It has been proposed that sidechains of Isoleucine (ILE), Leucine (LEU) and Valine (VAL)
often form hydrophobic clusters. These often called (ILV)-clusters prevent the intrusion
of water molecules and serve as cores of stability in high-energy partially folded states [1].
Although still not well understood, hydrophobic clusters seem to play a key role in protein
stability [2]. An available source to compute hydrophobic clusters was the BASIC web server
(unfortunately no longer available), which relied on the CSU algorithm [3, 4, 5].
The CSU algorithm was released as a web server application but is also unfortunately no longer maintained.
We have here reimplemented the original CSU algorithm to enable its use in a web server for the whole community.
ProteinTools allows computing cluster for user-defined selections and visualizing them in the protein structure.
The CSU algorithm for hydrophobic cluster detection shown
as a schema with 5 steps. We have included the algorithm into ProteinTools.
The figure above summarizes the algorithm, which proceeds as follows:
Two atoms A and B are considered to be in contact if a solvent molecule
placed at the surface of A’s sphere, overlaps with the sphere formed by a
solvent molecule plus the Van der Waals sphere of atom B [6].
The atoms are considered spheres of fixed radius [7]. If at any position a water molecule
penetrates several atoms’ spheres, the contact is considered to belong to that whose centre
is closest to the centre of atom A.
Each atom is discretized into many uniform small sections. The standard way to do this is
the Fibonacci grid [8], where by default each atom's sphere is discretized into 610 sections.
The area corresponds to 0.0016 of the total area of the sphere.
The algorithm then evaluates if any of the 610 sections overlaps with the neighbours,
and if so,
the contact in the section is declared to belong to the sphere whose centre is closest this atom’s centre.
The algorithm is followed for all the atoms until a matrix of residue-against-residue areas is computed.
By default, we define that two residues are in contact when they have an overlapping area of
at least 10 Å2. The adjacent matrix is converted to a graph,
where every component corresponds to a (hydrophobic) cluster.
The total area of the cluster is computed by the sum of the individual residue areas that comprise it.
References
[1] Kathuria,S. V et al. (2016) Clusters of isoleucine, leucine, and valine side chains define cores of stability in high-energy states of globular proteins: Sequence determinants of structure and stability. PROTEIN Sci., 25, 662–675.
[2] Basak,S. et al. (2019) Networks of electrostatic and hydrophobic interactions modulate the complex folding free energy surface of a designed βα protein. Proc. Natl. Acad. Sci. U. S. A., 116, 6806–6811.
[3] Sobolev,V. et al. (1996) Molecular docking using surface complementarity. Proteins Struct. Funct. Bioinforma., 25, 120–129.
[4] Sobolev,V. et al. (1999) Automated analysis of interatomic contacts in proteins. Bioinformatics, 15, 327–332.
[5] Wołek,K. et al. (2015) Determination of contact maps in proteins: A combination of structural and chemical approaches. J. Chem. Phys., 143.
[6] Sobolev,V. and Edelman,M. (1995) Modeling the quinone‐B binding site of the photosystem‐II reaction center using notions of complementarity and contact‐surface between atoms. Proteins Struct. Funct. Bioinforma., 21, 214–225.
[7] Shannon,R.D. (1976) Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallogr. Sect. A, 32, 751–767.
[8] González,Á. (2010) Measurement of Areas on a Sphere Using Fibonacci and Latitude-Longitude Lattices. Math. Geosci., 42, 49–64.
