Architectural Repertoire of Ligand‐Binding Pockets on Protein Surfaces

Weisel, Martin; Kriegl, Jan M.; Schneider, Gisbert

doi:10.1002/cbic.200900604

Cited by 21 publications

(16 citation statements)

References 86 publications

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“…[92] The use of pure geometric representations such as shape curvatures, spherical harmonics, wavelet coefficients or pocket frameworks has also been explored. [93,94] For example, Weisel et al [94] used graph-based pocket frameworks to show that ligand-binding cavities can be described with a limited number of topologies. Kahraman et al [95] used pure shape descriptors to compare binding sites and ligands and arrived to the conclusion that the assumption that binding sites that interact with similar ligands have similar geometries is only partially true.…”

Section: Binding Site Representationmentioning

confidence: 99%

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Jalencas

Mestres

2013

Molecular Informatics

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The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

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Section: Binding Site Representationmentioning

confidence: 99%

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Jalencas

Mestres

2013

Molecular Informatics

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“…Pocket frameworks encoding binding pocket similarities were also used to create protein binding site similarity networks (Weisel et al, 2010). Pocket frameworks are reduced, graph-based representations of pocket geometries generated by the software PocketGraph using a growing neural gas approach.…”

Section: Areas Of Drug Design: An Assessment Of Network-related Admentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

821

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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“…More recently the problem of drug solubility prediction from structure has been revisited (Jorgensen & Duffy, 2002). The prediction of physico-chemical properties of organic compounds from molecular structure has been extensively studied using hybrid techniques that include neural networks (Taskinen & Yliruusi, 2003;Weisel et al, 2010;Pal & Panja, 2013). Also identification of small-molecule ligands has been improved using neural techniques (Durrant & McCammon, 2010;Durrant and McCammon 2011;Romero Reyes et al, 2013).…”

Section: Neural Networkmentioning

confidence: 99%

Improving drug discovery using hybrid softcomputing methods

Pérez‐Sánchez

Cano

García-Rodríguez

2014

Applied Soft Computing

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Abstract. Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

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Architectural Repertoire of Ligand‐Binding Pockets on Protein Surfaces

Cited by 21 publications

References 86 publications

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Improving drug discovery using hybrid softcomputing methods

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