MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery.

MOTIVATION: Identification of interactions between bioactive small molecules and target proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target effects. Due to the tremendous size of the chemical space, experimental bioactivity screening efforts require the aid of computational approaches. Although deep learning models have been successful in predicting bioactive compounds, effective and comprehensive featurization of proteins, to be given as input to deep neural networks, remains a challenge. RESULTS: Here, we present a novel protein featurization approach to be used in deep learning-based compound-target protein binding affinity prediction. In the proposed method, multiple types of protein features such as sequence, structural, evolutionary and physicochemical properties are incorporated within multiple 2D vectors, which is then fed to state-of-the-art pairwise input hybrid deep neural networks to predict the real-valued compound-target protein interactions. The method adopts the proteochemometric approach, where both the compound and target protein features are used at the input level to model their interaction. The whole system is called MDeePred and it is a new method to be used for the purposes of computational drug discovery and repositioning. We evaluated MDeePred on well-known benchmark datasets and compared its performance with the state-of-the-art methods. We also performed in vitro comparative analysis of MDeePred predictions with selected kinase inhibitors' action on cancer cells. MDeePred is a scalable method with sufficiently high predictive performance. The featurization approach proposed here can also be utilized for other protein-related predictive tasks. AVAILABILITY AND IMPLEMENTATION: The source code, datasets, additional information and user instructions of MDeePred are available at https://github.com/cansyl/MDeePred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cytotoxic activities of some novel benzhydrylpiperazine derivatives.

This study presents the synthesis of nineteen 1-(substitutedbenzoyl)-4-benzhydrylpiperazine and 1-[(substitutedphenyl)sulfonyl]-4-benzhydrylpiperazine derivatives. In vitro cytotoxic activities of the compounds were screened against hepatocellular (HUH-7), breast (MCF-7) and colorectal (HCT-116) cancer cell lines by sulphorhodamine B assay. Among the test compounds, benzamide derivatives had high cytotoxic activity whereas sulfonamide derivatives showed variable 50% growth inhibition (GI50).

Synthesis and cytotoxic activity of novel 3-methyl-1-[(4-substitutedpiperazin-1-yl)methyl]-1H-indole derivatives.

A series of novel 3-methyl-1-[(4-substitutedpiperazin-1-yl)methyl]-1H-indoles (3a-l) were synthesized and their cytotoxicities were analyzed against 3 different human cell lines, including liver (HUH7), breast (MCF7) and colon (HCT116). The Mannich reaction of 3-methylindole (1) with 4-substitutedpiperazines (2) and formaldehyde resulted to the 3-methyl-1-[(4-substitutedpiperazin-1-yl)methyl]-1H-indoles (3a-l) in 38-69% yields. The investigation of anticancer screening revealed that the tested compounds showed comparable activity to the reference drug 5-fluorouracil and compounds 3g, 3h, 3i and 3k, had lower 50% inhibition (IC50) concentration than reference drug. Moreover, the cytotoxic effect of the most potent compound 3h on HUH7 and MCF7 cells through apoptosis was visualized by Hoechst staining and compared with paclitaxel, which is a mitotic inhibitor acting on microtubules. The morphological features of apoptosis were observed as condensed and fragmented nuclei that are similar to paclitaxel.

An ontology for collaborative construction and analysis of cellular pathways.

MOTIVATION: As the scientific curiosity in genome studies shifts toward identification of functions of the genomes in large scale, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. In this regard, it is essential to be able to store, integrate, access and analyze this data effectively with the help of software tools. Clearly this requires a strong ontology that is intuitive, comprehensive and uncomplicated. RESULTS: We define an ontology for an intuitive, comprehensive and uncomplicated representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information via collaboration, and supports manipulation of the stored data. In addition, it facilitates concurrent modifications to the data while maintaining its validity and consistency. Furthermore, novel structures for representation of multiple levels of abstraction for pathways and homologies is provided. Lastly, our ontology supports efficient querying of large amounts of data. We have also developed a software tool named pathway analysis tool for integration and knowledge acquisition (PATIKA) providing an integrated, multi-user environment for visualizing and manipulating network of cellular events. PATIKA implements the basics of our ontology.

PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways.

MOTIVATION: Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. RESULTS: We define an ontology for a comprehensive representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporation of the stored data, as well as multiple levels of abstraction. Based on this ontology, we present the architecture of an integrated environment named Patika (Pathway Analysis Tool for Integration and Knowledge Acquisition). Patika is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that Patika will be a valuable tool for rapid knowledge acquisition, microarray generated large-scale data interpretation, disease gene identification, and drug development. AVAILABILITY: A prototype of Patika is available upon request from the authors.

A monoclonal antibody against DNA binding helix of p53 protein.

Three monoclonal antibodies (Mabs) were generated against p53 DNA-binding core domain. When tested by immunoprecipitation, Western blot and immunofluorescence techniques, Mab 9E4, as well as 7D3 and 6B10 reacted with both wild-type and various mutant p53 proteins. The epitopes recognized by Mabs 7D3, 9E4 and 6B10 were located respectively within the amino acid residues 211-220, 281-290 and 291-300 of human p53 protein. The epitope recognized by 9E4 Mab coincides with helix 2, also called p53 DNA binding helix, which allows the direct contact of the protein with its target DNA sequences. This antibody may be useful to study transcription-dependent and transcription-independent activities of wild-type and mutant p53 proteins.

Identification of relative protein bands in polyacrylamide gel electrophoresis (PAGE) using a multi-resolution snake algorithm.

In polyacrylamide gel electrophoresis (PAGE) image analysis, it is important to determine the percentage of the protein of interest of a protein mixture. This study presents reliable computer software to determine this percentage. The region of interest containing the protein band is detected using the snake algorithm. The iterative snake algorithm is implemented in a multi-resolutional framework. The snake is initialized on a low-resolution image. Then, the final position of the snake at the low resolution is used as the initial position in the higher-resolution image. Finally, the area of the protein is estimated as the area enclosed by the final position of the snake.