A computational overview on phylogenetic characterization, pathogenic mutations, and drug targets for Ebola virus disease.

The World Health Organization declared Ebola virus disease (EVD) as the major outbreak in the 20th century. EVD was first identified in 1976 in South Sudan and the Democratic Republic of the Congo. EVD was transmitted from infected fruit bats to humans via contact with infected animal body fluids. The Ebola virus (EBOV) has a genome size of ∼18,959 bp. It encodes seven distinct proteins: nucleoprotein (NP), glycoprotein (GP), viral proteins VP24, VP30, VP35, matrix protein VP40, and polymerase L is considered a prime target for potential antiviral strategies. The current US FDA-approved anti-EVD vaccine, ERVERBO, and the other equally effective anti-EBOV combinations of three fully human monoclonal antibodies such as REGN-EB3, primarily target the envelope glycoprotein. This work elaborates on the EBOV's phylogenetic structure and the crucial mutations associated with viral pathogenicity.

Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams.

A promoter is a short region of DNA (100-1,000 bp) where transcription of a gene by RNA polymerase begins. It is typically located directly upstream or at the 5' end of the transcription initiation site. DNA promoter has been proven to be the primary cause of many human diseases, especially diabetes, cancer, or Huntington's disease. Therefore, classifying promoters has become an interesting problem and it has attracted the attention of a lot of researchers in the bioinformatics field. There were a variety of studies conducted to resolve this problem, however, their performance results still require further improvement. In this study, we will present an innovative approach by interpreting DNA sequences as a combination of continuous FastText N-grams, which are then fed into a deep neural network in order to classify them. Our approach is able to attain a cross-validation accuracy of 85.41 and 73.1% in the two layers, respectively. Our results outperformed the state-of-the-art methods on the same dataset, especially in the second layer (strength classification). Throughout this study, promoter regions could be identified with high accuracy and it provides analysis for further biological research as well as precision medicine. In addition, this study opens new paths for the natural language processing application in omics data in general and DNA sequences in particular.

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding.

An enhancer is a short (50-1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory bowel disease. Due to the importance of enhancers in genomics, the classification of enhancers has become a popular area of research in computational biology. Despite the few computational tools employed to address this problem, their resulting performance still requires improvements. In this study, we treat enhancers by the word embeddings, including sub-word information of its biological words, which then serve as features to be fed into a support vector machine algorithm to classify them. We present iEnhancer-5Step, a web server containing two-layer classifiers to identify enhancers and their strength. We are able to attain an independent test accuracy of 79% and 63.5% in the two layers, respectively. Compared to current predictors on the same dataset, our proposed method is able to yield superior performance as compared to the other methods. Moreover, this study provides a basis for further research that can enrich the field of applying natural language processing techniques in biological sequences. iEnhancer-5Step is freely accessible via http://biologydeep.com/fastenc/.

Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture.

Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we suggested a new strategy that includes gated recurrent units and position-specific scoring matrix profiles to predict vesicular transportation proteins, a biological function of great importance. Although it is difficult to discover its function, our model is able to achieve accuracies of 82.3% and 85.8% in the cross-validation and independent dataset, respectively. We also solve the problem of imbalance in the dataset via tuning class weight in the deep learning model. The results generated showed sensitivity, specificity, MCC, and AUC to have values of 79.2%, 82.9%, 0.52, and 0.861, respectively. Our strategy shows superiority in results on the same dataset against all other state-of-the-art algorithms. In our suggested research, we have suggested a technique for the discovery of more proteins, particularly proteins connected with vesicular transport. In addition, our accomplishment could encourage the use of gated recurrent units architecture in protein function prediction.

Virtual screening of the inhibitors targeting at the viral protein 40 of Ebola virus.

BACKGROUND: The Ebola virus is highly pathogenic and destructive to humans and other primates. The Ebola virus encodes viral protein 40 (VP40), which is highly expressed and regulates the assembly and release of viral particles in the host cell. Because VP40 plays a prominent role in the life cycle of the Ebola virus, it is considered as a key target for antiviral treatment. However, there is currently no FDA-approved drug for treating Ebola virus infection, resulting in an urgent need to develop effective antiviral inhibitors that display good safety profiles in a short duration. METHODS: This study aimed to screen the effective lead candidate against Ebola infection. First, the lead molecules were filtered based on the docking score. Second, Lipinski rule of five and the other drug likeliness properties are predicted to assess the safety profile of the lead candidates. Finally, molecular dynamics simulations was performed to validate the lead compound. RESULTS: Our results revealed that emodin-8-beta-D-glucoside from the Traditional Chinese Medicine Database (TCMD) represents an active lead candidate that targets the Ebola virus by inhibiting the activity of VP40, and displays good pharmacokinetic properties. CONCLUSION: This report will considerably assist in the development of the competitive and robust antiviral agents against Ebola infection.

Structural signature of the G719S-T790M double mutation in the EGFR kinase domain and its response to inhibitors.

Some individuals with non-small-cell lung cancer (NSCLC) benefit from therapies targeting epidermal growth factor receptor (EGFR), and the characterization of a new mechanism of resistance to the EGFR-specific antibody gefitinib will provide valuable insight into how therapeutic strategies might be designed to overcome this particular resistance mechanism. The G719S and T790M mutations and their combination were involved in causing different conformational redistribution of EGFR. In the present computational study, we analyzed the impact and structural influence of G719S/T790M double mutation (DM) in EGFR with ligand (gefitinib) through molecular dynamic simulation (50 ns) and docking analysis. We observed the escalation in distance between the functional loop and activation loop with respect to T790M mutation compared to the G719S mutation. Furthermore, we confirmed that the G719S mutation causes the ligand to move closer to the hinge region, whereas T790M makes the ligand escape from the binding pocket. Obtained results provide with an explanation for the resistance induced by T790M and a vital clue for the design of drugs to combat gefitinib resistance.

An integrated in silico approach to analyze the involvement of single amino acid polymorphisms in FANCD1/BRCA2-PALB2 and FANCD1/BRCA2-RAD51 complex.

Fanconi anemia (FA) is an autosomal recessive human disease characterized by genomic instability and a marked increase in cancer risk. The importance of FANCD1 gene is manifested by the fact that deleterious amino acid substitutions were found to confer susceptibility to hereditary breast and ovarian cancers. Attaining experimental knowledge about the possible disease-associated substitutions is laborious and time consuming. The recent introduction of genome variation analyzing in silico tools have the capability to identify the deleterious variants in an efficient manner. In this study, we conducted in silico variation analysis of deleterious non-synonymous SNPs at both functional and structural level in the breast cancer and FA susceptibility gene BRCA2/FANCD1. To identify and characterize deleterious mutations in this study, five in silico tools based on two different prediction methods namely pathogenicity prediction (SIFT, PolyPhen, and PANTHER), and protein stability prediction (I-Mutant 2.0 and MuStab) were analyzed. Based on the deleterious scores that overlap in these in silico approaches, and the availability of three-dimensional structures, structure analysis was carried out with the major mutations that occurred in the native protein coded by FANCD1/BRCA2 gene. In this work, we report the results of the first molecular dynamics (MD) simulation study performed to analyze the structural level changes in time scale level with respect to the native and mutated protein complexes (G25R, W31C, W31R in FANCD1/BRCA2-PALB2, and F1524V, V1532F in FANCD1/BRCA2-RAD51). Analysis of the MD trajectories indicated that predicted deleterious variants alter the structural behavior of BRCA2-PALB2 and BRCA2-RAD51 protein complexes. In addition, statistical analysis was employed to test the significance of these in silico tool predictions. Based on these predictions, we conclude that the identification of disease-related SNPs by in silico methods, in combination with MD approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. The methods reviewed here generated a considerable amount of valuable data, but also the need for further validation.

Molecular docking and molecular dynamics study on the effect of ERCC1 deleterious polymorphisms in ERCC1-XPF heterodimer.

Excision repair cross complementation group 1 (ERCC1) is an important protein in the nucleotide excision repair (NER) pathway, which is responsible for removing DNA adducts induced by platinum based compounds. The heterodimer ERCC1-XPF is one of two endonucleases required for NER. Genetic variations or polymorphisms in ERCC1 gene alter DNA repair capacity. Reduced DNA repair (NER) capacity may result in tumors and enhances cisplatin chemotherapy in cancer patients, which functions by causing DNA damage. Therefore, ERCC1 variants have the potential to be used as a strong candidate biomarker in cancer treatments. In this study we identified five variants V116M, R156Q, A199T, S267P, and R322C of ERCC1 gene as highly deleterious. Further structural and functional analysis has been conducted for ERCC1 protein in the presence of three variants V116M, R156Q, and A199T. Occurrence of theses variations adversely affected the regular interaction between ERCC1 and XPF protein. Analysis of 20 ns molecular dynamics simulation trajectories reveals that the predicted deleterious variants altered the ERCC1-XPF complex stability, flexibility, and surface area. Notably, the number of hydrogen bonds in ERCC1-XPF mutant complexes decreased in the molecular dynamic simulation periods. Overall, this study explores the link between the ERCC1 deleterious variants and cisplatin chemotherapy for various cancers with the help of molecular docking and molecular dynamic approaches.

In silico analysis of prion protein mutants: a comparative study by molecular dynamics approach.

Polymorphisms in the human prion proteins lead to amino acid substitutions by the conversion of PrPC to PrPSc and amyloid formation, resulting in prion diseases such as familial Creutzfeldt-Jakob disease, Gerstmann-Straussler-Scheinker disease and fatal familial insomnia. Cation-π interaction is a non-covalent binding force that plays a significant role in protein stability. Here, we employ a novel approach by combining various in silico tools along with molecular dynamics simulation to provide structural and functional insight into the effect of mutation on the stability and activity of mutant prion proteins. We have investigated impressions of prevalent mutations including 1E1S, 1E1P, 1E1U, 1E1P, 1FKC and 2K1D on the human prion proteins and compared them with wild type. Structural analyses of the models were performed with the aid of molecular dynamics simulation methods. According to our results, frequently occurred mutations were observed in conserved sequences of human prion proteins and the most fluctuation values appear in the 2K1D mutant model at around helix 4 with residues ranging from 190 to 194. Our observations in this study could help to further understand the structural stability of prion proteins.

In silico discrimination of nsSNPs in hTERT gene by means of local DNA sequence context and regularity.

Understanding and predicting the significance of novel genetic variants revealed by DNA sequencing is a major challenge to integrate and interpret in medical genetics with medical practice. Recent studies have afforded significant advances in characterization and predicting the association of single nucleotide polymorphisms in human TERT with various disorders, but the results remain inconclusive. In this context, a comparative study between disease causing and novel mutations in hTERT gene was performed computationally. Out of 59 missense mutations, five variants were predicted to be less stable with the most deleterious effect on hTERT gene by in silico tools, in which two mutations (L584W and M970T) were not previously reported to be involved in any of the human disorders. To get insight into the structural and functional impact due to the mutation, docking study and interaction analysis was performed followed by 6 ns molecular dynamics simulation. These results may provide new perspectives for the targeted drug discovery in the coming future.

Extrapolating the effect of deleterious nsSNPs in the binding adaptability of flavopiridol with CDK7 protein: a molecular dynamics approach.

BACKGROUND: Recent reports suggest the role of nonsynonymous single nucleotide polymorphisms (nsSNPs) in cyclin-dependent kinase 7 (CDK7) gene associated with defect in the DNA repair mechanism that may contribute to cancer risk. Among the various inhibitors developed so far, flavopiridol proved to be a potential antitumor drug in the phase-III clinical trial for chronic lymphocytic leukemia. Here, we described a theoretical assessment for the discovery of new drugs or drug targets in CDK7 protein owing to the changes caused by deleterious nsSNPs. METHODS: Three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on protein function by SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&GO, PhD-SNP, and screening for non-acceptable polymorphisms (SNAP). Furthermore, we analyzed the native and proposed mutant models in atomic level 10 ns simulation using the molecular dynamics (MD) approach. Finally, with the aid of Autodock 4.0 and PatchDock, we analyzed the binding efficacy of flavopiridol with CDK7 protein with respect to the deleterious mutations. RESULTS: By comparing the results of all seven prediction tools, three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on the protein function. The results of protein stability analysis inferred that I63R and H135R exhibited less deviation in root mean square deviation in comparison with the native and T285M protein. The flexibility of all the three mutant models of CDK7 protein is diverse in comparison with the native protein. Following to that, docking study revealed the change in the active site residues and decrease in the binding affinity of flavopiridol with mutant proteins. CONCLUSION: This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. The identification of disease related SNPs by computational methods has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. LAY ABSTRACT: Cell cycle regulatory protein, CDK7, is linked with DNA repair mechanism which can contribute to cancer risk. The main aim of this study is to extrapolate the relationship between the nsSNPs and their effects in drug-binding capability. In this work, we propose a new methodology which (1) efficiently identified the deleterious nsSNPs that tend to have functional effect on protein function upon mutation by computational tools, (2) analyze d the native protein and proposed mutant models in atomic level using MD approach, and (3) investigated the protein-ligand interactions to analyze the binding ability by docking analysis. This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. Overall, this approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.

Predicting the impact of single-nucleotide polymorphisms in CDK2-flavopiridol complex by molecular dynamics analysis.

Cyclic-dependent kinase 2 (CDK2) is one of the primary protein kinases involved in the regulation of cell cycle progression. Flavopiridol is a flavonoid derived from an indigenous plant act as a potent antitumor drug showing increased inhibitory activity toward CDK2. The presence of deleterious variations in CDK2 may produce different effects in drug-binding adaptability. Studies on nsSNPs of CDK2 gene will provide information on the most likely variants associated with the disease. Furthermore, investigating the relationship between deleterious variants and its ripple effect in the inhibitory action with drug will provide fundamental information for the development of personalized therapies. In this study, we predicted four variants Y15S, V18L, P45L, and V69A of CDK2 as highly deleterious. Occurrence of these variations seriously affected the normal binding capacity of flavopiridol with CDK2. Analysis of 10-ns molecular dynamics (MD) simulation trajectories indicated that the predicted deleterious variants altered the CDK2 stability, flexibility, and surface area. Notably, we noticed the decrease in number of hydrogen bonds between CDK2 and flavopiridol mutant complexes in the whole dynamic period. Overall, this study explores the possible relationship between the CDK2 deleterious variants and the drug-binding ability with the help of molecular docking and MD approaches.

LSHGD: a database for human leprosy susceptible genes.

Studies aiming to explore the involvement of host genetic factors to determine susceptibility to develop disease and individual's response to the infection with Mycobacterium leprae have increased in recent years. To address this issue, we have developed a Leprosy Susceptible Human Gene Database (LSHGD) to integrate leprosy and human associated 45 genes by profound literature search. This will serve as a user-friendly and interactive platform to understand the involvement of human polymorphisms (SNPs) in leprosy, independent genetic control over both susceptibility to leprosy and its association with multi-drug resistance of M. leprae. As the first human genetic database in leprosy it aims to provide information about the associated genes, corresponding protein sequences, available three dimensional structures and polymorphism related to leprosy. In conclusion, this will serve as a multifunctional valuable tool and convenient information platform which is freely available at http://www.vit.ac.in/leprosy/leprosy.htm and enables the user to retrieve information of their interest.

Predicting the impact of deleterious single point mutations in SMAD gene family using structural bioinformatics approach.

Functional alteration in SMAD proteins leads to dis-regulation of its mechanism results in possibilities of high risk diseases like fibrosis, cancer, juvenile polyposis etc. Studying single nucleotide polymorphism (SNP) in SMAD genes helps understand the malfunction of these proteins. In this study, we focused on deleterious effects of nsSNPs in both structural and functional level using publically available bioinformatics tools. We have mainly focused on identifying deleterious nsSNPs in both structural and functional level in SMAD genes by using SIFT, PolyPhen, SNPs&GO, I-Mutant 3.0, MUpro and PANTHER. Structure analysis was carried out with the major mutation that occurred in the native protein coded by SMAD genes and its amino acid positions (R358W, K306S, R310G, S433R and R361C). SRide was used to check the stability of the native and mutant modelled proteins. In addition, we used MAPPER to identify SNPs present in transcription factor binding sites. These findings demonstrate that the in silico approaches can be used efficiently to identify potential candidate SNPs in large scale analysis.

Investigating the structural impacts of I64T and P311S mutations in APE1-DNA complex: a molecular dynamics approach.

BACKGROUND: Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA. PRINCIPAL FINDINGS: In this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis. SIGNIFICANCE: Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis.

Exploration of structural stability in deleterious nsSNPs of the XPA gene: A molecular dynamics approach.

BACKGROUND: Distinguishing the deleterious from the massive number of non-functional nsSNPs that occur within a single genome is a considerable challenge in mutation research. In this approach, we have used the existing in silico methods to explore the mutation-structure-function relationship in the XPAgene. MATERIALS AND METHODS: We used the Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping (PolyPhen), I-Mutant 2.0, and the Protein Analysis THrough Evolutionary Relationships methods to predict the effects of deleterious nsSNPs on protein function and evaluated the impact of mutation on protein stability by Molecular Dynamics simulations. RESULTS: By comparing the scores of all the four in silico methods, nsSNP with an ID rs104894131 at position C108F was predicted to be highly deleterious. We extended our Molecular dynamics approach to gain insight into the impact of this non-synonymous polymorphism on structural changes that may affect the activity of the XPAgene. CONCLUSION: Based on the in silico methods score, potential energy, root-mean-square deviation, and root-mean-square fluctuation, we predict that deleterious nsSNP at position C108F would play a significant role in causing disease by the XPA gene. Our approach would present the application of in silicotools in understanding the functional variation from the perspective of structure, evolution, and phenotype.