This paper presents a biology inspired neural learning algorithm called bio-basis function neural network (BBFNN) for analysing protein sequences. The basic principle is to replace radial basis functions of conventional radial basis function neural networks with amino acid similarity measurement matrices. From this, model complexity can be significantly reduced and hence model robustness can be enhanced dramatically. We have applied the algorithm to the prediction of the phosphorylation sites in proteins and the cleavage sites in hepatitis C virus (HCV) polyproteins with success.