AIMC Topic: Proteins

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DrugormerDTI: Drug Graphormer for drug-target interaction prediction.

Computers in biology and medicine
Drug-target interactions (DTI) prediction is a crucial task in drug discovery. Existing computational methods accelerate the drug discovery in this respect. However, most of them suffer from low feature representation ability, significantly affecting...

DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes.

Computational biology and chemistry
B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including des...

Deep learning for optimization of protein expression.

Current opinion in biotechnology
Advances in high-throughput DNA synthesis and sequencing have fuelled the use of massively parallel reporter assays for strain characterization. These experiments produce large datasets that map DNA sequences to protein expression levels, and have sp...

Novel machine learning method allerStat identifies statistically significant allergen-specific patterns in protein sequences.

The Journal of biological chemistry
Cutting-edge technologies such as genome editing and synthetic biology allow us to produce novel foods and functional proteins. However, their toxicity and allergenicity must be accurately evaluated. It is known that specific amino acid sequences in ...

Top-down design of protein architectures with reinforcement learning.

Science (New York, N.Y.)
As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approa...

Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit.

Journal of chemical information and modeling
Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein...

PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.

Nature communications
Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a ...

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization.

Current opinion in chemical biology
The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) h...

Bifunctional robots inducing targeted protein degradation.

European journal of medicinal chemistry
The gaining importance of Targeted Protein Degradation (TPD) and PROTACs (PROteolysis-TArgeting Chimeras) have drawn the scientific community's attention. PROTACs are considered bifunctional robots owing to their avidity for the protein of interest (...

Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Journal of chemical information and modeling
Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as...