SeedMatchR: identify off-target effects mediated by siRNA seed regions in RNA-seq experiments.

MOTIVATION: On-target gene knockdown, using siRNA, ideally results from binding fully complementary regions in mRNA transcripts to induce direct cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ∼8 nucleotides at the 5' end of the guide strand, called a seed region, bind the 3' untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with off-target effects driven by the seed region. However, there are limited computational tools designed specifically for detecting siRNA off-target effects mediated by the seed region in differential gene expression experiments. RESULTS: SeedMatchR is an R package developed to provide users a single, unified resource for detecting and visualizing seed-mediated off-target effects of siRNA using RNA-seq experiments. SeedMatchR is designed to extend current differential expression analysis tools, such as DESeq2, by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of SeedMatchR to detect cumulative changes in differential gene expression attributed to siRNA seed region activity. AVAILABILITY: SeedMatchR is available on CRAN. Documentation and example workflows are available through the SeedMatchR GitHub page at https://github.com/tacazares/SeedMatchR.

[Low disease activity and remission status of systemic lupus erythematosus in a real-world study].

OBJECTIVE: To investigate the rates of low disease activity and clinical remission in patients with systemic lupus erythematosus (SLE) in a real-world setting, and to analyze the related factors of low disease activity and clinical remission. METHODS: One thousand patients with SLE were enrolled from 11 teaching hospitals. Demographic, clinical and laboratory data, as well as treatment regimes were collec-ted by self-completed questionnaire. The rates of low disease activity and remission were calculated based on the lupus low disease activity state (LLDAS) and definitions of remission in SLE (DORIS). Charac-teristics of patients with LLDAS and DORIS were analyzed. Multivariate Logistic regression analysis was used to evaluate the related factors of LLDAS and DORIS remission. RESULTS: 20.7% of patients met the criteria of LLDAS, while 10.4% of patients achieved remission defined by DORIS. Patients who met LLDAS or DORIS remission had significantly higher proportion of patients with high income and longer disease duration, compared with non-remission group. Moreover, the rates of anemia, creatinine elevation, increased erythrocyte sedimentation rate (ESR) and hypoalbuminemia was significantly lower in the LLDAS or DORIS group than in the non-remission group. Patients who received hydroxychloroquine for more than 12 months or immunosuppressant therapy for no less than 6 months earned higher rates of LLDAS and DORIS remission. The results of Logistic regression analysis showed that increased ESR, positive anti-dsDNA antibodies, low level of complement (C3 and C4), proteinuria, low household income were negatively related with LLDAS and DORIS remission. However, hydroxychloroquine usage for longer than 12 months were positively related with LLDAS and DORIS remission. CONCLUSION: LLDAS and DORIS remission of SLE patients remain to be improved. Treatment-to-target strategy and standar-dized application of hydroxychloroquine and immunosuppressants in SLE are recommended.

Dissecting the Imbalance of Synovial Macrophages in Rheumatoid Arthritis.

Objective: This study sought to identify candidate genes of rheumatoid arthritis (RA) synovial macrophages using bioinformatics and to explore their pathways in the pathogenesis of RA. Methods: The microarray datasets GSE10500 and GSE97779 were obtained from the Gene Express Omnibus and analyzed with synovial macrophages of 14 RA patients and 8 healthy donors. The researchers used R software to identify differentially expressed genes and determine functional enrichment pathways. A protein-protein interaction network was then constructed using STRING and Cytoscape. Gene expression was validated with the GSE71370 dataset and RT-qPCR analysis. Results: 102 DEGs were identified in RA synovial macrophages relative to normal samples. Of these, 72 were upregulated; 30 were downregulated. GO and KEGG pathway analyses suggested that DEGs mainly regulated the immune response and signaling pathways associated with inflammatory activation, apoptosis, and cancer. The top five hub genes and top 1 gene module from the PPI network of DEGs were VEGFA, MMP9, FN1, IGF1, CXCL9, ISG20, RSAD2, IFI27, GBP2, and GBP1. The GSE71370 dataset and RT-qPCR analysis showed that CXCL9 and GBP1 were significantly upregulated (P ≤ .05). Conclusions: CXCL9 and GBP1 may contribute to RA pathogenesis and serve as potential biomarkers and therapeutic targets for RA.

Pregnancy in patients with systemic lupus erythematosus: a systematic review.

Systemic lupus erythematosus (SLE)-a most common disorder in women of reproductive age-has been described to be associated with adverse pregnancy outcomes. Despite the increased health risks for the mother (preeclampsia, lupus flare, arterial hypertension, gestational diabetes mellitus and thrombotic risk when antiphospholipid antibodies are present) and fetus (miscarriage, stillbirth, premature birth, intrauterine growth restriction and neonatal lupus), the majority of patients can deliver healthy neonates. With appropriate management by a multidisciplinary team, composing rheumatologists, obstetricians and neonatologists, women with SLE can achieve better pregnancy outcomes by monitoring associated predictive indicators, raising major concern for severe complications and somewhat early delivery if necessary. In this review, we summarize the latest advances in secondary infertility and pregnancy-related risk perception for lupus patients, with an emphasis on the safety of biological agents (mainly belimumab and rituximab) and traditional therapeutic regimens.

Erythropoiesis signature and ubiquitin-mediated proteolysis are enriched in systematic juvenile idiopathic arthritis.

Systemic Juvenile Idiopathic Arthritis (sJIA) is a distinctive subtype of Juvenile Idiopathic Arthritis (JIA). The pathogenesis of sJIA is still unclear with the limited treatment options. Although previous bioinformatics analyses have identified some genetic factors underlying sJIA, these studies were mostly single centre with a small sample size and the results were often inconsistent. Herein, we combined two data sets of GSE20307 and GSE21521 and select the matrix of patients diagnosed as sJIA in it for further analysis. The GSE20307 and GSE21521 matrixes downloaded from the Gene Expression Omnibus (GEO) were analysed using online tools GEO2R, Venny, Metascape, STRING and Cytoscape to identify differentially expressed genes (DEGs), enrichment pathways, protein-protein interaction (PPI), main module and hub genes between sJIA individuals and healthy controls. A total of 289 overlapping genes (consisting of 41 downregulated genes and 248 upregulated genes) were identified. Hub genes were primarily related to erythropoiesis. And the KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis of overlapping DEGs were mainly involved in malaria and non-small cell lung cancer. Besides, DEGs in main module were involved in ubiquitin-mediated proteolysis. Our study suggests that the erythropoiesis signature indeed exists in sJIA similar to previous reports. And ubiquitin-mediated proteolysis is important in sJIA.

Cerebral Organoids for Modeling of HSV-1-Induced-Amyloid ß Associated Neuropathology and Phenotypic Rescue.

Herpes simplex virus type I (HSV-1) infection is a potential risk factor involved in the Amyloid ß (Aß) associated neuropathology. However, further understanding of the neuropathological effects of the HSV-1 infection is hampered by the limitations of existing infection models due to the distinct differences between human brains and other mammalians' brains. Here we generated cerebral organoid models derived from pluripotent stem cells to investigate the HSV-induced Aß associated neuropathology and the role of antiviral drugs in the phenotypic rescue. Our results identified that the HSV-1-infected cerebral organoids recapitulated Aß associated neuropathology including the multicellular Aß deposition, dysregulated endogenous AD mediators, reactive gliosis, neuroinflammation, and neural loss, indicating that cerebral organoids offer an opportunity for modeling the interaction of HSV-1 with the complex phenotypes across the genetic, cellular, and tissue levels of the human Alzheimer's disease (AD). Furthermore, we identified that two antiviral drugs, namely Ribavirin (RBV) and Valacyclovir (VCV), inhibited HSV-1 replication and rescued the neuropathological phenotypes associated with AD in the HSV-1-infected cerebral organoids, implying their therapeutic potential to slow down the progression of AD. Our study provides a high-fidelity human-relevant in-vitro HSV-1 infection model to reconstitute the multiscale neuropathological features associated with AD and discover therapeutic drug candidates relevant to the AD viral hypothesis.

Metabolomic analysis of synovial fluids from rheumatoid arthritis patients using quasi-targeted liquid chromatography-mass spectrometry/mass spectrometry.

OBJECTIVES: Synovial fluid (SF) accumulates extensively in joints of individuals with rheumatoid arthritis (RA), which reflects the pathological state of the synovium and disease activity. This study applied quasi-targeted liquid chromatography-mass spectrometry/mass spectrometry, an advanced metabolomics technique, to find characteristic metabolisms in RA. METHODS: SF samples from the patients (n=20) were collected and examined using the metabolomic technique. SF samples from patients with osteoarthritis (OA) (n=20) were used as controls. RESULTS: Four hundred and seventy-nine variable metabolites were detected, and 250 of these metabolites were identified by searching the Human Metabolome Database (HMDB) and a self-constructed information list of possible metabolites. S-plot and volcano plot analysis detected 22 metabolites with differential levels in RA SF compared with those in OA SF. With these 22 candidate metabolites, pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database detected upregulation of pyrimidine metabolism and purine metabolism, and downregulation of fatty acid biosynthesis and unsaturated fatty acid biosynthesis in RA SF. Receiver operating characteristic (ROC) analysis and logistic regression models detected increased levels of guaiacol, naringenin, phenylpropanolamine and vanillylmandelic acid in RA SF. Furthermore, the naringenin level showed positive correlation with rheumatic factor (RF) and anti-cyclic citrillinated peptides (anti-CCP) levels. CONCLUSIONS: Our study suggests disturbed pyrimidine metabolism, purine metabolism, fatty acid biosynthesis and unsaturated fatty acid biosynthesis, as well as increased naringenin level, are characteristic metabolisms in RA.

[Recent advances in emerging three-dimensional in vitro models for sport-related traumatic brain injury].

Sports-related traumatic brain injury (srTBI) is a traumatic brain injury (TBI) caused by sports, which can result in cognitive and motor dysfunction. Currently, research on the molecular mechanism of srTBI and related drug development mainly relies on monolayer culture models and animal models. However, many differences exist in cell populations and inflammatory responses between these models and human pathophysiological processes. Most of the researches derived from the models can't effectively conducted translational research. Emerging three-dimensional (3D) in vitro models bridge the limitations of traditional models in simulating the pathophysiological processes of human srTBI and provide new means to understand srTBI. A literature has reported the research progress of emerging 3D in vitro models in neurological diseases, but there is a lack of systematic summary of the mentioned models in srTBI studies. Here, we review the research progress of emerging 3D in vitro models of srTBI, discuss the advantages and limitations of existing models, and further prospect the future trend of srTBI models. This paper aims to provide a new research perspective for researchers in tissue engineering and sports medicine to study the molecular mechanisms of srTBI and develop neuroprotective drugs.

The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization.

Matched Molecular Pairs (MMP) analysis is a well-established technique for Structure Activity and Property Analysis (SAR and SPR). Summarizing multiple MMPs that describe the same structural change into a single chemical transform can be a powerful tool for prediction (termed Transform from here on). This is particularly useful in the area of Absorption, Distribution, Metabolism, and Elimination (ADME) analysis that is less influenced by 3D structural binding effects. The creation of a knowledge database containing many of these Transforms across typical ADME assays promises to be a powerful approach to aid multidimensional optimization. We present a detailed workflow for the derivation of such a database. We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. This is made freely available as part of the LillyMol software package. We describe the application of this method to several ADME/Tox (Toxicity) assay data sets and highlight multiple cases where the impact of traditional medicinal chemistry Transforms is contradicted by MMP data. We also describe the internal software interface used by medicinal chemists to aid the design of new compounds via automated suggestion. This approach utilizes the matched pairs database to "suggest" improved compounds in an automated design scenario. A nonvisual script-based version of the automated suggestions code with an associated set of described chemical Transforms is also made freely available along with this paper and as part of the LillyMol software package. Finally, we contrast this knowledge database against a larger database of all MMPs derived from a 2 million compound diversity set and a subset of MMPs seen in historical discovery projects. The comparison against all transforms in the diversity collection highlights the very low coverage of the transform database as compared to all possible transforms involving 15 atom fragments. The comparison against a smaller subset of Transforms seen on internal Medicinal Chemistry projects shows better coverage of the transform database for a small set of common medicinal chemistry strategies. Within the context of all possible transforms available to a medicinal chemistry project team, the challenge remains to move beyond mere idea generation from past projects toward high quality prediction for novel ADME/Tox modulating Transforms.

Context Aware Data-Driven Retrosynthetic Analysis.

Modern drug discovery is an iterative process relying on hypothesis generation through exploitation of available data and hypothesis testing that produces informative results necessary for subsequent rounds of exploration. In this setting, hypothesis generation consists of designing chemical structures likely to meet the pharmaceutically relevant objectives of the discovery project pursued while hypothesis testing involves the compound synthesis and biological assays to query the hypothesis. While much attention has been placed on effective compound design, it is often the case that hypothesis generation efforts lead to novel chemical structure designs with no established chemical synthesis route. We introduce a chemical context aware data-driven method built upon millions of available reactions, with attractive run-time characteristics, to recommend synthetic routes matching a precedent-derived template. Coupled with modern automated synthesis platforms and available building block collections, the method enables drug discovery researchers to identify easy to interpret and implement routes for target compounds. Results of this in-house computer-aided synthesis platform termed ChemoPrint are presented here demonstrating how such tools can bridge chemical synthesis knowledge with synthetic resources and facilitate hypothesis testing, thereby reducing the time required to complete an idea-to-data drug discovery cycle.

High melatonin levels are related to spinal ossification in patients with ankylosing spondylitis.

Objectives: To investigate associations of serum melatonin with spinal ossification and cytokines in ankylosing spondylitis (AS).Methods: Serum was obtained from 52 AS patients and 25 healthy controls. Melatonin was measured by ELISA kit; bone morphogenetic protein (BMP)-2, dickkopf-related protein (Dkk)-1, IL-1ß, IL-6, IL-17 and TNF-α concentrations were assayed using Luminex multiplex bead system. Osteocalcin and ß isomer of C-terminal telopeptide of type I collagen (ß-CTX) were measured using electrochemiluminescence immunoassay. Spinal damages were assessed using the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) on radiographs.Results: Serum melatonin was significantly increased in AS patients. Serum melatonin correlated positively with mSASSS after multivariate adjustment for age and disease duration (r = 0.70, p < .01). Patients with spinal bone bridge have higher levels of melatonin than those without spinal bone bridge [16.69 (4.65, 41.10) pg/ml vs. 7.43 (3.29, 15.30) pg/ml, p = .03]. The multiple linear regression analysis found that melatonin was a risk factor for spinal bone formation (ß = 0.35, p < .05). Additionally, melatonin correlated positively with osteocalcin (r = 0.34, p = .04) and IL-1ß (r = 0.39, p = .04) in AS.Conclusion: Melatonin is increased in AS patients, especially in patients with spinal bone bridge. It suggests that melatonin may play an important role in the pathological osteogenesis of AS.

Proliferation and apoptosis of rheumatoid arthritis fibroblast-like synoviocytes following signal transducer and activator of transcription 3 RNA interference delivery.

OBJECTIVE: In this study, we investigated the effects of delivering small interfering RNA (siRNA) for efficient STAT3 downregulation on propagation and apoptosis of rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS). METHODS: The FLSs were transfected with three different siRNAs. RNAi-1 was selected for further experiments. The expression levels of both STAT3 messenger RNA (mRNA) and its protein were detected by a real-time polymerase chain reaction and Western blot analysis. The proliferation of FLSs was examined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The apoptosis of FLSs was examined by flow cytometry. The expression levels of cell apoptotic-related genes Bcl-2, Bax, and caspase-3 were detected by Western blot analysis. RESULTS: RNAi-1 was selected as the RNAi group for its lowest expression levels of STAT3 mRNA. In RNAi group, the proliferation of synoviocytes was much lower and the apoptosis rate was significantly higher. FLSs of RNAi-1 group showed significantly lower expression level of apoptotic-inhibiting gene Bcl-2 and significantly higher expression levels of proapoptotic gene Bax and apoptotic protease caspase-3. CONCLUSION: Transfection with targeted STAT3 recombinant plasmids effectively inhibited the expression of STAT3 mRNA and its protein in RA-FLSs. RNAi-mediated silencing of STAT3 reduced the proliferation and promoted the apoptosis of FLSs.

MiR-548a-3p regulates inflammatory response via TLR4/NF-κB signaling pathway in rheumatoid arthritis.

Currently published studies have implicated that microRNAs (miRNAs) including exosomes-encapsulated miRNAs play a critical role in rheumatoid arthritis (RA). Previously, we have found that exosomes-encapsulated miR-548a-3p was significantly decreased in serum samples from RA patients by miRNAs microarray analysis. However, little is known of the role of miR-548a-3p in the development and progression of RA. In this study, we aim to investigate the underlying molecular mechanisms of miR-548a-3p in RA, which will provide new insight into understanding the pathogenesis of RA and identifying novel therapeutics targets for this disease. As validated by quantitative real-time polymerase chain reaction (qRT-PCR), the expression of miR-548a-3p in serum exosomes and peripheral blood mononuclear cells (PBMCs) of RA patients (n = 76) was obviously down-regulated compared with healthy controls (n = 20). Serum exosomal miR-548a-3p was negatively associated with levels of CRP, RF, and ESR in serum of patients with RA. MiR-548a-3p could inhibit the proliferation and activation of pTHP-1 cells by regulating the TLR4/NF-κB signaling pathway. Accordingly, exosomes-delivered miR-548a-3p may be a critical factor predicting the disease activity of RA. MiR-548a-3p/TLR4/NF-κB axis can serve as promising targets for RA diagnosis and treatment.

MicroRNA-204-5p suppresses IL6-mediated inflammatory response and chemokine generation in HK-2 renal tubular epithelial cells by targeting IL6R.

During the pathogenetic process of varied kidney diseases, renal tubules are the major sites in response to detrimental insults, including pro-inflammatory stimuli. MicroRNA-204-5p (miR-204-5p) can be detected in the renal tubular epithelial cells in the normal kidney; its expression, however, is downregulated in the kidney with pathological changes. This study aimed to investigate the role of miR-204-5p in interleukin 6 (IL6) mediated inflammatory response and chemokine production in HK-2 renal tubular cells. In HK-2 cells, the expression of miR-204-5p was downregulated in response to exogenous pro-inflammatory stimulus, tumor necrosis factor α (TNFα), or IL1ß, while that of IL6 receptor α (IL6R) was upregulated. Dual-luciferase results confirmed that miRNA-204-5p directly targeted IL6R. In addition to suppressing IL6R expression, miRNA-204-5p agomir also inhibited the phosphorylation of signal transducer and activator of transcription 3 (STAT3) in HK-2 cells exposed to exogenous IL6. Further, miRNA-204-5p suppressed the overproduction of pro-inflammatory mediators (cyclooxygenase 2 and prostaglandin E2) and chemokines (C-C motif chemokine ligand 2 and C-X-C motif chemokine ligand 8). The anti-inflammatory effects of miRNA-204-5p were attenuated when IL6R was reexpressed in HK-2 cells. Collectively, our study reveals that miR-204-5p inhibits the inflammation and chemokine generation in renal tubular epithelial cells by modulating the IL6/IL6R axis.

Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets.

Deep learning has drawn significant attention in different areas including drug discovery. It has been proposed that it could outperform other machine learning algorithms, especially with big data sets. In the field of pharmaceutical industry, machine learning models are built to understand quantitative structure-activity relationships (QSARs) and predict molecular activities, including absorption, distribution, metabolism, and excretion (ADME) properties, using only molecular structures. Previous reports have demonstrated the advantages of using deep neural networks (DNNs) for QSAR modeling. One of the challenges while building DNN models is identifying the hyperparameters that lead to better generalization of the models. In this study, we investigated several tunable hyperparameters of deep neural network models on 24 industrial ADME data sets. We analyzed the sensitivity and influence of five different hyperparameters including the learning rate, weight decay for L2 regularization, dropout rate, activation function, and the use of batch normalization. This paper focuses on strategies and practices for DNN model building. Further, the optimized model for each data set was built and compared with the benchmark models used in production. Based on our benchmarking results, we propose several practices for building DNN QSAR models.

MiR-130a protects against lipopolysaccharide-induced glomerular cell injury by upregulation of Klotho.

AIMS: Lupus nephritis is a frequent and serious complication of systemic lupus erythematosus (SLE). Therefore, better understanding regarding the underlying mechanism of renal tubular injury induced by SLE, is beneficial to develop different therapeutic strategies for lupus nephritis. The study aimed to investigate the role of miR-130a against lipopolysaccharide-induced glomerular cell injury. METHODS: HK-2 cells (human renal proximal tubule cells) were used for detecting miR-130a levels. Cells were divided into scramble, miR-130 mimic, siNC, si-miR-130a and si-Klotho groups apoptosis and CCK-8 assays were performed to investigate the cell apoptosis and proliferation rates. qRT-PCR, ELISA, and western blotting were performed to detect the proteins and their expressions. RESULTS: LPS induced inflammatory injury in HK-2 cells by inducing cell apoptosis (P < 0.01) and by expressing the inflammatory factors such as IL-1ß, IL-6, IL-8 and TNF-α in HK-2 cells. LPS increased the expression of miR-130a compared to control group of cells (P < 0.01). miR-130a was highly expressed in HK-2 cells (P < 0.001). Overexpression of miR-130a reversed LPS-induced apoptosis (P < 0.05), increased expression of inflammatory mediators and decreased cell viability (P < 0.05), and miR-130a knockdown in HK-2 cells revealed to just the opposite effects upon treatment with LPS. Western blotting results showed that overexpression of miR-130a promoted the expression of Klotho and activated the PI3K/AKT pathway but inhibited Wnt and NF-κB pathways. CONCLUSIONS: These findings demonstrated that miR-130a promoted PI3K/AKT pathway but inhibited Wnt and NF-κB pathways through upregulation of Klotho. Furthermore, miR-130a protects against lipopolysaccharide-induced glomerular cell injury by upregulating Klotho expression.

The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space.

Venturing into the immensity of the small molecule universe to identify novel chemical structure is a much discussed objective of many methods proposed by the chemoinformatics community. To this end, numerous approaches using techniques from the fields of computational de novo design, virtual screening and reaction informatics, among others, have been proposed. Although in principle this objective is commendable, in practice there are several obstacles to useful exploitation of the chemical space. Prime among them are the sheer number of theoretically feasible compounds and the practical concern regarding the synthesizability of the chemical structures conceived using in silico methods. We present the Proximal Lilly Collection initiative implemented at Eli Lilly and Co. with the aims to (i) define the chemical space of small, drug-like compounds that could be synthesized using in-house resources and (ii) facilitate access to compounds in this large space for the purposes of ongoing drug discovery efforts. The implementation of PLC relies on coupling access to available synthetic knowledge and resources with chemo/reaction informatics techniques and tools developed for this purpose. We describe in detail the computational framework supporting this initiative and elaborate on the characteristics of the PLC virtual collection of compounds. As an example of the opportunities provided to drug discovery researchers by easy access to a large, realistically feasible virtual collection such as the PLC, we describe a recent application of the technology that led to the discovery of selective kinase inhibitors.

PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.

Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .

Synthesis of p-O-Alkyl Salicylanilide Derivatives as Novel EGFR Inhibitors.

Preclinical Research Epidermal growth factor receptor (EGFR), a validated target for anticancer drugs, plays a critical role in tumorigenesis and tumor development. A series of p-O-alkyl salicylanilide derivatives were designed and synthesized as novel EGFR inhibitors using a salicylic acid scaffold. A simulated six-membered ring strategy formed through intramolecular hydrogen bonds was employed to mimic the planar quinazoline of the EGFR antagonist, gefitinib. The derived compounds with hydroxyl at the ortho position were more potent than ones with methoxyl group. In particular, compounds 5d and 5b displayed significant EGFR inhibitory (IC50 values = 0.30 and 0.45 µM, respectively) activity as well as potent antiproliferative activity in A431 and HCT-116 tumor cells. These salicylanilides could be considered as promising lead compounds for developing novel EGFR inhibitors.

Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization.

Accurately predicting how a small molecule binds to its target protein is an essential requirement for structure-based drug design (SBDD) efforts. In structurally enabled medicinal chemistry programs, binding pose prediction is often applied to ligands after a related compound's crystal structure bound to the target protein has been solved. In this article, we present an automated pose prediction protocol that makes extensive use of existing X-ray ligand information. It uses spatial restraints during docking based on maximum common substructure (MCS) overlap between candidate molecule and existing X-ray coordinates of the related compound. For a validation data set of 8784 docking runs, our protocol's pose prediction accuracy (80-82%) is almost two times higher than that of one unbiased docking method software (43%). To demonstrate the utility of this protocol in a project setting, we show its application in a chronological manner for a number of internal drug discovery efforts. The accuracy and applicability of this algorithm (>70% of cases) to medicinal chemistry efforts make this the approach of choice for pose prediction in lead optimization programs.