Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Machine Learning for Modelling Tissue Distribution of Drugs and the Impact of Transporters

The ability to predict human pharmacokinetics in early stages of drug development is of paramount importance to prevent late stage attrition as well as in managing toxicity. This thesis explores the machine learning modelling of one of the main pharmacokinetics parameters that determines the therapeutic success of a drug - volume of distribution. In order to do so, a variety of physiological phenomena with known mechanisms of impact on drug distribution were considered as input features during the modelling of volume of distribution namely, Solute Carriers-mediated uptake and ATP-binding Cassette-mediated efflux, drug-induced phospholipidosis and plasma protein binding. These were paired with molecular descriptors to provide both chemical and biological information to the building of the predictive models. Since biological data used as input is limited, prior to modelling volume of distribution, the various types of physiological descriptors were also modelled. Here, a focus was placed on harnessing the information contained in correlations within the two transporter families, which was done by using multi-label classification. The application of such approach to transporter data is very recent and its use to model Solute Carriers data, for example, is reported here for the first time. On both transporter families, there was evidence that accounting for correlations between transporters offers useful information that is not portrayed by molecular descriptors. This effort also allowed uncovering new potential links between members of the Solute Carriers family, which are not obvious from a purely physiological standpoint. The models created for the different physiological parameters were then used to predict these parameters and fill in the gaps in the available experimental data, and the resulting merging of experimental and predicted data was used to model volume of distribution. This exercise improved the accuracy of volume of distribution models, and the generated models incorporated a wide variety of the different physiological descriptors supplied along with molecular features. The use of most of these physiological descriptors in the modelling of distribution is unprecedented, which is one of the main novelty points of this thesis. Additionally, as a parallel complementary work, a new method to characterize the predictive reliability of machine learning classification model was proposed, and an in depth analysis of mispredictions, their trends and causes was carried out, using one of the transporter models as example. This is an important complement to the main body of work in this thesis, as predictive performance is necessarily tied to prediction reliability

PURIFICATION AND CHARACTERIZATION OF BcsC; AN INTEGRAL COMPONENT OF BACTERIAL CELLULOSE EXPORT

Biofilms are a growing concern in the medical field due to their increased resistance to antibiotics. When found in a biofilm, bacteria can have antibiotic resistance 10-1000 times that of their planktonic counterparts. Therefore, it is important to study the formation of biofilms. Cellulose biofilms are formed by Enterobacteriaceae, such as many Escherichia coli and Salmonella spp. strains. Biofilms provide these species with benefits including antimicrobial protection, development of bacterial communities, promotion of DNA exchange, uptake of nutrients, and, in the case of cellulose biofilms, immune system evasion. Cellulose biofilms are controlled by the Bacterial cellulose synthesis (Bcs) complex located at the cell membrane of bacteria able to form cellulose-based biofilms. Proteins, BcsA and BcsB, have been characterized for cellulose synthesis, however, cellulose export has yet to be described. BcsC is believed to play a role in this export process due to its homology to other polysaccharide export proteins in the alginate and poly β-1,6-GlcNAc (PGA) systems. Herein, a series of bioinformatics analysis was performed that supported the hypothesis that BcsC consists of an outer membrane β-barrel connected to a periplasmic tetratricopeptide repeat (TPR) region and that these two regions play different roles in the export process. To begin addressing this hypothesis, the research focused on the structure-function characterization of these regions of BcsC. While practical quantities of the β-barrel region could be purified, this region proved to be recalcitrant to folding into its native state following purification. However, high yields of all TPR constructs were obtained and subjected to further analyses. Circular dichroism studies confirmed our bioinformatics analyses that the secondary structure of the TPR constructs have a predominantly α-helical content. This technique also provided preliminary evidence that there are structural changes upon binding of the TPR to soluble carboxymethyl cellulose (CMC). Intrinsic fluorescence spectroscopy quenching results further demonstrated that the TPR region has a single binding site along with high KD values (ex. 416 µM for the longest construct) for carboxymethyl cellulose. These results were further confirmed with an Avicel insoluble substrate binding assay which also demonstrated that binding of the TPR to cellulose occurred across a biologically relevant pH range (pH 6-8) and that the majority of the binding may be due to an N-terminal portion of the TPR region (amino acids 24-342). Thus, this collective evidence supports that the TPR region of BcsC plays an integral role in the transport of cellulose polymers across the bacterial cell wall into the external environment where biofilm formation can occur. Future studies regarding BcsC would benefit in investigating potential protein-protein interactions with periplasmic proteins, such as BcsG, as well as profiling the β-barrel domain

IN SILICO SCREENING OF TASTE RECEPTORS: AN INTEGRATE MODELING APPROACH.

Taste is one of the five senses and accounts for the sensory impression of food or other substances on the tongue. It represents an innate mechanism of defence by which humans and animals detect safety or threat in food. Notably, taste is a whole-body experience since taste receptors, besides being located in the taste buds, are also found in non-sensory tissues, like the gut or the airways, playing still not completely known roles, for example, in glucose metabolism as well as in energy homeostasis. This clearly lays the groundwork for scientific investigations aimed to develop chemical tools through which modulate these physiopathological mechanisms. Although both GPCR and Ion Channels mediate these processes, this Thesis focuses on the latter class, so far less explored than the former one, involving four members of the Transient Potential Receptors family, namely TRPM8, TRPM5, TRPV1 and TRPV4. Although if each study presented its own objectives, peculiarities and relative computational approaches, a common path can be traced for all of them. First, the three-dimensional structure was generated by homology modelling techniques, by exploiting a well validated fragmental approach, then the obtained homology model was tested by docking calculations, which while including preliminary correlative studies, were always aimed at developing reliable strategies for virtual screening campaigns. The here reported results provide further remarkable confirmations for the reliability of the already modelled (and exploited) TRPM8 model, while the here generated TRPM5 and TRPV4 models afford results (despite obtained in a validating preliminary phase) in line with those of TRPM8 further emphasizing the reliability of the fragmental approach. Not to mention that the described targeted strategy to model TRPV1 suggests that previously generated homology models can be then exploited to assist the modeling of highly homologous proteins still obtaining encouraging results but with a significant saving of the required computational efforts. Finally, the here proposed TRPM8 results offer a convincing proof of the potential improvements that may be obtained combining ligand-based and structure-based approaches in a virtual screening analysis

Multiscale Simulations of Biological Membranes : The Challenge To Understand Biological Phenomena in a Living Substance

Biological membranes are tricky to investigate. They are complex in terms of molecular composition and structure, functional over a wide range of time scales, and characterized by nonequilibrium conditions. Because of all of these features, simulations are a great technique to study biomembrane behavior. A significant part of the functional processes in biological membranes takes place at the molecular level; thus computer simulations are the method of choice to explore how their properties emerge from specific molecular features and how the interplay among the numerous molecules gives rise to function over spatial and time scales larger than the molecular ones. In this review, we focus on this broad theme. We discuss the current state-of-the-art of biomembrane simulations that, until now, have largely focused on a rather narrow picture of the complexity of the membranes. Given this, we also discuss the challenges that we should unravel in the foreseeable future. Numerous features such as the actin-cytoskeleton network, the glycocalyx network, and nonequilibrium transport under ATP-driven conditions have so far received very little attention; however, the potential of simulations to solve them would be exceptionally high. A major milestone for this research would be that one day we could say that computer simulations genuinely research biological membranes, not just lipid bilayers.Peer reviewe

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

IN SILICO APPROACHES IN DRUG DESIGN AND DEVELOPMENT: APPLICATIONS TO RATIONAL LIGAND DESIGN AND METABOLISM PREDICTION

In the last decades, the applications of computational methods in medicinal chemistry have experienced significant changes which have incredibly expanded their approaches, and more importantly their objectives. The overall aim of the present research project is to explore the different fields of the modelling studies by using well-known computational methods as well as different and innovative techniques. Indeed, computational methods traditionally consisted in ligand-based and the structure-based approaches substantially aimed at optimizing the ligand structure in terms of affinity, potency and selectivity. The studies concerning the muscarinic receptors in the present thesis applied these approaches for the rational design of novel improved bioactive molecules, interacting both in the orthosteric (e.g., 1,4-dioxane agonist) and in the allosteric sites. The research includes also the application of a novel method for target optimization, which consists in the generation of the so-called conformational chimeras to explore the flexibility of the modelled GPCR structures. In parallel, computational methods are finding successful applications in the research phase which precedes the ligand design and which is focused on a detailed validation and characterization of the biological target. A proper example of this kind of studies is given by the study regarding the purinergic receptors, which is aimed at the identification and characterization of potential allosteric binding pockets for the already reported inhibitors, exploiting also innovative approaches for binding site predictions (e.g., PELE, SPILLO-PBSS). Over time, computational applications felt a rich extension of their objectives and one of the clearest examples is represented by the ever increasing attempts to optimize the ADME/Tox profile of the novel compounds, so reducing the marked attrition in drug discovery caused by unsuitable pharmacokinetic profiles. Coherently, the first and main project of the present thesis regards the field of metabolism prediction and is founded on the meta-analysis and the corresponding database called MetaSar, manually collected from the recent specialized literature. This ongoing extended project includes different studies which are overall aimed at developing a comprehensive method for metabolism prediction. In detail, this Thesis reports an interesting application of the database which exploits an innovative predictive technique, the Proteochemometric modelling (PCM). This approach is indeed at the forefront of the latest modelling techniques, as it perfectly fits the growing request of new solutions to deal with the incredibly huge amount of data recently produced by the \u201comics\u201d disciplines. In this context, MetaSar represents an alternative and still appropriate source of data for PCM studies, which also enables the extension of its fields of application to a new avenue, such as the prediction of metabolism biotransformation. In the present thesis, we present the first example of these applications, which involves the building of a classification model for the prediction of the glucuronidation reaction. The field of glucuronidation reactions is exhaustively explored also through an homology modelling study aimed at defining the complete three-dimensional structure of the enzyme UGT2B7, the main isoform of glucuronidation enzymes in humans, in complex with the cofactor UDPGA and a typical substrate, such as Naproxen. The paths of the substrate entering to the binding site and the egress of the product have been investigated by performing Steered Molecular Dynamics (SMD) simulations, which were also useful to gain deeper insights regarding the full mechanism of action and the movements of the cofactor

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes