Analytical model of ion transport and conversion of light energy in chloroplasts

An Effective Multi-Label Protein Sub-Chloroplast Localization Prediction by Skipped-Grams of Evolutionary Profiles Using Deep Neural Network

Chloroplast is one of the most classic organelles in algae and plant cells. Identifying the locations of chloroplast proteins in the chloroplast organelle is an important as well as a challenging task in deciphering their functions. Biological-based experiments to identify the Protein Sub-Chloroplast Localization (PSCL) is time-consuming and cost-intensive. Over the last decade, a few computational methods have been developed to predict PSCL in which earlier works assumed to predict only single-location; whereas, recent works are able to predict multiple-locations of chloroplast organelle. However, the performances of all the state-of-the-art predictors are poor. This article proposes a novel skip-gram technique to extract highly discriminating patterns from evolutionary profiles and a multi-label deep neural network to predict the PSCL. The proposed model is assessed on two publicly available datasets, i.e., Benchmark and Novel. Experimental results demonstrate that the proposed work outperforms significantly when compared to the state-of-the-art multi-label PSCL predictors. A multi-label prediction accuracy (i.e., Overall Actual Accuracy) of the proposed model is enhanced by an absolute minimum margin of 6.7 percent on Benchmark dataset and 7.9 percent on Novel dataset when compared to the best PSCL predictor from the literature. Further, result of statistical t-test concludes that the performance of the proposed work is significantly improved and thus, the proposed work is an effective computational model to solve multi-label PSCL prediction. The proposed prediction model is hosted on web-server and available at https://nitkit-vgst727-nppsa.nitk.ac.in/deeplocpred/.

From leaves to roots: Biophysical models of transport of substances in plants

The transport processes of substances in various plant tissues are extremely diverse. However, models aimed at elucidating the mechanisms of such processes are almost absent in the literature. A unified view of all these transport processes is necessary, considering the laws of statistical physics and thermodynamics. A model of active ion transport was constructed based on the laws of statistical physics. Using this model, we traced the entire pathway of substances and energy in a plant. The pathway included aspects of the production of energy in the process of photosynthesis, consumption of energy to obtain nutrients from the soil, transport of such substances to the main organelles of all types of plant cells, the rise of water with dissolved substances along the trunk to the leaves, and the evaporation of water, accompanied by a change in the percentage of isotopes caused by different rates of evaporation. Models of ion transport in the chloroplasts and mitochondria of plant cells have been constructed. A generalized model comprising plant cells and their vacuoles was analyzed. A model of the transport of substances in the roots of plants was also developed. Based on this model, the problem of transport of substances in tall trees has been considered. The calculated concentrations of ions in the vacuoles of cells and resting potentials agreed well with the experimental data.

Protocells and LUCA: Transport of substances from first physicochemical principles

Models of the transport of substances in protocells are considered from first physicochemical principles. Functional similarities and differences in the transport systems of archaea, cyanobacteria, E. coli, and diatoms have been analyzed. Based on the selection of the most important transport systems, a model of transport of substances through the membrane of the last universal common ancestor, LUCA, was constructed. Models of isotope separation in protocells were considered. Based on the proposed models, the difference in isotope concentrations in rocks can be predicted, which can serve as an indicator of the presence of life in the early stages of evolution. Mechanisms of energy conversion for the simplest forms of directed motion in protocells are considered. A special stage in the evolution of protocells is proposed - the minimal mobile cell.

Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction

Predicting the localization of chloroplast proteins at the sub-subcellular level is an essential yet challenging step to elucidate their functions. Most of the existing subchloroplast localization predictors are limited to predicting single-location proteins and ignore the multi-location chloroplast proteins. While recent studies have led to some multi-location chloroplast predictors, they usually perform poorly. This paper proposes an ensemble transductive learning method to tackle this multi-label classification problem. Specifically, given a protein in a dataset, its composition-based sequence information and profile-based evolutionary information are respectively extracted. These two kinds of features are respectively compared with those of other proteins in the dataset. The comparisons lead to two similarity vectors which are weighted-combined to constitute an ensemble feature vector. A transductive learning model based on the least squares and nearest neighbor algorithms is proposed to process the ensemble features. We refer to the resulting predictor to as EnTrans-Chlo. Experimental results on a stringent benchmark dataset and a novel dataset demonstrate that EnTrans-Chlo significantly outperforms state-of-the-art predictors and particularly gains more than 4% (absolute) improvement on the overall actual accuracy. For readers' convenience, EnTrans-Chlo is freely available online at http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/.

Antenna entropy in plant photosystems does not reduce the free energy for primary charge separation

We have investigated the concept of the so-called "antenna entropy" of higher plant photosystems. Several interesting points emerge: 1. In the case of a photosystemwhich harbours an excited state, the “antenna entropy” is equivalent to the configurational (mixing) entropy of a thermodynamic canonical ensemble. The energy associated with this parameter has been calculated for a hypothetical isoenergetic photosystem, photosystem I and photosystem II, and comes out in the range of 3.5 - 8% of the photon energy considering 680 nm. 2. The “antenna entropy” seems to be a rather unique thermodynamic phenomenon, in as much as it does not modify the free energy available for primary photochemistry, as has been previously suggested. 3. It is underlined that this configurational (mixing) entropy, unlike heat dispersal in a thermal system, does not involve energy dilution. This points out an important difference between thermal and electronic energy dispersal.

Origin of the directed movement of protocells in the early stages of the evolution of life

The origin of the directed motion of protocells during the early stages of evolution was discussed. The expenditures for movement, space orientation, and reception of information about the environment were taken into consideration, and it was shown that directed movement is evolutionarily advantageous in the following cases: when opposite gradients of different resources (for example, matter and energy) are great enough and when there is a rapid change in environmental parameters. It was also shown that the advantage of directed movement strategies depends greatly on how information about the environment is obtained by a protocell.

Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction

It is very challenging and complicated to predict protein locations at the sub-subcellular level. The key to enhancing the prediction quality for protein sub-subcellular locations is to grasp the core features of a protein that can discriminate among proteins with different subcompartment locations. In this study, a different formulation of pseudoamino acid composition by the approach of discrete wavelet transform feature extraction was developed to predict submitochondria and subchloroplast locations. As a result of jackknife cross-validation, with our method, it can efficiently distinguish mitochondrial proteins from chloroplast proteins with total accuracy of 98.8% and obtained a promising total accuracy of 93.38% for predicting submitochondria locations. Especially the predictive accuracy for mitochondrial outer membrane and chloroplast thylakoid lumen were 82.93% and 82.22%, respectively, showing an improvement of 4.88% and 27.22% when other existing methods were compared. The results indicated that the proposed method might be employed as a useful assistant technique for identifying sub-subcellular locations. We have implemented our algorithm as an online service called SubIdent (http://bioinfo.ncu.edu.cn/services.aspx).