3D domain swapping is a protein structural phenomenon that evolved as a mechanism for oligomeric assembly. Several experimental and computational studies are performed earlier to understand various aspects of domain swapping. 3DSwap-Pred use a RandomForest based algorithm for the prediction. 3D domain swapping is a protein structural phenomenon that mediates the formation of the higher oligomers in a variety of proteins with different structural and functional properties. This phenomenon plays important role in mediating functions ranging from oligomerization to pathological conformational diseases. 3D swapping can be observed only when a protein structure is solved in the swapped conformation in the oligomeric state. This is a limiting step to understand this important structural phenomenon in a large scale. 3Dswap-pred algorithm is designed to classify a given protein sequence as "swapping" or "non-swapping" based on the Random Forest based classifier. We have used literature curated sequences of proteins involved in 3D swapping as positive dataset. Negative data set is derived using a new sequence mining method to improve the accuracy of the algorithm. A set of 126 sequence based features are employed as vectors in the classifier. Using an independent validation dataset of 68 positive sequences and 313 negative sequences, 3DSwap-Pred achieved an accuracy of 63.78 in testing and accuracy of 62.34 during training.

Sequence derived features are used in this study this study can be broadly classified as composition based features, sequence derived fusion features and physico-chemical features derived from AAINDEX database. Composition based features refers to the feature derived from composition of amino acids present in the sequences used in positive and negative datasets. Sequence derived fusion features refers to the new class of hybrid features where two distinct features are combined to define a new, distinct features. Physicochemical properties derived from AAINDEX database is used to compute the properties.

Decision trees provide an effective approach in a tree-like graph of model to predict the probable decision of a system by analysing various associated parameters. The Random Forest classification extends the concept of decision trees and has been successfully employed in developing solutions for a variety of problems in biology. Random Forest is an ensemble decision tree classifier, which incorporates two effective machine learning techniques (bagging and selection of feature from random subspace) in to a single method. Random forest is a collection of decision trees, where each tree is grown using a subset of the possible attributes in the input feature vector. Instead of using all features in all trees, Random Forest randomly selects a subset of features to split at each node when growing a tree and the final decision is derived by combining results from all the trees generated during a simulation. It has been shown that combining multiple decision trees produced in randomly selected subspaces can improve the generalization accuracy. Random forest constructs an ensemble of decision trees from randomly sampled subspaces of the input space, and the final classification is obtained by combining the results from the trees via "voting". The random subspace method is used to avoid overfitting on the training set while preserving the maximum accuracy when training a decision tree classifier. Prediction assessment of the 3Dswap-Pred classifier is measured using the cross-validation using out-of-bag (OOB) samples in the Random Forest. 3DSwap-pred Random Forest algorithm is implemented using the randomForest R package.