Visualization of biomolecular channels and cavities

People involved: Talha Bin Masood and Raghavendra G.S.

Fundamentally all biological processes are molecular in nature. So, it is essential to understand biomolecules and their interactions to gain better insight into living systems. Proteins are constituted of chains of small building blocks called amino acids. These chains of amino acids fold in 3D space to define structure of a protein. It is known that structure of biomolecules plays an important role in defining its function. Biomolecular structures contain complex features such as pockets and protrusions on the surface, internal cavities and voids, channel and tunnel like structures connecting external surface to functional sites buried deep inside the molecule. Analysis of these features is very important for understanding of structure-function relationships, engineering new proteins with required functional properties, or designing inhibitors for existing proteins.

Proteins are often represented in space-fill model as a union of balls, where each ball corresponds to an atom (See Figure 1). This model is ideal for application of geometric and topological techniques for detailed analysis. For example, geometric algorithms have been developed for extraction of molecular surface which is extremely important in the study of any protein. Similarly, for accurate measurement of molecular volumes, identification and characterization of empty space within a molecule, methods from computation geometry and topology are applied. This project is a contribution to this area of research with special focus on integrated geometric and topological methods for visual analysis of cavities and channels in biomolecules.

With increasing availability of structures of large proteins and protein complexes at atomic detail through advancements in the field of crystallography, there is a need of designing faster and more space efficient algorithms for their analysis. Another driver for the need of efficient geometric algorithms is the availability of larger molecular dynamics trajectories, which are essentially time varying molecular structures. Designing algorithms to address these challenges is the second major focus of this project.

Figure 1. ACH receptor transmembrane protein (PDB id: 1OED). (Left) The space-fill model. (Middle) The molecular surface. (Right) The central transmembrane pore through this protein.

In the first part, we describe two methods: one for extraction and visualization of biomolecular channels, and the other for extraction of cavities in uncertain data. We also describe the two software tools based on the proposed methods targeted at the end-user, the biologists. These two web server tools publicly available for use are called ChExVis and RobustCavities. In the second part, we describe efficient parallel algorithms for two geometric structures widely used in the study of biomolecules. One of the structures we discuss is discrete Voronoi diagram which finds applications in channel visualization, while the other structure is alpha complex which is extremely useful in studying geometric and topological properties of biomolecules.

Extraction and visualization of channels

Extraction of robust voids and pockets in proteins

Connecting cavities in biomolecules

Parallel computation of discrete Voronoi diagram

Parallel computation of alpha complex


  1. Talha Bin Masood, Sankaran Sandhya, Nagasuma Chandra and Vijay Natarajan.
    ChExVis: a tool for molecular channel extraction and visualization.
    BMC Bioinformatics, 2015, 16:119.

  2. Raghavendra Sridharamurthy, Talha Bin Masood, Harish Doraiswamy, Siddharth Patel, Raghavan Varadarajan and Vijay Natarajan.
    Extraction of robust voids and pockets in proteins.
    In Visualization in Medicine and Life Sciences III.
    Lars Linsen, Hans-Christian Hege, and Bernd Hamann (Eds.)
    Springer-Verlag, Mathematics and Visualization Series
    , 2016, 329-349.
    Cover Image

  3. Talha Bin Masood and Vijay Natarajan.
    An integrated geometric and topological approach to connecting cavities in biomolecules.
    PacificVis 2016: Proc. IEEE Pacific Visualization Symposium, 2016, 104-111.

  4. Talha Bin Masood, Hari Krishna Malladi and Vijay Natarajan.
    Facet-JFA: Faster computation of discrete Voronoi diagrams.
    ICVGIP 2014: Proc. Indian Conference on Computer Vision, Graphics and Image Processing, 2014, 20:1-20:8.


  1. ChExVis
    A web server for molecular Channel Extraction and Visualization.

  2. RobustCavities
    Web portal for a software which computes cavities in proteins robustly taking into account uncertainties in the atomic radii.


Contact: talha [at] iisc [dot] ac [dot] in.