AIMC Topic: Proteins

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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...

Graph kernels combined with the neural network on protein classification.

Journal of bioinformatics and computational biology
At present, most of the researches on protein classification are based on graph kernels. The essence of graph kernels is to extract the substructure and use the similarity of substructures as the kernel values. In this paper, we propose a novel graph...

Protein model quality assessment using 3D oriented convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein model quality assessment (QA) is a crucial and yet open problem in structural bioinformatics. The current best methods for single-model QA typically combine results from different approaches, each based on different input features...

DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability.

refineD: improved protein structure refinement using machine learning based restrained relaxation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraint...

Machine-learning techniques for the prediction of protein-protein interactions.

Journal of biosciences
Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental tec...

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...

Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion o...

Capsule network for protein post-translational modification site prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Computational methods for protein post-translational modification (PTM) site prediction provide a useful approach for studying protein functions. The prediction accuracy of the existing methods has significant room for improvement. A rece...

ConDo: protein domain boundary prediction using coevolutionary information.

Bioinformatics (Oxford, England)
MOTIVATION: Domain boundary prediction is one of the most important problems in the study of protein structure and function. Many sequence-based domain boundary prediction methods are either template-based or machine learning (ML) based. ML-based met...