DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks

Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in computer vision and natural language processing due to the prevention of overfitting and efficient training. Here, we propose DEEPred, a hierarchical stack of multi-task feed-forward deep neural networks, as a solution to Gene Ontology (GO) based protein function prediction. DEEPred was optimized through rigorous hyper-parameter tests, and benchmarked using three types of protein descriptors, training datasets with varying sizes and GO terms form different levels. Furthermore, in order to explore how training with larger but potentially noisy data would change the performance, electronically made GO annotations were also included in the training process. The overall predictive performance of DEEPred was assessed using CAFA2 and CAFA3 challenge datasets, in comparison with the state-of-the-art protein function prediction methods. Finally, we evaluated selected novel annotations produced by DEEPred with a literature-based case study considering the 'biofilm formation process' in Pseudomonas aeruginosa. This study reports that deep learning algorithms have significant potential in protein function prediction; particularly when the source data is large. The neural network architecture of DEEPred can also be applied to the prediction of the other types of ontological associations. The source code and all datasets used in this study are available at: https://github.com/cansyl/DEEPred

Synthesis of Some Substituted 6-Phenyl Purine Analogues and Their Biological Evaluation as Cytotoxic Agents

A series of 6-(4-substituted phenyl)-9-(tetrahydropyran-2-yl) purines 3-9, 6-(4-substituted phenyl) purines 10-16, 9-((4-substituted phenyl) sulfonyl)-6-(4-substituted phenyl) purines 17-32 were prepared and screened initially for their in vitro anticancer activity against selected human cancer cells (liver Huh7, colon HCT116, breast MCF7). 6-(4-Phenoxyphenyl) purine analogues 9, 16, 30-32, had potent cytotoxic activities. The most active purine derivatives 5-9, 14, 16, 18, 28-32 were further screened for their cytotoxic activity in hepatocellular cancer cells. 6-(4-Phenoxyphenyl)-9(tetrahydropyran-2-yl)-9H-purine (9) had better cytotoxic activity (IC50 5.4 mu M) than the well-known nucleobase analogue 5-FU and known nucleoside drug fludarabine on Huh7 cells. The structure-activity relationship studies reported that the substitution at C-6 positions in purine nucleus with the 4-phenoxyphenyl group is responsible for the anti-cancer activity

Synthesis of novel diflunisal hydrazide hydrazones as anti-hepatitis C virus agents and hepatocellular carcinoma inhibitors

Hepatitis C virus (HCV) infection is a main cause of chronic liver disease, leading to liver cirrhosis and hepatocellular carcinoma (HCC). The objective of our research was to develop effective agents against viral replication. We have previously identified the hydrazide hydrazone scaffold as a promising hepatitis C virus (HCV) and hepatocelluler inhibitor. Herein we describe the design a number of 2',4'-difluoro-4-hydroxy-N'-(arylmethylidene) biphenyl-3-carbohydrazide (3a-t) as anti-HCV and anticancer agents. Results from evaluation of anti-HCV activity indicated that most of the synthesized hydrazone derivatives inhibited viral replication in the Huh7/Rep-Feo1b and Huh 7.5-FGR-JCI-Rluc2A reporter systems. Antiproliferative activities of increasing concentrations of 2',4'-difluoro-4-hydroxy-N'-(2-pyridyl methylidene)biphenyl-3-carbohydrazide 3b and diflunisal (2.5-40 mu M) were assessed in liver cancer cell lines (Huh7, HepG2, Hep3B, Mahlavu, FOCUS and SNU-475) with sulforhodamine B assay for 72 h. Compound 3b with 2-pyridinyl group in the hydrazone part exhibited promising cytotoxic activity against all cell lines with IC50 values of 10, 1034 16.21 4.74, 9.29 and 8.33 mu M for Huh7, HepG2, Hep3B, Mahlavu, FOCUS and SNU-475 cells, respectively, and produced dramatic cell cycle arrest at SubG1/G0 phase as an indicator of apoptotic cell death induction. (C) 2015 Elsevier Masson SAS. All rights reserved

PATZ1 is a DNA damage-responsive transcription factor that inhibits p53 function

Insults to cellular health cause p53 protein accumulation, and loss of p53 function leads to tumorigenesis. Thus, p53 has to be tightly controlled. Here we report that the BTB/POZ domain transcription factor PATZ1 (MAZR), previously known for its tran- scriptional suppressor functions in T lymphocytes, is a crucial regulator of p53. The novel role of PATZ1 as an inhibitor of the p53 protein marks its gene as a proto-oncogene. PATZ1-deficient cells have reduced proliferative capacity, which we assessed by transcriptome sequencing (RNA-Seq) and real-time cell growth rate analysis. PATZ1 modifies the expression of p53 target genes associated with cell proliferation gene ontology terms. Moreover, PATZ1 regulates several genes involved in cellular adhesion and morphogenesis. Significantly, treatment with the DNA damage-inducing drug doxorubicin results in the loss of the PATZ1 transcription factor as p53 accumulates. We find that PATZ1 binds to p53 and inhibits p53-dependent transcription activation. We examine the mechanism of this functional inhibitory interaction and demonstrate that PATZ1 excludes p53 from DNA bind- ing. This study documents PATZ1 as a novel player in the p53 pathway

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Implicit motif distribution based hybrid computational kernel for sequence classification

Motivation: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive

Protein Kinaz İnhibitörlerinin Hücre Sinyal Yollarındaki Yeni Hedeflerinin Sistem Biyolojisi Yöntemleri İle Tanımlanması

ÖZETProtein kinazlar hücresel sinyal ileti yolaklarında anahtar görevi alarak alan hücrenin sağkalım ve ölüm mekanizmalarını kontrol ederler. Son yıllarda kanser tedavisinde kullanılmaya başlayan yeni kanser ilaçlarının büyük çoğunluğunu kinaz inhibitörü kemoterapötik ajanlar oluşturmaktadır. Bu ilaçların büyük çoğunluğunda, doza bağımlı olarak bir tek proteinin değil birden çok proteinin hücre sinyal yollarına etki ettiği gösterilmiştir. Bu bağlamda önerilen bu projenin özgün amacı tedaviye dirençli ve duyarlı karaciğer kanseri hücrelerinde protein kinaz inhibitörü-hücre sinyal yanıtı ilişkisinin PI3K/Akt sinyal yolu inhibitörleri ile sistem biyolojisi yöntemleri kullanılarak belirlenmesidir. Sonuç olarak kullanılacak inhibitörlerin daha önce ilaç yan etkisi olarak ifade edilen ancak son zamanlarda hedef dışı (off-target) etki olarak belirtilen etkileri tanımlanacaktır. Önerilen bu proje kapsamında RNA-Seq transkriptom dizileme verileri geniş ölçekli veri analizi ve sistem biyolojisi yaklaşımı ile analiz edilecektir. Bu etkiler hem kullanılan ilaçların yeni hedefe yönlendirilmesi (drug repurposing) ve yeni ilaç hedeflerinin belirlenmesini sağlayacaktır

Prediction of protein subcellular localization based on primary sequence data

Subcellular localization is crucial for determining the functions of proteins. A system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order is designed. The approach for prediction is to find the most frequent motifs for each protein in a given class based on clustering via self organizing maps and then to use these most frequent motifs as features for classification by the help of multi layer perceptrons. This approach allows a classification independent of the length of the sequence. In addition to these, the use of a new encoding scheme is described for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. The statistical test results of the system is presented on a four class problem. P2SL achieves slightly higher prediction accuracy than the similar studies

Prediction of protein subcellular localization based on primary sequence data

This paper describes a system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order. Our approach for prediction is to find the most frequent motifs for each protein (class) based on clustering and then to use these most frequent motifs as features for classification. This approach allows a classification independent of the length of the sequence. Another important property of the approach is to provide a means to perform reverse analysis and analysis to extract rules. In addition to these and more importantly, we describe the use of a new encoding scheme for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. We present preliminary results of our system on a two class (dichotomy) classifier. However, it can be extended to multiple classes with some modifications