Miguel Sousa

A molecular tool kit for the variable design of logic operations (NOR, INH, EnNOR)

A simple set of five components was used to design molecular logic gates based on phthalimide-sensitised Tb(III) luminescence, including the first report of an enabled NOR (EnNOR) gate.

Amino acid pair- and triplet-wise groupings in the interior of α-helical segments in proteins

A statistical approach has been applied to analyse primary structure patterns at inner positions of α-helices in proteins. A systematic survey was carried out in a recent sample of non-redundant proteins selected from the Protein Data Bank, which were used to analyse α-helix structures for amino acid pairing patterns. Only residues more than three positions apart from both termini of the α-helix were considered as inner. Amino acid pairings i, i+k (k=1, 2, 3, 4, 5), were analysed and the corresponding 20×20 matrices of relative global propensities were constructed. An analysis of (i, i+4, i+8) and (i, i+3, i+4) triplet patterns was also performed. These analysis yielded information on a series of amino acid patterns (pairings and triplets) showing either high or low preference for α-helical motifs and suggested a novel approach to protein alphabet reduction. In addition, it has been shown that the individual amino acid propensities are not enough to define the statistical distribution of these patterns. Global pair propensities also depend on the type of pattern, its composition and orientation in the protein sequence. The data presented should prove useful to obtain and refine useful predictive rules which can further the development and fine-tuning of protein structure prediction algorithms and tools.

Is Internet Industry Facing an IPO Bubble 2.0

During the so called dot-com bubble period in the late 1990s, the question of whether the Internet stock prices could be explained or not by companies’ fundamentals was intensely discussed and studied. Ten years later, the high valuations of recent Internet IPOs, justified mostly on the basis of a growing number of Internet users and less on the basis of the companies’ fundamentals, led many to wonder if the Internet industry is facing a valuation bubble 2.0. This study explores the impact of the major (financial and non-financial) value-drivers, identified during the previous dot-com bubble, on the recent Internet companies’ IPO stock price and whether that impact differs from the one observed in the previous dot-com wave. Additionally, by analyzing more than one non-financial measure at the same time we are able to identify the relative incremental explanatory power on the IPO stock price of those non-financial measures.

Predicting the secondary structure of proteins using machine learning algorithms

The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.

Is There Still a Berlin Wall in the Post-Issue Operating Performance of European IPOs?

This paper studies the post‐IPO operating performance of a sample of 555 European firms that went public between 1995 and 2006. Consistent with previous findings, we observe a decline in post‐issue operating performance of IPO firms. However, firms located in emerging European countries perform even worse after the IPO than firms located in developed European countries. Our results suggest that this less successful post‐issue operating performance by firms located in emerging countries can be explained by a more aggressive use of accruals and a better timing of the IPO in order to coincide with a period of high operating performance.

Prot-SSP: A Tool for Amino Acid Pairing Pattern Analysis in Secondary Structures

It is known that individual amino acids can have a decisive role in the stabilization of a protein structure. Moreover, it is likely that specific amino acid combinations also fulfil structural and stabilizing roles in protein structure. We present Prot-SSP, an analytical Python tool designed to gather and parse sequence and structural data from sets of PDB files and determine amino acid residue pairing propensities and correlations in alpha helices and beta strands, in various secondary structure contexts. This versatile and user-friendly bioinformatic tool has proven useful for the analysis of a selected set of protein structures as shown in an illustrative example.

Predicting the Start of Protein α-Helices Using Machine Learning Algorithms

Proteins are complex structures synthesised by living organisms. They are actually a fundamental type of molecules and can perform a large number of functions in cell biology. Proteins can assume catalytic roles and accelerate or inhibit chemical reactions in our body. They can assume roles of transportation of smaller molecules, storage, movement, mechanical support, immunity and control of cell growth and differentiation [25]. All of these functions rely on the 3D-structure of the protein. The process of going from a linear sequence of amino acids, that together compose a protein, to the protein’s 3D shape is named protein folding. Anfinsen’s work [29] has proven that primary structure determines the way protein folds. Protein folding is so important that whenever it does not occur correctly it may produce diseases such as Alzheimer’s, Bovine Spongiform Encephalopathy (BSE), usually known as mad cows disease, Creutzfeldt-Jakob (CJD) disease, a Amyotrophic Lateral Sclerosis (ALS), Huntingtons syndrome, Parkinson disease, and other diseases related to cancer.