CAVER Analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories

Jurčík, Adam; Bednář, David; Byška, Jan; Marques, Sérgio M.; Furmanová, Katarína; Daniel, Lukáš; Kokkonen, Piia; Brezovský, Jan; Strnad, Ondřej; Štourač, Jan; Pavelka, Antonín; Maňák, Martin; Damborský, Jiřı́; Kozlíková, Barbora

doi:10.1093/bioinformatics/bty386

Cited by 279 publications

(236 citation statements)

References 12 publications

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“…The proposed method is a part of the software tool CAVER Analyst 2.0 . The source code for computing Voronoi diagrams, extreme points, bridges, groups representing probe regions, and elements of their Connolly surfaces is available in the open‐source library AwVoronoi…”

Section: Discussionmentioning

confidence: 99%

“…Probe regions are represented as parts of the Voronoi 1‐skeleton and Connolly surfaces are used for visualization. CAVER Analyst (a software tool for the analysis and visualization of molecular structures) can display these surfaces in high quality thanks to GPU‐based ray‐casting . We will need to estimate the volume of probe regions and compare them with other methods.…”

Section: Methodsmentioning

confidence: 99%

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Voronoi‐based detection of pockets in proteins defined by large and small probes

Maňák

2019

J Comput Chem

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The function of enzymatic proteins is given by their ability to bind specific small molecules into their active sites. These sites can often be found in pockets on a hypothetical boundary between the protein and its environment. Detection, analysis, and visualization of pockets find its use in protein engineering and drug discovery. Many definitions of pockets and algorithms for their computation have been proposed. Kawabata and Go defined them as the regions of empty space into which a small spherical probe can enter but a large probe cannot and developed programs that can compute their approximate shape. In this article, this definition was slightly modified in order to capture the existence of large internal holes, and a Voronoi‐based method for the computation of the exact shape of these modified regions is introduced. The method first puts a finite number of large probes on the protein exterior surface and then, considering both large probes and atomic balls as obstacles for the small probe, the method computes the exact shape of the regions for the small probe. This is all achieved with Voronoi diagrams, which help with the safe navigation of spherical probes among spherical obstacles. Detected regions are internally represented as graphs of vertices and edges describing possible movements of the center of the small probe on Voronoi edges. The surface bounding each region is obtained from this representation and used for visualization, volume estimation, and comparison with other approaches. © 2019 Wiley Periodicals, Inc.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Voronoi‐based detection of pockets in proteins defined by large and small probes

Maňák

2019

J Comput Chem

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“…Further, the sidechains of Met128 in PepEdr and Met135 in PepElm are pushed into a conformation that crowds the pocket. The volumes of the P1 pockets of PepEse and PepEdr were estimated using caver analyst software . A 99 Å 3 cavity was found in the location of P1 pocket of PepEse using default cavity computing settings with probe radius of 1.4 Å.…”

Section: Discussionmentioning

confidence: 99%

Catalytic triad heterogeneity in S51 peptidase family: Structural basis for functional variability

et al. 2019

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Peptidase E (PepE) is a nonclassical serine peptidase with a Ser‐His‐Glu catalytic triad. It is specific for dipeptides with an N‐terminal aspartate residue (Asp‐X dipeptidase activity). Its homolog from Listeria monocytogenes (PepElm) has a Ser‐His‐Asn “catalytic triad.” Based on sequence alignment we predicted that the PepE homolog from Deinococcus radiodurans (PepEdr) would have a Ser‐His‐Asp “catalytic triad.” We confirmed this by solving the crystal structure of PepEdr to 2.7 Å resolution. We show that PepElm and PepEdr lack the Asp‐X dipeptidase activity. Our analyses suggest that absence of P1 pocket in the active site could be the main reason for this lack of typical activity. Sequence and structural data reveal that the PepE homologs can be divided into long and short PepEs based on presence or absence of a C‐terminal tail which adopts a β‐hairpin conformation in the canonical PepE from Salmonella enterica. A long PepE from Bacillus subtilis with Ser‐His‐Asp catalytic triad exhibits Asp‐X dipeptidase activity. Whereas the three long PepEs enzymatically characterized till date have been found to possess the Asp‐X dipeptidase activity, the three enzymatically characterized short PepEs lack this activity irrespective of the nature of their catalytic triads. This study illuminates the structural and functional heterogeneity in the S51 family and also provides structural basis for the functional variability among PepE homologs.

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“…Protein complexes, irrespective of the number of units, can be in general visualized in one of the traditional and widely used software tools, such as UCSF Chimera [22], PyMol [27], VMD [8], Aquaria [20], CAVER Analyst [11], and others. However, these tools provide the users only with general molecular representations, without specific support for the analysis of protein complexes and their visual exploration.…”

Section: Visualization Of Protein Complexesmentioning

confidence: 99%

Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes

Furmanová

Jurčík

Kozlíková

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Exploration of an ensemble of 500 possible human nucleosome configurations, based on the following components: a) Overview Graph with main application controls; b) 3D View, showing the exploded Complex Configuration, amino acids from contact interfaces are colored according to the frequency of their interactions; c) Property View, showing properties of all Complex Configurations; d) Protein View with range filter panel; e) Residue Matrix, which also can be switched to Contact Zone List View, and f) Filter View.Abstract-When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the datafrom the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels and at each level, we offer a set of selection and filtering operations enabling the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.

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CAVER Analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories

Cited by 279 publications

References 12 publications

Voronoi‐based detection of pockets in proteins defined by large and small probes

Voronoi‐based detection of pockets in proteins defined by large and small probes

Catalytic triad heterogeneity in S51 peptidase family: Structural basis for functional variability

Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes

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