Related Experiment Video
Updated: Aug 18, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
A bi-recursive neural network architecture for the prediction of protein coarse contact maps
Alessandro Vullo1, Paolo Frasconi
1Department of Systems and Computer Science, University of Florence, Firenze, Italy, I-50139, Italy. vullo@dsi.unifi.it
Abstract:
Prediction of contact maps may be seen as a strategic step towards the solution of fundamental open problems in structural genomics. In this paper we focus on coarse grained maps that describe the spatial neighborhood relation between secondary structure elements (helices, strands, and coils) of a protein. We introduce a new machine learning approach for scoring candidate contact maps. The method combines a specialized noncausal recursive connectionist architecture and a heuristic graph search algorithm. The network is trained using candidate graphs generated during search. We show how the process of selecting and generating training examples is important for tuning the precision of the predictor.
Related Concept Videos
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-protein Interfaces
Multi-pass Transmembrane Proteins and β-barrels
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as G-protein-linked receptors (GPCRs) and...
Conserved Binding Sites
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Protein Organization
The primary structure of a protein is its amino acid sequence.

