The effect of acute sleep deprivation on cortisol level: a systematic review and meta-analysis.

Acute sleep deprivation has aroused widespread concern and the relationship between acute sleep deprivation and cortisol levels is inconsistent. This study aimed to explore additional evidence and details. The PubMed, Web of Science, EMBASE, CLINAHL and Cochrane databases were searched for eligible studies published up to June 7, 2023. All analyses were performed using Review Manager 5.4 and Stata/SE 14.0. A total of 24 studies contributed to this meta-analysis. There was no significant difference in cortisol levels between participants with acute sleep deprivation and normal sleep in 21 crossover-designed studies (SMD = 0.18; 95% CI: -0.11, 0.45; p = 0.208) or 3 RCTs (SMD = 0.26; 95% CI: -0.22, 0.73; p = 0.286). Subgroup analysis revealed that the pooled effects were significant for studies using serum as the sample (SMD = 0.46; 95%CI: 0.11, 0.81; p = 0.011). Studies reporting cortisol levels in the morning, in the afternoon and in the evening did not show significant difference (p > 0.05). The pooled effects were statistically significant for studies with multiple measurements (SMD = 0.28; 95%CI: 0.03, 0.53; p = 0.027) but not for studies with single cortisol assessments (p = 0.777). When the serum was used as the test sample, the cortisol levels of individuals after acute sleep deprivation were higher than those with normal sleep.

Effect of high-fat diet on the fatty acid profiles of brain in offspring mice exposed to maternal gestational diabetes mellitus.

OBJECTIVE: Fatty acids play a critical role in the proper functioning of the brain. This study investigated the effects of a high-fat (HF) diet on brain fatty acid profiles of offspring exposed to maternal gestational diabetes mellitus (GDM). METHODS: Insulin receptor antagonist (S961) and HF diet were used to establish the GDM animal model. Brain fatty acid profiles of the offspring mice were measured by gas chromatography at weaning and adulthood. Protein expressions of the fatty acid transport pathway Wnt3/ß-catenin and the target protein major facilitator superfamily domain-containing 2a (MFSD2a) were measured in the offspring brain by Western blot. RESULTS: Maternal GDM increased the body weight of male offspring (P < 0.05). In weaning offspring, factorial analysis showed that maternal GDM increased the monounsaturated fatty acid (MUFA) percentage of the weaning offspring's brain (P < 0.05). Maternal GDM decreased offspring brain arachidonic acid (AA), but HF diet increased brain linoleic acid (LA) (P < 0.05). Maternal GDM and HF diet reduced offspring brain docosahexaenoic acid (DHA), and the male offspring had higher DHA than the female offspring (P < 0.05). In adult offspring, factorial analysis showed that HF diet increased brain MUFA in offspring, and male offspring had higher brain MUFA than female offspring (P < 0.05). The HF diet increased brain LA in the offspring. Male offspring had higher level of AA than female offspring (P < 0.05). HF diet reduced DHA in the brains of female offspring. The brain protein expression of ß-catenin and MFSD2a in both weaning and adult female offspring was lower in the HF + GDM group than in the CON group (P < 0.05). CONCLUSIONS: Maternal GDM increased the susceptibility of male offspring to HF diet-induced obesity. HF diet-induced adverse brain fatty acid profiles in both male and female offspring exposed to GDM.

Gestational Diabetes Mellitus Remodels the Fetal Brain Fatty Acid Profile Through Placenta-Brain Lipid Axis in C57BL/6J Mice.

BACKGROUND: Polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA), are critical for proper fetal brain growth and development. Gestational diabetes mellitus (GDM) could affect maternal-fetal fatty acid metabolism. OBJECTIVE: This study aimed to explore the effect of GDM and high-fat (HF) diet on the DHA transport signaling pathway in the placenta-brain axis and fatty acid concentrations in the fetal brain. METHODS: Insulin receptor antagonist (S961) and HF diet were used to establish an animal model of GDM. Eighty female C57BL/6J mice were randomly divided into control (CON), GDM, HF, and HF+GDM groups. The fatty acid profiles of the maternal liver and fetal brain were analyzed by gas chromatography. In addition, we analyzed the protein amounts of maternal liver fatty acid desaturase (FADS1/3), elongase (ELOVL2/5) and the regulatory factor sterol-regulatory element-binding protein (SREBP)-1c, and the DHA transport signaling pathway (Wnt3/ß-catenin/MFSD2a) of the placenta and fetal brain using western blotting. RESULTS: GDM promoted the decrease of maternal liver ELOVL2, ELOVL5, and SREBP-1c. Accordingly, we observed a significant decrease in the amount of maternal liver arachidonic acid (AA), DHA, and total n-3 PUFA and n-6 PUFA induced by GDM. GDM also significantly decreased the amount of DHA and n-3 PUFA in the fetal brain. GDM downregulated the Wnt3/ß-catenin/MFSD2a signaling pathway, which transfers n-3 PUFA in the placenta and fetal brain. The HF diet increased n-6 PUFA amounts in the maternal liver, correspondingly increasing linoleic acid, gamma-linolenic acid, AA, and total n-6 PUFA in the fetal brain, but decreased DHA amount in the fetal brain. However, HF diet only tended to decrease placental ß-catenin and MFSD2a amounts (P = 0.074 and P = 0.098, respectively). CONCLUSIONS: Maternal GDM could affect the fatty acid profile of the fetal brain both by downregulating the Wnt3/ß-catenin/MFSD2a pathway of the placental-fetal barrier and by affecting maternal fatty acid metabolism.

Effects of Gestational Diabetes Mellitus and Selenium Deficiency on the Offspring Growth and Blood Glucose Mechanisms of C57BL/6J Mice.

This study aimed to explore the effects and mechanisms of maternal gestational diabetes mellitus (GDM) and selenium (Se) deficiency on the growth and glucose metabolism of offspring. Female C57BL/6J mice were divided into four groups as follows: a control group, a GDM group, a Se deficiency group, and a GDM with Se deficiency group. GDM animal models were established via S961. Pregnant mice fed their offspring until weaning. Then, offspring continued to be fed with a basic diet until adulthood. Body weight and fasting blood glucose were measured weekly. Se content, oxidative stress indicators, and the protein expression of the PI3K/Akt signaling pathway were detected. GDM increased susceptibility to obesity in lactating offspring, with gender differences observed in adult offspring. The effect of Se deficiency on SOD activity only appeared in female offspring during adulthood but was shown in male offspring during weaning though it disappeared during adulthood. GDM and Se deficiency increased the risk of abnormal glucose metabolism in female offspring from weaning to adulthood but gradually decreased in male offspring. The influence on the expression of PI3K/Akt signaling pathway-related proteins showed the same trend. GDM and Se deficiency affected the growth and glucose metabolism of offspring through oxidative stress and PI3K/Akt signaling pathway-related proteins, and gender differences existed.

Effects of dietary PUFA patterns and FADS genotype on breast milk PUFAs in Chinese lactating mothers.

BACKGROUND: Breastfeeding affects the growth and development of infants, and polyunsaturated fatty acids (PUFAs) play a crucial role in this process. To explore the factors influencing the PUFA concentration in breast milk, we conducted research on two aspects: dietary fatty acid patterns and single nucleotide polymorphisms (SNPs) in maternal fatty acid desaturase genes. METHODS: Three hundred seventy Chinese Han lactating mothers were recruited. A dietary semi-quantitative food frequency questionnaire (FFQ) was used to investigate the dietary intake of lactating mothers from 22 to 25 days postpartum for 1 year. Meanwhile, breast milk samples were collected from the participants and tested for the concentrations of 8 PUFAs and 10 SNP genotypes. We sought to determine the effect of dietary PUFA patterns and SNPs on breast milk PUFAs. We used SPSS 24.0 statistical software for data analysis. Statistical tests were all bilateral tests, with P < 0.05 as statistically significant. RESULTS: Under the same dietary background, PUFA contents in breast milk expressed by most major allele homozygote mothers tended to be higher than that expressed by their counterparts who carried minor allele genes. Moreover, under the same gene background, PUFA contents in breast milk expressed by the mother's intake of essential PUFA pattern tended to be higher than that expressed by their counterparts who took the other two kinds of dietary. CONCLUSIONS: Our study suggests that different genotypes and dietary PUFA patterns affect PUFA levels in breast milk. We recommend that lactating mothers consume enough essential fatty acids to ensure that their infants ingest sufficient PUFAs.

Association of Prepregnancy Obesity and Remodeled Maternal-Fetal Plasma Fatty Acid Profiles.

Background: Fatty acids, especially polyunsaturated fatty acid (PUFA), are found abundantly in the brain and are fundamental for a fetus's growth. The fatty acid profiles of mothers and fetuses may be affected by maternal prepregnancy body mass index (pre-BMI), thus affecting fetal growth and development. Methods: A total of 103 mother-fetus pairs were divided into overweight/obese (OW, n = 26), normal weight (NW, n = 60), and underweight (UW, n = 17) groups according to pre-BMI. Fatty acid profiles in maternal and umbilical cord plasma were analyzed by gas chromatography. Results: The infant birth BMI z-score of the OW group was higher than that of the NW and UW groups (p < 0.05). The OW mothers had significantly higher plasma n-6 PUFA and n-6/n-3, but lower docosahexaenoic acid (DHA) and n-3 PUFA (p < 0.05). In cord plasma, the proportions of DHA and n-3 PUFA were lower in the OW group (p < 0.05), whereas the n-6/n-3 ratio was higher in the OW group (p < 0.05). The pre-BMI was negatively correlated with cord plasma DHA in all subjects (r = -0.303, p = 0.002), and the same negative correlation can be observed in the OW group (r = -0.561, p = 0.004), but not in the NW and UW groups (p > 0.05). The pre-BMI was positively correlated with cord plasma n-6/n-3 in all subjects (r = 0.325, p = 0.001), and the same positive correlation can be found in the OW group (r = 0.558, p = 0.004), but not in NW and UW groups (p > 0.05). Conclusions: Maternal pre-BMI was associated with the maternal-fetal plasma fatty acid profiles, whereas the adverse fatty acid profiles are more noticeable in the prepregnancy OW mothers.

Knowledge and Attitudes Towards Obesity and Bariatric Surgery in University Students: a National Survey.

BACKGROUND: Obesity has become a noticeable public health problem, especially in the young population. Bariatric surgery is growing in China but it is still new to the general public. Knowledge and attitudes towards obesity and bariatric surgery in university students are important to national health decision-making. OBJECTIVES: To study the knowledge and attitudes towards obesity and bariatric surgery among Chinese university students. METHODS: A self-designed questionnaire was distributed to university students to fill in online from June to July 2021. RESULTS: A total of 3199 questionnaires were received, with an effective rate of 98.58% (3154 questionnaires). 65.44% of whom had normal BMI and 12.14% of whom were overweight or in obesity. More than 80% of them had a high knowledge of the common causes and complications of obesity but lacked knowledge of the relationship between obesity and bone and joint, tumor or cancer, and reproductive diseases. 51.55% of them thought they needed to lose weight, especially female students (P < 0.01). Only 39% had regular physical exercise habits, 58.62% of them could reasonably control their diet, and more than 2/3 of them (67.82%) often stayed up late. Safety (29.77%) and effectiveness (29.3%) of bariatric surgery were not well accepted. Among medical students, education positively affected knowledge and attitudes towards bariatric surgery (P < 0.05). Mass media and social platforms were the main sources for obtaining obesity and weight-loss information. CONCLUSIONS: Chinese university students have insufficient knowledge of obesity and related complications, lack of scientific management of their own weight, and poor acceptance of bariatric surgery. More education is needed on this issue.

A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing.

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.

Frequency-specific alterations of regional homogeneity in subcortical stroke patients with different outcomes in hand function.

Emerging evidence has suggested that abnormalities in regional spontaneous brain activity following stroke may be detected by intrinsic low-frequency oscillations (LFO) in resting-state functional MRI (R-fMRI). However, the relationship between hand function outcomes following stroke and local LFO synchronization in different frequency bands is poorly understood. In this study, we performed R-fMRI to examine the regional homogeneity (ReHo) at three different frequency bands (slow-5: .01-.027 Hz; slow-4: .027-.08 Hz; and typical band: .01-.1 Hz) in 26 stroke patients with completely paralyzed hands (CPH) and 26 matched patients with partially paralyzed hands (PPH). Compared to the PPH group, decreased ReHo in the bilateral cerebellum posterior lobes and the contralesional cerebellum anterior lobe was observed in the slow-5 band and the slow-4 band in the CPH group, respectively. The mean ReHo values in these regions were positively correlated with the Fugl-Meyer assessment (FMA) scores. In contrast, increased ReHo in the contralesional supplementary motor area and the contralesional superior temporal gyrus was observed in the slow-4 band and the slow-5 band, respectively. The mean ReHo values in these regions were negatively correlated with the FMA scores. Importantly, significant interactions were identified between the frequency bands and the subgroups of patients in the contralesional precentral gyrus and middle frontal gyrus. These findings indicate that frequency-dependent R-fMRI patterns may serve as potential biomarkers of the neural substrates associated with hand function outcomes following stroke.

Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke.

Motor functions are supported through functional integration across the extended motor system network. Individuals following stroke often show deficits on motor performance requiring coordination of multiple brain networks; however, the assessment of connectivity patterns after stroke was still unclear. This study aimed to investigate the changes in intra- and inter-network functional connectivity (FC) of multiple networks following stroke and further correlate FC with motor performance. Thirty-three left subcortical chronic stroke patients and 34 healthy controls underwent resting-state functional magnetic resonance imaging. Eleven resting-state networks were identified via independent component analysis (ICA). Compared with healthy controls, the stroke group showed abnormal FC within the motor network (MN), visual network (VN), dorsal attention network (DAN), and executive control network (ECN). Additionally, the FC values of the ipsilesional inferior parietal lobule (IPL) within the ECN were negatively correlated with the Fugl-Meyer Assessment (FMA) scores (hand + wrist). With respect to inter-network interactions, the ipsilesional frontoparietal network (FPN) decreased FC with the MN and DAN; the contralesional FPN decreased FC with the ECN, but it increased FC with the default mode network (DMN); and the posterior DMN decreased FC with the VN. In sum, this study demonstrated the coexistence of intra- and inter-network alterations associated with motor-visual attention and high-order cognitive control function in chronic stroke, which might provide insights into brain network plasticity following stroke.

Altered Effective Connectivity of the Primary Motor Cortex in Stroke: A Resting-State fMRI Study with Granger Causality Analysis.

The primary motor cortex (M1) is often abnormally recruited in stroke patients with motor disabilities. However, little is known about the alterations in the causal connectivity of M1 following stroke. The purpose of the present study was to investigate whether the effective connectivity of the ipsilesional M1 is disturbed in stroke patients who show different outcomes in hand motor function. 23 patients with left-hemisphere subcortical stroke were selected and divided into two subgroups: partially paralyzed hands (PPH) and completely paralyzed hands (CPH). Further, 24 matched healthy controls (HCs) were recruited. A voxel-wise Granger causality analysis (GCA) on the resting-state fMRI data between the ipsilesional M1 and the whole brain was performed to explore differences between the three groups. Our results showed that the influence from the frontoparietal cortices to ipsilesional M1 was diminished in both stroke subgroups and the influence from ipsilesional M1 to the sensorimotor cortices decreased greater in the CPH group than in the PPH group. Moreover, compared with the PPH group, the decreased influence from ipsilesional M1 to the contralesional cerebellum and from the contralesional superior parietal lobe to ipsilesional M1 were observed in the CPH group, and their GCA values were positively correlated with the FMA scores; Conversely, the increased influence from ipsilesional M1 to the ipsilesional middle frontal gyrus and middle temporal gyrus were observed, whose GCA values were negatively correlated with the FMA scores. This study suggests that the abnormalities of casual flow in the ipsilesional M1 are related to the severity of stroke-hand dysfunction, providing valuable information to understand the deficits in resting-state effective connectivity of motor execution and the frontoparietal motor control network during brain plasticity following stroke.

Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs.

PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand-target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand-pharmacophore fit score. Two retrospective tests were performed which confirmed that the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reaction related off-targets.

The plasticity of intrinsic functional connectivity patterns associated with rehabilitation intervention in chronic stroke patients.

INTRODUCTION: It has been demonstrated that rehabilitative interventions can promote motor function recovery in stroke patients. However, little is known regarding the neural mechanisms that underlie the rehabilitation treatments. The aim of this study was to investigate the plasticity of intrinsic functional connectivity patterns that are associated with rehabilitation intervention in chronic stroke patients. METHODS: Twelve chronic stroke patients with subcortical lesions in the left motor pathway participated in a 4-week rehabilitation intervention and underwent resting-state functional magnetic resonance imaging (fMRI) scanning before and after the intervention. Both functional connectivity analyses of the ipsilesional (left) primary motor cortex (M1) and measurements of the lateralization index of the connectivity patterns were performed in both the stroke patients and healthy controls (HC). RESULTS: Compared with the HC, the decreased connectivity of the ipsilesional M1 with the contralesional sensorimotor cortex (SMC), bilateral supplementary motor areas, and inferior parietal lobule due to stroke were remarkably restored after the intervention. More specifically, the lateralization index of the bilateral SMC tends to be the normal level. Moreover, comparing post- with pre-intervention, we observed significantly increased connectivity of ipsilesional M1 with the contralesional M1 and medial superior frontal gyrus (mSFG). Additionally, the index of pre-intervention connectivity with the contralesional mSFG was positively correlated with motor improvement. CONCLUSION: The impact of rehabilitation intervention on intrinsic functional connectivity patterns throughout the brain was measurable on resting-state fMRI, and systematic assessment of resting-state functional connectivity can provide prognostic insight for later motor improvement.

Are Topological Properties of Drug Targets Based on Protein-Protein Interaction Network Ready to Predict Potential Drug Targets?

Identification of potential druggable targets utilizing protein-protein interactions network (PPIN) has been emerging as a hotspot in drug discovery and development research. However, it remains unclear whether the currently used PPIN topological properties are enough to discriminate the drug targets from non-drug targets. In this study, three-step classification models using different network topological properties were designed and implemented using support vector machine (SVM) to compare the enrichment of known drug targets from non-targets. Surprisingly, none of the models was able to identify more than 75% of the true targets in the test set. It appears that the currently used simple PPIN topological properties are not likely robust enough for prediction of potential drug targets with high confidence, which also echoes similarly unsatisfying prediction data reported previously. However, we proposed that quality and quantity improvement of the protein-protein interactions (PPI) data for model training will help increasing the prediction accuracy.

iDrug: a web-accessible and interactive drug discovery and design platform.

BACKGROUND: The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed. RESULTS: We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work. CONCLUSIONS: iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug.

SimG: an alignment based method for evaluating the similarity of small molecules and binding sites.

In this study, a Gaussian volume overlap and chemical feature based molecular similarity metric was devised, and a downhill simplex searching was carried out to evaluate the corresponding similarity. By representing the shapes of both the candidate small molecules and the binding site with chemical features and comparing the corresponding Gaussian volumes overlaps, the active compounds could be identified. These two aspects compose the proposed method named SimG which supports both structure-based and ligand-based strategies. The validity of the proposed method was examined by analyzing the similarity score variation between actives and decoys as well as correlation among distinct reference methods. A retrospective virtual screening test was carried out on DUD data sets, demonstrating that the performance of structure-based shape matching virtual screening in DUD data sets is substantially dependent on some physical properties, especially the solvent-exposure extent of the binding site: The enrichments of targets with less solvent-exposed binding sites generally exceeds that of the one with more solvent-exposed binding sites and even surpasses the corresponding ligand-based virtual screening.

ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method.

SUMMARY: ChemMapper is an online platform to predict polypharmacology effect and mode of action for small molecules based on 3D similarity computation. ChemMapper collects >350 000 chemical structures with bioactivities and associated target annotations (as well as >3 000 000 non-annotated compounds for virtual screening). Taking the user-provided chemical structure as the query, the top most similar compounds in terms of 3D similarity are returned with associated pharmacology annotations. ChemMapper is designed to provide versatile services in a variety of chemogenomics, drug repurposing, polypharmacology, novel bioactive compounds identification and scaffold hopping studies. AVAILABILITY: http://lilab.ecust.edu.cn/chemmapper/. CONTACT: xfliu@ecust.edu.cn or hlli@ecust.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.

BACKGROUND: DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence. RESULTS: In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively. CONCLUSIONS: The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.

PTID: an integrated web resource and computational tool for agrochemical discovery.

SUMMARY: Although in silico drug discovery approaches are crucial for the development of pharmaceuticals, their potential advantages in agrochemical industry have not been realized. The challenge for computer-aided methods in agrochemical arena is a lack of sufficient information for both pesticides and their targets. Therefore, it is important to establish such knowledge repertoire that contains comprehensive pesticides' profiles, which include physicochemical properties, environmental fates, toxicities and mode of actions. Here, we present an integrated platform called Pesticide-Target interaction database (PTID), which comprises a total of 1347 pesticides with rich annotation of ecotoxicological and toxicological data as well as 13 738 interactions of pesticide-target and 4245 protein terms via text mining. Additionally, through the integration of ChemMapper, an in-house computational approach to polypharmacology, PTID can be used as a computational platform to identify pesticides targets and design novel agrochemical products. AVAILABILITY: http://lilab.ecust.edu.cn/ptid/. CONTACT: hlli@ecust.edu.cn; xhqian@ecust.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel, customizable and optimizable parameter method using spherical harmonics for molecular shape similarity comparisons.

A novel molecular shape similarity comparison method, namely SHeMS, derived from spherical harmonic (SH) expansion, is presented in this study. Through weight optimization using genetic algorithms for a customized reference set, the optimal combination of weights for the translationally and rotationally invariant (TRI) SH shape descriptor, which can specifically and effectively distinguish overall and detailed shape features according to the molecular surface, is obtained for each molecule. This method features two key aspects: firstly, the SH expansion coefficients from different bands are weighted to calculate similarity, leading to a distinct contribution of overall and detailed features to the final score, and thus can be better tailored for each specific system under consideration. Secondly, the reference set for optimization can be totally configured by the user, which produces great flexibility, allowing system-specific and customized comparisons. The directory of useful decoys (DUD) database was adopted to validate and test our method, and principal component analysis (PCA) reveals that SH descriptors for shape comparison preserve sufficient information to separate actives from decoys. The results of virtual screening indicate that the proposed method based on optimal SH descriptor weight combinations represents a great improvement in performance over original SH (OSH) and ultra-fast shape recognition (USR) methods, and is comparable to many other popular methods. Through combining efficient shape similarity comparison with SH expansion method, and other aspects such as chemical and pharmacophore features, SHeMS can play a significant role in this field and can be applied practically to virtual screening by means of similarity comparison with 3D shapes of known active compounds or the binding pockets of target proteins.