  Recently, there has been great interest in learning how to best represent
proteins, specifically with fixed-length embeddings. Deep learning has become a
popular tool for protein representation learning as a model's hidden layers
produce potentially useful vector embeddings. TAPE introduced a number of
benchmark tasks and showed that semi-supervised learning, via pretraining
language models on a large protein corpus, improved performance on downstream
tasks. Two of the tasks (fluorescence prediction and stability prediction)
involve learning fitness landscapes. In this paper, we show that CNN models
trained solely using supervised learning both compete with and sometimes
outperform the best models from TAPE that leverage expensive pretraining on
large protein datasets. These CNN models are sufficiently simple and small that
they can be trained using a Google Colab notebook. We also find for the
fluorescence task that linear regression outperforms our models and the TAPE
models. The benchmarking tasks proposed by TAPE are excellent measures of a
model's ability to predict protein function and should be used going forward.
However, we believe it is important to add baselines from simple models to put
the performance of the semi-supervised models that have been reported so far
into perspective.
