Title: Dihedral angle prediction using generative adversarial networks,
Abstract: Several dihedral angles prediction methods were developed for protein
structure prediction and their other applications. However, distribution of
predicted angles would not be similar to that of real angles. To address this
we employed generative adversarial networks (GAN). Generative adversarial
networks are composed of two adversarially trained networks: a discriminator
and a generator. A discriminator distinguishes samples from a dataset and
generated samples while a generator generates realistic samples. Although the
discriminator of GANs is trained to estimate density, GAN model is intractable.
On the other hand, noise-contrastive estimation (NCE) was introduced to
estimate a normalization constant of an unnormalized statistical model and thus
the density function. In this thesis, we introduce noise-contrastive estimation
generative adversarial networks (NCE-GAN) which enables explicit density
estimation of a GAN model. And a new loss for the generator is proposed. We
also propose residue-wise variants of auxiliary classifier GAN (AC-GAN) and
Semi-supervised GAN to handle sequence information in a window. In our
experiment, the conditional generative adversarial network (C-GAN), AC-GAN and
Semi-supervised GAN were compared. And experiments done with improved
conditions were invested. We identified a phenomenon of AC-GAN that
distribution of its predicted angles is composed of unusual clusters. The
distribution of the predicted angles of Semi-supervised GAN was most similar to
the Ramachandran plot. We found that adding the output of the NCE as an
additional input of the discriminator is helpful to stabilize the training of
the GANs and to capture the detailed structures. Adding regression loss and
using predicted angles by regression loss only model could improve the
conditional generation performance of the C-GAN and AC-GAN.