AIMC Topic: Proteins

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Predicting protein-peptide interaction sites using distant protein complexes as structural templates.

Scientific reports
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details...

Electrochemical protein recognition based on macromolecular self-assembly of molecularly imprinted polymer: a new strategy to mimic antibody for label-free biosensing.

Journal of materials chemistry. B
A versatile strategy, based on the use of an amphiphilic copolymer as a macromonomer, was developed for the preparation of a fully synthetic MIP (molecularly imprinted polymer) sensor for protein recognition. A UV-crosslinkable copolymer poly(DMA-co-...

DESTINI: A deep-learning approach to contact-driven protein structure prediction.

Scientific reports
The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein's sequence is one of most challenging problems in computational biology. In this work, we introduce DEST...

Predicting protein-ligand binding residues with deep convolutional neural networks.

BMC bioinformatics
BACKGROUND: Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categ...

DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks.

Journal of chemical information and modeling
Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural network-based method, fo...

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

Journal of computational chemistry
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets...

Identification of coenzyme-binding proteins with machine learning algorithms.

Computational biology and chemistry
The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on th...

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Journal of chemical information and modeling
Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds...

Prediction of TF-Binding Site by Inclusion of Higher Order Position Dependencies.

IEEE/ACM transactions on computational biology and bioinformatics
Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to extract higher order dependencies. In this work, we first propose a novel method to extract higher or...

DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

PloS one
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving t...