AIMC Topic: Proteins

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iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

IEEE/ACM transactions on computational biology and bioinformatics Apr 6, 2021
Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association,...
Models, Statistical Proteins Computational Biology Sequence Analysis, Protein Histidine Phosphorylation Amino Acid Sequence Neural Networks, Computer
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Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

PLoS computational biology Mar 26, 2021
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by en...
Reproducibility of Results Computational Biology Neural Networks, Computer Protein Folding Proteins Sequence Analysis, Protein Protein Conformation
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Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches.

Scientific reports Mar 25, 2021
The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were ...
Proteins Sequence Alignment Biological Coevolution Models, Genetic Evolution, Molecular Machine Learning
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Disentangling the Contribution of Each Descriptive Characteristic of Every Single Mutation to Its Functional Effects.

Journal of chemical information and modeling Mar 23, 2021
Mutational effects predictions continue to improve in accuracy as advanced artificial intelligence (AI) algorithms are trained on exhaustive experimental data. The next natural questions to ask are if it is possible to gain insights into which attrib...
Artificial Intelligence Position-Specific Scoring Matrices Algorithms Proteins Mutation
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Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

Journal of chemical information and modeling Mar 23, 2021
Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite th...
Ligands Proteins Neural Networks, Computer Software Protein Binding
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Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Mar 16, 2021
Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC (OPRC), for the molecular featurization and feature engineering,...
Proteins Databases, Protein Protein Binding Ligands Machine Learning
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AptaNet as a deep learning approach for aptamer-protein interaction prediction.

Scientific reports Mar 16, 2021
Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, the...
Aptamers, Nucleotide Proteins Databases, Protein Deep Learning
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Stereoelectronic effects in stabilizing protein-N-glycan interactions revealed by experiment and machine learning.

Nature chemistry Mar 15, 2021
The energetics of protein-carbohydrate interactions, central to many life processes, cannot yet be manipulated predictably. This is mostly due to an incomplete quantitative understanding of the enthalpic and entropic basis of these interactions in aq...
Machine Learning Proteins Humans Protein Folding Thermodynamics
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GGL-Tox: Geometric Graph Learning for Toxicity Prediction.

Journal of chemical information and modeling Mar 15, 2021
Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large libr...
Algorithms Drug Design Proteins Machine Learning
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PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.

International journal of molecular sciences Mar 8, 2021
Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular functio...
Protein Processing, Post-Translational Proteins Computational Biology Machine Learning Tyrosine Support Vector Machine Sequence Analysis, Protein
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