Analysis of time course Omics datasets.

Over the past 20 years, Omics technologies emerged as the consensual denomination of holistic molecular profiling. These techniques enable parallel measurements of biological -omes, or "all constituents considered collectively", and utilize the latest advancements in transcriptomics, proteomics, metabolomics, imaging, and bioinformatics. The technological accomplishments in increasing the sensitivity and throughput of the analytical devices, the standardization of the protocols and the widespread availability of reagents made the capturing of static molecular portraits of biological systems a routine task. The next generation of time course molecular profiling already allows for extensive molecular snapshots to be taken along the trajectory of time evolution of the investigated biological systems. Such datasets provide the basis for application of the inverse scientific approach. It consists in the inference of scientific hypotheses and theories about the structure and dynamics of the investigated biological system without any a priori knowledge, solely relying on data analysis to unveil the underlying patterns. However, most temporal Omics data still contain a limited number of time points, taken over arbitrary time intervals, through measurements on biological processes shifted in time. The analysis of the resulting short and noisy time series data sets is a challenge. Traditional statistical methods for the study of static Omics datasets are of limited relevance and new methods are required. This chapter discusses such algorithms which enable the application of the inverse analysis approach to short Omics time series.

Functional peptides by genome reverse engineering.

Computational biology and chemistry combined with high-throughput analytical technologies contribute to reduce operational costs and foster innovation in every phase of the discovery of bioactive molecules. In order for life science industries to continue to deliver at the required market rate, new concepts need to be implemented in research and development, and new sources of bioactive molecules should be investigated. The genomic revolution provides the necessary information to generate novel bioactive peptides by the computational dissection of genomes.

Computational studies of ligand-receptor interactions in bitter taste receptors.

Phenylthiocarbamide tastes intensely bitter to some individuals, but others find it completely tasteless. Recently, it was suggested that phenylthiocarbamide elicits bitter taste by interacting with a human G protein-coupled receptor (hTAS2R38) encoded by the PTC gene. The phenylthiocarbamide nontaster trait was linked to three single nucleotide polymorphisms occurring in the PTC gene. Using the crystal structure of bovine rhodopsin as template, we generated the 3D structure of hTAS2R38 bitter taste receptor. We were able to map on the receptor structure the amino acids affected by the genetic polymorphisms and to propose molecular functions for two of them that explained the emergence of the nontaster trait. We used molecular docking simulations to find that phenylthiocarbamide exhibited a higher affinity for the target receptor than the structurally similar molecule 6-n-propylthiouracil, in line with recent experimental studies. A 3D model was constructed for the hTAS2R16 bitter taste receptor as well, by applying the same protocol. We found that the recently published experimental ligand binding affinity data for this receptor correlated well with the binding scores obtained from our molecular docking calculations.

Comparison of a homology model and the crystallographic structure of human 11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) in a structure-based identification of inhibitors.

Human 11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) catalyzes the interconversion of cortisone into active cortisol. 11betaHSD1 inhibition is a tempting target for the treatment of a host of human disorders that might benefit from blockade of glucocorticoid action, such as obesity, metabolic syndrome, and diabetes type 2. Here, we report an in silico screening study aimed at identifying new selective inhibitors of human 11betaHSD1 enzyme. In the first step, homology modeling was employed to build the 3D structure of 11betaHSD1. Further, molecular docking was used to validate the predicted model by showing that it was able to discriminate between known 11betaHSD1 inhibitors or substrates and non-inhibitors. The homology model was found to reproduce closely the crystal structure that became publicly available in the final stages of this work. Finally, we carried out structure-based virtual screening experiments on both the homology model and the crystallographic structure with a database of 114,000 natural molecules. Among these, 15 molecules were consistently selected as inhibitors based on both the model and crystal structures of the enzyme, implying a good quality for the homology model. Among these putative 11betaHSD1 inhibitors, two were flavonone derivatives that have already been shown to be potent inhibitors of the enzyme.

Global dynamics of biological systems from time-resolved omics experiments.

The emergent properties of biological systems, organized around complex networks of irregularly connected elements, limit the applications of the direct scientific method to their study. The current lack of knowledge opens new perspectives to the inverse scientific paradigm where observations are accumulated and analysed by advanced data-mining techniques to enable a better understanding and the formulation of testable hypotheses about the structure and functioning of these systems. The current technology allows for the wide application of omics analytical methods in the determination of time-resolved molecular profiles of biological samples. Here it is proposed that the theory of dynamical systems could be the natural framework for the proper analysis and interpretation of such experiments. A new method is described, based on the techniques of non-linear time series analysis, which is providing a global view on the dynamics of biological systems probed with time-resolved omics experiments.

Similarity networks of protein binding sites.

An increasing attention has been dedicated to the characterization of complex networks within the protein world. This work is reporting how we uncovered networked structures that reflected the structural similarities among protein binding sites. First, a 211 binding sites dataset has been compiled by removing the redundant proteins in the Protein Ligand Database (PLD) (http://www-mitchell.ch.cam.ac.uk/pld/). Using a clique detection algorithm we have performed all-against-all binding site comparisons among the 211 available ones. Within the set of nodes representing each binding site an edge was added whenever a pair of binding sites had a similarity higher than a threshold value. The generated similarity networks revealed that many nodes had few links and only few were highly connected, but due to the limited data available it was not possible to definitively prove a scale-free architecture. Within the same dataset, the binding site similarity networks were compared with the networks of sequence and fold similarity networks. In the protein world, indications were found that structure is better conserved than sequence, but on its own, sequence was better conserved than the subset of functional residues forming the binding site. Because a binding site is strongly linked with protein function, the identification of protein binding site similarity networks could accelerate the functional annotation of newly identified genes. In view of this we have discussed several potential applications of binding site similarity networks, such as the construction of novel binding site classification databases, as well as the implications for protein molecular design in general and computational chemogenomics in particular.

Global properties of biological networks.

This article discusses the most recent achievements in understanding the biological implications of the small-world and scale-free global topological properties of genetic, proteomic and metabolic networks. Most importantly, these networks are highly clustered and have small node-to-node distances. With their few very connected nodes, which are statistically unlikely to fail under random conditions, the proper functioning of these systems is maintained under external perturbations.

Descriptor-based protein remote homology identification.

Here, we report a novel protein sequence descriptor-based remote homology identification method, able to infer fold relationships without the explicit knowledge of structure. In a first phase, we have individually benchmarked 13 different descriptor types in fold identification experiments in a highly diverse set of protein sequences. The relevant descriptors were related to the fold class membership by using simple similarity measures in the descriptor spaces, such as the cosine angle. Our results revealed that the three best-performing sets of descriptors were the sequence-alignment-based descriptor using PSI-BLAST e-values, the descriptors based on the alignment of secondary structural elements (SSEA), and the descriptors based on the occurrence of PROSITE functional motifs. In a second phase, the three top-performing descriptors were combined to obtain a final method with improved performance, which we named DescFold. Class membership was predicted by Support Vector Machine (SVM) learning. In comparison with the individual PSI-BLAST-based descriptor, the rate of remote homology identification increased from 33.7% to 46.3%. We found out that the composite set of descriptors was able to identify the true remote homolog for nearly every sixth sequence at the 95% confidence level, or some 10% more than a single PSI-BLAST search. We have benchmarked the DescFold method against several other state-of-the-art fold recognition algorithms for the 172 LiveBench-8 targets, and we concluded that it was able to add value to the existing techniques by providing a confident hit for at least 10% of the sequences not identifiable by the previously known methods.

Design of virtual libraries of umami-tasting molecules.

The introduction of molecular tools in food research offers the possibility to the food industry to benefit from the experience gained in the field by pharmaceutical companies. In this work we are showing how in silico virtual screening techniques based on molecular similarity were applied for identifying novel umami-tasting compounds. The results obtained suggest that 5'-ribonucleotides and monosodium glutamate might elicit the fifth basic taste via the same molecular mechanism. New algorithms were developed and used in this work, such as the dimension reduction of data sets by singular value decomposition and the introduction of the correlation dimension as a natural dimension of a chemical space. It is shown that the representations of molecular data sets in chemical spaces possess self-similar properties, characteristic of fractal objects.