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1.
BMC Bioinformatics ; 20(Suppl 26): 628, 2019 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-31839008

RESUMO

BACKGROUND: Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters. CONCLUSIONS: The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.


Assuntos
Biologia Computacional , Reposicionamento de Medicamentos , Análise por Conglomerados , Biologia Computacional/métodos , Reposicionamento de Medicamentos/métodos , Humanos , Aprendizado de Máquina
2.
Chem Biol Drug Des ; 94(1): 1390-1401, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30916462

RESUMO

Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

3.
Bioinformatics ; 35(20): 3989-3995, 2019 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-30873528

RESUMO

MOTIVATION: Studies have shown that the accuracy of random forest (RF)-based scoring functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of classical SFs, such as X-Score, does not. Nevertheless, the impact of the similarity between training and test samples on this matter has not been studied in a systematic manner. It is therefore unclear how these SFs would perform when only trained on protein-ligand complexes that are highly dissimilar or highly similar to the test set. It is also unclear whether SFs based on machine learning algorithms other than RF can also improve accuracy with increasing training set size and to what extent they learn from dissimilar or similar training complexes. RESULTS: We present a systematic study to investigate how the accuracy of classical and machine-learning SFs varies with protein-ligand complex similarities between training and test sets. We considered three types of similarity metrics, based on the comparison of either protein structures, protein sequences or ligand structures. Regardless of the similarity metric, we found that incorporating a larger proportion of similar complexes to the training set did not make classical SFs more accurate. In contrast, RF-Score-v3 was able to outperform X-Score even when trained on just 32% of the most dissimilar complexes, showing that its superior performance owes considerably to learning from dissimilar training complexes to those in the test set. In addition, we generated the first SF employing Extreme Gradient Boosting (XGBoost), XGB-Score, and observed that it also improves with training set size while outperforming the rest of SFs. Given the continuous growth of training datasets, the development of machine-learning SFs has become very appealing. AVAILABILITY AND IMPLEMENTATION: https://github.com/HongjianLi/MLSF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

4.
Sci Rep ; 8(1): 15186, 2018 10 12.
Artigo em Inglês | MEDLINE | ID: mdl-30315195

RESUMO

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.


Assuntos
Epistasia Genética , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Polimorfismo de Nucleotídeo Único , Psoríase/genética , Alelos , Estudos de Casos e Controles , Biologia Computacional/métodos , Redes Reguladoras de Genes , Genótipo , Haplótipos , Humanos , Fenótipo
5.
Oncol Rep ; 40(3): 1592-1600, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-29956794

RESUMO

Since cyclin­dependent kinases 4/6 (CDK4/6) play pivotal roles in cell cycle regulation and are overexpressed in human skin cancers, CDK4/6 inhibitors are potentially effective drugs for skin cancer. In the present study, we present a mixed computational and experimental study attempting to repurpose approved small­molecule drugs as dual CDK4/6 inhibitors for skin cancer treatment. We performed structure­based virtual screening using the docking software idock, targeting an ensemble of CDK4/6 structures. We identified and selected nine compounds with significant predicted scores, and evaluated their cytotoxic effects in vitro in A375 and A431 human skin cancer cell lines. Rafoxanide was found to exhibit the highest cytotoxic effects (IC50: 1.09 µM for A375 and 1.31 µM for A431 cells). Consistent with the expected properties of CDK4/6 inhibitors, rafoxanide significantly increased the G1 phase population. Notably, we revealed that rafoxanide specifically decreased the expression of CDK4/6, cyclin D, retinoblastoma protein (Rb) and the phosphorylation of CDK4/6 and Rb. Furthermore, the anticancer effect of rafoxanide was demonstrated in vivo in BALB/C nude mice subcutaneously xenografted with human skin cancer A375 cells. Rafoxanide (40 mg/kg, i.p.) exhibited significant antitumor activity, comparable to that of oxaliplatin (5 mg/kg, i.p.). The combined administration of rafoxanide and oxaliplatin produced a synergistic therapeutic effect. To the best of our knowledge, the present study is the first to indicate that rafoxanide inhibits CDK4/6 activity and is a potential candidate drug for the treatment of human skin cancer.


Assuntos
Biomarcadores Tumorais/metabolismo , Quinase 4 Dependente de Ciclina/antagonistas & inibidores , Quinase 6 Dependente de Ciclina/antagonistas & inibidores , Inibidores de Proteínas Quinases/farmacologia , Rafoxanida/farmacologia , Neoplasias Cutâneas/tratamento farmacológico , Bibliotecas de Moléculas Pequenas/farmacologia , Animais , Antinematódeos/farmacologia , Antineoplásicos/farmacologia , Apoptose/efeitos dos fármacos , Proliferação de Células/efeitos dos fármacos , Descoberta de Drogas , Feminino , Regulação Enzimológica da Expressão Gênica/efeitos dos fármacos , Ensaios de Triagem em Larga Escala , Humanos , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Nus , Neoplasias Cutâneas/enzimologia , Neoplasias Cutâneas/patologia , Células Tumorais Cultivadas , Ensaios Antitumorais Modelo de Xenoenxerto
6.
Biomolecules ; 8(1)2018 03 14.
Artigo em Inglês | MEDLINE | ID: mdl-29538331

RESUMO

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.


Assuntos
Aprendizado de Máquina , Simulação de Acoplamento Molecular/normas , Mapeamento de Interação de Proteínas/métodos , Análise de Sequência de Proteína/normas , Mapeamento de Interação de Proteínas/normas
7.
Front Oncol ; 7: 288, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29238696

RESUMO

In advanced lung cancer, epidermal growth factor tyrosine kinase inhibitors (EGFR TKIs) have extraordinary clinical efficacy. However, their usefulness is severely compromised by drug resistance mediated by various mechanisms, the most important of which is the secondary EGFR T790M mutation. The mutation blocks the binding of EGFR TKIs to the receptor kinase, thereby abolishing the therapeutic efficacy. In this study, we used our free and open-source protein-ligand docking software idock to screen worldwide approved small-molecule drugs against EGFR T790M. The computationally selected drug candidates were evaluated in vitro in resistant non-small cell lung cancer (NSCLC) cell lines. The specificity of the drugs toward the mutant EGFR was demonstrated by cell-free kinase inhibition assay. The inhibition of EGFR kinase activity and its downstream signaling pathways in NSCLC cells was shown by immunoblot analysis. The positive hints were revealed to be indacaterol, canagliflozin, and cis-flupenthixol, all of which were shown to induce apoptosis in NSCLC cells harboring the EGFR T790M mutation. Moreover, the combination of indacaterol with gefitinib was also found to produce synergistic anticancer effect in NSCLC cells bearing EGFR T790M. The observed synergistic effect was likely contributed by the enhanced inhibition of EGFR and its downstream signaling molecules.

8.
Sci Rep ; 7(1): 17987, 2017 12 21.
Artigo em Inglês | MEDLINE | ID: mdl-29269744

RESUMO

The phosphatidylinositol-3-kinase (PI3K)/AKT signaling pathway plays a pivotal role in many cellular processes, including the proliferation, survival and differentiation of lung cancer cells. Thus, PI3K is a promising therapeutic target for lung cancer treatment. In this study, we applied free and open-source protein-ligand docking software, screened 3167 FDA-approved small molecules, and identified putative PI3Kα inhibitors. Among them, econazole nitrate, an antifungal agent, exhibited the highest activity in decreasing cell viability in pathological types of NSCLC cell lines, including H661 (large cell lung cancer) and A549 (adenocarcinoma). Econazole decreased the protein levels of p-AKT and Bcl-2, but had no effect on the phosphorylation level of ERK. It inhibited cell growth and promote apoptosis in a dose-dependent manner. Furthermore, the combination of econazole and cisplatin exhibited additive and synergistic effects in the H661 and A549 lung cancer cell lines, respectively. Finally, we demonstrated that econazole significantly suppressed A549 tumor growth in nude mice. Our findings suggest that econazole is a new PI3K inhibitor and a potential drug that can be used in lung cancer treatment alone or in combination with cisplatin.


Assuntos
Antineoplásicos/uso terapêutico , Apoptose/efeitos dos fármacos , Econazol/uso terapêutico , Neoplasias Pulmonares/tratamento farmacológico , Células A549 , Animais , Linhagem Celular Tumoral , Humanos , Masculino , Camundongos Endogâmicos BALB C , Camundongos Nus , Transplante de Neoplasias , Proteína Oncogênica v-akt/metabolismo , Proteínas Proto-Oncogênicas c-bcl-2/metabolismo , Transdução de Sinais/efeitos dos fármacos
9.
J Pharmacol Sci ; 135(3): 114-120, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-29132796

RESUMO

Hyperuricemia, a long-term purine metabolic disorder, is a well-known risk factor for gout, hypertension and diabetes. In maintaining normal whole-body purine levels, xanthine oxidase (XOD) is a key enzyme in the purine metabolic pathway, as it catalyzes the oxidation of hypoxanthine to xanthine and finally to uric acid. Here we used the protein-ligand docking software idock to virtually screen potential XOD inhibitors from 3167 approved small compounds/drugs. The inhibitory activities of the ten compounds with the highest scores were tested on XOD in vitro. Interestingly, all the ten compounds inhibited the activity of XOD at certain degrees. Particularly, the anti-ulcerative-colitis drug olsalazine sodium demonstrated a great inhibitory activity for XOD (IC50 = 3.4 mg/L). Enzymatic kinetic studies revealed that the drug was a hybrid-type inhibitor of xanthine oxidase. Furthermore, the drug strikingly decreased serum urate levels, serum/hepatic activities of XOD at a dose-dependent manner in vivo. Thus, we demonstrated a successful hunting process of compounds/drugs for hyperuricemia through virtual screening, supporting a potential usage of olsalazine sodium in the treatment of hyperuricemia.


Assuntos
Ácidos Aminossalicílicos/farmacologia , Antiulcerosos/farmacologia , Ácido Úrico/sangue , Xantina Desidrogenase/antagonistas & inibidores , Xantina Desidrogenase/metabolismo , Ácidos Aminossalicílicos/uso terapêutico , Animais , Relação Dose-Resposta a Droga , Avaliação Pré-Clínica de Medicamentos , Hiperuricemia/tratamento farmacológico , Técnicas In Vitro , Masculino , Camundongos , Relação Estrutura-Atividade
10.
Artigo em Inglês | MEDLINE | ID: mdl-26336137

RESUMO

Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and gene regulation. Limited by expensive experiments, it is promising to discover them with variations directly from sequence data. Although existing computational methods have produced satisfactory results, they are one-to-one mappings with no site-specific information on residue/nucleotide variations, where these variations in binding cores may impact binding specificity. This study presents a new representation for modeling binding cores by incorporating variations and an algorithm to discover them from only sequence data. Our algorithm takes protein and DNA sequences from TRANSFAC (a Protein-DNA Binding Database) as input; discovers from both sets of sequences conserved regions in Aligned Pattern Clusters (APCs); associates them as Protein-DNA Co-Occurring APCs; ranks the Protein-DNA Co-Occurring APCs according to their co-occurrence, and among the top ones, finds three-dimensional structures to support each binding core candidate. If successful, candidates are verified as binding cores. Otherwise, homology modeling is applied to their close matches in PDB to attain new chemically feasible binding cores. Our algorithm obtains binding cores with higher precision and much faster runtime ( ≥ 1,600x) than that of its contemporaries, discovering candidates that do not co-occur as one-to-one associated patterns in the raw data. AVAILABILITY: http://www.pami.uwaterloo.ca/~ealee/files/tcbbPnDna2015/Release.zip.


Assuntos
Análise por Conglomerados , Biologia Computacional/métodos , Proteínas de Ligação a DNA/química , DNA/química , Alinhamento de Sequência/métodos , Algoritmos , DNA/análise , DNA/genética , DNA/metabolismo , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Mineração de Dados , Ligação Proteica , Análise de Sequência de DNA , Análise de Sequência de Proteína
11.
Chem Biol Drug Des ; 89(4): 505-513, 2017 04.
Artigo em Inglês | MEDLINE | ID: mdl-27664399

RESUMO

Bladder carcinoma (BC) is the ninth most common cause of cancer worldwide. Surgical resection and conventional chemotherapy and radiotherapy will ultimately fail due to tumor recurrence and resistance. Thus, the development of novel treatment is urgently needed. Fibroblast growth factor receptor 3 (FGFR3) is an important and well-established target for BC treatment. In this study, we utilized the free and open-source protein-ligand docking software idock to prospectively identify potential inhibitors of FGFR3 from 3,167 worldwide approved small-molecule drugs using a repositioning strategy. Six high-scoring compounds were purchased and tested in vitro. Among them, the acaricide drug fluazuron exhibited the highest antiproliferative effect in human BC cell lines RT112 and RT4. We further demonstrated that fluazuron treatment significantly increased the percentage of apoptosis cells, and decreased the phosphorylation level of FGFR3 and its downstream proteins FRS2-α, AKT, and ERK. We also investigated the anticancer effect of fluazuron in vivo in BALB/C nude mice subcutaneously xenografted with RT112 cells. Our results showed that oral treatment with fluazuron (80 mg/kg) significantly inhibited tumor growth. These results suggested for the first time that fluazuron is a potential inhibitor of FGFR3 and a candidate anticancer drug for the treatment of BC.


Assuntos
Acaricidas/farmacologia , Antineoplásicos/farmacologia , Compostos de Fenilureia/farmacologia , Receptor Tipo 3 de Fator de Crescimento de Fibroblastos/antagonistas & inibidores , Neoplasias da Bexiga Urinária/tratamento farmacológico , Acaricidas/química , Antineoplásicos/química , Apoptose/efeitos dos fármacos , Linhagem Celular Tumoral , Cristalografia por Raios X , Humanos , Técnicas In Vitro , Simulação de Acoplamento Molecular , Compostos de Fenilureia/química , Fosforilação , Receptor Tipo 3 de Fator de Crescimento de Fibroblastos/química , Receptor Tipo 3 de Fator de Crescimento de Fibroblastos/metabolismo , Transdução de Sinais , Neoplasias da Bexiga Urinária/patologia
12.
Front Pharmacol ; 7: 374, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27812335

RESUMO

San-Huang-Xie-Xin-Tang (SHXXT), one of the most important traditional Chinese medicinal formulas, is comprised by three herbal medicines, the rhizome of Rheum officinale [or Rheum tanguticum (Polygonaceae) (Dahuang in Chinese)], the root of Scutellaria baicalensis (Labiatae) (Huangqin in Chinese), and the rhizome of Coptis chinensis (Ranunculaceae) (Huanglian in Chinese) in the ratios of 2:1:1 or 1:1:1. This study is aimed to quantitate and qualify of SHXXT, by a rapid, convenient, and effective HPLC-PDA approach associated with LC-MS technique. Of which method, nine chosen major bioactive components in SHXXT, including aloe-emodin (Ale), baicalin (Ba), berberine (Be), coptisine (Co), palmatine (Pa), resveratroloside (Res), rhein (Rh), sennoside A (Se-A), and wogonin (Wo), were evaluated within 30 min. The nine chemical markers were monitored in a high sensitivity with a low detection limit of 0.01-0.55 µg/mL and the correlation coefficient of the regression curve revealed a good linearity with R2 > 0.99. Moreover, the extraction solution system and the HPLC elution conditions were also optimized in the present study. This present developed protocol was then successfully applied to quantify nine chemical markers of 10 SHXXT products from eight Taiwanese TCM pharmaceutical companies. In quantitative results, Res was found as the major compound in SHXXT-1~5 and 8 with significantly higher amounts than those in other products, indicating the products SHXXT-1~5 and 8 may use R. tanguticum as the raw material, which possessed a higher concentration of the bioactive composition Res, instead of R. officinale. Simultaneously, Ale, Rh, and Wo were < 2% in these 10 products. Different chemical profiles of commercial products indicated that, probably, each product with the same named formula might be regarded as a sole medicine and need to be investigated individually. Importantly, it is never too much to emphasize the importance of quality control in TCM development.

13.
BMC Bioinformatics ; 17(Suppl 11): 308, 2016 Sep 22.
Artigo em Inglês | MEDLINE | ID: mdl-28185549

RESUMO

BACKGROUND: Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes. RESULTS: Against commonly-held views, we have found that pose generation error has generally a small impact on the accuracy of binding affinity prediction. This is also true for large pose generation errors and it is not only observed with machine-learning scoring functions, but also with classical scoring functions such as AutoDock Vina. Furthermore, we propose a procedure to correct a substantial part of this error which consists of calibrating the scoring functions with re-docked, rather than co-crystallised, poses. In this way, the relationship between Vina-generated protein-ligand poses and their binding affinities is directly learned. As a result, test set performance after this error-correcting procedure is much closer to that of predicting the binding affinity in the absence of pose generation error (i.e. on crystal structures). We evaluated several strategies, obtaining better results for those using a single docked pose per ligand than those using multiple docked poses per ligand. CONCLUSIONS: Binding affinity prediction is often carried out on the docked pose of a known binder rather than its co-crystallised pose. Our results suggest than pose generation error is in general far less damaging for binding affinity prediction than it is currently believed. Another contribution of our study is the proposal of a procedure that largely corrects for this error. The resulting machine-learning scoring function is freely available at http://istar.cse.cuhk.edu.hk/rf-score-4.tgz and http://ballester.marseille.inserm.fr/rf-score-4.tgz .


Assuntos
Simulação de Acoplamento Molecular/normas , Proteínas Nucleares/metabolismo , Pirazinas/metabolismo , Software , Fatores de Transcrição/metabolismo , Humanos , Ligantes , Proteínas Nucleares/química , Ligação Proteica , Conformação Proteica , Pirazinas/química , Fatores de Transcrição/química
14.
Artigo em Inglês | MEDLINE | ID: mdl-26357085

RESUMO

Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and for the deep understanding of gene regulation. Traditionally, binding cores are identified in resolved high-resolution 3D structures. However, it is expensive, labor-intensive and time-consuming to obtain these structures. Hence, it is promising to discover binding cores computationally on a large scale. Previous studies successfully applied association rule mining to discover binding cores from TF-TFBS binding sequence data only. Despite the successful results, there are limitations such as the use of tight support and confidence thresholds, the distortion by statistical bias in counting pattern occurrences, and the lack of a unified scheme to rank TF-TFBS associated patterns. In this study, we proposed an association rule mining algorithm incorporating statistical measures and ranking to address these limitations. Experimental results demonstrated that, even when the threshold on support was lowered to one-tenth of the value used in previous studies, a satisfactory verification ratio was consistently observed under different confidence levels. Moreover, we proposed a novel ranking scheme for TF-TFBS associated patterns based on p-values and co-support values. By comparing with other discovery approaches, the effectiveness of our algorithm was demonstrated. Eighty-four binding cores with PDB support are uniquely identified.


Assuntos
Sítios de Ligação , Biologia Computacional/métodos , Proteínas de Ligação a DNA/química , DNA/química , Modelos Estatísticos , Algoritmos , DNA/metabolismo , Proteínas de Ligação a DNA/metabolismo , Mineração de Dados , Ligação Proteica
15.
PLoS One ; 10(7): e0132072, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-26147897

RESUMO

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Surgical resection and conventional chemotherapy and radiotherapy ultimately fail due to tumor recurrence and HCC's resistance. The development of novel therapies against HCC is thus urgently required. The cyclin-dependent kinase (CDK) pathways are important and well-established targets for cancer treatment. In particular, CDK2 is a key factor regulating the cell cycle G1 to S transition and a hallmark for cancers. In this study, we utilized our free and open-source protein-ligand docking software, idock, prospectively to identify potential CDK2 inhibitors from 4,311 FDA-approved small molecule drugs using a repurposing strategy and an ensemble docking methodology. Sorted by average idock score, nine compounds were purchased and tested in vitro. Among them, the anti-psychotic drug fluspirilene exhibited the highest anti-proliferative effect in human hepatocellular carcinoma HepG2 and Huh7 cells. We demonstrated for the first time that fluspirilene treatment significantly increased the percentage of cells in G1 phase, and decreased the expressions of CDK2, cyclin E and Rb, as well as the phosphorylations of CDK2 on Thr160 and Rb on Ser795. We also examined the anti-cancer effect of fluspirilene in vivo in BALB/C nude mice subcutaneously xenografted with human hepatocellular carcinoma Huh7 cells. Our results showed that oral fluspirilene treatment significantly inhibited tumor growth. Fluspirilene (15 mg/kg) exhibited strong anti-tumor activity, comparable to that of the leading cancer drug 5-fluorouracil (10 mg/kg). Moreover, the cocktail treatment with fluspirilene and 5-fluorouracil exhibited the highest therapeutic effect. These results suggested for the first time that fluspirilene is a potential CDK2 inhibitor and a candidate anti-cancer drug for the treatment of human hepatocellular carcinoma. In view of the fact that fluspirilene has a long history of safe human use, our discovery of fluspirilene as a potential anti-HCC drug may present an immediately applicable clinical therapy.


Assuntos
Antipsicóticos/farmacologia , Carcinoma Hepatocelular/tratamento farmacológico , Quinase 2 Dependente de Ciclina/antagonistas & inibidores , Fluspirileno/farmacologia , Neoplasias Hepáticas/tratamento farmacológico , Simulação de Acoplamento Molecular , Proteínas de Neoplasias/antagonistas & inibidores , Animais , Carcinoma Hepatocelular/enzimologia , Carcinoma Hepatocelular/patologia , Simulação por Computador , Quinase 2 Dependente de Ciclina/metabolismo , Feminino , Fase G1/efeitos dos fármacos , Células Hep G2 , Humanos , Neoplasias Hepáticas/enzimologia , Neoplasias Hepáticas/patologia , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Nus , Proteínas de Neoplasias/metabolismo , Fase S/efeitos dos fármacos , Ensaios Antitumorais Modelo de Xenoenxerto
16.
Molecules ; 20(6): 10947-62, 2015 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-26076113

RESUMO

Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.


Assuntos
Modelos Teóricos , Relação Estrutura-Atividade
17.
Mol Inform ; 34(2-3): 115-26, 2015 02.
Artigo em Inglês | MEDLINE | ID: mdl-27490034

RESUMO

There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on how accurate their predicted ranking is. However, despite their proven advantages, machine-learning scoring functions are still not widely applied. This seems to be due to insufficient understanding of their properties and the lack of user-friendly software implementing them. Here we present a study where the accuracy of AutoDock Vina, arguably the most commonly-used docking software, is strongly improved by following a machine learning approach. We also analyse the factors that are responsible for this improvement and their generality. Most importantly, with the help of a proposed benchmark, we demonstrate that this improvement will be larger as more data becomes available for training Random Forest models, as regression models implying additive functional forms do not improve with more training data. We discuss how the latter opens the door to new opportunities in scoring function development. In order to facilitate the translation of this advance to enhance structure-based molecular design, we provide software to directly re-score Vina-generated poses and thus strongly improve their predicted binding affinity. The software is available at http://istar.cse.cuhk.edu.hk/rf-score-3.tgz and http://crcm. marseille.inserm.fr/fileadmin/rf-score-3.tgz.


Assuntos
Aprendizado de Máquina , Modelos Teóricos , Simulação de Acoplamento Molecular , Software
18.
BMC Bioinformatics ; 15: 291, 2014 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-25159129

RESUMO

BACKGROUND: State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. RESULTS: In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study. CONCLUSIONS: Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.


Assuntos
Inteligência Artificial , Biologia Computacional/métodos , Proteínas/metabolismo , Ligantes , Modelos Lineares , Ligação Proteica
19.
Adv Mater ; 26(34): 5962-8, 2014 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-25042007

RESUMO

The palladium nanoparticle (Pd NP)-decorated LaAlO3 /SrTiO3 (LAO/STO) heterostructure is for the first time used as a hydrogen-gas sensor with very high sensitivity and workability at room temperature. The outstanding gas-sensing properties are due to the Pd NPs' catalytic effect to different gases, resulting in charge coupling between the gas molecules and the two-dimensional electron gas (2DEG) through the Pd NPs by either a direct charge exchange or a change of the electron affinity. These results provide insight into the emerging properties at LAO/STO interfaces.

20.
BMC Bioinformatics ; 15: 56, 2014 Feb 25.
Artigo em Inglês | MEDLINE | ID: mdl-24564583

RESUMO

BACKGROUND: Visualization of protein-ligand complex plays an important role in elaborating protein-ligand interactions and aiding novel drug design. Most existing web visualizers either rely on slow software rendering, or lack virtual reality support. The vital feature of macromolecular surface construction is also unavailable. RESULTS: We have developed iview, an easy-to-use interactive WebGL visualizer of protein-ligand complex. It exploits hardware acceleration rather than software rendering. It features three special effects in virtual reality settings, namely anaglyph, parallax barrier and oculus rift, resulting in visually appealing identification of intermolecular interactions. It supports four surface representations including Van der Waals surface, solvent excluded surface, solvent accessible surface and molecular surface. Moreover, based on the feature-rich version of iview, we have also developed a neat and tailor-made version specifically for our istar web platform for protein-ligand docking purpose. This demonstrates the excellent portability of iview. CONCLUSIONS: Using innovative 3D techniques, we provide a user friendly visualizer that is not intended to compete with professional visualizers, but to enable easy accessibility and platform independence.


Assuntos
Biologia Computacional/métodos , Simulação de Acoplamento Molecular/métodos , Proteínas/química , Proteínas/metabolismo , Software , Gráficos por Computador , Internet , Ligantes , Ligação Proteica , Interface Usuário-Computador
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