A new method to improve network topological similarity search: applied to fold recognition

Lhota, John; Hauptman, Ruth; Hart, Thomas D.; Ng, Clara; Xie, Lei

doi:10.1093/bioinformatics/btv125

Cited by 12 publications

(20 citation statements)

References 61 publications

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“…We have developed several methods, e.g. ENTS55 and case-based reasoning5657 for this purpose. In our on-going work, we plan to integrate these methods into the COSINE algorithm.…”

Section: Discussionmentioning

confidence: 99%

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

Lim

Gray

Xie

et al. 2016

Sci Rep

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Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

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“…We have developed several methods, e.g. ENTS55 and case-based reasoning5657 for this purpose. In our on-going work, we plan to integrate these methods into the COSINE algorithm.…”

Section: Discussionmentioning

confidence: 99%

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

Lim

Gray

Xie

et al. 2016

Sci Rep

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“…For example, the data fusion of several chemical structure fingerprints enables the prediction of binding affinity based on chemical similarity [9]. Lhota et al have developed a new similarity framework, Enrichment of Network Topological Similarity (ENTS), to relate similarities of different attributes of biological entities, and to assess the statistical significance of similarity measurements [10]. ENTS has demonstrated superior performance in linking protein sequence similarity to its structural similarity.…”

Section: Fundamental Concepts Of Data Sciences and Their Impacts Omentioning

confidence: 99%

Providing data science support for systems pharmacology and its implications to drug discovery

Hart

Xie

2016

Expert Opinion on Drug Discovery

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Introduction The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. Areas covered This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Expert opinion Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

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“…ADASS [39] compares and classifies protein domain architectures by recognizing similarity between the domain architectures even if the proteins share very poor sequence similarity; it includes neighbor information in it’s score. The Enrichment of Network Topological Similarity (ENTS), is a framework to improve the performance of large-scale similarity searches; it considers a continuous protein space and performs well on the fold recognition problem [40]. ArchSchema [41] uses a domain-graph of related domain arrangements.…”

Section: Grammar Of Proteinsmentioning

confidence: 99%

The language of the protein universe

Scaiewicz

Levitt

2015

Current Opinion in Genetics & Development

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Proteins, the main cell machinery which play a major roll in nearly every cellular process, have always been a central focus in biology. We live in the post-genomic era, and inferring information from massive data sets is a steadily growing universal challenge. The increasing availability of fully sequenced genomes can be regarded as the “Rosetta Stone” of the protein universe, allowing the understanding of genomes and their evolution, just as the original Rosetta Stone allowed Champollion to decipher the ancient Egyptian hieroglyphics. In this review, we consider aspects of the protein domain architectures repertoire that are closely related to those of human languages and aim to provide some insights about the language of proteins.

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A new method to improve network topological similarity search: applied to fold recognition

Abstract: : lxie@iscb.org.

Cited by 12 publications

References 61 publications

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

Providing data science support for systems pharmacology and its implications to drug discovery

The language of the protein universe

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