AIMC Topic: Protein Conformation

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Generative artificial intelligence for de novo protein design.

Current opinion in structural biology Apr 24, 2024
Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forwar...
Protein Engineering Artificial Intelligence Models, Molecular Protein Conformation Proteins
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Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.

Journal of chemical information and modeling Apr 18, 2024
Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully...
Models, Molecular Genetic Variation Machine Learning Computational Biology NF-E2-Related Factor 2 Proto-Oncogene Proteins c-myc Genome, Human Protein Conformation Humans
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Encoding the space of protein-protein binding interfaces by artificial intelligence.

Computational biology and chemistry Apr 18, 2024
The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underl...
Models, Molecular Protein Conformation Binding Sites Protein Binding Proteins Artificial Intelligence
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Protein Engineering with Lightweight Graph Denoising Neural Networks.

Journal of chemical information and modeling Apr 17, 2024
Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establish...
Protein Conformation Protein Engineering Models, Molecular Mutation Neural Networks, Computer Deep Learning Proteins
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Apprehensions and emerging solutions in ML-based protein structure prediction.

Current opinion in structural biology Apr 16, 2024
The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in ...
Protein Conformation Proteins Models, Molecular Machine Learning Computational Biology
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ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes.

Journal of chemical information and modeling Apr 4, 2024
Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources...
Deep Learning Models, Molecular Biocatalysis Protein Conformation Enzymes
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FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics.

International journal of biological macromolecules Mar 27, 2024
Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework ...
Computational Biology Proteins Protein Conformation Software Deep Learning Humans Neural Networks, Computer
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Prediction of order parameters based on protein NMR structure ensemble and machine learning.

Journal of biomolecular NMR Mar 26, 2024
The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. Th...
Nuclear Magnetic Resonance, Biomolecular Proteins Protein Conformation Machine Learning
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A suite of designed protein cages using machine learning and protein fragment-based protocols.

Structure (London, England : 1993) Mar 20, 2024
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, se...
Protein Conformation Cryoelectron Microscopy Machine Learning Models, Molecular Hydrophobic and Hydrophilic Interactions Molecular Docking Simulation Proteins
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Flattening the curve-How to get better results with small deep-mutational-scanning datasets.

Proteins Mar 19, 2024
Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the ...
Machine Learning Software Algorithms Deep Learning Humans Neural Networks, Computer Databases, Protein Mutation Computational Biology Proteins Protein Conformation
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