AIMC Topic: Proteins

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PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Journal of computational chemistry
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better te...

Predicting residues involved in anti-DNA autoantibodies with limited neural networks.

Medical & biological engineering & computing
Computer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for ...

Protein p Prediction by Tree-Based Machine Learning.

Journal of chemical theory and computation
Protonation states of ionizable protein residues modulate many essential biological processes. For correct modeling and understanding of these processes, it is crucial to accurately determine their p values. Here, we present four tree-based machine l...

EnsembleFam: towards more accurate protein family prediction in the twilight zone.

BMC bioinformatics
BACKGROUND: Current protein family modeling methods like profile Hidden Markov Model (pHMM), k-mer based methods, and deep learning-based methods do not provide very accurate protein function prediction for proteins in the twilight zone, due to low s...

PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information.

PeerJ
Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based o...

Large-scale design and refinement of stable proteins using sequence-only models.

PloS one
Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be ...

EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Journal of molecular biology
Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tools for estimating rates take as input a large set of homologous proteins, a probabilistic model of ev...

Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) Based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes.

Molecular informatics
Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. I...

BioS2Net: Holistic Structural and Sequential Analysis of Biomolecules Using a Deep Neural Network.

International journal of molecular sciences
BACKGROUND: For decades, the rate of solving new biomolecular structures has been exceeding that at which their manual classification and feature characterisation can be carried out efficiently. Therefore, a new comprehensive and holistic tool for th...

Accurate positioning of functional residues with robotics-inspired computational protein design.

Proceedings of the National Academy of Sciences of the United States of America
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geom...