Abstract
Computational methods for protein structure prediction allow us to determine a three-dimensional structure of a protein based on its pure amino acid sequence. These methods are a very important alternative to costly and slow experimental methods, like X-ray crystallography or Nuclear Magnetic Resonance. However, conventional calculations of protein structure are time-consuming and require ample computational resources, especially when carried out with the use of ab initio methods that rely on physical forces and interactions between atoms in a protein. Fortunately, at the present stage of the development of computer science, such huge computational resources are available from public cloud providers on a pay-as-you-go basis. We have designed and developed a scalable and extensible system, called Cloud4PSP, which enables predictions of 3D protein structures in the Microsoft Azure commercial cloud. The system makes use of the Warecki-Znamirowski method as a sample procedure for protein structure prediction, and this prediction method was used to test the scalability of the system. The results of the efficiency tests performed proved good acceleration of predictions when scaling the system vertically and horizontally. In the paper, we show the system architecture that allowed us to achieve such good results, the Cloud4PSP processing model, and the results of the scalability tests. At the end of the paper, we try to answer which of the scaling techniques, scaling out or scaling up, is better for solving such computational problems with the use of Cloud computing.
Highlights
Protein structure prediction is one of the most important and yet difficult processes for modern computational biology and structural bioinformatics [21]
The practical role of protein structure prediction is becoming even more important in the face of the dynamically growing number of protein sequences obtained through the translation of DNA sequences coming from large-scale sequencing projects
Computational procedures that allow for the determination of a protein structure from its amino acid sequence are great alternatives for experimental methods for protein structure determination, like X-ray crystallography and Nuclear Magnetic Resonance (NMR)
Summary
Protein structure prediction is one of the most important and yet difficult processes for modern computational biology and structural bioinformatics [21]. Experimental methods for the determination of protein structures, such as X-ray crystallography or Nuclear Magnetic Resonance (NMR), are lagging behind the number of protein sequences. Computational procedures that allow for the determination of a protein structure from its amino acid sequence are great alternatives for experimental methods for protein structure determination, like X-ray crystallography and NMR. Protein structure prediction refers to the computational procedure that delivers a three-dimensional structure of a protein based on its amino acid sequence (Fig. 1). There are various approaches to the problem and many algorithms have been developed These methods generally fall into two groups: (1) physical and (2) comparative [35, 72]. Most of them try to reproduce nature’s algorithm and implement it as a computational procedure in order to give proteins their unique 3D native conformations [43].
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