Assessing Structural Predictions of Protein–Protein Recognition: The CAPRI Experiment

Janin, Joël; Wodak, Shoshana J.; Lensink, Marc F.; Velankar, Sameer

doi:10.1002/9781118889886.ch4

Cited by 5 publications

(6 citation statements)

References 165 publications

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“…To conclude the Results, we briefly overview the performance of our team in CAPRI rounds 26, 27, and 30, where the KSENIA scoring function was used. First, we used the Hex software in order to generate preliminary docking poses.…”

Section: Resultsmentioning

confidence: 99%

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Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates

Popov

Grudinin

2015

J. Chem. Inf. Model.

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Selection of putative binding poses is a challenging part of virtual screening for protein-protein interactions. Predictive models to filter out binding candidates with the highest binding affinities comprise scoring functions that assign a score to each binding pose. Existing scoring functions are typically deduced by collecting statistical information about interfaces of native conformations of protein complexes along with interfaces of a large generated set of non-native conformations. However, the obtained scoring functions become biased toward the method used to generate the non-native conformations, i.e., they may not recognize near-native interfaces generated with a different method. The present study demonstrates that knowledge of only native protein-protein interfaces is sufficient to construct well-discriminative predictive models for the selection of binding candidates. Here we introduce a new scoring method that comprises a knowledge-based potential called KSENIA deduced from structural information about the native interfaces of 844 crystallographic protein-protein complexes. We derive KSENIA using convex optimization with a training set composed of native protein complexes and their near-native conformations obtained using deformations along the low-frequency normal modes. As a result, our knowledge-based potential has only marginal bias toward a method used to generate putative binding poses. Furthermore, KSENIA is smooth by construction, which allows it to be used along with rigid-body optimization to refine the binding poses. Using several test benchmarks, we demonstrate that our method discriminates well native and near-native conformations of protein complexes from non-native ones. Our methodology can be easily adapted to the recognition of other types of molecular interactions, such as protein-ligand, protein-RNA, etc. KSENIA will be made publicly available as a part of the SAMSON software platform at https://team.inria.fr/nano-d/software .

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Section: Resultsmentioning

confidence: 99%

“…Finally, we want to note that we have successfully used KSENIA in the CAPRI protein docking experiment starting from Round 26. 60 We will make KSENIA publicly available as a part of the SAMSON software platform developed in our group at https://team.inria.fr/nano-d/software.…”

Section: Discussionmentioning

confidence: 99%

Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates

Popov

Grudinin

2015

J. Chem. Inf. Model.

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“…These computational tools leverage three-dimensional protein structures, the protein’s ability to form complexes, and the dynamic behavior of proteins. Methodologically, computational molecular biophysics and biochemistry take advantage of well-validated parameter-based mathematical models, the strengths and weaknesses of which are under continuous evaluation [ 9 , 10 ] and their potential for translational value has been previously noted [ 11 ]. The combination of experimental studies with molecular modeling and molecular dynamics simulations has led to progressively greater understanding of kinase domain functionality at atomic resolution and the role that each residue plays in the native structure [ 12 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing

et al. 2017

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Variants in the TGFBR2 kinase domain cause several human diseases and can increase propensity for cancer. The widespread application of next generation sequencing within the setting of Individualized Medicine (IM) is increasing the rate at which TGFBR2 kinase domain variants are being identified. However, their clinical relevance is often uncertain. Consequently, we sought to evaluate the use of molecular modeling and molecular dynamics (MD) simulations for assessing the potential impact of variants within this domain. We documented the structural differences revealed by these models across 57 variants using independent MD simulations for each. Our simulations revealed various mechanisms by which variants may lead to functional alteration; some are revealed energetically, while others structurally or dynamically. We found that the ATP binding site and activation loop dynamics may be affected by variants at positions throughout the structure. This prediction cannot be made from the linear sequence alone. We present our structure-based analyses alongside those obtained using several commonly used genomics-based predictive algorithms. We believe the further mechanistic information revealed by molecular modeling will be useful in guiding the examination of clinically observed variants throughout the exome, as well as those likely to be discovered in the near future by clinical tests leveraging next-generation sequencing through IM efforts.

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“…One important current area of application of implicit solvation models is in conjunction with molecular dynamics to study of the strength of protein‐ligand and protein‐protein binding . Volume and surface area geometrical measures are also widely used in protein design, structural biology and medicinal chemistry to, for example, assess the stability of macromolecules, discover and characterize drug binding sites, investigate the effects of molecular crowding, and measure the level of surface complementarity between macromolecules …”

Section: Introductionmentioning

confidence: 99%

Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units

et al. 2017

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We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion–exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many‐body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area‐based non‐polar implicit solvent model implemented as an open source plug‐in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time‐step for protein‐sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.

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Assessing Structural Predictions of Protein–Protein Recognition: The CAPRI Experiment

Cited by 5 publications

References 165 publications

Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates

Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates

Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing

Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units

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