Reservoir Computing (RC) is a highly efficient machine learning algorithm specially suited for processing temporal dataset. RC system extracts features from input by projecting them into a high dimensional space. A major advantage of RC framework is that it only requires the readout layer to be trained which significantly reduces the training cost for complex temporal data. In recent years, memristors have become extremely popular in neuromorphic applications due to their attractive analogy to biological synapses. Alamethicin-doped, synthetic biomembrane can emulate key synaptic functions due to its volatile memristive property which can enable learning and computation. In contrast to its solid-state counterparts, this two-terminal biomolecular memristor features similar structure, switching mechanism, and ionic transport modality as biological synapses while consuming considerably lower power. In this work, we have shown biomolecular memristor-based reservoir system to solve tasks such as classification and time-series analysis in a simulation based environment. Our work may pave the way towards highly energy efficient and biocompatible memristor-based reservoir computing systems capable of handling complex temporal tasks in hardware in the near future.