Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs

Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features

5-Methylcytosine (m5C) is a well-known post-transcriptional modification that plays significant roles in biological processes, such as RNA metabolism, tRNA recognition, and stress responses. Traditional high-throughput techniques on identification of m5C sites are usually time consuming and expensive. In addition, the number of RNA sequences shows explosive growth in the post-genomic era. Thus, machine-learning-based methods are urgently requested to quickly predict RNA m5C modifications with high accuracy. Here, we propose a noval support-vector-machine (SVM)-based tool, called iRNA-m5C_SVM, by combining multiple sequence features to identify m5C sites in Arabidopsis thaliana. Eight kinds of popular feature-extraction methods were first investigated systematically. Then, four well-performing features were incorporated to construct a comprehensive model, including position-specific propensity (PSP) (PSNP, PSDP, and PSTP, associated with frequencies of nucleotides, dinucleotides, and trinucleotides, respectively), nucleotide composition (nucleic acid, di-nucleotide, and tri-nucleotide compositions; NAC, DNC, and TNC, respectively), electron-ion interaction pseudopotentials of trinucleotide (PseEIIPs), and general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-general). Evaluated accuracies over 10-fold cross-validation and independent tests achieved 73.06% and 80.15%, respectively, which showed the best predictive performances in A. thaliana among existing models. It is believed that the proposed model in this work can be a promising alternative for further research on m5C modification sites in plant.

MetaFS: Performance assessment of biomarker discovery in metaproteomics

Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. So far, a variety of feature selection methods have been developed for metaproteomic study. However, due to FS's performance depended heavily on the data characteristics of a given research, the well-suitable feature selection method must be carefully selected to obtain the reproducible differential proteins. Moreover, it is critical to evaluate the performance of each FS method according to comprehensive criteria, because the single criterion is not sufficient to reflect the overall performance of the FS method. Therefore, we developed an online tool named MetaFS, which provided 13 types of FS methods and conducted the comprehensive evaluation on the complex FS methods using four widely accepted and independent criteria. Furthermore, the function and reliability of MetaFS were systematically tested and validated via two case studies. In sum, MetaFS could be a distinguished tool for discovering the overall well-performed FS method for selecting the potential biomarkers in microbiome studies. The online tool is freely available at https://idrblab.org/metafs/.

The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years

Despite The Central Dogma states the destiny of gene as 'DNA makes RNA and RNA makes protein', the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.

PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.

Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?

Pseudouridine (Ψ) is the most abundant RNA modification and has been found in many kinds of RNAs, including snRNA, rRNA, tRNA, mRNA, and snoRNA. Thus, Ψ sites play a significant role in basic research and drug development. Although some experimental techniques have been developed to identify Ψ sites, they are expensive and time consuming, especially in the post-genomic era with the explosive growth of known RNA sequences. Thus, highly accurate computational methods are urgently required to quickly detect the Ψ sites on uncharacterized RNA sequences. Several predictors have been proposed using multifarious features, but their evaluated performances are still unsatisfactory. In this study, we first identified Ψ sites for H. sapiens, S. cerevisiae, and M. musculus using the sequence features from the bi-profile Bayes (BPB) method based on the random forest (RF) and support vector machine (SVM) algorithms, where the performances were evaluated using 5-fold cross-validation and independent tests. It was found that the SVM-based accuracies were 3.55% and 5.09% lower than the iPseU-CUU predictor for the H_990 and S_628 datasets, respectively. Almost the same-level results were obtained for M_994 and an independent H_200 dataset, even showing a 5.0% improvement for S_200. Then, three different kinds of features, including basic Kmer, general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-General), and nucleotide chemical property (NCP) and nucleotide density (ND) from the iRNA-PseU method, were combined with BPB to show their comprehensive performances, where the effective features are selected by the max-relevance-max-distance (MRMD) method. The best evaluated accuracies of the combined features for the S_628 and M_994 datasets were achieved at 70.54% and 72.45%, which were 2.39% and 0.65% higher than iPseU-CUU. For the S_200 dataset, it was also improved 8% from 69% to 77%. However, there was no obvious improvement for H. sapiens, which was evaluated as approximately 63.23% and 72.0% for the H_990 and H_200 datasets, respectively. The overall performances for Ψ identification using BPB features as well as the combined features were not obviously improved. Although some kinds of feature extraction methods based on the RNA sequence information have been applied to construct the predictors in previous studies, the corresponding accuracies are generally in the range of 60%-70%. Thus, researchers need to reconsider whether there is any sequence feature in the RNA Ψ modification prediction problem.

The binding mode of vilazodone in the human serotonin transporter elucidated by ligand docking and molecular dynamics simulations

Vilazodone is a novel antidepressant used for the treatment of major depressive disorder (MDD) with a primary action mechanism of inhibiting the human serotonin reuptake transporter (hSERT) and acting as a 5-HT_1A receptor partial agonist. The interaction between vilazodone and the 5-HT_1A receptor has been reported, however, the binding mode of vilazodone in the hSERT remains elusive. In the current study, to elucidate the molecular mechanism of vilazodone binding in the hSERT, the drug and its five analogs were docked into the hSERT crystal structure as initial conformations and were sampled by 400 ns molecular dynamics (MD) simulations. Through the analysis of the profiles of protein-ligand binding free energies, interaction fingerprints, and conformational rearrangements, the binding mode of vilazodone in the hSERT was revealed. As a result, unlike the classical antidepressants located in the S1 site of the hSERT, vilazodone adopted a linear pose in the binding pocket. Its arylpiperazine fragment occupies the central site (S1) and interacts with Y95, D98, I172, Y176, F335, F341, S438, and T439, while the indole fragment extends to the allosteric site (S2) via interacting with the ionic switch (R104/E403) between the two sites. The new insights obtained are not only helpful in understanding the binding mode of vilazodone in the hSERT, but also provide valuable guidance to the discovery of novel antidepressant drugs.

Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics

Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/).

Ion Channels as Therapeutic Targets for Type 1 Diabetes Mellitus

Ion channels are integral proteins expressed in almost all living cells and are involved in muscle contraction and nutrient transport. They play a critical role in the normal functioning of the excitable tissues of the nervous system and regulate the action potential and contraction events. Dysfunction of genes encodes ion channel proteins, which disrupt the channel function and lead to a number of diseases, among which is type 1 diabetes mellitus (T1DM). Therefore, understanding the complex mechanism of ion channel receptors is necessary to facilitate the diagnosis and management of treatment. In this review, we summarize the mechanism of important ion channels and their potential role in the regulation of insulin secretion along with the limitations of ion channels as therapeutic targets. Furthermore, we discuss the recent investigations of the mechanism regulating the ion channels in pancreatic beta cells, which suggest that ion channels are active participants in the regulation of insulin secretion.

Identification of Anti-cancer Peptides Based on Multi-classifier System

AIMS AND OBJECTIVE

Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti-cancer peptides through experiments take a lot of time and money, therefore, it is necessary to develop a fast and accurate calculation model to identify the anti-cancer peptide. Machine learning algorithms are a good choice.

MATERIALS AND METHODS

In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting.

RESULTS AND CONCLUSION

The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

Integrating the Ribonucleic Acid Sequencing Data From Various Studies for Exploring the Multiple Sclerosis-Related Long Noncoding Ribonucleic Acids and Their Functions

Multiple sclerosis (MS) is a chronic fatal central nervous system (CNS) disease involving in complex immunity dysfunction. Recently, long noncoding RNAs (lncRNAs) were discovered as the important regulatory factors for the pathogenesis of MS. However, these findings often cannot be repeated and confirmed by the subsequent studies. We considered that the small-scale samples or the heterogeneity among various tissues may result in the divergence of the results. Currently, RNA-seq has become a powerful approach to quantify the abundances of lncRNA transcripts. Therefore, we comprehensively collected the MS-related RNA-seq data from a variety of previous studies, and integrated these data using an expression-based meta-analysis to identify the differentially expressed lncRNA between MS patients and controls in whole samples and subgroups. Then, we performed the Jensen-Shannon (JS) divergence and cluster analysis to explore the heterogeneity and expression specificity among various tissues. Finally, we investigated the potential function of identified lncRNAs for MS using weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA), and 5,420 MS-related lncRNAs specifically expressed in the brain tissue were identified. The subgroup analysis found a small heterogeneity of the lncRNA expression profiles between brain and blood tissues. The results of WGCNA and GSEA showed that a potential important function of lncRNAs in MS may be involved in the regulation of ribonucleoproteins and tumor necrosis factor cytokines receptors. In summary, this study provided a strategy to explore disease-related lncRNAs on genome-wide scale, and our findings will be benefit to improve the understanding of MS pathogenesis.

AOPs-SVM: A Sequence-Based Classifier of Antioxidant Proteins Using a Support Vector Machine

Antioxidant proteins play important roles in countering oxidative damage in organisms. Because it is time-consuming and has a high cost, the accurate identification of antioxidant proteins using biological experiments is a challenging task. For these reasons, we proposed a model using machine-learning algorithms that we named AOPs-SVM, which was developed based on sequence features and a support vector machine. Using a testing dataset, we conducted a jackknife cross-validation test with the proposed AOPs-SVM classifier and obtained 0.68 in sensitivity, 0.985 in specificity, 0.942 in average accuracy, 0.741 in MCC, and 0.832 in AUC. This outperformed existing classifiers. The experiment results demonstrate that the AOPs-SVM is an effective classifier and contributes to the research related to antioxidant proteins. A web server was built at http://server.malab.cn/AOPs-SVM/index.jsp to provide open access.

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility

Aims

As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ.

Methods

In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted.

Results

Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ.

Conclusion

A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.

How Does Chirality Determine the Selective Inhibition of Histone Deacetylase 6? A Lesson from Trichostatin A Enantiomers Based on Molecular Dynamics

Histone deacetylase 6 (HDAC6) plays a key role in a variety of neurological disorders, which makes it attractive drug target for the treatment of Alzheimer's disease, Parkinson's disease, and memory/learning impairment. The selectivity of HDAC6 inhibitors (sHDAC6Is) are widely considered to be susceptible to the sizes of their Cap group and the physicochemical properties of their linker or zinc-binding group, which makes the discovery of new sHDAC6Is extremely difficult. With the discovery of the distinct selectivity between Trichostatin A (TSA) enantiomers, the chirality residing in the connective units between TSA's Cap and linker shows a great impact on its selectivity. However, the mechanism underlining ( S)-TSA's selectivity is still elusive, and the way chirality switches the selective ( S)-TSA to nonselective ( R)-TSA is unknown. In this study, multiple computational approaches were collectively applied to explore, validate, and differentiate the binding modes of two TSA enantiomers in HDACs (especially the HDAC6) at atomic level. First, two nonconservative residues (G200/M205 and Y197/F202 in HDAC1/6) in loop3 and four conservative residues deep inside the hydrophobic binding pocket were discovered as the decisive residues of ( S)-TSA's selectivity toward HDAC6. Then, a novel mechanism underlying the selectivity of ( S)-TSA toward HDAC6 was proposed, which was composed of the trigger by two nonconservative residues F202 and M205 in HDAC6 and a subsequently improved fit of ( S)-TSA deep inside HDAC6's hydrophobic binding pocket. TSA enantiomers were used as a molecular probe to explore the mechanism underlying sHDAC6Is' selectivity in this study. Because of their decisive roles in ( S)-TSA's selectivity to HDAC6, both F202 and M205 in HDAC6 should be especially considered in the discovery of novel sHDAC6Is.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein⁻protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.