AIMC Topic: Proteins

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ProtPlat: an efficient pre-training platform for protein classification based on FastText.

BMC bioinformatics
BACKGROUND: For the past decades, benefitting from the rapid growth of protein sequence data in public databases, a lot of machine learning methods have been developed to predict physicochemical properties or functions of proteins using amino acid se...

Machine learning modeling of family wide enzyme-substrate specificity screens.

PLoS computational biology
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their nat...

A backbone-centred energy function of neural networks for protein design.

Nature
A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type...

Machine learning based predictive model for the analysis of sequence activity relationships using protein spectra and protein descriptors.

Journal of biomedical informatics
Accurately establishing the connection between a protein sequence and its function remains a focal point within the field of protein engineering, especially in the context of predicting the effects of mutations. From this, there has been a continued ...

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.

ACS biomaterials science & engineering
Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino ac...

De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.

Journal of chemical information and modeling
Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeuti...

Has DeepMind's AlphaFold solved the protein folding problem?

BioTechniques
DeepMind released AlphaFold 2.0 in 2020, an artificial intelligence model to predict the structure of proteins, which could mean that proteins can be characterized without the need for tedious and costly lab analysis.

SPP-CPI: Predicting Compound-Protein Interactions Based On Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying interactions between compound and protein is a substantial part of the drug discovery process. Accurate prediction of interaction relationships can greatly reduce the time of drug development. The uniqueness of our method lies in three as...

Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and...

Predicting Local Protein 3D Structures Using Clustering Deep Recurrent Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Since protein 3D structure prediction is very important for biochemical study and drug design, researchers have developed many machine learning algorithms to predict protein 3D structures using the sequence information only. Understanding the sequenc...