Multiple protein sequence alignment (MPSA) intend to realize the similarity between multiple protein sequences and increasing accuracy. MPSA turns into a critical bottleneck for large scale protein sequence data sets. It is vital for existing MPSA tools to be kept running in a parallelized design.  Joining MPSA tools with cloud computing will improve the speed and accuracy in case of large scale data sets.  PROBCONS is probabilistic consistency for progressive MPSA based on hidden Markov models.  PROBCONS is an MPSA tool that achieves the maximum expected accuracy, but it has a time-consuming problem. In this paper firstly, the proposed approach is to cluster the large multiple protein sequences into structurally similar protein sequences. This classification is done based on secondary structure, LCS, and amino acids features. Then PROBCONS MPSA tool will be performed in parallel to clusters. The last step is to merge the final PROBCONS of clusters. The proposed algorithm is in the Amazon Elastic Cloud (EC2). The proposed algorithm achieved the highest alignment accuracy. Feature classification understands protein sequence, structure and function, and all these features affect accuracy strongly and reduce the running time of searching to produce the final alignment result.

The perfect alignment between three or more sequences of Protein, RNA or DNA is a very difficult task in bioinformatics. There are many techniques for alignment multiple sequences. Many techniques maximize speed and do not concern with the accuracy of the resulting alignment. Likewise, many techniques maximize accuracy and do not concern with the speed. Reducing memory and execution time requirements and increasing the accuracy of multiple sequence alignment on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the most well-known programs (CLUSTAL-OMEGA, MAFFT, BROBCONS, KALIGN, RETALIGN, and MUSCLE). For programs’ testing and evaluating, benchmark protein datasets are used. Both the execution time and alignment quality are two important metrics. The obtained results show that no single MSA tool can always achieve the best alignment for all datasets.