The executable tool of DeepRIN is available for download at. The yellow reference planes of the two peptides occupy the same space, and therefore since the phi values are different the orange planes are in different locations. The peptides diverge at the with the carbonyl groups of Leu47 being in different locations. Compared to the recently released state-of-the-art tool, SPIDER3, DeepRIN reduced the Psi angle prediction error by more than 5 degrees and the Phi angle prediction error by more than 2 degrees on average. The peptides were overlaid so that the Tyr and Ile of the two peptides overlay each other. Extensive experimental results show that DeepRIN outperformed the best existing tools significantly. The architecture of DeepRIN enables effective encoding of local and global interatcions between amino acids in a protein sequence to achieve accruacte prediction. Uses a backbone-dependent rotamer library based on kernel density estimates to provide rotamer frequencies and torsional angles, a tree decomposition algorithm to solve the side chain packing problem, specific potentials (anisotropic hydrogen-bonding, soft pairwise van der Waals), and fast collision detection. DeepRIN is designed based on inception networks and residual networks that have performed well on image classification and text recognition. The input to DeepRIN is a feature matrix representing a composition of physico-chemical properties of amino acids, a 20-dimensional position-specific substitution matrix (PSSM) generated by PSI-BLAST, a 30-dimensional hidden Markov Model sequence profile generated by HHBlits, and predicted eight-state secondary structure features. In this paper, a new deep residual inception network architecture, called DeepRIN, is proposed for the prediction of Psi-Phi angles. ![]() Existing methods for Psi-Phi angle prediction have significant room for improvement. ![]() Prediction of protein backbone torsion angles (Psi and Phi) can provide important information for protein structure prediction and sequence alignment.
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