AIMC Topic: Sequence Analysis, Protein

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Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Mar 7, 2019
Antimicrobial peptides are short amino acid sequences that may be antibacterial, antifungal, and antiviral. Most machine learning methodologies applied to identifying antibacterial peptides have developed feature vectors of identical lengths for each...
Pore Forming Cytotoxic Proteins Databases, Protein Sequence Analysis, Protein Neural Networks, Computer Anti-Bacterial Agents Computational Biology Algorithms
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RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.

International journal of molecular sciences Mar 1, 2019
Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-bi...
Humans Sequence Analysis, RNA Machine Learning RNA, Long Noncoding Software Sequence Analysis, Protein Animals Protein Binding
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Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.

International journal of molecular sciences Feb 21, 2019
It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolu...
Binding Sites Fourier Analysis Saccharomyces cerevisiae Proteins Protein Binding Support Vector Machine Sequence Analysis, Protein Humans Animals
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SignalP 5.0 improves signal peptide predictions using deep neural networks.

Nature biotechnology Feb 18, 2019
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish ...
Algorithms Computational Biology Bacterial Proteins Neural Networks, Computer Software Archaeal Proteins Eukaryota Sequence Analysis, Protein Protein Sorting Signals Amino Acid Sequence Biotechnology
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TargetDBP: Accurate DNA-Binding Protein Prediction Via Sequence-Based Multi-View Feature Learning.

IEEE/ACM transactions on computational biology and bioinformatics Jan 18, 2019
Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targ...
DNA-Binding Proteins Sequence Analysis, Protein Databases, Protein Position-Specific Scoring Matrices Algorithms Computational Biology Support Vector Machine Machine Learning
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DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

PloS one Jan 8, 2019
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving t...
Amino Acid Sequence Protein Structure, Tertiary Sequence Analysis, Protein Proteins Databases, Protein Algorithms Computational Biology Models, Molecular Deep Learning Support Vector Machine
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Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

Scientific reports Oct 15, 2018
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...
Sequence Analysis, Protein Proteome Databases, Protein Amino Acid Sequence Computational Biology Algorithms RNA-Binding Proteins Sensitivity and Specificity Neural Networks, Computer Deep Learning Machine Learning
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Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs.

International journal of molecular sciences Oct 7, 2018
Using computational techniques to identify intrinsically disordered residues is practical and effective in biological studies. Therefore, designing novel high-accuracy strategies is always preferable when existing strategies have a lot of room for im...
Humans Software Intrinsically Disordered Proteins Neural Networks, Computer Sequence Analysis, Protein
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Predicting improved protein conformations with a temporal deep recurrent neural network.

PloS one Sep 4, 2018
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally re...
Amino Acid Sequence Algorithms Protein Conformation Deep Learning Neural Networks, Computer Protein Folding Sequence Analysis, Protein Molecular Dynamics Simulation Software
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BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC.

Journal of theoretical biology Aug 20, 2018
Antibiotics of β-lactam class account for nearly half of the global antibiotic use. The β-lactamase enzyme is a major element of the bacterial arsenals to escape the lethal effect of β-lactam antibiotics. Different variants of β-lactamases have evolv...
beta-Lactamases Databases, Protein beta-Lactam Resistance Bacterial Proteins Sequence Analysis, Protein Support Vector Machine
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