Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

BACKGROUND:Overactive osteoclasts disrupt bone homeostasis and play a bad role in the pathological mechanisms of related skeletal diseases,such as osteoporosis,fragility fractures,and osteoarthritis.Studies have confirmed that ellagic acid and ellagtannin have the potential to inhibit osteoclast differentiation.As their natural metabolites,urolithin A has antioxidant,anti-inflammatory,anti-proliferative and anti-cancer effects,but its effect on osteoclast differentiation and its underlying molecular mechanisms remain unclear. OBJECTIVE:To explore the effect of urolithin A on osteoclast differentiation induced by receptor activator for nuclear factor-κB ligand and its mechanism. METHODS:Mouse mononuclear macrophage leukemia cells(RAW264.7)that grew stably were cultured in vitro.Toxicity of urolithin A(0,0.1,0.5,1.5,2.5 μmol/L)to RAW264.7 cells were detected by cytotoxic MTS assay to screen out the safe concentration.Different concentrations of urolithin A were used again to intervene with receptor activator for nuclear factor-κB ligand-induced differentiation of RAW264.7 cells in vitro.Then,tartrate-resistant acid phosphatase staining and F-actin ring and nucleus staining were performed to observe its effect on the formation and function of osteoclasts.Finally,the expressions of urolithin A on upstream and downstream genes and proteins in the MAPK signaling pathway were observed by western blot and RT-qPCR assays. RESULTS AND CONCLUSION:Urolithin A inhibited osteoclast differentiation and F-actin ring formation in a concentration-dependent manner and 2.5 μmol/L had the strongest inhibitory effect.Urolithin A inhibited the mRNA expression of Nfatc1,Ctsk,Mmp9 and Atp6v0d2 and the protein synthesis of Nfatc1 and Ctsk,related to osteoclast formation and bone resorption.Urolithin A inhibited the activity of osteoclasts by downregulating the phosphorylation of p38 protein to inhibit the mitogen-activated protein kinase signaling pathway.

In order to reveal the association between single nucleotide-based polymorphism(low density lipoprotein receptor-related protein 5,LRP5),DNA of 98 Chinese Holstein cows were ex-tracted and the LRP 5 gene polymorphism locus was detected by direct sequencing,and related dif-ferent genotypes and milk quality traits at the SNP loci was analyzed.The results showed that three SNPs were found on the fourth intron of the LRP 5 gene,I4-90 C＞T,I4-229 C＞A and I4-570 A＞G.Among them,I4-90 C＞T had TT,CC and TC genotypes,and TC genotype was signifi-cantly higher than TT and CC genotypes(P＜0.05).However,mutations at this locus were not significantly associated with milk yield and fat content(P＞0.05);genotypes CC and C for I4-229 C＞A;genotypes AA,GG and GA for I4-570 A＞G.The two mutation sites showed no significant correlation with milk quality traits in Chinese Holstein dairy cows(P＞0.05).The above results indicate that the I4-90 C＞T site of LRP 5 gene can be used for marker-assisted selection as molec-ular markers affecting milk quality traits in Chinese Holstein cows.

This study aims to investigate the relationship between single nucleotide polymorphisms(SNPs)in the 3'-phosphoadenosine 5'-phosphosulfate synthase 2(PAPSS2)gene and milk quality traits in Chinese Holstein cows.A total of 141 Chinese Holstein cows were sampled,and their DNA was extracted to detect polymorphic sites in the PAPSS2 gene using direct sequencing technology.The genotypes at different SNP sites were then correlated with milk quality traits.The results i-dentified three SNP sites in the second intron of the PAPSS2 gene:I2-3686 A＞G,I2-3854 G＞C,and I2-3917 G＞A,each exhibiting three genotypes.Specifically,at the first SNP site,individuals with the AG genotype demonstrated significantly higher milk fat and solids content compared to those with the GG genotype(P＜0.05).At the second SNP site,individuals with the GG genotype exhibited significantly higher urinary nitrogen content than those with the GC and CC genotypes(P＜0.05).No significant differences were observed in milk quality traits associated with the third SNP site.These findings suggest that SNP sites in the PAPSS2 gene can serve as molecular mark-ers influencing milk quality traits in Chinese Holstein cows,providing potential applications for marker-assisted selection in dairy cattle.

The peroxisome proliferator-activated receptor gamma(peroxisome proliferator-activated receptor gamma,coactivator 1 beta,PPARGC1B)gene is an intranuclear receptor transcription fac-tor responsible for regulating the expression of target genes.To comprehend the characteristics and mutations of the PPARGC1B gene within the Sichuan yak population,the SNP loci of the PPARGC1B gene were identified through direct sequencing of PCR products.Additionally,the cod-ing region of the PPARGC1B gene was obtained via PCR amplification and sequencing.Bioinforma-tics analyses were conducted to predict protein-coding and mRNA secondary structure.This study identified four exon SNP mutation sites(E9-189A→C,E9-387G→A,E9-542C→T,and E9-554T→C)based on the single nucleotide polymorphism analysis of the PPARGC1B gene in Sichuan yaks.Notably,the E9-387G→A and E9-554T→C loci exhibited significant correlations with shear force and backfat thickness in Sichuan yaks.Subsequently,bioinformatics analysis of the four mutation sites revealed that the PPARGC1B protein is an acidic,unstable,non-transmembrane,and non-secretory hydrophilic protein with a coiled helix structure.It lacks a signal peptide and transmembrane region,predominantly functions in the nucleus,and features 106 phosphorylation sites,one glycosylation site,and one conserved RRM structure.The secondary structure comprises mainly α-helix and random coils.Although the protein structure of the PPARGC1B gene remained unchanged post-mutation,there were significant differences in mRNA secondary structure.These findings suggest that the polymorphic loci of the PPARGC1B gene in Sichuan yaks could serve as a theoretical basis for enhancing meat quality traits through molecular biological methods,presen-ting practical applications in breeding.