AIMC Topic: Sequence Alignment

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Distance-based protein folding powered by deep learning.

Proceedings of the National Academy of Sciences of the United States of America Aug 9, 2019
Direct coupling analysis (DCA) for protein folding has made very good progress, but it is not effective for proteins that lack many sequence homologs, even coupled with time-consuming conformation sampling with fragments. We show that we can accurate...
Membrane Proteins Models, Molecular Protein Folding Sequence Alignment Algorithms Deep Learning Time Factors
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Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins.

Neural computation Jul 1, 2019
A restricted Boltzmann machine (RBM) is an unsupervised machine learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. RBMs were recently proposed for characterizi...
Amino Acid Sequence Probability Static Electricity Stochastic Processes Computer Simulation Sequence Alignment Unsupervised Machine Learning Models, Statistical Principal Component Analysis Proteins Models, Molecular
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ProteinNet: a standardized data set for machine learning of protein structure.

BMC bioinformatics Jun 11, 2019
BACKGROUND: Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized ...
Proteins Machine Learning Databases, Protein Amino Acid Sequence Reference Standards Sequence Alignment
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Deep convolutional neural networks for accurate somatic mutation detection.

Nature communications Mar 4, 2019
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...
Neoplasms Computational Biology Databases, Genetic Sequence Analysis, DNA Humans Neural Networks, Computer Machine Learning Mutation Genes, Neoplasm DNA Mutational Analysis Exome Sequence Alignment Diploidy
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Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach.

BMC genomics Dec 27, 2018
BACKGROUND: Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both poss...
Sequence Analysis, RNA Genome, Human Sequence Alignment RNA Splicing Software Gene Expression Profiling Deep Learning Exons Humans
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Computational design of thermostabilizing point mutations for G protein-coupled receptors.

eLife Jun 21, 2018
Engineering of GPCR constructs with improved thermostability is a key for successful structural and biochemical studies of this transmembrane protein family, targeted by 40% of all therapeutic drugs. Here we introduce a comprehensive computational ap...
Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Point Mutation Serotonin 5-HT2 Receptor Agonists Serotonin 5-HT2 Receptor Antagonists Protein Engineering Thermodynamics Sequence Alignment Sequence Homology, Amino Acid Receptor, Serotonin, 5-HT2C Crystallography, X-Ray Binding Sites Machine Learning Animals Humans Protein Binding Gene Expression Models, Molecular Kinetics Amino Acid Sequence Protein Stability
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Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks.

Cell systems Dec 20, 2017
While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferri...
Neural Networks, Computer Machine Learning Algorithms Forecasting Animals Computational Biology Proteins Humans Databases, Protein Protein Conformation Models, Molecular Protein Folding Probability Sequence Alignment
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Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

Amino acids Nov 18, 2017
Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithm...
Models, Theoretical Databases, Protein Receptors, G-Protein-Coupled Amino Acid Sequence Sequence Alignment Sequence Analysis, Protein Machine Learning Neural Networks, Computer Algorithms Reproducibility of Results
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Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

Proteins Nov 14, 2017
We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successf...
Protein Folding Sequence Alignment Sequence Analysis, Protein Models, Molecular Crystallography, X-Ray Algorithms Protein Conformation Computational Biology Proteins Humans Databases, Protein Machine Learning
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Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

Proteins Oct 31, 2017
In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on co...
Datasets as Topic Computational Biology Crystallography, X-Ray Protein Conformation Humans Protein Folding Proteins Machine Learning Sequence Alignment Databases, Protein Algorithms Sequence Analysis, Protein Models, Molecular
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