AIMC Topic: Capsid

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Prediction of Adeno-Associated Virus Fitness with a Protein Language-Based Machine Learning Model.

Human gene therapy
Adeno-associated virus (AAV)-based therapeutics have the potential to transform the lives of patients by delivering one-time treatments for a variety of diseases. However, a critical challenge to their widespread adoption and distribution is the high...

Advances in AAV capsid engineering: Integrating rational design, directed evolution and machine learning.

Molecular therapy : the journal of the American Society of Gene Therapy
Adeno-associated virus (AAV) has emerged as a highly promising vector for human gene therapy due to its favorable safety profile, versatility, and ability to transduce a wide range of tissues. However, natural AAV serotypes have shortcomings, includi...

Machine-learning a virus assembly fitness landscape.

PloS one
Realistic evolutionary fitness landscapes are notoriously difficult to construct. A recent cutting-edge model of virus assembly consists of a dodecahedral capsid with 12 corresponding packaging signals in three affinity bands. This whole genome/pheno...

Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning.

Frontiers in immunology
A key hurdle to making adeno-associated virus (AAV) capsid mediated gene therapy broadly beneficial to all patients is overcoming pre-existing and therapy-induced immune responses to these vectors. Recent advances in high-throughput DNA synthesis, mu...

Improved automatic detection of herpesvirus secondary envelopment stages in electron microscopy by augmenting training data with synthetic labelled images generated by a generative adversarial network.

Cellular microbiology
Detailed analysis of secondary envelopment of the herpesvirus human cytomegalovirus (HCMV) by transmission electron microscopy (TEM) is crucial for understanding the formation of infectious virions. Here, we present a convolutional neural network (CN...

SPECTRUS: A Dimensionality Reduction Approach for Identifying Dynamical Domains in Protein Complexes from Limited Structural Datasets.

Structure (London, England : 1993)
Identifying dynamical, quasi-rigid domains in proteins provides a powerful means for characterizing functionally oriented structural changes via a parsimonious set of degrees of freedom. In fact, the relative displacements of few dynamical domains us...

Machine Learning of Molecular Dynamics Simulations Provides Insights into the Modulation of Viral Capsid Assembly.

Journal of chemical information and modeling
An effective approach in the development of novel antivirals is to target the assembly of viral capsids by using capsid assembly modulators (CAMs). CAMs targeting hepatitis B virus (HBV) have two major modes of function: they can either accelerate nu...

Integrating different approaches for the identification of new disruptors of HIV-1 capsid multimerization.

Biochemical and biophysical research communications
Human Immunodeficiency Virus (HIV) belongs to the Lentivirus genus, Retroviridae family, enveloped by a lipid bilayer within which the capsid protein encases the viral genome, reverse transcriptase, and integrase proteins, key components for viral re...

Machine-learning-guided Directed Evolution for AAV Capsid Engineering.

Current pharmaceutical design
Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as i...