AIMC Topic: Proteins

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HormoNet: a deep learning approach for hormone-drug interaction prediction.

BMC bioinformatics Feb 28, 2024
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is ...
Amino Acids Deep Learning Proteins Drug Interactions Humans Hormones
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Automated model building and protein identification in cryo-EM maps.

Nature Feb 26, 2024
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs. Here we present ModelAngelo, a machine-learning approa...
Machine Learning Proteins Neural Networks, Computer Markov Chains Protein Conformation Cryoelectron Microscopy Computer Graphics Amino Acid Sequence Models, Molecular
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Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches.

Current opinion in structural biology Feb 24, 2024
Protein-protein interactions play crucial roles in many biological processes. Traditionally, protein complex structures are normally built by protein-protein docking. With the rapid development of artificial intelligence and its great success in mono...
Proteins Deep Learning Artificial Intelligence
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Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Journal of chemical information and modeling Feb 22, 2024
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated si...
Machine Learning Ligands Proteins
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Protein design using structure-based residue preferences.

Nature communications Feb 22, 2024
Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at in...
Amino Acids Proteins Mutation Neural Networks, Computer
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graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Journal of chemical information and modeling Feb 17, 2024
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of bin...
Drug Discovery Protein Binding Neural Networks, Computer Ligands Proteins
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An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values.

Molecular informatics Feb 15, 2024
When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequent...
Protein Binding Machine Learning Ligands Proteins
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Machine learning for functional protein design.

Nature biotechnology Feb 15, 2024
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelera...
Machine Learning Proteins Amino Acid Sequence Biotechnology Antibodies
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Machine learning approaches in predicting allosteric sites.

Current opinion in structural biology Feb 13, 2024
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked t...
Machine Learning Proteins Artificial Intelligence Allosteric Site Allosteric Regulation Binding Sites Ligands
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Recent Progress of Protein Tertiary Structure Prediction.

Molecules (Basel, Switzerland) Feb 13, 2024
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI)...
Software Artificial Intelligence Models, Molecular Databases, Protein Protein Folding Algorithms Computational Biology Protein Conformation Proteins
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