Search arXivSearch

arXiv · 1406.1400

The Structural Biology and Critical Evaluation of Bacterial Proteases as Targets in New Drug Design

Abstract

Bacteria produce a range of proteolytic enzymes, for which a number human equivalent or structurally similar examples exist and the primary focus of this study was to analyse the published literature to find proteolytic enzymes, specifically endoproteses and to examine the similarity in the substrates that they act on so as to predict a suitable structural motif which can be used as the basis for preparation of useful prodrug carriers against diseases caused by specific bacteria like Salmonella. Also, the similarities between the bacterial proteases and the action of human matrix metalloproteinases (MMPs), together with the MMP-like activity of bacterial endoproteases to activate human MMPs, were also analysed. This information was used to try to identify substrates on which the MMPs and bacterial proteases act, to aid the design of oligopeptide prodrug carriers to treat cancer and its metastatic spread. MMPs are greatly involved in cancer growth and progression, a few MMPs and certain proteases share a similar type of activity in degrading the extra cellular matrix (ECM) and substrates including gelatin. Our primary targets of study were to identify the proteases and MMPs that facilitate the migration of bacteria and growth of tumour cells respectively. The study was thus a two-way approach to study the substrate specificity of both bacterial proteases and MMPs, thereby to help in characterisation of their substrates. Various bioinformatics tools were used in the characterisation of the proteases and substrates as well as in the identification of possible binding sites and conserved regions in a range of candidate proteins. Central to this project was the salmonella derived PgtE surface protease that has been shown recently to act upon the pro-forms of human MMP-9.

Explore related subjects

Keep this discovery

BibTeXRIS

Maria Antony Dhivyan JE. 2014-06-03. The Structural Biology and Critical Evaluation of Bacterial Proteases as Targets in New Drug Design. https://arxiv.org/abs/1406.1400

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