Final Report Summary - PROTDNABINDSPEC (Inferring DNA binding specificities through in silico folding of natively unstructured protein regions)
The study of protein-DNA binding specificities has important ramifications for the analysis and prediction of the gene regulatory networks that govern several crucial biological processes during cell cycle. Over the past decades, much attention has been paid to the thorough characterisation of individual nucleic acid binding proteins through manifold approaches ranging from genetics to molecular biology and biochemistry. Computational tools are now being explored alone or in combination with high throughput techniques with various degrees of success. Structure-based approaches are particularly promising, as they can predict previously undetected binding sites and open the road to the rational design of novel regulatory molecules.
The final goal of obtaining an unbiased and definite understanding of these important molecular interactions is very challenging. This project primarily aimed at expanding current knowledge through the use of structural bioinformatics methods, focusing on natively unfolded protein regions - flexible segments that do not assume a fixed conformation in the native state, but become ordered upon binding. Recent studies have shown that only specific functional classes are associated with such proteins - including DNA and protein binding, transcription and translational regulation, and cell cycle regulation. Despite the availability of many predictors of disordered regions as a binary feature of proteins, there are no methods that provide information about their ligand types and mode of interaction.
Research activities have encompassed the design, implementation, testing and benchmarking of a method to predict the DNA-bound conformation of disordered protein regions at atomic resolution. This required the initial development of knowledge based pair-wise potentials to model the interaction energy between different amino acids and nucleotides. These statistical potentials have been integrated into FRAGFOLD, one of the first fragment-based approaches for new fold prediction. The accuracy of the resulting methodology is currently under investigation using available experimental reference data and well-established benchmarking techniques.
The final goal of obtaining an unbiased and definite understanding of protein-DNA interactions is very challenging. The results of this project will improve our understanding of the molecular details of these macromolecular interactions, assist the annotation of whole genome sequences, and prioritise experiments aimed at verifying the predicted specificities. On a more general level, this research will further advance knowledge in the field of macromolecular interactions, a central problem in systems biology.
The final goal of obtaining an unbiased and definite understanding of these important molecular interactions is very challenging. This project primarily aimed at expanding current knowledge through the use of structural bioinformatics methods, focusing on natively unfolded protein regions - flexible segments that do not assume a fixed conformation in the native state, but become ordered upon binding. Recent studies have shown that only specific functional classes are associated with such proteins - including DNA and protein binding, transcription and translational regulation, and cell cycle regulation. Despite the availability of many predictors of disordered regions as a binary feature of proteins, there are no methods that provide information about their ligand types and mode of interaction.
Research activities have encompassed the design, implementation, testing and benchmarking of a method to predict the DNA-bound conformation of disordered protein regions at atomic resolution. This required the initial development of knowledge based pair-wise potentials to model the interaction energy between different amino acids and nucleotides. These statistical potentials have been integrated into FRAGFOLD, one of the first fragment-based approaches for new fold prediction. The accuracy of the resulting methodology is currently under investigation using available experimental reference data and well-established benchmarking techniques.
The final goal of obtaining an unbiased and definite understanding of protein-DNA interactions is very challenging. The results of this project will improve our understanding of the molecular details of these macromolecular interactions, assist the annotation of whole genome sequences, and prioritise experiments aimed at verifying the predicted specificities. On a more general level, this research will further advance knowledge in the field of macromolecular interactions, a central problem in systems biology.