SearcharxivSearch

arXiv · 1512.00423

Implementation of the Tangent Sphere and Cutting Plane Methods in the Quantitative Determination of Ligand Binding Site Burial Depths in Proteins Using FORTRAN 77/90 Language

Abstract

Ligand burial depth is an indicator of protein flexibility, as the extent of receptor conformational change required to bind a ligand in general varies directly with its depth of burial. In a companion paper (Reyes, V.M. 2015a), we report on the Tangent Sphere (TS) and Cutting Plane (CP) methods -- complementary methods to quantify, independent of protein size, the degree of ligand burial in a protein receptor. In this report, we present results that demonstrate the effectiveness of a set of FORTRAN 77 and 90 source codes used in the implementation of the two related procedures, as well as the precise implementation of the procedures. Particularly, we show here that application of the TS and CP methods on a theoretical model protein in the form of a spherical grid of points accurately portrays the behavior of the TS and CP indices, the predictive parameters obtained from the two methods. We also show that results of the implementation of the TS and CP methods on six protein receptors (Laskowski et al. 1996) are inagreement with their findings regarding cavity sizes in these proteins. The six FORTRAN programs we present here are: find_molec_centr.f, tangent_sphere.f, find_CP_coeffs.f, CPM_Neg_Side.f, CPM_Pos_Side.f and CPM_Zero_Side.f. The first program calculates the x-, y- and z-coordinates of the molecular geometric centroid of the protein (global centroid, GC), the center of the TS. Its radius is the distance between the GC and the local centroid (LC), the centroid of the bound ligand or a portion of its binding site. The second program finds the number of protein atoms inside, outside and on the TS. The third determines the four coefficients A, B, C and D of the equation of the CP, Ax + By + Cz + D = 0. The CP is tangent to the TS at GC. The fourth, fifth and sixth programs determine the number of protein atoms lying on the negative side, positive side, and on the CP.

Explore related subjects

Keep this discovery

BibTeXRIS

Vicente M. Reyes. 2015-11-30. Implementation of the Tangent Sphere and Cutting Plane Methods in the Quantitative Determination of Ligand Binding Site Burial Depths in Proteins Using FORTRAN 77/90 Language. https://arxiv.org/abs/1512.00423

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--$\tau$ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.

q-bio.BM

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid$_{3\text{D}}$ from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.

q-bio.BM

Predicting directional flexibility in proteins

Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge movements, catalytic loop rearrangements and domain reorientations, which can be characterized by directional flexibility and correlated structural motions of the protein backbone. While Molecular Dynamics (MD) simulations provide an established but often prohibitively expensive approach, recent deep generative models aim to reduce this cost by directly predicting conformational ensembles, emulating MD. However, due to their large size and the need to generate several states until the derived dynamical properties converge, these models remain expensive. In this work, we propose BackFlip-2: a fast SE(3)-equivariant graph neural network trained to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure. In a series of experiments, we show that our model matches the accuracy of substantially larger ensemble generation models while being orders of magnitude faster, and demonstrate that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins. BackFlip-2 model weights, training and inference code are available at https://github.com/graeter-group/backflip.

q-bio.BM