VASP-S: A Volumetric Analysis and Statistical Model for Predicting Steric Influences on Protein-Ligand Binding Specificity

Chen, Brian Y.; Bandyopadhyay, Soutir

doi:10.1109/bibm.2011.33

Cited by 16 publications

(21 citation statements)

References 53 publications

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“…VASP-S can distinguish variations in original binding cavity shape that are large enough to create different binding preferences from variations that are too small to affect specificity [8]. We hypothesized that the variations we observed between different conformational samples of the same binding cavities were so large that VASP-S would incorrectly classify them …”

Section: Comparing Fava Against Statistical Models For Rigid Comparisonmentioning

confidence: 85%

A flexible volumetric comparison of protein cavities can reveal patterns in ligand binding specificity

Guo

Kuhlengel

Stinson

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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Conformational flexibility is an underlying cause of error in all comparisons of protein structure. Using flexible representations, some comparison algorithms can identify subtle functional similarities among distantly related proteins even when they exhibit different backbone conformations. The same techniques are not designed to identify subtle variations among closely related proteins that might cause differences in specificity. In such cases, molecular flexibility obscures structural details that influence the specific recognition of similar but non-identical ligands.To enhance the analysis of ligand binding specificity, this paper presents FAVA (Flexible Aggregate Volumetric Analysis), a conformationally robust tool for comparing similar binding cavities with different binding preferences. FAVA examines a large number of conformational samples to characterize local flexibility using Constructive Solid Geometry. Using molecular dynamics simulations as a source for conformational samples, we used FAVA to analyze a nonredundant sample of serine protease and enolase structures. Different snapshots from the same proteins exhibited significant variations in binding cavity shape. Nonetheless, analysis with FAVA revealed subfamilies with different binding preferences. FAVA also identified amino acids associated with differences in binding preferences, predicting established experimental results. These results illustrate a new approach to flexible comparison that uses sampled conformational data. It reveals that detailed comparisons of very similar proteins, such as those within small ligand binding cavities, are possible even in the presence of conformational flexibility. Identifying influences on specificity in this manner points to new applications of protein engineering and drug design. * equal contribution.

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Section: Comparing Fava Against Statistical Models For Rigid Comparisonmentioning

confidence: 85%

A flexible volumetric comparison of protein cavities can reveal patterns in ligand binding specificity

Guo

Kuhlengel

Stinson

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Self Cite

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“…A fragment can accommodate parts of a ligand molecule that cannot be accommodated by the other cavity. A statistical model trained on the volumes of fragments that occur between cavities with identical specificity can reveal, between other cavities, fragments too large to suggest identical specificity [8].…”

Section: Statistical Modelingmentioning

confidence: 99%

“…To verify the predictive accuracy of our method, we constructed statistical models of volumetric similarity [8] and fragment volume [9] based on trypsin and enolase cavities aligned with ska. Using DFO-VASP, we computed all against all superpositions of serine protease cavities and enolase cavities with the latin hypercube starting strategy.…”

Section: Large-scale Validationmentioning

confidence: 99%

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Aligning ligand binding cavities by optimizing superposed volume

Chen

Scheinberg

Chen

2012

2012 IEEE International Conference on Bioinformatics and Biomedicine

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We describe an optimization-based method that seeks the superposition of ligand binding cavities that maximizes their overlapping volume. Our method, called DFO-VASP, iteratively uses Boolean set operations to evaluate overlapping volume in intermediate superpositions while searching for the maximal one. Our results verify that the superpositions identified are biologically relevant, and demonstrate that DFO-VASP generally discovers cavity superpositions with similar or occasionally larger overlapping volume than those of superpositions generated with existing means.

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“…As a result, the shape of the focusing region can be compared independently, without considering atomic structure, or it can be modified by atomic structure, to provide an integrated analysis. Given that influences on binding specificity can already be identified by solid representations of protein binding sites [7,8], the integration of electrostatic focusing represents an opportunity to detect a second, nearly orthogonal range of influences on specificity.…”

Section: Introductionmentioning

confidence: 99%

A volumetric method for representing and comparing regions of electrostatic focusing in molecular structure

Chen

Paul

2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Algorithms for protein structure comparison employ diverse and effective representations of molecular shape. However, they do not generally represent the shape of the electrostatic potential field, except at the molecular surface. This approach neglects the geometry of the field on the outside of the molecular surface, where electrostatic focusing can play an important role in molecular recognition: Narrow clefts and grooves can partially shield charged atoms from the high dielectric solvent, enhancing potentials inside the cavity and projecting the lines of the electric field outwards from the cavity. This interplay between molecular shape and electrostatic potential is an essential means of recognition in many biomolecular systems. To leverage this phenomenon for more accurate protein structure comparison algorithms, this paper presents the first comparative representation of the region where focusing occurs. We first verified our representation in a case study of superoxide dismutase, where electrostatic focusing was first observed. Our method accurately identified the site where electrostatic focusing was established in the past. We then applied our representation to compare regions of electrostatic focusing with the positions of charged amino acids, to determine where they coincide. Over 866 protein-DNA complexes, our representations correctly detected the enrichment of arginines that contact regions of electrostatic focusing in the minor grooves of DNA. These results indicate that our novel methods precisely represent and accurately compare regions where electrostatic focusing occurs. They also describe a novel and elegant technique for seamlessly integrating molecular shape and electrostatic focusing into the same structure comparison framework. Figure 1: Boolean Set operations on three dimensional solids (left) can detect regions where two solids coincide (intersection), where either solid occupies space (union), or regions inside one solid and not inside the other (difference).

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VASP-S: A Volumetric Analysis and Statistical Model for Predicting Steric Influences on Protein-Ligand Binding Specificity

Cited by 16 publications

References 53 publications

A flexible volumetric comparison of protein cavities can reveal patterns in ligand binding specificity

A flexible volumetric comparison of protein cavities can reveal patterns in ligand binding specificity

Aligning ligand binding cavities by optimizing superposed volume

A volumetric method for representing and comparing regions of electrostatic focusing in molecular structure

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