---
title: "Combining Machine Learning and Optimization Techniques to Determine 3-D Structures of Polypeptides"
authors:
  - "Márcio Dorn"
  - "Luciana S. Buriol"
  - "Luís C. Lamb"
doi: "10.5591/978-1-57735-516-8/IJCAI11-469"
url: "https://ijcai.org/proceedings/2011/469"
pdf: "https://ijcai.org/proceedings/2011/0469.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "2794-2794"
track: ""
keywords:
---

# Combining Machine Learning and Optimization Techniques to Determine 3-D Structures of Polypeptides

Márcio Dorn, Luciana S. Buriol, Luís C. Lamb

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 2794-2794. DOI: [10.5591/978-1-57735-516-8/IJCAI11-469](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-469)

## Abstract

One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.