AIMC Topic: Sequence Alignment

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Analysis of distance-based protein structure prediction by deep learning in CASP13.

Proteins Sep 13, 2019
This paper reports the CASP13 results of distance-based contact prediction, threading, and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by...
Software Algorithms Deep Learning Computational Biology Protein Conformation Machine Learning Proteins Sequence Alignment Protein Folding Models, Molecular Sequence Analysis, Protein Crystallography, X-Ray
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rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments.

PloS one Aug 15, 2019
In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins fun...
Proteins Sequence Alignment Deep Learning Computational Biology
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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...
Protein Folding Sequence Alignment Algorithms Membrane Proteins Models, Molecular 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...
Computer Simulation Models, Statistical Proteins Stochastic Processes Sequence Alignment Amino Acid Sequence Probability Static Electricity Unsupervised Machine Learning Principal Component Analysis 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 ...
Machine Learning Proteins Sequence Alignment Databases, Protein Amino Acid Sequence Reference Standards
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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...
Exome Diploidy DNA Mutational Analysis Sequence Alignment Genes, Neoplasm Neural Networks, Computer Machine Learning Neoplasms Sequence Analysis, DNA Mutation Computational Biology Databases, Genetic Humans
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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...
Exons Software Sequence Alignment Humans RNA Splicing Gene Expression Profiling Sequence Analysis, RNA Deep Learning Genome, Human
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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...
Receptor, Serotonin, 5-HT2C Crystallography, X-Ray Binding Sites Sequence Alignment Sequence Homology, Amino Acid Protein Engineering Thermodynamics Kinetics Amino Acid Sequence Protein Stability Protein Binding Models, Molecular Gene Expression Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Point Mutation Humans Serotonin 5-HT2 Receptor Antagonists Machine Learning Protein Conformation, beta-Strand Serotonin 5-HT2 Receptor Agonists Animals
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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...
Computational Biology Proteins Neural Networks, Computer Databases, Protein Sequence Alignment Humans Algorithms Models, Molecular Forecasting Animals Probability Protein Conformation Protein Folding Machine Learning
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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...
Reproducibility of Results Receptors, G-Protein-Coupled Neural Networks, Computer Sequence Analysis, Protein Models, Theoretical Amino Acid Sequence Machine Learning Databases, Protein Algorithms Sequence Alignment
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