Current Protein and Peptide Science - Volume 19, Issue 5, 2018

Background: Sphingosine kinase 1 (SK1) overexpression and elevated sphingosine-1-phosphate (S1P) levels have been correlated with many disease states from cancer to inflammatory diseases to diabetes. Even though SK1 inhibitors are of consideberable interest as effective chemotherapeutic agents, poor potency, lack of selectivity and poor pharmacokinetic properties have been major problems in the first generation SK1 inhibitors. Objective: There is an urgent need for the discovery of novel in vivo, stable selective SK1 inhibitors with improved potency. The primary object of this study was to identify potential novel leads for orthosteric inhibition of SK1. Methods: We propose a series of compounds from different chemotypes as potential selective SK1 inhibitors via virtual screening of the ZINC database using ligand-based and structure-based pharmacophore models, molecular docking, substructure search, selectivity calculations. Molecular dynamics (MD) simulations revealed key insights into the binding mode and the stability of the SK1-ligand complex. Results: Ten ligands were proposed as potential SK1 inhibitors based on the high induced fit docking scores, BEI, LLE and %HOA. Ligands 2, 3, 5 and 9 were found to be selective toward SK1 with favorable binding free energy of - 95 ± 5 kcal/mol. MD simulation of ligand 5 showed that the ligand-SK1 complex reached equilibrium with favorable hydrogen bonding and hydrophobic interactions. The four selective compounds have less than 0.24 similarity with previously discovered potent inhibitors. Conclusion: The proposed compounds may serve as potential novel leads for orthosteric inhibition of SK1.

Background: Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). Methods: In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Results: Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Conclusions: Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.

Background: Inferring drug-target interaction (DTI) candidates for new drugs or targets without any interaction information is a critical challenge for modern drug design and discovery. Results from existing DTI inference methods indicate that these approaches necessitate further improvement. Methods: In this paper, we developed a novel DTI identification model (PreNNDS) by integrating Neighbor interaction profiles, Nonnegative matrix factorization, Discriminative low-rank representation, and Sparse representation classification into a unified framework. Results: AUPR values on four types of datasets show that PreNNDS can efficiently identify potential DTIs for new drugs or targets. We listed predicted top 20 drugs interacting with hsa1132 and hsa1124 and top 20 targets interacting with D00255 and D00195. Conclusions: PreNNDS can be applied to identify multi-target drugs and multi-drug resistance proteins, as well as to provide clues for microRNA-disease and gene-disease association prediction.

Background: Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Objective: Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. Method: More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. Results: The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. Conclusion: The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features.

Background: A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Objective: Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. Method: In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. Results: We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Conclusion: Performance evaluation of ODT shows that it can be potentially useful. It confirms that predicting potential or missing DTIs based on the known interactions is a promising direction to solve problems related to the use of uncertain and unreliable negative samples and those related to the great demand in computational resources.

Background: Experimental approaches to identify drug-target interactions (DTIs) among a large number of chemical compounds and proteins are still costly and time-consuming. As an assistant, computational approaches are able to rapidly infer potential drug or target candidates for diverse screenings on a large scale. The most difficult scenario (S4) of screenings tries to explore the pairwise interacting candidates between newly designed chemical compounds (new potential drugs) and proteins (new target candidates). Few of current computational approaches can be applied to the inference of potential DTIs in S4 because the new potential drugs have no known target and the new target candidates have no existing drug at all. In addition, due to the essential issues among DTI, such as missing DTIs and the imbalance between few approved DTIs and many unknown drug-target pairs, existing metrics of DTI inference may not reflect the performance of inferring approaches fairly. Methods: To address these issues, this paper develops three instance neighborhood-based models: individualto- individual (I2I), individual-to-group (I2G) and nearest-neighbor-zone (NNZ). In I2I, if a new drug tends to interact with individual targets similar to a new target of interest, it likely interacts with the new target. In I2G, the new drug possibly interacts with the new target if it tends to interact with a target group, in which member targets are similar to each other and one or more of them are similar to the new target. In NNZ, the pair of the new drug and the new target is a potential DTI if it is similar to known existing DTIs. This paper also designs a topological dense index to guide the selection of the appropriate models when given different datasets. Moreover, an additional metric Coverage is introduced to enhance the assessment of DTI inference. Results: Performed on four benchmark datasets, our models demonstrate that the instance neighborhood can improve the DTI inference significantly. Under the guidance of our topological dense index, the best models for the datasets are chosen and achieve inspiring performances, including ~85%, ~81%, ~86% and ~81% in terms of AUC and ~29%, ~32%, ~32% and ~33% in terms of AUPR respectively. The superiority of our models is demonstrated by both the comparison with two state-of-the-art approaches and the novel DTI inference. Conclusion: By leveraging the instance neighborhood, our models are able to infer DTIs in the most difficult scenario S4. Moreover, our topological dense index can guide the appropriate models when given different datasets.

Background: During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. Objective: In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. Method: In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. Results: In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Conclusion: Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can freely available at https://github.com/NwpuSY/DT_all.git for academic users

The mitochondrial carrier family (solute carrier family 25) is a super family of nuclearencoded transporters localized on the inner mitochondrial membranes. In human, the mitochondrial carrier family has 53 members, all with a ternary structure of six transmembrane α-helices. The structure of mitochondrial carrier family has three repeats of conservative motifs. The members of this family connect the inter membrane space and matrix of mitochondria, and transport various important small molecules across the inner membrane. In the present review, we have highlighted the limitations of traditional research methods to gain the accurate knowledge of membrane proteins. We have focused on recent emerging computational strategies such as molecular modeling, molecular dynamics, and quantitative structure–activity relationship to predict the structure and function of membrane proteins at atomic resolution in the absence of experimental data. This review aims to summarize a comprehensive introduction of recent discoveries about the biological investigations of the structure and transport mechanism of mitochondrial carriers, which are useful for further investigation on diseases and drug developments related to mitochondrial carrier deficiency, especially the combination of traditional experiments and emerging computational strategies.