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1

Protein-ligand binding site detection as an alternative route to molecular docking and drug repurposing

Toti D., Macari G., Polticelli F.

Bio-Algorithms and Med-Systems

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2018

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Vol. 14, no. 2

art. no. 20180004

EN

After the onset of the genomic era, the detection of ligand binding sites in proteins has emerged over the last few years as a powerful tool for protein function prediction. Several approaches, both sequence and structure based, have been developed, but the full potential of the corresponding tools has not been exploited yet. Here, we describe the development and classification of a large, almost exhaustive, collection of protein-ligand binding sites to be used, in conjunction with the Ligand Binding Site Recognition Application Web Application developed in our laboratory, as an alternative to virtual screening through molecular docking simulations to identify novel lead compounds for known targets. Ligand binding sites derived from the Protein Data Bank have been clustered according to ligand similarity, and given a known ligand, the binding mode of related ligands to the same target can be predicted. The collection of ligand binding sites contains more than 200,000 sites corresponding to more than 20,000 different ligands. Furthermore, the ligand binding sites of all Food and Drug Administration-approved drugs have been classified as well, allowing to investigate the possible binding of each of them (and related compounds) to a given target for drug repurposing and redesign initiatives. Sample usage cases are also described to demonstrate the effectiveness of this approach.

2

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.

3

An improved structural model of the human iron exporter ferroportin. Insight into the role of pathogenic mutations in hereditary hemochromatosis type 4

Tortosa V., Bonaccorsi di Patti M. C., Brandi V., Musci G., Polticelli F.

Bio-Algorithms and Med-Systems

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2017

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Vol. 13, no. 4

215--222

EN

Ferroportin (Fpn) is a membrane protein representing the major cellular iron exporter, essential for metal translocation from cells into plasma. Despite its pivotal role in human iron homeostasis, many questions on Fpn structure and biology remain unanswered. In this work, we present two novel and more reliable structural models of human Fpn (hFpn; inward-facing and outwardfacing conformations) obtained using as templates the recently solved crystal structures of a bacterial homologue of hFpn, Bdellovibrio bacteriovorus Fpn. In the absence of an experimentally solved structure of hFpn, the structural predictions described here allow to analyze the role of pathogenic mutations in the Fpn-linked hereditary hemochromatosis disease and represent a valuable alternative for reliable structure-based functional studies on this human iron exporter.

4

The human iron exporter ferroportin. Insight into the transport mechanism by molecular modeling

Tortosa V., Bonaccorsi di Patti M. C., Musci G., Polticelli F.

Bio-Algorithms and Med-Systems

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2016

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Vol. 12, no. 1

1--7

EN

Ferroportin, a membrane protein belonging to the major facilitator superfamily of transporters, is the only vertebrate iron exporter known so far. Several ferroportin mutations lead to the so-called ferroportin disease or type 4 hemochromatosis, characterized by two distinct iron accumulation phenotypes depending on whether the mutation affects the activity of the protein or its degradationdługo pathway. Through extensive molecular modeling analyses using the structure of all known major facilitator superfamily members as templates, multiple structural models of ferroportin in the three mechanistically relevant conformations (inward open, occluded, and outward open) have been obtained. The best models, selected on the ground of experimental data available on wild-type and mutant ferroportion, provide for the first time a prediction at the atomic level of the dynamics of the transporter. Based on these results, a possible mechanism for iron export is proposed.

5

Molecular models of human visual pigments: insight into the atomic bases of spectral tuning

Centola F., Polticelli F.

Bio-Algorithms and Med-Systems

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2016

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Vol. 12, no. 3

141--146

EN

The cycle of vision is a chain of biochemical reactions that occur after exposure of the pigments to the light. The known mechanisms of the transduction of the light pulse derive mainly from studies on bovine rhodopsin. The objective of this work is to construct molecular models of human rhodopsin and opsins, for which threedimensional structures are not available, to analyze the retinal environment and identify the similarities and differences that characterize the human visual pigments. One of the main results of this work is the identification of Glu102 as the probable second counterion of the Schiff base in M opsin (green pigments) and L opsin (red pigments). Further, the analysis of the molecular models allows uncovering the molecular bases of the different absorption maxima of M and L opsins with respect to rhodopsin and S opsin. These differences appear to be due to both an increase in the polarity of the retinal environment and specific electrostatic interactions, which determine a reorganization of the electronic distribution of retinal by selectively stabilizing one of the two resonance forms.

6

Automatic protein abbreviations discovery and resolution from full-text scientific papers: the PRAISED framework

Toti D, Atzeni P, Polticelli F

Bio-Algorithms and Med-Systems

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2012

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Vol. 8, no. 1

13--51

EN

This paper describes a methodology for discovering and resolving protein names abbreviations from the full-text versions of scientific articles, implemented in the PRAISED framework with the ultimate purpose of building up a publicly available abbreviation repository. Three processing steps lie at the core of the framework: i) an abbreviation identification phase, carried out via domain-independent metrics, whose purpose is to identify all possible abbreviations within a scientific text; ii) an abbreviation resolution phase, which takes into account a number of syntactical and semantic criteria in order to match an abbreviation with its potential explanation; and iii) a dictionary-based protein name identification, which is meant to select only those abbreviations belonging to the protein science domain. A local copy of the UniProt database is used as a source repository for all the known proteins. The PRAISED implementation has been tested against several known annotated corpora, such as the Medstract Gold Standard Corpus, the AB3P Corpus, the BioText Corpus and the Ao and Takagi Corpus, obtaining significantly high levels of recall and extremely fast performance, while also keeping promising levels of precision and overall f-measure, in comparison to the most relevant similar methods. This comparison has been carried out up to Phase 2, since those methods stop at expanding abbreviations, without performing any entity recognition. Instead, the entity recognition performed in the last phase provides PRAISED with an effective strategy for protein discovery, thus moving further from existing context-free techniques. Furthermore, this implementation also addresses the complexity of full-text papers, instead of the simpler abstracts more generally used. As such, the whole PRAISED process (Phase 1, 2 and 3) has been also tested against a manually annotated subset of full-text papers retrieved from the PubMed repository, with significant results as well.

7

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.

8

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.

9

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.