A novel strategy for designing the magic shotguns for distantly related target pairs.

Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics.

Glioblastoma (GBM) is the most common and aggressive intracranial malignant brain tumor, and the abnormal expression of HDAC1 is closely correlated to the progression, recurrence and metastasis of GBM cells, making selective inhibition of HDAC1 a promising strategy for GBM treatments. Among all available selective HDAC1 inhibitors, the macrocyclic peptides have gained great attention due to their remarkable inhibitory selectivity on HDAC1. However, the binding mechanism underlying this selectivity is still elusive, which increases the difficulty of designing and synthesizing the macrocyclic peptide-based anti-GBM drug. Herein, multiple computational approaches were employed to explore the binding behaviors of a typical macrocyclic peptide FK228 in both HDAC1 and HDAC6. Starting from the docking conformations of FK228 in the binding pockets of HDAC1&6, relatively long MD simulation (500 ns) shown that the hydrophobic interaction and hydrogen bonding of E91 and D92 in the Loop2 of HDAC1 with the Cap had a certain traction effect on FK228, and the sub-pocket formed by Loop1 and Loop2 in HDAC1 could better accommodate the Cap group, which had a positive effect on maintaining the active conformation of FK228. While the weakening of the interactions between FK228 and the residues in the Loop2 of HDAC6 during the MD simulation led to the large deflection of FK228 in the binding site, which also resulted in the decrease in the interactions between the Linker region of FK228 and the previously identified key amino acids (H134, F143, H174, and F203). Therefore, the residues located in Loop1 and Loop2 contributed in maintaining the active conformation of FK228, which would provide valuable hints for the discovery and design of novel macrocyclic polypeptide HDAC inhibitors.

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-HT1A receptor partial agonist. The interaction between vilazodone and the 5-HT1A 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.

VARIDT 1.0: variability of drug transporter database.

The absorption, distribution and excretion of drugs are largely determined by their transporters (DTs), the variability of which has thus attracted considerable attention. There are three aspects of variability: epigenetic regulation and genetic polymorphism, species/tissue/disease-specific DT abundances, and exogenous factors modulating DT activity. The variability data of each aspect are essential for clinical study, and a collective consideration among multiple aspects becomes crucial in precision medicine. However, no database is constructed to provide the comprehensive data of all aspects of DT variability. Herein, the Variability of Drug Transporter Database (VARIDT) was introduced to provide such data. First, 177 and 146 DTs were confirmed, for the first time, by the transporting drugs approved and in clinical/preclinical, respectively. Second, for the confirmed DTs, VARIDT comprehensively collected all aspects of their variability (23 947 DNA methylations, 7317 noncoding RNA/histone regulations, 1278 genetic polymorphisms, differential abundance profiles of 257 DTs in 21 781 patients/healthy individuals, expression of 245 DTs in 67 tissues of human/model organism, 1225 exogenous factors altering the activity of 148 DTs), which allowed mutual connection between any aspects. Due to huge amount of accumulated data, VARIDT made it possible to generalize characteristics to reveal disease etiology and optimize clinical treatment, and is freely accessible at: https://db.idrblab.org/varidt/ and http://varidt.idrblab.net/.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies.

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were 'first' discovered to be non-CWP. 'Then', 21 new strategies that combined the 'sample'-based method with the 'metabolite'-based one were found to be CWP. 'Finally', a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery.

The type IV bacterial secretion system (SS) is reported to be one of the most ubiquitous SSs in nature and can induce serious conditions by secreting type IV SS effectors (T4SEs) into the host cells. Recent studies mainly focus on annotating new T4SE from the huge amount of sequencing data, and various computational tools are therefore developed to accelerate T4SE annotation. However, these tools are reported as heavily dependent on the selected methods and their annotation performance need to be further enhanced. Herein, a convolution neural network (CNN) technique was used to annotate T4SEs by integrating multiple protein encoding strategies. First, the annotation accuracies of nine encoding strategies integrated with CNN were assessed and compared with that of the popular T4SE annotation tools based on independent benchmark. Second, false discovery rates of various models were systematically evaluated by (1) scanning the genome of Legionella pneumophila subsp. ATCC 33152 and (2) predicting the real-world non-T4SEs validated using published experiments. Based on the above analyses, the encoding strategies, (a) position-specific scoring matrix (PSSM), (b) protein secondary structure & solvent accessibility (PSSSA) and (c) one-hot encoding scheme (Onehot), were identified as well-performing when integrated with CNN. Finally, a novel strategy that collectively considers the three well-performing models (CNN-PSSM, CNN-PSSSA and CNN-Onehot) was proposed, and a new tool (CNN-T4SE, https://idrblab.org/cnnt4se/) was constructed to facilitate T4SE annotation. All in all, this study conducted a comprehensive analysis on the performance of a collection of encoding strategies when integrated with CNN, which could facilitate the suppression of T4SS in infection and limit the spread of antimicrobial resistance.

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.

Prevalence of phycotoxin contamination in shellfish from the Northern Bering Sea and the Chukchi Sea.

To understand phycotoxin contamination in shellfish in the sub-Arctic and Arctic areas, scanning for the presence of 13 hydrophilic and lipophilic toxin components each was by liquid chromatography tandem quadrupole mass spectrometry analysis in shellfish samples collected from the Northern Bering Sea and the Chukchi Sea in 2014. The results showed that shellfish collected in both areas werecontaminated to different extents. Saxitoxin (STX), decarbamoylsaxitoxin (dcSTX) and decarbamoylneosaxitoxin (dcNEO) were the most frequently detected hydrophilic components, with maximum concentrations of 90.1 µg/kg, 112.25 µg/kg and 23.09 µg/kg, respectively. Although gonyautoxins (GTXs) were only detected in 3 samples, they were the main contributors to overall toxicity of high-latitude samples, especially GTX1. For lipophilic toxins, spirolide-1 (SPX1) and yessotoxin (YTX) were present in all samples at low levels (< 7 µg/kg and < 50 µg/kg, respectively). Only 5 samples showed evidence of okadaic acid (OA) and dinophysistoxin-2 (DTX-2) at low concentrations, ranging from 0.42 µg/kg to 7.23 µg/kg and 3.03 µg/kg to 30.59 µg/kg, respectively. Notably, a high level of pectenotoxin-1 (PTX-1) at 467.40 µg/kg was found in the shellfish collected at the northernmost station, exceeding the safety regulation standard by nearly 3 times. For both lipophilic and hydrophilic toxins, contamination in shellfish in the sub-Arctic and the Arctic area may be much more widespread and severe than was previously thought. This study highlighted the need to monitor toxins in a wider variety of shellfish, especially economic or commercial species, and across a wider range of sub-Arctic and Arctic waters, as well as the potential sources of these toxins.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target.

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure-therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein-protein interaction (PPI) network and biological system profile on the drugs' efficacy-safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification.

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.

Improved source apportionment of PAHs and Pb by integrating Pb stable isotopes and positive matrix factorization application (PAHs): A historical record case study from the northern South China Sea.

To obtain the historical changes of pyrogenic sources, integrated source apportionment methods, which include PAH compositions, diagnostic ratios (DRs), Pb isotopic ratios, and positive matrix factorization (PMF) model, were developed and applied in sediments of the northern South China Sea. These methods provided a gradually clear picture of energy structural change. Spatially, Σ15PAH (11.3 to 95.5ng/g) and Pb (10.2 to 74.6µg/g) generally exhibited decreasing concentration gradient offshore; while the highest levels of PAHs and Pb were observed near the southern Taiwan Strait, which may be induced by accumulation of different fluvial input. Historical records of pollutants followed closely with the economic development of China, with fast growth of Σ15PAH and Pb occurring since the 1980s and 1990s, respectively. The phasing-out of leaded gasoline in China was captured with a sharp decrease of Pb after the mid-1990s. PAHs and Pb correlated well with TOC and clay content for core sediments, which was not observed for surface sediments. There was an up-core increase of high molecular PAH proportions. Coal and biomass burning were then qualitatively identified as the major sources of PAHs with DRs. Furthermore, shift toward less radiogenic signatures of Pb isotopic ratios after 1900 revealed the start and growing importance of industrial sources. Finally, a greater separation and quantification of various input was achieved by a three-factor PMF model, which made it clear that biomass burning, coal combustion, and vehicle emissions accounted for 40±20%, 41±13%, and 19±12% of PAHs through the core. Biomass and coal combustion acted as major sources before 2000, while contributions from vehicle emission soared thereafter. The integrated multi-methodologies here improved the source apportionment by reducing biases with a step-down and cross-validation perspective, which could be similarly applied to other aquatic systems.

Fate of polycyclic aromatic hydrocarbons from the North Pacific to the Arctic: Field measurements and fugacity model simulation.

Polycyclic aromatic hydrocarbons (PAHs) have accumulated ubiquitously inArctic environments, where re-volatilization of certain organic pollutants as a result of climate change has been observed. To investigate the fate of semivolatile organic compounds in the Arctic, dissolved PAHs in the surface seawaters from the temperate Pacific Ocean to the Arctic Ocean, as well as a water column in the Arctic Ocean, were collected during the 4th Chinese National Arctic Research Expedition in summer 2010. The total concentrations of seven dissolved PAHs in surface water ranged from 1.0 to 5.1 ng L-1, decreasing with increasing latitude. The vertical profile of PAHs in the Arctic Ocean was generally characteristic of surface enrichment and depth depletion, which emphasized the role of vertical water stratification and particle settling processes. A level III fugacity model was developed in the Bering Sea under steady state assumption. Model results quantitatively simulated the transfer processes and fate of PAHs in the air and water compartments, and highlighted a summer air-to-sea flux of PAHs in the Bering Sea, which meant that the ocean served as a sink for PAHs, at least in summer. Acenaphthylene and acenaphthene reached equilibrium in air-water diffusive exchange, and any perturbation, such as a rise in temperature, might lead to disequilibrium and remobilize these compounds from their Arctic reservoirs.

Fate of Polycyclic Aromatic Hydrocarbons in Seawater from the Western Pacific to the Southern Ocean (17.5°N to 69.2°S) and Their Inventories on the Antarctic Shelf.

Semivolatile organic compounds such as polycyclic aromatic hydrocarbons (PAHs) have the potential to reach pristine environments through long-range transport. To investigate the long-range transport of the PAHs and their fate in Antarctic seawater, dissolved PAHs in the surface waters from the western Pacific to the Southern Ocean (17.5°N to 69.2°S), as well as down to 3500 m PAH profiles in Prydz Bay and the adjacent Southern Ocean, were observed during the 27th Chinese National Antarctic Research Expedition in 2010. The concentrations of Σ9PAH in the surface seawater ranged from not detected (ND) to 21 ng L(-1), with a mean of 4.3 ng L(-1); and three-ring PAHs were the most abundant compounds. Samples close to the Australian mainland displayed the highest levels across the cruise. PAHs originated mainly from pyrogenic sources, such as grass, wood, and coal combustion. Vertical profiles of PAHs in Prydz Bay showed a maximum at a depth of 50 m and less variance with depth. In general, we inferred that the water masses as well as the phytoplankton were possible influencing factors on PAH surface-enrichment depth-depletion distribution. Inventory estimation highlighted the contribution of intermediate and deep seawater on storing PAHs in seawater from Prydz Bay, and suggested that climate change rarely shows the rapid release of the PAHs currently stored in the major reservoirs (intermediate and deep seawater).

[Matrix effect and retention efficiency of hydrophilic-lipophilic balance cartridges in multi-residual determination of veterinary drugs in river water].

Matrix effect is an important interfering factor in LC-MS quantitative analysis. In this paper, matrix effects and retention efficiencies of 33 veterinary drugs spiked in river water were studied on hydrophilic-lipophilic balance (HLB) cartridges of 3 brands (Waters, Supelco, and CNW), using LC-MS/MS for detection and reverse osmosis (RO) water as the control under 500-fold concentration. In RO water, only the exogenous matrix effects were observed on three brands of HLB cartridges. Most quinolones and tetracyclines showed positive matrix effects. Estrogens showed negative matrix effects on two brands of HLB cartridges. Sulfonamides were not obviously affected by matrix effects. Chloramphenicols showed negative matrix effects on one brand of HLB cartridge. In river water, matrix effects were different from those of the RO water due to the combined exogenous and endogenous interfering substances. Sulfonamides showed slight matrix effects as those in RO water. Most quinolones and tetracyclines showed positive matrix effects. Chloramphenicols and estrogens showed negative matrix effects. Compared to the external standard method, matrix matched calibration method effectively overcame the matrix effects with better quantitative results. The recoveries of 33 target veterinary drugs spiked in river water at 50 ng/L and 200 ng/L levels were in the ranges of 40.3%-146.0% (Waters), 37.8%-104.2% (Supelco), and 52.9%-150.1% (CNW) with RSDs (n = 4) of 0.2%-14.6%. The results indicated that there was no significant difference in the retention efficiency between the 3 HLB cartridges with the matrix matched calibration method. This study provided supporting data for the HLB cartridge selection in multi-residual determination of the veterinary drugs in river water samples.