MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens.

Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure).

NL MIND-BEST: a web server for ligands and proteins discovery--theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum.

There are many protein ligands and/or drugs described with very different affinity to a large number of target proteins or receptors. In this work, we selected Ligands or Drug-target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments.

MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Trichomonas gallinae.

Many drugs with very different affinity to a large number of receptors are described. Thus, in this work, we selected drug-target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity for different targets. Quantitative structure-activity relationship (QSAR) models become a very useful tool in this context because they substantially reduce time and resource-consuming experiments.

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.

Machine learning techniques for single nucleotide polymorphism--disease classification models in schizophrenia.

Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need of new genetic or proteomic patterns that can diagnose patients using biological information.

Ontologies of drug discovery and design for neurology, cardiology and oncology.

The complex diseases in the field of Neurology, Cardiology and Oncology have the most important impact on our society. The theoretical methods are fast and they involve some efficient tools aimed at discovering new active drugs specially designed for these diseases. The ontology of all the items that are linked with the molecule metabolism and the treatment of these diseases gives us the possibility to correlate information from different levels and to discover new relationships between complex diseases such as common drug targets and disease patterns.

Drug discovery and design for complex diseases through QSAR computational methods.

There is a need for a study of the complex diseases due to their important impact on our society. One of the solutions involves the theoretical methods which are fast and efficient tools that can lead to the discovery of new active drugs specially designed for these diseases. The Quantitative Structure - Activity Relationship models (QSAR) and the complex network theory become important solutions for screening and designing efficient pharmaceuticals by coding the chemical information of the molecules into molecular descriptors.

Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic.

Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions.

Trypanosoma brucei causes African trypanosomiasis in humans (HAT or African sleeping sickness) and Nagana in cattle. The disease threatens over 60 million people and uncounted numbers of cattle in 36 countries of sub-Saharan Africa and has a devastating impact on human health and the economy. On the other hand, Trypanosoma cruzi is responsible in South America for Chagas disease, which can cause acute illness and death, especially in young children.

Complex network spectral moments for ATCUN motif DNA cleavage: first predictive study on proteins of human pathogen parasites.

The development of methods that can predict the metal-mediated biological activity based only on the 3D structure of metal-unbound proteins has become a goal of major importance. This work is dedicated to the amino terminal Cu(II)- and Ni(II)-binding (ATCUN) motifs that participate in the DNA cleavage and have antitumor activity. We have calculated herein, for the first time, the 3D electrostatic spectral moments for 415 different proteins, including 133 potential ATCUN antitumor proteins.

3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites.

The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a new and fast Markov chain model (MCM) to predict the enzyme classification (EC) number.

Generalized lattice graphs for 2D-visualization of biological information.

Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein-protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations.

Prot-2S: a new python web tool for protein secondary structure studies.

Prot-2S is a bioinformatics web application devised to analyse the protein chain secondary structures (2S) (http:/ /www.requimte.pt:8080/Prot-2S/).

Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.

The cancer diagnostic is a complex process and, sometimes, the specific markers can interfere or produce negative results. Thus, new simple and fast theoretical models are required. One option is the complex network graphs theory that permits us to describe any real system, from the small molecules to the complex genetic, neural or social networks by transforming real properties in topological indices.

QSAR and complex network study of the chiral HMGR inhibitor structural diversity.

Efficient drugs such as statins or mevinic acids are inhibitors of the rate-limiting enzyme of cholesterol biosynthesis, 3-hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGR), an enzyme responsible for the double reduction of 3-hydroxy-3-methyl-glutaryl coenzyme A into mevalonic acid. These compounds promoted the synthesis and evaluation of new inhibitors for HMGR, named HMGRIs. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the HMGRI synthesis.

Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices.

The importance of the promoter sequences in the function regulation of several important mycobacterial pathogens creates the necessity to design simple and fast theoretical models that can predict them. This work proposes two DNA promoter QSAR models based on pseudo-folding lattice network (LN) and star-graphs (SG) topological indices. In addition, a comparative study with the previous RNA electrostatic parameters of thermodynamically-driven secondary structure folding representations has been carried out.

Quantitative Proteome-Property Relationships (QPPRs). Part 1: finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra.

Numerical parameters of the molecular networks, also referred as Topological Indices or Connectivity Indices (CIs), have been used in Bioorganic and Medicinal Chemistry to find Quantitative Structure-Activity, Property or Toxicity Relationship (QSAR, QSPR and QSTR) models. QSPR models generally use CIs as inputs to predict the biological activity of compounds. However, the literature does not evidence a great effort to find QSAR-like models for other biologically and chemically relevant systems.

Natural/random protein classification models based on star network topological indices.

The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randic's star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model. The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbrack's Web Lab.

Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices.

The huge amount of new proteins that need a fast enzymatic activity characterization creates demands of protein QSAR theoretical models. The protein parameters that can be used for an enzyme/non-enzyme classification includes the simpler indices such as composition, sequence and connectivity, also called topological indices (TIs) and the computationally expensive 3D descriptors. A comparison of the 3D versus lower dimension indices has not been reported with respect to the power of discrimination of proteins according to enzyme action.

Enhancing obstetric and gynecology ultrasound images by adaptation of the speckle reducing anisotropic diffusion filter.

Objective: So far there is no ideal speckle reduction filtering technique that is capable of enhancing and reducing the level of noise in medical ultrasound (US) images, while efficiently responding to medical experts' validation criteria which quite often include a subjective component. This paper presents an interactive tool called evolutionary speckle reducing anisotropic diffusion filter (EVOSRAD) that performs adaptive speckle filtering on ultrasound B-mode still images. The medical expert runs the algorithm interactively, having a permanent control over the output, and guiding the filtering process towards obtaining enhanced images that agree to his/her subjective quality criteria.

Speckle reduction through interactive evolution of a general order statistics filter for clinical ultrasound imaging.

This communication presents an interactive tool performing adaptive speckle filtering so that the medical expert who runs the algorithm has permanent control over the output and guides the process towards obtaining enhanced images that agree to his/her subjective quality criteria. The core of the filtering tool is an Interactive Genetic Algorithm that adapts online the coefficients of a general order statistics filter. Preliminary results show the potential of the method in comparison to other powerful speckle reduction filters on a test bed comprising obstetrics and gynecology ultrasound images.

Accurate intermolecular ground-state potential-energy surfaces of the HCCH-He, Ne, and Ar van der Waals complexes.

Accurate ground-state intermolecular potential-energy surfaces are obtained for the HCCH-He, Ne, and Ar van der Waals complexes. The interaction energies are calculated at the coupled cluster singles and doubles including connected triple excitations level and fitted to analytic functions. For the three complexes we start with systematic basis set studies carried out at several intermolecular geometries, and using augmented correlation consistent polarized valence basis sets x-aug-cc-pVXZ (x=-,d; X=D,T,Q,5), also extended with a set of 3s3p2d1f1g midbond functions.

Accurate intermolecular ground state potential of the Ar-N2 van der Waals complex.

After carrying out a systematic basis set convergence study, we evaluate several ground state potential energy surfaces of the Ar-N(2) van der Waals complex at the coupled cluster singles and doubles model including connected triples corrections. We use the aug-cc-pVXZ (X=5,Q,D) and the daug-cc-pVQZ basis sets augmented with a set of 3s3p2d1f1g (denoted 33211) and 3s3p2d2f1g (denoted 33221) midbond functions, respectively. aug-cc-pVTZ-33211 results were available in the literature.

Gray-scale image enhancement as an automatic process driven by evolution.

Image enhancement is the task of applying certain transformations to an input image such as to obtain a visually more pleasant, more detailed, or less noisy output image. The transformation usually requires interpretation and feedback from a human evaluator of the output result image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention.

Accurate intermolecular ground state potential of the Ne-N2 van der Waals complex.

Ab initio ground state potential energy surfaces are obtained from interaction energies calculated with the coupled cluster singles and doubles model including connected triples corrections [CCSD(T)] and the aug-cc-pVXZ (X=5,Q,T,D) basis sets augmented with two different sets of midbond functions (denoted 33221 and 33211). The aug-cc-pV5Z-33221 surface is characterized by a T-shaped 49.5 cm(-1) minimum at Re=3.