The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications

Halperin, Inbal; Glazer, Dariya S.; Wu, Sheng Nan; Altman, Russ B.

doi:10.1186/1471-2164-9-s2-s2

Cited by 56 publications

(62 citation statements)

References 60 publications

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“…97 They found numerous PROSITE patterns with common three-dimensional structure characteristics which could be used to create templates defining 3D functional patterns. Wu et al 98 recently improved on a previous study 99 showing that 3D information is significantly more relevant than PROSITE patterns. Our work suggests that common and distinct characteristics can be associated with a given pattern and that distinct patterns share common local features.…”

Section: Prosite Patternsmentioning

confidence: 95%

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Doppelt-Azeroual

Delfaud²,

Moriaud³

et al. 2010

Protein Science

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Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.

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Section: Prosite Patternsmentioning

confidence: 95%

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Doppelt-Azeroual

Delfaud²,

Moriaud³

et al. 2010

Protein Science

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“…Next, the algorithm checks whether the increase is "large enough, " i.e., greater than or equal to δ (line 11). If so, the set of SDPs and the sets of not-yet-covered structures are updated (line [13][14]. If not, the algorithm terminates and returns the set of SDPs.…”

Section: Structure-guided Selection Of Specificity Determining Positionsmentioning

confidence: 99%

Structure-guided selection of Specificity Determining Positions in the human kinome

Moll

Finn

Kavraki

2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Background:The human kinome contains many important drug targets. It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. The increased availability of protein 3D structures has provided much information on the structural variation within a given protein family. However, the relationship between structural variations and binding specificity is complex and incompletely understood. We have developed a structural bioinformatics approach which provides an analysis of key determinants of binding selectivity as a tool to enhance the rational design of drugs with a specific selectivity profile. Results: We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm's performance is demonstrated using an extensive dataset for the human kinome. Conclusion: We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site. We show for several inhibitors that we are able to identify residues that are known to be functionally important.

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“…The most common way to perform such a search is to identify patterns specific from a given function for example and to design a prediction method. Information taken into account can consist in different levels: only sequence (Ansari & Raghave, 2010;Sigrist et al, 2010), sequence and structure (Halperin et al, 2008;Pugalenthi et al, 2008), only structure (Manikandan et al, 2008;Polacco & Babbitt, 2006) or use of more general classifications: GO (Espadaler et al, 2006), SCOP (Tendulkar et al, 2010) ... In this section, the objective is to design a prediction method only based on AA sequence in order to provide information for only sequenced proteins.…”

Section: Using Hmm To Detect Interesting Hmm-sa Patternsmentioning

confidence: 99%

Application of HMM to the Study of Three-Dimensional Protein Structure

Reynès¹,

Regad²,

Pérot³

et al. 2011

Hidden Markov Models, Theory and Applications

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The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications

Cited by 56 publications

References 60 publications

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Structure-guided selection of Specificity Determining Positions in the human kinome

Application of HMM to the Study of Three-Dimensional Protein Structure

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