A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins. Results: In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC) in conjunction with multiple physicochemical properties of amino acids to predict odorantbinding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively). Conclusion: Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information

Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

BACKGROUND: Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins. RESULTS: By using leave-one-out cross validation, the prediction accuracy is 85.5% for inner membrane, 94.5% for matrix and 51.2% for outer membrane. The overall prediction accuracy for submitochondria location prediction is 85.2%. For proteins predicted to localize at inner membrane, the accuracy is 94.6% for membrane protein type prediction. CONCLUSION: Our method is an effective method for predicting protein submitochondria location. But even with our method or the methods at subcellular level, the prediction of protein submitochondria location is still a challenging problem. The online service SubMito is now available at

A Prediction Model for Membrane Proteins Using Moments Based Features

The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies

Amyloid beta oligomers induce neuronal elasticity changes in age-dependent manner: a force spectroscopy study on living hippocampal neurons

Small soluble species of amyloid-beta (Aβ) formed during early peptide aggregation stages are responsible for several neurotoxic mechanisms relevant to the pathology of Alzheimer's disease (AD), although their interaction with the neuronal membrane is not completely understood. This study quantifies the changes in the neuronal membrane elasticity induced by treatment with the two most common Aβ isoforms found in AD brains: Aβ40 and Aβ42. Using quantitative atomic force microscopy (AFM), we measured for the first time the static elastic modulus of living primary hippocampal neurons treated with pre-aggregated Aβ40 and Aβ42 soluble species. Our AFM results demonstrate changes in the elasticity of young, mature and aged neurons treated for a short time with the two Aβ species pre-aggregated for 2 hours. Neurons aging under stress conditions, showing aging hallmarks, are the most susceptible to amyloid binding and show the largest decrease in membrane stiffness upon Aβ treatment. Membrane stiffness defines the way in which cells respond to mechanical forces in their environment and has been shown to be important for processes such as gene expression, ion-channel gating and neurotransmitter vesicle transport. Thus, one can expect that changes in neuronal membrane elasticity might directly induce functional changes related to neurodegeneration

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques

Efficacy of different protein descriptors in predicting protein functional families using support vector machine

Master'sMASTER OF SCIENCE (PHARMACY

El efecto de los aceites de oleaginosas en rumen en el sistema de fermentación in vitro

Los subproductos de plantas oleaginosas han sido utilizadas para mejorar el desempeño productivo en ruminates ya sea por la alta energía que proporciona o por su composición proteica, sin embargo, pocos artículos reportan la cantidad que debe proporcionarse en forma adecuada en la dieta de ruminates como suplemento. Es por lo anterior, que el trabajo fue elaborado para evaluar el efecto de cártamo (Carthamus tinctorius L., SFM)/canola (Brassica napus, CAS) con semilla de sorgo en una proporción (0%, 25%, 50%, 75%, 100%, respectivamente) en una proporción concentrado-forraje (1:1) en una prueba in vitro de fermentación ruminal de ovino, como es la cinética de producción de gas, nitrógeno amoniacal (NH3-N), pH, producción de metano (CH4) y la desaparición de materia seca in vitro (IVDMD) de rastrojo de maíz. Los resultados mostraron que la producción de gas, y de metano significativamente disminuyeron, pero IVDMD y pH incrementaron con el incremento de ambas proporciones de SFM y CAS en alimentación a base de concentrado. Para tomar en consideración el desempeño de fermentación y ambiente, nuestros resultados sugieren que la suplementación más adecuada de canola y cartamo en alimentación a base de concentrado son de 25% a 50%, y de 25% a 75%, respectivamente. Modificar el metabolismo microbiano en rumen a través de la adición de aceites derivados de plantas es una manera efectiva de aumentar los ácidos grasos funcionales de los productos derivados de ruminates. Poco es conocido de la influencia de ácidos grasos exogenos en los procesos del metabolismo de lípidos en las membranas de bacterias y protozoarios. Es por lo anterior, que el presente trabajo se enfocó en investigar los ácidos grasos de cadena larga (LCFA), ácidos grasos volétiles (VFA) y metano (CH4) a las 48 horas de suplemtento exógeno de aceites principalmente conteniendo ácidos grasos poli/mono insaturados, PUFA/MUFA, (C18:3, C18:2 y C18:1 de aceites de linaza, cártamo y canola respectivamente) y ácidos grasos de cadena mediana MCFA, (C12:0 de aceite de coco). Los resultados mostraron que la composición de ácidos grasos entre bacteria y protozoarios fueron diferentes. La suplementación de aceite de linaza, principalmente ácido linoleico (C18:3), al incrementar las proporciones de C18:2 n6c, C18:2 n6t, C18:1 n9c, C18:1 n9t y cis- ácidos grasos en la membrane de bacterias y protozoarios en diferente medida, mejora ligeramente las concentraciones de ácido acético y propiónico pero no tiene impacto en CH4. La suplementación con aceite de coco, aumenta la composición de MCFA, y por lo tanto aumenta la biosíntesis de MCFA en fracciones tanto de bacterias como de protozoarios (del C12:0 al C14:0), para inhibir la actividad de metanogénesis en cierta medida. Los mayores ácidos grasos saturados, saturados/insaturados, trans-, even-carbon insaturados fueron obtenidos de membranas de bacterias cuando se suplementaba con aceites de cártamo y canola, principalmente C18:2 y C18:1, respectivamente, pero más bajo que aceite de coco. Sin embargo, en ambos con cártamo y canola aumentaron las concentraciones de ácido acético y propiónico, disminuye la Ac/Pro ratio pero no el impacto de la actividad de metanogénesis. En resumen, los resultados implican que diferentes grados de ácidos grasos insaturados de cadena corta o larga puede impactar en la fermentación ruminal