Proteins are macromolecules involved in the biological functions. Protein-protein interactions that occur between two or more proteins are called protein complexes. These complexes are of vital importance as they play a key role in most biological processes, such as biosynthesis, cellular regulation and signal translation. For this reason, the researchers try to understand these protein complexes in their three-dimensional structure, through data that are generated from methods such as X-ray crystallography or nuclear magnetic resonance. Because the tertiary structure is unique to each protein it is also directly responsible for its biological properties. These data are used for the prediction of protein-protein interactions. The prediction seeks by means of computational tools, to identify and to categorize the type of interaction that occurs in a given protein complex. This through the use of different features of the complex. In this study we try to determine if using only the distance between amino acids belonging to the interaction zone in protein complexes influences whether the interaction is classified as transient or permanent (duration time of interaction). For this we use a set of protein complexes already classified as transient or permanent, in addition to its three-dimensional structure. The distance measures used as a feature, are selected considering the energy produced in the interaction zone and the structure of the amino acids involved and their position. A 75% accuracy was obtained, using distance as a discriminant factor in the classification of protein complexes. This suggests that, there is a relation between the distance as a discriminating factor to classify these complexes as transient or permanent.
Protein-protein interactions (PPIs) are known for its important role in diverse biological processes. One of the crucial issues to understand and classify PPI is to characterize their interfaces in order to discriminate between transient and permanent complexes. The stability of protein-protein interactions depends on the energetic features of interaction surfaces. This work explores the surfaces of complex interaction classified as permanent and transient, in order to find those energetic features that can differentiate between both type of complexes. We claim that the number of energetic features and their contribution to the interactions can be key factors to predict between transient and permanent interactions. Moreover, the features used can be adjusted according to the size of the complex studied. We evaluate different classifiers to predict these interactions, using a set of 298 complexes extracted from databases of protein complexes -in terms of their known three-dimensional structure-, and which were already classified as transient or permanent. As a result, we obtained an improved accuracy up to 86.6% when using SVM with kernel linear.
In this paper we outline important differences between (1) protein interaction networks and (2) social and other complex networks, in terms of fine-grained network community profiles. While these families of networks present some general similarities, they also have some stark differences in the way the communities are formed. Namely, we find that the sizes of the best communities in such biological networks are an order of magnitude smaller than in social and other complex networks. We furthermore find that the generative model describing biological networks is very different from the model describing social networks. While for latter the Forest-Fire model best approximates their network community profile, for biological networks it is a random rewiring model that generates networks with the observed profiles. Our study suggests that these families of networks should be treated differently when deriving results from network analysis, and a fine-grained analysis is needed to better understand their structure.
DOI : 10.1109/asonam.2014.6921669 Anahtar Kelimeler :
Communities, Social network services, Proteins, Biological system modeling, Clustering algorithms, Conferences, bioinformatics, social networking (online), random rewiring model, network community profile, forest-fire model, generative model, fine-grained network community profiles, complex networks, protein interaction networks, social networks, biological networks