AIMC Topic: Proteins

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HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes.

Journal of chemical information and modeling
HADDOCK is a widely used resource for integrative modeling of a variety of biomolecular complexes that is able to incorporate experimental knowledge into physics-based calculations during complex prediction, refinement, scoring and analysis. Here we ...

Improved Prediction of Drug-Protein Interactions through Physics-Based Few-Shot Learning.

Journal of chemical information and modeling
Accurate prediction of drug-protein interactions is crucial for drug discovery. Due to the bottleneck of traditional scoring functions, many machine learning scoring functions (MLSFs) have been proposed for structure-based drug screening. However, ex...

The continuous evolution of biomolecular force fields.

Structure (London, England : 1993)
Biomolecular force fields have continuously evolved to improve their accuracy and broaden their applications in biological and therapeutic discoveries. The rapid adaptation of advanced computational technology, in particular the recent deep learning ...

'Intelligent' proteins.

Cellular and molecular life sciences : CMLS
We present an idea of protein molecules that challenges the traditional view of proteins as simple molecular machines and suggests instead that they exhibit a basic form of "intelligence". The idea stems from suggestions coming from Integrated Inform...

MMSol: Predicting Protein Solubility with an Antinoise Multimodal Deep Model.

Journal of chemical information and modeling
Protein solubility plays a critical role in determining its biological function, such as enabling proper protein delivery and ensuring that proteins remain soluble during cellular processes or therapeutic applications. Accurate prediction of protein ...

High-Throughput Analysis of Protein Adsorption to a Large Library of Polymers Using Liquid Extraction Surface Analysis-Tandem Mass Spectrometry (LESA-MS/MS).

Analytical chemistry
Biomaterials play an important role in medicine from contact lenses to joint replacements. High-throughput screening coupled with machine learning has identified synthetic polymers that prevent bacterial biofilm formation, prevent fungal cell attachm...

Designing diverse and high-performance proteins with a large language model in the loop.

PLoS computational biology
We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse...

NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.

Interdisciplinary sciences, computational life sciences
Non-coding RNAs (ncRNAs) are one of the components of epigenetic mechanisms that regulates gene expression. Studying ncRNA-protein interactions (NPI) can help to explore a wide range of biological features and related diseases. Traditional NPI resear...

Multi-view based heterogeneous graph contrastive learning for drug-target interaction prediction.

Journal of biomedical informatics
Drug-Target Interaction (DTI) prediction plays a pivotal role in accelerating drug discovery and development by identifying novel interactions between drugs and targets. Most previous studies on Drug-Protein Pair (DPP) networks have primarily focused...

Measuring alignment of structural proteins in engineered tissue constructs using polarized Raman spectroscopy.

PloS one
Measures of structural protein alignment within biological and engineered tissues are needed for improved understanding of their mechanical behavior and functionality. We advance our method of measuring protein alignment using polarized Raman spectro...