AIMC Topic: Models, Molecular

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Evolutionary insights into the active-site structures of the metallo-β-lactamase superfamily from a classification study with support vector machine.

Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry Sep 18, 2020
The metallo-β-lactamase (MβL) superfamily, which is intriguing due to its enzyme promiscuity, is a good model enzyme superfamily for studies of catalytic function evolution. Our previous study traced the evolution of the phosphotriesterase activity o...
beta-Lactamases Catalytic Domain Computational Biology Models, Molecular Support Vector Machine Kinetics Evolution, Molecular
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ODiNPred: comprehensive prediction of protein order and disorder.

Scientific reports Sep 8, 2020
Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however...
Neural Networks, Computer Software Computational Biology Models, Molecular Humans Proteins Algorithms Databases, Protein Amino Acids Intrinsically Disordered Proteins Protein Folding
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Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data.

Molecules (Basel, Switzerland) Aug 29, 2020
Activity landscape (AL) models are used for visualizing and interpreting structure-activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different t...
Drug Discovery Structure-Activity Relationship Models, Molecular Machine Learning Drug Design
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Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning.

ACS combinatorial science Aug 17, 2020
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computat...
Machine Learning Peptides Protein Binding Molecular Structure Amino Acid Sequence Ligands High-Throughput Screening Assays Peptide Library Structure-Activity Relationship Models, Molecular Combinatorial Chemistry Techniques
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A deep attention network for predicting amino acid signals in the formation of [Formula: see text]-helices.

Journal of bioinformatics and computational biology Aug 6, 2020
The secondary and tertiary structure of a protein has a primary role in determining its function. Even though many folding prediction algorithms have been developed in the past decades - mainly based on the assumption that folding instructions are en...
Protein Structure, Secondary Databases, Protein Protein Conformation, alpha-Helical Machine Learning Models, Molecular Amino Acids Software
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Predicting protein model correctness in Coot using machine learning.

Acta crystallographica. Section D, Structural biology Jul 27, 2020
Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that ...
Models, Molecular Machine Learning Crystallography, X-Ray Protein Conformation Software Proteins
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Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN).

Molecules (Basel, Switzerland) Jul 17, 2020
This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron micros...
Solutions Thermodynamics Neural Networks, Computer Algorithms Water Copper Kinetics Diffusion Water Pollutants, Chemical Sulfur Adsorption Models, Molecular
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In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Journal of applied toxicology : JAT Jul 13, 2020
Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor compounds. Given the concern of increased use and the appeal of ENDS by young people, evaluating the potential of flavors to induce DNA damage is impo...
Risk Assessment Electronic Nicotine Delivery Systems Mice Consumer Product Safety Rats Machine Learning Biomarkers Humans Quantitative Structure-Activity Relationship Cell Line Models, Molecular Animals Phosphorylation Flow Cytometry Mutagenicity Tests DNA Damage Histones Tumor Suppressor Protein p53 Flavoring Agents
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Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints.

Toxicology letters Jul 3, 2020
The human ether-a-go-go-related gene (hERG) encodes a tetrameric potassium channel called Kv11.1. This channel can be blocked by certain drugs, which leads to long QT syndrome, causing cardiotoxicity. This is a significant problem during drug develop...
Cricetinae Peptide Mapping Cardiotoxicity Potassium Channel Blockers ERG1 Potassium Channel Cricetulus Support Vector Machine Area Under Curve Predictive Value of Tests Models, Molecular CHO Cells Humans Databases, Factual Animals Machine Learning Algorithms
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DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins.

Journal of bioinformatics and computational biology Jun 23, 2020
Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are f...
Area Under Curve Deep Learning Neural Networks, Computer Models, Molecular Membrane Proteins Reproducibility of Results Protein Domains Databases, Protein Sensitivity and Specificity
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