AI Medical Compendium Topic:
Amino Acid Sequence

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Sequence-Based Prediction of Fuzzy Protein Interactions.

Journal of molecular biology
It is becoming increasingly recognised that disordered proteins may be fuzzy, in that they can exhibit a wide variety of binding modes. In addition to the well-known process of folding upon binding (disorder-to-order transition), many examples are em...

Exploration and Evaluation of Machine Learning-Based Models for Predicting Enzymatic Reactions.

Journal of chemical information and modeling
Unannotated gene sequences in databases are increasing due to sequencing advances. Therefore, computational methods to predict functions of unannotated genes are needed. Moreover, novel enzyme discovery for metabolic engineering applications further ...

Computational Identification and Analysis of Ubiquinone-Binding Proteins.

Cells
Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will...

A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.

Molecular informatics
Drug-Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and understanding the drug side effects which helps to identify new therapeutic profiles for various diseases. However, the exponential growth in the genomic and...

Prediction of Contact Residues in Anti-HIV Neutralizing Antibody by Deep Learning.

Japanese journal of infectious diseases
The monoclonal antibody 1C10 targets the V3 loop of HIV-1 and neutralizes a broad range of clade B viruses. However, the mode of interaction between 1C10 and the V3 loop remains unclear because crystallization of 1C10 and the V3 peptide was unsuccess...

Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.

Biochimica et biophysica acta. General subjects
Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding...

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

Scientific reports
Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoprot...

Improved protein structure prediction using potentials from deep learning.

Nature
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence. This problem is of fundamental importance as the structure of a protein largely determines its function; however, protein str...

Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-Protein Interactions (PPIs) play an irreplaceable role in biological activities of organisms. Although many high-throughput methods are used to identify PPIs from different kinds of organisms, they have some shortcomings, such as high cost an...

DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment.

BMC bioinformatics
BACKGROUND: Recently developed methods of protein contact prediction, a crucially important step for protein structure prediction, depend heavily on deep neural networks (DNNs) and multiple sequence alignments (MSAs) of target proteins. Protein seque...