AIMC Topic: Protein Aggregates

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Deconvoluting Biophysical Factors that Influence Long-Term Aggregation Rates of High-Concentration Monoclonal Antibody Formulations.

Molecular pharmaceutics
Efficient determination of developable protein drug candidates and stable solution conditions is a key challenge in industrial drug development. Protein aggregation is difficult to predict and can lead to challenges in manufacturing, storage, and pat...

Machine Learning-Based Bioactivity Prediction and Descriptor-Guided Rational Design of Amyloid-β Aggregation Inhibitors.

ACS chemical neuroscience
Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which amyloid-β (Aβ) aggregation plays a pivotal role in its onset and progression. Inhibiting Aβ aggregation is a promising therapeutic strategy; however, its intrinsically diso...

Recurrent Neural Networks Predict Future Peptide Aggregation for Drug Development.

Molecular pharmaceutics
Physical stability of an active pharmaceutical ingredient (API) is a key consideration in the development of a pharmaceutical drug. Solution conditions such as pH, excipient concentrations, and storage temperatures can impact the physical stability o...

Prediction of aggregation in monoclonal antibodies from molecular surface curvature.

Scientific reports
Protein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the ...

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.

Nature communications
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in d...

Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.

Science advances
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively pertur...

In Silico Screening of Small Molecule Inhibitors for Amyloid-β Aggregation.

Journal of chemical information and modeling
The self-aggregation of amyloid-β (Aβ) into fibrils is a hallmark of Alzheimer's disease (AD). Inhibition of Aβ aggregation with small molecule compounds represents a promising therapeutic strategy for AD. However, designing effective ligands is chal...

Massive experimental quantification allows interpretable deep learning of protein aggregation.

Science advances
Protein aggregation is a pathological hallmark of more than 50 human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experi...

Molecular Insights into α-Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach.

ACS chemical neuroscience
The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. The aggregation pathway of α-synuclein typically involves a defined sequence of nucleation, elongation, a...

Sulfonic acid functionalized β-amyloid peptide aggregation inhibitors and antioxidant agents for the treatment of Alzheimer's disease: Combining machine learning, computational, in vitro and in vivo approaches.

International journal of biological macromolecules
Alzheimer's disease (AD) is characterized as a neurodegenerative disorder that is caused by plaque formation by accumulating β-amyloid (Aβ), leading to neurocognitive function and impaired mental development. Thus, targeting Aβ represents a promising...