Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

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

Machine Learning Methods for Effectively Discovering Complex Relationships in Graph Data

Graphs are extensively employed in many systems due to their capability to capture the interactions (edges) among data (nodes) in many real-life scenarios. Social networks, biological networks and molecular graphs are some of the domains where data have inherent graph structural information. Built graphs can be used to make predictions in Machine Learning (ML) such as node classifications, link predictions, graph classifications, etc. But, existing ML algorithms hold a core assumption that data instances are independent of each other and hence prevent incorporating graph information into ML. This irregular and variable sized nature of non-Euclidean data makes learning underlying patterns of the graph more sophisticated. One approach is to convert the graph information into a lower dimensional space and use traditional learning methods on the reduced space. Meanwhile, Deep Learning has better performance than ML due to convolutional layers and recurrent layers which consider simple correlations in spatial and temporal data, respectively. This proves the importance of taking data interrelationships into account and Graph Convolutional Networks (GCNs) are inspired by this fact to exploit the structure of graphs to make better inference in both node-centric and graph-centric applications. In this dissertation, the graph based ML prediction is addressed in terms of both node classification and link prediction tasks. At first, GCN is thoroughly studied and compared with other graph embedding methods specific to biological networks. Next, we present several new GCN algorithms to improve the prediction performance related to biomedical networks and medical imaging tasks. A circularRNA (circRNA) and disease association network is modeled for both node classification and link prediction tasks to predict diseases relevant to circRNAs to demonstrate the effectiveness of graph convolutional learning. A GCN based chest X-ray image classification outperforms state-of-the-art transfer learning methods. Next, the graph representation is used to analyze the feature dependencies of data and select an optimal feature subset which respects the original data structure. Finally, the usability of this algorithm is discussed in identifying disease specific genes by exploiting gene-gene interactions

Molecular Science for Drug Development and Biomedicine

With the avalanche of biological sequences generated in the postgenomic age, molecular science is facing an unprecedented challenge, i.e., how to timely utilize the huge amount of data to benefit human beings. Stimulated by such a challenge, a rapid development has taken place in molecular science, particularly in the areas associated with drug development and biomedicine, both experimental and theoretical. The current thematic issue was launched with the focus on the topic of “Molecular Science for Drug Development and Biomedicine”, in hopes to further stimulate more useful techniques and findings from various approaches of molecular science for drug development and biomedicine

Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks

Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to efficiently identify a network\u27s set of attractors and to predict the likelihood with which an arbitrary state leads to each of the network’s attractors. These probabilities will be represented using a fuzzy membership vector. Predicting fuzzy membership vectors using machine learning techniques may address the intractability posed by networks of realistic size and complexity. Modeling and simulation can be used to provide the necessary training sets for machine learning methods to predict fuzzy membership vectors. The experiments comprise several GRNs, each represented by a set of output classes. These classes consist of thresholds τ and ¬τ, where τ = [τlaw,τhigh]; state s belongs to class τ if the probability of its transitioning to attractor belongs to the range [τlaw,τhigh]; otherwise it belongs to class ¬τ. Finally, each machine learning classifier was trained with the training sets that was previously collected. The objective is to explore methods to discover patterns for meaningful classification of states in realistically complex regulatory networks. The research design took a GRN and a machine learning method as input and produced output class \u3c Ατ \u3e and its negation ¬ \u3c Ατ \u3e. For each GRN, attractors were identified, data was collected by sampling each state to create fuzzy membership vectors, and machine learning methods were trained to predict whether a state is in a healthy attractor or not. For T-LGL, SVMs had the highest accuracy in predictions (between 93.6% and 96.9%) and precision (between 94.59% and 97.87%). However, naive Bayesian classifiers had the highest recall (between 94.71% and 97.78%). This study showed that all experiments have extreme significance with pvalue \u3c 0.0001. The contribution this research offers helps clinical biologist to submit genetic states to get an initial result on their outcomes. For future work, this implementation could use other machine learning classifiers such as xgboost or deep learning methods. Other suggestions offered are developing methods that improves the performance of state transition that allow for larger training sets to be sampled

Protein Domain Linker Prediction: A Direction for Detecting Protein – Protein Interactions

Protein chains are generally long and consist of multiple domains. Domains are the basic of elements of protein structures that can exist, evolve and function independently. The accurate and reliable identification of protein domains and their interactions has very important impacts in several protein research areas. The accurate prediction of protein domains is a fundamental stage in both experimental and computational proteomics. The knowledge is an initial stage of protein tertiary structure prediction which can give insight into the way in which protein works. The knowledge of domains is also useful in classifying the proteins, understanding their structures, functions and evolution, and predicting protein-protein interactions (PPI). However, predicting structural domains within proteins is a challenging task in computational biology. A promising direction of domain prediction is detecting inter-domain linkers and then predicting the reigns of the protein sequence in which the structural domains are located accordingly. Protein-protein interactions occur at almost every level of cell function. The identification of interaction among proteins and their associated domains provide a global picture of cellular functions and biological processes. It is also an essential step in the construction of PPI networks for human and other organisms. PPI prediction has been considered as a promising alternative to the traditional drug design techniques. The identification of possible viral-host protein interaction can lead to a better understanding of infection mechanisms and, in turn, to the development of several medication drugs and treatment optimization. In this work, a compact and accurate approach for inter-domain linker prediction is developed based solely on protein primary structure information. Then, inter-domain linker knowledge is used in predicting structural domains and detecting PPI. The research work in this dissertation can be summarized in three main contributions. The first contribution is predicting protein inter-domain linker regions by introducing the concept of amino acid compositional index and refining the prediction by using the Simulated Annealing optimization technique. The second contribution is identifying structural domains based on inter-domain linker knowledge. The inter-domain linker knowledge, represented by the compositional index, is enhanced by the in cooperation of biological knowledge, represented by amino acid physiochemical properties. To develop a well optimized Random Forest classifier for predicting novel domain and inter-domain linkers. In the third contribution, the domain information knowledge is utilized to predict protein-protein interactions. This is achieved by characterizing structural domains within protein sequences, analyzing their interactions, and predicting protein interaction based on their interacting domains. The experimental studies and the higher accuracy achieved is a valid argument in favor of the proposed framework

Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level