Closing the side-chain gap in protein loop modeling

Rossi, Karen A.; Nayeem, Akbar; Weigelt, Carolyn A.; Krystek, Stanley R.

doi:10.1007/s10822-009-9274-3

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…This process can be simulated by induced fit docking which allows for ligand and protein flexibility. [41, 57, 58] To improve the recognition of active compounds in the PI3Kα model (2rd0), we used induced fit docking with Glide 5.6 and Prime 2.2 to construct of multiple receptor conformations [59, 60] representative of the binding site residues’ response to the template ligand AS-605240. This generated a set of eighteen models.…”

Section: Resultsmentioning

confidence: 99%

Thiazolidinedione‐Based PI3Kα Inhibitors: An Analysis of Biochemical and Virtual Screening Methods

Pinson¹,

Schmidt‐Kittler

Zhu

et al. 2011

ChemMedChem

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A series of synthesized and commercially available compounds were assessed against PI3Kα for in vitro inhibitory activity and the results compared to binding calculated in silico. Using published crystal structures of PI3Kγ and PI3Kδ co-crystallized with inhibitors as a template, docking was able to identify the majority of potent inhibitors from a decoy set of 1000 compounds. On the other hand, PI3Kα as the apo-form or modelled by induced fit docking or built as a homology model gave only poor results. A PI3Kα homology model derived from a liganded PI3Kδ crystal structure was developed which has good ability to identify active compounds. The docking results identified binding poses for active compounds that differ from those identified to date and can contribute to our understanding of structure-activity relationships for PI3K inhibitors.

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Section: Resultsmentioning

confidence: 99%

Thiazolidinedione‐Based PI3Kα Inhibitors: An Analysis of Biochemical and Virtual Screening Methods

Pinson¹,

Schmidt‐Kittler

Zhu

et al. 2011

ChemMedChem

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“…This method has been shown to perform well for loop modeling in generic proteins (Rossi et al , 2007, 2009) and the protocol can be applied to CDR-H3 modeling with accuracies high enough that the models could be used as surrogates for experimental structures in many applications (Sellers et al , 2010). …”

Section: Antibody Cdr Modelingmentioning

confidence: 99%

Computer-aided antibody design

Kuroda

Shirai

Jacobson

et al. 2012

Protein Engineering Design and Selection

212

151

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Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (VH) and light (VL) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody–antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.

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Protein Homology Modelling

Peitsch

Schwede

Diemand

et al. 2011

Encyclopedia of Life Sciences

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Protein structure prediction aims to model the three‐dimensional (3D) structure of so far structurally uncharacterised proteins from their amino acid sequence. Motivated by the observation that homologous proteins with related amino acid sequences have similar 3D structures, protein homology modelling uses comparative methods to generate models for a target protein based on one or more related proteins with known 3D structure. The coordinates of the model are generated based on alignments between the target's and template's amino acid sequences, which define the correspondence between residues in both proteins. Ultimately, the quality of a computational model determines its usefulness for specific biomedical applications. Therefore, model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models, and to estimate the overall accuracy of a model. Homology modelling (or comparative modelling) is currently the most accurate computational method available to routinely generate models of sufficient quality for various applications in life science research. Comparative protein modelling methods have been completely automated in recent years, and several Internet servers offer protein modelling services which are reliable and easy to use – also for the nonexpert in computational biology. Key Concepts: Protein structure prediction aims to model the three‐dimensional structure of so far structurally uncharacterised proteins (‘target’) based on their amino acid sequence. Homologous proteins with related amino acid sequences have similar three‐dimensional structures. Protein homology modelling uses information from one or more related proteins with known three‐dimensional structure (‘template’) to generate models for the target protein. Sensitive sequence searching methods are applied to identify template proteins with known structures in large databases. An alignment between the target's and template's amino acid sequences describes the correspondence between residues in both proteins. The coordinates of the model are constructed by extracting positional information from the corresponding structural template. Segments of the target protein not covered by template information (e.g. insertions/deletion in the alignment) have to be constructed using de novo modelling methods. Model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models. Ultimately, the quality of a structural model determines its usefulness for specific biomedical applications.

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Closing the side-chain gap in protein loop modeling

Cited by 3 publications

References 24 publications

Thiazolidinedione‐Based PI3Kα Inhibitors: An Analysis of Biochemical and Virtual Screening Methods

Thiazolidinedione‐Based PI3Kα Inhibitors: An Analysis of Biochemical and Virtual Screening Methods

Computer-aided antibody design

Protein Homology Modelling

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