...

EVOLVE: A Web Platform for AI-Based Protein Mutation Prediction and Evolutionary Phase Exploration.

Journal of chemical information and modeling
While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effec...

Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.

Molecular biology and evolution
Phylogenetic inference aims at reconstructing the tree describing the evolution of a set of sequences descending from a common ancestor. The high computational cost of state-of-the-art maximum likelihood and Bayesian inference methods limits their us...

Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time.

Nucleic acids research
In infected individuals, viruses are present as a population consisting of dominant and minor variant genomes. Most databases contain information on the dominant genome sequence. Since the emergence of SARS-CoV-2 in late 2019, variants have been sele...

An efficient deep learning method for amino acid substitution model selection.

Journal of evolutionary biology
Amino acid substitution models play an important role in studying the evolutionary relationships among species from protein sequences. The amino acid substitution model consists of a large number of parameters; therefore, it is estimated from hundred...

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Nucleic acids research
Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importance, their prevalence and the range of regulated gene functions are largely unknown. We introduced D...