  Proteins are the major building blocks of life, and actuators of almost all
chemical and biophysical events in living organisms. Their native structures in
turn enable their biological functions which have a fundamental role in drug
design. This motivates predicting the structure of a protein from its sequence
of amino acids, a fundamental problem in computational biology. In this work,
we demonstrate state-of-the-art protein structure prediction (PSP) results
using embeddings and deep learning models for prediction of backbone atom
distance matrices and torsion angles. We recover 3D coordinates of backbone
atoms and reconstruct full atom protein by optimization. We create a new gold
standard dataset of proteins which is comprehensive and easy to use. Our
dataset consists of amino acid sequences, Q8 secondary structures, position
specific scoring matrices, multiple sequence alignment co-evolutionary
features, backbone atom distance matrices, torsion angles, and 3D coordinates.
We evaluate the quality of our structure prediction by RMSD on the latest
Critical Assessment of Techniques for Protein Structure Prediction (CASP) test
data and demonstrate competitive results with the winning teams and AlphaFold
in CASP13 and supersede the results of the winning teams in CASP12. We make our
data, models, and code publicly available.
