AIMC Topic: Proteins

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PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability.

International journal of molecular sciences
Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful f...

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.

eLife
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allo...

Evaluation of a Self-Supervised Machine Learning Method for Screening of Particulate Samples: A Case Study in Liquid Formulations.

Journal of pharmaceutical sciences
Imaging is commonly used as a characterization method in the pharmaceuticals industry, including for quantifying subvisible particles in solid and liquid formulations. Extracting information beyond particle size, such as classifying morphological sub...

DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model.

IEEE/ACM transactions on computational biology and bioinformatics
Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies ...

ASFold-DNN: Protein Fold Recognition Based on Evolutionary Features With Variable Parameters Using Full Connected Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Protein fold recognition contribute to comprehend the function of proteins, which is of great help to the gene therapy of diseases and the development of new drugs. Researchers have been working in this direction and have made considerable achievemen...

A Semi-Supervised Autoencoder-Based Approach for Protein Function Prediction.

IEEE journal of biomedical and health informatics
After the development of next-generation sequencing techniques, protein sequences are abundantly available. Determining the functional characteristics of these proteins is costly and time-consuming. The gap between the number of protein sequences and...

Single-sequence protein structure prediction using a language model and deep learning.

Nature biotechnology
AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain...

Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features.

Computational and mathematical methods in medicine
Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it ...

Drug-target binding affinity prediction method based on a deep graph neural network.

Mathematical biosciences and engineering : MBE
The development of new drugs is a long and costly process, Computer-aided drug design reduces development costs while computationally shortening the new drug development cycle, in which DTA (Drug-Target binding Affinity) prediction is a key step to s...

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods.

Journal of chemical information and modeling
We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for red...