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In silico study of the structure and function of Streptococcus mutans plasmidic proteins

Caprari S., Minervini G., Brandi V., Polticelli F.

Bio-Algorithms and Med-Systems

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2017

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Vol. 13, no. 2

51--61

EN

The Gram-positive bacterium Streptococcus mutans is the principal causative agent of human tooth decay, an oral disease that affects the majority of the world’s population. Although the complete S. mutans genome is known, approximately 700 proteins are still annotated as hypothetical proteins, as no threedimensional structure or homology with known proteins exists for them. Thus, the significant portion of genomic sequences coding for unknown-function proteins makes the knowledge of pathogenicity and survival mechanisms of S. mutans still incomplete. Plasmids are found in virtually every species of Streptococcus, and some of these mediate resistance to antibiotics and pathogenesis. However, there are strains of S. mutans that contain plasmids, such as LM7 and UA140, to which no function has been assigned yet. In this work, we describe an in silico study of the structure and function of all the S. mutans proteins encoded by pLM7 and pUA140 plasmids to gain insight into their biological function. A combination of different structural bioinformatics methodologies led to the identification of plasmidic proteins potentially required for the bacterial survival and pathogenicity. The structural information obtained on these proteins can be used to select novel targets for the design of innovative therapeutic agents towards S. mutans.

2

Ab initio protein structure prediction – the hydrophobicity distribution analysis

Evangelista G., Minervini G., Polticelli F, Malawski M., Szepieniec T., Flis Ł., Prymula K., Kochańczyk M., Matczyńska E., Wiśniowski Z., Salapa K., Banach M., Roterman I.

Bio-Algorithms and Med-Systems

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2011

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Vol. 7, no. 2

5--12

EN

The three-dimensional structures generated for 20 “never born proteins” (NBP – random amino acid sequence with no significant homology to existing proteins) using two different techniques: ROSETTA (called R in the paper) and “fuzzy oil drop” model (called S in the paper) were compared to estimate the accordance with the assumed model estimating the influence of an external force field on the final structure of the protein. Selected structures are those corresponding to the highest (10 proteins) and lowest (10 proteins) RMS-D values obtained measuring the similarity between the R and S structures. The R structures generated according to an internal force field (the individual inter-molecular interaction) including solvation effects were analyzed using the “fuzzy oil drop” model as target model. The second applied model “fuzzy oil drop” generated structures characterized by an ordered hydrophobic core structure. 13 of the 20 selected S structures appeared to be accordant with the “fuzzy oil drop” model while 6 out of the 20 structures appeared to be accordant with external force field for R structures which suggests a general interpretation of the influence of an external force field on the folding simulation.

3

RandomBlast a tool to generate random “never born protein” sequences

Evangelista G., Minervini G., Luisi P. L., Polticelli F

Bio-Algorithms and Med-Systems

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2007

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Vol. 3, no. 5

27--31

EN

In an accompanying paper by Minervini et al., we deal with the scientific problem of studying the sequence to structure relationships in “never born proteins” (NBPs), i.e. protein sequences which have never been observed in nature. The study of the structural and functional properties of "never born proteins" requires the generation of a large library of protein sequences characterized by the absence of any significant similarity with all the known protein sequences. In this paper we describe the implementation of a simple command-line software utility used to generate random amino acid sequences and to filter them against the NCBI non redundant protein database, using as a threshold the value of the Evalue parameter returned by the well known sequence comparison software Blast. This utility, named RandomBlast, has been written using C programming language for Windows operating systems. The structural implications of NBPs random amino acid composition are discussed as compared to natural proteins of comparable length.

4

High throughput protein structure prediction in a GRID environment

Minervini G., La Rocca G., Luisi P. L., Polticelli F

Bio-Algorithms and Med-Systems

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2007

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Vol. 3, no. 5

39--43

EN

The number of known natural protein sequences, though quite large, is infinitely small as compared to the number of proteins theoretically possible with the twenty natural amino acids. Thus, there exists a huge number of protein sequences which have never been observed in nature, the so called “never born proteins”. The study of the structural and functional properties of "never born proteins" represents a way to improve our knowledge on the fundamental properties that make existing protein sequences so unique. Furthermore it is of great interest to understand if the extant proteins are only the result of contingency or else the result of a selection process based on the peculiar physico-chemical properties of their protein sequence. Protein structure prediction tools combined with the use of large computing resources allow to tackle this problem. In fact, the study of never born proteins requires the generation of a large library of protein sequences not present in nature and the prediction of their three-dimensional structure. This is not trivial when facing 105-107 protein sequences. Indeed, on a single CPU it would require years to predict the structure of such a large library of protein sequences. On the other hand, this is an embarassingly parallel problem in which the same computation (i.e. the prediction of the three-dimensional structure of a protein sequence) must be repeated several times (i.e. on a large number of protein sequences). The use of grid infrastructures makes feasible to approach this problem in an acceptable time frame. In this paper we describe the set up of a simulation environment within the EUChinaGRID infrastructure that allows user friendly exploitation of grid resources for largescale protein structure prediction.