Evaluation of the Wilma-SIE Virtual Screening Method in Community Structure–Activity Resource 2013 and 2014 Blind Challenges

Hogues, Hervé; Sulea, Traian; Purisima, Enrico O.

doi:10.1021/acs.jcim.5b00278

Cited by 10 publications

(18 citation statements)

References 36 publications

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“…For more information about the various methods, the reader is encouraged to read the manuscripts that the participants have submitted to this special issue. Their papers properly describe the unique features of their methods and what they have learned from the CSAR 2014 exercise. ,,,,− …”

Section: Methodsmentioning

confidence: 99%

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

Carlson

Smith

Damm-Ganamet

et al. 2016

J. Chem. Inf. Model.

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The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant’s method and the setup protocol we used. This problem is inherent to decoys and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock1, Glide-XP2, PLANTS3, Wilma4, Gold5, SMINA6, Glide-XP2/PELE7, FlexX8, and MedusaDock9. In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term11, SMINA6 using Autodock-Vina12,13, FlexX8 using HYDE14, and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity16. Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

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Section: Methodsmentioning

confidence: 99%

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

Carlson

Smith

Damm-Ganamet

et al. 2016

J. Chem. Inf. Model.

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“…22 ) Structural refinement was then carried out by energy minimization using the AMBER force field 39,40 with a distance-dependent dielectric (4r ij ) and infinite cutoff for nonbonded interactions, constraining the non-hydrogen atoms at their crystallographic positions with a harmonic force of 5 kcal mol −1 A −2 . Additional refinement was also applied for each sampling−scoring protocol described below.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Assessment of Solvated Interaction Energy Function for Ranking Antibody–Antigen Binding Affinities

Sulea

Vivcharuk

Corbeil

et al. 2016

J. Chem. Inf. Model.

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Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. Questions? Contact the NRC Publications Archive team atPublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. ABSTRACT: Affinity modulation of antibodies and antibody fragments of therapeutic value is often required in order to improve their clinical efficacies. Virtual affinity maturation has the potential to quickly focus on the critical hotspot residues without the combinatorial explosion problem of conventional display and library approaches. However, this requires a binding affinity scoring function that is capable of ranking single-point mutations of a starting antibody. We focus here on assessing the solvated interaction energy (SIE) function that was originally developed for and is widely applied to scoring of protein−ligand binding affinities. To this end, we assembled a structure−function data set calledS i n g l e -P o i n tM u t a n t Antibody Binding (SiPMAB) comprising several antibody− antigen systems suitable for this assessment, i.e., based on high-resolution crystal structures for the parent antibodies and coupled with high-quality binding affinity measurements for sets of single-point antibody mutants in each system. Using this data set, we tested the SIE function with several mutation protocols based on the popular methods SCWRL, Rosetta, and FoldX. We found that the SIE function coupled with a protocol limited to sampling only the mutated side chain can reasonably predict relative binding affinities with a Spearman rank-order correlation coefficient of about 0.6, outperforming more aggressive sampling protocols. Importantly, this performance is maintained for each of the seven system-specific component subsets as well as for other relevant subsets including non-alanine and charge-altering mutations. The transferability and enrichment in affinityimproving mutants can be further enhanced using consensus ranking over multiple methods, including the SIE, Talaris, and FOLDEF energy functions. The knowledge gained from this study can lead to successful prospective applications of virtual affinity maturation.

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“…For example, Cui et al showed the advantage of SIE over MM/GBSA in calculating absolute G in the simulation of Ras-Raf and Ras-RalGDS [17]. Additionally, SIE approach has been ranked as one of the best scoring functions in several rounds of Community Structure-Activity Resource (CSAR) and Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenges [18][19][20][21]. However, the performance of SIE approach on achieving accurate G prediction is still far from perfect, which might be related to its usage of Amber force field that cannot tackle electrostatic polarization, an indispensable term for predicting G accurately [22][23][24][25][26].…”

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confidence: 99%