AIMC Topic: Proteins

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Elucidating the druggability of the human proteome with eFindSite.

Journal of computer-aided molecular design Mar 19, 2019
Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and r...
5-Aminolevulinate Synthetase Proteome Proteins Serine Proteases Binding Sites Supervised Machine Learning Drug Design Humans Drug Discovery Software Protein Binding
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Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

Journal of chemical information and modeling Mar 18, 2019
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual scr...
Molecular Docking Simulation User-Computer Interface Proteins Machine Learning Protein Conformation Thermodynamics Drug Evaluation, Preclinical Cheminformatics Ligands
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Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier.

Mathematical biosciences Mar 15, 2019
Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore...
Support Vector Machine Proteins Models, Biological Amino Acids Aptamers, Nucleotide Computational Biology Humans
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Predicting protein residue-residue contacts using random forests and deep networks.

BMC bioinformatics Mar 14, 2019
BACKGROUND: The ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the c...
Amino Acid Sequence Computational Biology Proteins Machine Learning
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Predicting protein-peptide interaction sites using distant protein complexes as structural templates.

Scientific reports Mar 12, 2019
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details...
Humans Machine Learning Molecular Docking Simulation Protein Binding Computational Biology Software Peptides Binding Sites Amino Acid Sequence Protein Interaction Mapping Datasets as Topic Cluster Analysis Proteins
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Electrochemical protein recognition based on macromolecular self-assembly of molecularly imprinted polymer: a new strategy to mimic antibody for label-free biosensing.

Journal of materials chemistry. B Mar 6, 2019
A versatile strategy, based on the use of an amphiphilic copolymer as a macromonomer, was developed for the preparation of a fully synthetic MIP (molecularly imprinted polymer) sensor for protein recognition. A UV-crosslinkable copolymer poly(DMA-co-...
Polymers Proteins Molecular Imprinting Electrochemical Techniques Biosensing Techniques Nanoparticles
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DESTINI: A deep-learning approach to contact-driven protein structure prediction.

Scientific reports Mar 5, 2019
The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein's sequence is one of most challenging problems in computational biology. In this work, we introduce DEST...
Protein Structure, Tertiary Computational Biology Deep Learning Proteins
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Predicting protein-ligand binding residues with deep convolutional neural networks.

BMC bioinformatics Feb 26, 2019
BACKGROUND: Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categ...
Databases, Protein Amino Acid Sequence Protein Binding Proteins Ligands Algorithms Deep Learning Neural Networks, Computer
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DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks.

Journal of chemical information and modeling Feb 25, 2019
Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural network-based method, fo...
Computational Biology Proteins Transition Temperature Point Mutation Protein Stability Neural Networks, Computer
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PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

Journal of computational chemistry Feb 15, 2019
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets...
Computational Biology Amino Acid Sequence Support Vector Machine Databases, Protein Proteins Models, Chemical Protein Binding
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