LSTM for binary classification using multiple attributes

I haven't used neural networks for many years, so excuse my ignorance. I was wondering what is the most appropriate way to train a LSTM model based on my dataset. I have 3 attributes as follows:

Attribute 1: small int e.g., [123, 321, ...]

Attribute 2: text sequence ['cgtaatta', 'ggcctaaat', ... ]

Attribute 3: text sequence ['ttga', 'gattcgtt', ... ]

Class label: binary [0, 1, ...]

The length of each sample's attributes (2 or 3) is arbitrary; therefore I do not want to use them as words rather as sequences (that's why I want to use RNN/LSTM models).

Is it possible to have more than one (sequence) inputs to the LSTM model (are there examples)? Or should I concatenate them into one e.g., input 1: [123 cgtaatta ttga, 0]