AIMC Topic: Thermodynamics

Clear Filters Showing 261 to 270 of 333 articles

Co-combustion of sewage sludge and coffee grounds under increased O/CO atmospheres: Thermodynamic characteristics, kinetics and artificial neural network modeling.

Bioresource technology Nov 13, 2017
(Co-)combustion characteristics of sewage sludge (SS), coffee grounds (CG) and their blends were quantified under increased O/CO atmosphere (21, 30, 40 and 60%) using a thermogravimetric analysis. Observed percentages of CG mass loss and its maximum ...
Neural Networks, Computer Coffee Thermogravimetry Thermodynamics Atmosphere Kinetics Carbon Dioxide Sewage
View on PubMed DOI

De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture.

Proceedings of the National Academy of Sciences of the United States of America Oct 31, 2017
Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing ...
Genome, Human Molecular Conformation Thermodynamics Phase Transition Epigenesis, Genetic In Situ Hybridization, Fluorescence Cell Nucleus Chromatin Chromosomes, Human Neural Networks, Computer Animals Humans
View on PubMed DOI

The Thermodynamic Basis of the Fuzzy Interaction of an Intrinsically Disordered Protein.

Angewandte Chemie (International ed. in English) Oct 10, 2017
Many intrinsically disordered proteins (IDP) that fold upon binding retain conformational heterogeneity in IDP-target complexes. The thermodynamics of such fuzzy interactions is poorly understood. Herein we introduce a thermodynamic framework, based ...
Protein Folding Calorimetry Circular Dichroism Amino Acid Sequence Intrinsically Disordered Proteins Protein Binding Thermodynamics Kinetics Molecular Dynamics Simulation Fuzzy Logic Models, Molecular
View on PubMed DOI

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

Biochemical and biophysical research communications Oct 7, 2017
Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) ...
Molecular Docking Simulation Drug Design Antineoplastic Agents ROC Curve Humans Thermodynamics Cyclin-Dependent Kinase 2 Databases, Protein Inhibitory Concentration 50 Supervised Machine Learning Datasets as Topic Ligands Protein Kinase Inhibitors
View on PubMed DOI

Bias-Free Chemically Diverse Test Sets from Machine Learning.

ACS combinatorial science Jul 27, 2017
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal an...
Models, Chemical Quantum Theory Databases, Chemical Thermodynamics Cluster Analysis Structure-Activity Relationship Principal Component Analysis Machine Learning
View on PubMed DOI

Editorial-Sequences and topology.

Current opinion in structural biology Jun 4, 2017
Computational Biology Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Proteins Binding Sites Thermodynamics Protein Multimerization Machine Learning Humans Protein Interaction Mapping Protein Interaction Domains and Motifs Molecular Docking Simulation Animals Protein Binding
View on PubMed DOI

Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics.

Journal of chemical theory and computation May 4, 2017
The free energy calculations of complex chemical and biological systems with molecular dynamics (MD) are inefficient due to multiple local minima separated by high-energy barriers. The minima can be escaped using an enhanced sampling method such as m...
Neural Networks, Computer Molecular Dynamics Simulation Thermodynamics
View on PubMed DOI

FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains.

Journal of computational chemistry Mar 10, 2017
The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a krigi...
Models, Molecular Amino Acids Protein Conformation Physical Phenomena Static Electricity Thermodynamics Peptides Models, Theoretical Machine Learning Proteins
View on PubMed DOI

A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

Proteins Jan 20, 2017
Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and th...
Protein Interaction Mapping Structural Homology, Protein Software Research Design Molecular Docking Simulation Benchmarking Binding Sites Machine Learning Protein Binding Computational Biology Protein Conformation Thermodynamics Cluster Analysis Proteins
View on PubMed DOI

Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility.

Proteins Dec 5, 2016
Predicting protein conformational changes from unbound structures or even homology models to bound structures remains a critical challenge for protein docking. Here we present a study directly addressing the challenge by reducing the dimensionality a...
Models, Statistical Proteins Benchmarking Binding Sites Molecular Docking Simulation Protein Conformation Machine Learning Protein Multimerization Thermodynamics Protein Binding Software Dimensional Measurement Accuracy Structural Homology, Protein Computational Biology Research Design
View on PubMed DOI
Popular Topics
Recent Journals