Orthotropic Piezoelectricity in 2D Nanocellulose

The control of electromechanical responses within bonding regions is essential to face frontier challenges in nanotechnologies, such as molecular electronics and biotechnology. Here, we present I\b{eta}-nanocellulose as a potentially new orthotropic 2D piezoelectric crystal. The predicted in-layer piezoelectricity is originated on a sui-generis hydrogen bonds pattern. Upon this fact and by using a combination of ab-initio and ad-hoc models, we introduce a description of electrical profiles along chemical bonds. Such developments lead to obtain a rationale for modelling the extended piezoelectric effect originated within bond scales. The order of magnitude estimated for the 2D I\b{eta}-nanocellulose piezoelectric response, ~pm V-1, ranks this material at the level of currently used piezoelectric energy generators and new artificial 2D designs. Such finding would be crucial for developing alternative materials to drive emerging nanotechnologies.Comment: 5 figures included. Supp. Mat. available on the online version: https://www.nature.com/articles/srep34616, Others on: http://www.nanowerk.com/nanotechnology-news/newsid=44806.ph

Novel Ligand-Based Approach to Screening of Large Databases for Paramphistomicide Lead Generation

In this report, non-stochastic and stochastic 2D atom-based linear indices were used to the discrimination of paramphistomicide compounds from inactive ones. Two linear classification-based QSAR models were obtained. These equations, performed considering both non-stochastic and stochastic TOMOCOMD-CARDD descriptors, classify correctly 88.57% of chemicals in database, for a good Mathew’s correlation coefficient of 0.77. A few anthelmintics compounds and other drugs from the Merck Index, Negwer handbook, and Goodman & Gilman were selected/identified by the models as possible paramphistomicide, one of them was found in the recent literature as possessing this activity. The results demonstrate the usefulness of TOMOCOMD-CARDD method for drug discovery of new lead paramphistomicide compounds.En este informe se emplearon índices lineales estocásticos y no estocásticos en 2D, basados en átomos, para discriminar los compuestos de acción paramfistomicida de los inactivos. Se obtuvieron dos modelos lineales QSAR basados en la clasificación. Estas ecuaciones, llevadas a cabo teniendo en cuenta descriptores TOMOCOMD-CARDD tanto estocásticos como no estocásticos, clasifican correctamente el 88,57% de los elementos químicos de la base de datos, arrojando un buen coeficiente de correlación de Mathews del 0,77. Los modelos seleccionaron/identificaron algunos compuestos antihelmínticos y otros fármacos del índice Merck, del manual Negwer y de Goodman & Gilman como posibles paramfistomicidas, y la literatura reciente incluye a uno de ellos como poseedor de esta actividad. Los resultados demuestran la utilidad del método TOMOCOMD-CARDD para el descubrimiento de fármacos y de nuevos compuestos líderes de acción paramfistomicida.Ciencias Experimentale

Overlap and diversity in antimicrobial peptide databases: Compiling a non-redundant set of sequences

Motivation: The large variety of antimicrobial peptide (AMP) databases developed to date are characterized by a substantial overlap of data and similarity of sequences. Our goals are to analyze the levels of redundancy for all available AMP databases and use this information to build a new nonredundant sequence database. For this purpose, a new software tool is introduced. Results: A comparative study of 25 AMP databases reveals the overlap and diversity among them and the internal diversity within each database. The overlap analysis shows that only one database (Peptaibol) contains exclusive data, not present in any other, whereas all sequences in the LAMP-Patent database are included in CAMP-Patent. However, the majority of databases have their own set of unique sequences, as well as some overlap with other databases. The complete set of non-duplicate sequences comprises 16 990 cases, which is almost half of the total number of reported peptides. On the other hand, the diversity analysis identifies the most and least diverse databases and proves that all databases exhibit some level of redundancy. Finally, we present a new parallel-free software, named Dover Analyzer, developed to compute the overlap and diversity between any number of databases and compile a set of non-redundant sequences. These results are useful for selecting or building a suitable representative set of AMPs, according to specific needs. © The Author 2015. Published by Oxford University Press. All rights reserved.Antimicrobial Cationic Peptide

ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins

Background: The exponential growth of protein structural and sequence databases is enabling multifaceted approaches to understanding the long sought sequence-structure-function relationship. Advances in computation now make it possible to apply well-established data mining and pattern recognition techniques to these data to learn models that effectively relate structure and function.

Physico-Chemical and structural interpretation of discrete derivative indices on N-tuples atoms

This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity of the atoms and bonds, respectively. On the other hand, it is demonstrated that the GDIs are sensitive to progressive structural modifications in terms of: size, ramifications, electronic richness, conjugation effects and molecular symmetry. Moreover, it is observed that the GDIs quantify the interaction capacity among molecules and codify information on the activation entropy. A structure property relationship study reveals that there exists a direct correspondence between the individual frequencies of atoms and Hückel’s Free Valence, as well as between the atomic GDIs and the chemical shift in NMR, which collectively validates the theory that these indices codify steric and electronic information of the atoms in a molecule. Taking in consideration the regularity and coherence found in experiments performed with the GDIs, it is possible to say that GDIs possess plausible interpretation in structural and physicochemical terms. © 2016 by the authors; licensee MDPI, Basel, Switzerland.Pharmaceutical Preparation

In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance

Infections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes.A set of compounds active against parasitic nematodes were collated from various literature sources including PubChem while the inactive set was derived from DrugBank database. The support vector machine (SVM) algorithm was used for model development, and stratified ten-fold cross validation was used to evaluate the performance of each classifier. The best results were obtained using the radial basis function kernel. The SVM method achieved an accuracy of 81.79% on an independent test set. Using the model developed above, we were able to indentify novel compounds with potential anthelmintic activity.In this study, we successfully present the SVM approach for predicting compounds active against parasitic nematodes which suggests the effectiveness of computational approaches for antiparasitic drug discovery. Although, the accuracy obtained is lower than the previously reported in a similar study but we believe that our model is more robust because we intentionally employed stringent criteria to select inactive dataset thus making it difficult for the model to classify compounds. The method presents an alternative approach to the existing traditional methods and may be useful for predicting hitherto novel anthelmintic compounds.12 page(s

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

Data for: LEGO-based Generalized Set of Two Linear Algebraic 3D Bio-Macro-Molecular Descriptors: Theory and Validation by QSARs

SI3-1: 15 suggested theoretical configurations for the calculation of MDs (defined with the name projects). The selected configuration for the projects used in this study are also indicated in Table SI2-1 and are available at SI3-1. SI3-2: The experiments employed a dataset containing 152 representatives, non-homologous proteins (see SI3-2 to review the protein files). (Fleming and Richards, 2000). SI3-3: The evaluation of this application in protein science requires the use of two datasets. The first data set, employed as a training set, was proposed by Ouyang (Ouyang and Liang, 2008) and contains 80 proteins (the case “2BLM” was removed since it only considered an alpha carbon representation). The second dataset, employed as a test set, was proposed by Ruiz-Blanco (Ruiz-Blanco et al., 2015) and contains 17 proteins. SI3-4: For the generation of the models, a dataset of 204 proteins was employed. (Chou, 1999) The original dataset was split into two groups: a training set with 149 proteins and a test set containing 55 proteins to ensure a proper comparison (Marrero Ponce et al., 2015a