Abstract
Many cell functions in all living organisms rely on protein-based molecular recognition involving disorder-to-order transitions upon binding by molecular recognition features (MoRFs). A well accepted computational tool for identifying likely protein-protein interactions is sequence alignment. In this paper, we propose the combination of sequence alignment and disorder prediction as a tool to improve the confidence of identifying MoRF-based protein-protein interactions. The method of reverse sequence alignment is also rationalized here as a novel approach for finding additional interaction regions, leading to the concept of a retro-MoRF, which has the reversed sequence of an identified MoRF. The set of retro-MoRF binding partners likely overlap the partner-sets of the originally identified MoRFs. The high abundance of MoRF-containing intrinsically disordered proteins in nature suggests the possibility that the number of retro-MoRFs could likewise be very high. This hypothesis provides new grounds for exploring the mysteries of protein-protein interaction networks at the genome level.
Highlights
All the challenges in biological research may come down to the molecular level and be conquered by various physical and chemical interactions among various bio-molecules, such as proteins, DNA, and RNA
We describe an integrated analysis of the relation between the reversed sequence, sequence alignment, and intrinsic disorder
By combining these three features, we developed a novel protocol for identification of potential protein-protein interaction sites, called retro-MoRFs, which are reversed-sequence molecular recognition features
Summary
All the challenges in biological research may come down to the molecular level and be conquered by various physical and chemical interactions among various bio-molecules, such as proteins, DNA, and RNA. Identifying possible interactions among various bio-molecules is of great importance for current biological science. Traditional experimental methods are both time- and cost-consuming Advanced experimental methods, such as yeast 2 hybrid, fast and efficient, have significant false positive and false negative rates. In spite of these experimental difficulties, developments in bioinformatics have made a great contribution in this area. Techniques based on sequence alignment become the most basic but very powerful tools for identifying structures, functions, and mutual interactions among bio-molecules
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