Stochastic molecular descriptors for polymers. 2. Spherical truncation of electrostatic interactions on entropy based polymers 3D-QSAR

3-D maps and coupling numbers for protein sequences

“…[31,32,[38][39][40][41][42]). Motivated by their work, in this study, we give a numerical characterization of the 3-D graphical representation that will facilitate quantitative comparisons of protein sequences.…”

3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

“…Neglecting direct interactions between distant aa in 1 P does not avoid the possibility that electrostatic interactions propagate between those aa within the protein backbone in an indirect manner. Consequently, in the present model long-range interactions are possible (not forbidden) but are estimated indirectly using the natural powers of 1 P. The use of MM theory thus allows a simple model to be developed to calculate the average electrostatic potentials (n k ) for the indirect interaction between any aa j and the other aa i located at a distance k within the protein backbone [25,28]:…”

“…As a result, on considering five sets or orbits of amino acids (core, inner, middle, outer, total) and six ranges for the electrostatic interactions (0, 1, 2, 3, 4, 5) we calculated a total of 30 (5 · 6 = 30) potentials to characterize each of the 265 proteins. Previous publications should be consulted for details on similar models [23,25,27,28]. For all calculations, we used the in-house software BIOMARKS, version 1.0 Ò [30].…”

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

“…1). 68,69 It can be seen in Figure 1 that the different orbits are represented in gray colors scale in an effort to provide a clearer understanding of how they were calculated. In this example we used a kinase protein with PDB code 2C5X 70 and the software Chimera.…”

“…This fact could be due to the differences and similarities between these descriptors concerning the structural information that they are able to encode because of their mathematical definition. 29,68,69 All of these methodologies have different final equations despite the fact that they all consider vdw interactions. Nonetheless, in spite of predicting non-kinases more easily, the Spectral moments have the least separation between the percentage of good classification for these kinds of groups (95.55% (non-K) to 85.40% (K) ¼ 10.15) and the best total percentage (89.71).…”