AIMC Topic: Amino Acids

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iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion.

Immunogenetics
Cancer is a terrible disease, recent studies reported that tumor T cell antigens (TTCAs) may play a promising role in cancer treatment. Since experimental methods are still expensive and time-consuming, it is highly desirable to develop automatic com...

DLPacker: Deep learning for prediction of amino acid side chain conformations in proteins.

Proteins
Prediction of side chain conformations of amino acids in proteins (also termed "packing") is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have...

Using Steady-State Kinetics to Quantitate Substrate Selectivity and Specificity: A Case Study with Two Human Transaminases.

Molecules (Basel, Switzerland)
We examined the ability of two human cytosolic transaminases, aspartate aminotransferase (GOT1) and alanine aminotransferase (GPT), to transform their preferred substrates whilst discriminating against similar metabolites. This offers an opportunity ...

Deep Neural Network and Extreme Gradient Boosting Based Hybrid Classifier for Improved Prediction of Protein-Protein Interaction.

IEEE/ACM transactions on computational biology and bioinformatics
Understanding the behavioral process of life and disease-causing mechanism, knowledge regarding protein-protein interactions (PPI) is essential. In this paper, a novel hybrid approach combining deep neural network (DNN) and extreme gradient boosting ...

Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism.

Interdisciplinary sciences, computational life sciences
As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locati...

Artificial intelligence based methods for hot spot prediction.

Current opinion in structural biology
Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved i...

Amino acid environment affinity model based on graph attention network.

Journal of bioinformatics and computational biology
Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that ...

Learning the local landscape of protein structures with convolutional neural networks.

Journal of biological physics
One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received...

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine.

Scientific reports
The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubilit...

Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles.

Computational and mathematical methods in medicine
Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often...