AI Medical Compendium Topic:
Protein Binding

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ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual bind...

Machine learning-based chemical binding similarity using evolutionary relationships of target genes.

Nucleic acids research
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional ac...

Neural networks with circular filters enable data efficient inference of sequence motifs.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleic acids and proteins often have localized sequence motifs that enable highly specific interactions. Due to the biological relevance of sequence motifs, numerous inference methods have been developed. Recently, convolutional neural n...

Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data.

Bioinformatics (Oxford, England)
MOTIVATION: Studies have shown that the accuracy of random forest (RF)-based scoring functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of classical SFs, such as X-Score, does not. Nevertheless, the impact of the...

Biological sequence modeling with convolutional kernel networks.

Bioinformatics (Oxford, England)
MOTIVATION: The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a mo...

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Nucleic acids research
The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-re...

Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Base...

GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs.

Bioinformatics (Oxford, England)
SUMMARY: Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, interpreting predictive sequence patterns learned by gkm-SVMs can be challenging. Existing interpreta...

The PSIPRED Protein Analysis Workbench: 20 years on.

Nucleic acids research
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 year...

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Nucleic acids research
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predic...