Predicting the three-dimensional structure of proteins is a long-standing 
challenge of computational biology, as the structure (or lack of a rigid 
structure) is well known to determine a protein's function. Predicting relative 
solvent accessibility (RSA) of amino acids within a protein is a significant 
step towards resolving the protein structure prediction challenge especially in 
cases in which structural information about a protein is not available by 
homology transfer. Today, arguably the core of the most powerful prediction 
methods for predicting RSA and other structural features of proteins is some 
form of deep learning, and all the state-of-the-art protein structure prediction 
tools rely on some machine learning algorithm. In this article we present a deep 
neural network architecture composed of stacks of bidirectional recurrent neural 
networks and convolutional layers which is capable of mining information from 
long-range interactions within a protein sequence and apply it to the prediction 
of protein RSA using a novel encoding method that we shall call "clipped". The 
final system we present, PaleAle 5.0, which is available as a public server, 
predicts RSA into two, three and four classes at an accuracy exceeding 80% in 
two classes, surpassing the performances of all the other predictors we have 
benchmarked.