Performance of ZDOCK and ZRANK in CAPRI rounds 13–19

Hwang, Howook; Vreven, Thom; Pierce, Brian G.; Hung, Jui-Hung; Weng, Zhiping

doi:10.1002/prot.22764

Cited by 73 publications

(70 citation statements)

References 37 publications

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“…Detailed comparison against other docking algorithms, which goes beyond the scope of this study, should be performed for a systematic evaluation. Because ZRANK is widely used and has shown considerable success in critical assessment of predicted interactions (CAPRI) experiments (35,36), we believe our method will perform comparatively well when evaluated against other top docking algorithms. The benchmarked test dataset contained a diverse set of 44 different targets that varied in size and in secondary and tertiary structures (SI Appendix, Table S3); these difficult test cases provided an opportunity to comprehensively validate the MLRbased structure discrimination.…”

Section: Discussionmentioning

confidence: 99%

Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency

Tharakaraman¹,

Robinson²,

Hatas³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Affinity improvement of proteins, including antibodies, by computational chemistry broadly relies on physics-based energy functions coupled with refinement. However, achieving significant enhancement of binding affinity (>10-fold) remains a challenging exercise, particularly for cross-reactive antibodies. We describe here an empirical approach that captures key physicochemical features common to antigen-antibody interfaces to predict proteinprotein interaction and mutations that confer increased affinity. We apply this approach to the design of affinity-enhancing mutations in 4E11, a potent cross-reactive neutralizing antibody to dengue virus (DV), without a crystal structure. Combination of predicted mutations led to a 450-fold improvement in affinity to serotype 4 of DV while preserving, or modestly increasing, affinity to serotypes 1-3 of DV. We show that increased affinity resulted in strong in vitro neutralizing activity to all four serotypes, and that the redesigned antibody has potent antiviral activity in a mouse model of DV challenge. Our findings demonstrate an empirical computational chemistry approach for improving protein-protein docking and engineering antibody affinity, which will help accelerate the development of clinically relevant antibodies. (1). Engineering improved affinity and specificity of these compounds can augment their potency and safety while decreasing required dosages. Production of antibodies with binding properties of interest typically relies on methods involving screening large numbers of clones generated by the immune system or by mutant libraries (2, 3). Alternatively, computer-based design offers the potential to rationally mutate available antibodies for improved properties, including enhanced affinity and specificity to target antigens. Recently, several successful examples of antibody affinity improvement by computational methods using physical modeling with energy minimization have been described (4-6). However, such approaches require a 3D structure of the antibody-antigen complex and rarely result in affinity gains greater than 10-fold. Further, these approaches are sensitive to precise atomic coordinates, rendering them inapplicable to computer-generated models. More significantly, enhancement of affinity in the context of an antibody that recognizes multiple antigens (i.e., crossreactive) remains a particular challenge.Dengue is the most medically relevant arboviral disease in humans, with an estimated 3.6 billion people at risk for infection. More than 200 million infections of dengue virus (DV) are estimated to occur globally each year (7). The incidence, geographical outreach, and number of severe disease cases of dengue are increasing (8, 9), making DV of increasing concern as a human pathogen. The complex of DVs is composed of four distinct serotypes (designated DV1-4) (10), which vary from one another at the amino acid level by 25-40%. The sequence and antigenic variability of DVs have challenged efforts to develop an effective vaccine or therapeutic against a...

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Section: Discussionmentioning

confidence: 99%

Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency

Tharakaraman¹,

Robinson²,

Hatas³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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“…Several groups have proposed that contact analysis of docking models can be used to predict the interface of the proteins (interface post-prediction) (Ferná ndez- Recio et al, 2004;Hwang et al, 2010b;Lensink and Wodak, 2010a;Sacquin-Mora et al, 2008;Saladin et al, 2014;de Vries and Bonvin, 2011). We thus also wanted to evaluate how well the binding site was predicted regardless of the peptide conformation.…”

Section: Binding Site Predictionmentioning

confidence: 99%

Fully Blind Peptide-Protein Docking with pepATTRACT

2015

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Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. Here, we present a new fully blind flexible peptide-protein docking protocol, pepATTRACT, which combines a rapid coarse-grained global peptide docking search of the entire protein surface with a two-stage atomistic flexible refinement. Global unbound-unbound docking yielded near-native models for 70% of the docking cases when testing the protocol on the largest benchmark of peptide-protein complexes available to date. This performance is similar to that of state-of-the-art local docking protocols that rely on information about the binding site. Upon restricting the search to the peptide binding region, the resulting pepATTRACT-local approach outperformed existing methods. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html.

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“…The protein considered as ligand is moved rotationally and translationally searching for the best tridimensional shape complementarity between receptor and ligand, and the corresponding electrostatic interactions. ZDock is a recommendable protein-protein docking protocol 45,46,47 , mainly when there is no previous information about the residues involved in the interactions between both proteins, neither about the specific region of potential binding sites. In this way, the convenience of using a global systematic search method, like ZDock, in combination with other accurate docking methods has been shown to be a successful strategy to establish protein-protein interactions 48 .…”

Section: Peptidomimetic-protein Blind Docking Using Zdockmentioning

confidence: 99%