SearcharxivSearch

arXiv · 2012.10197

The impact of physiological stress conditions on protein structure and trypsin inhibition of serine protease inhibitor Kazal type 1 (SPINK1) and its N34S variant

Abstract

One of the most common mutations in the serine protease inhibitor Kazal type 1 (SPINK1) gene is the N34S variant which is strongly associated with chronic pancreatitis. Although it is assumed that N34S mutation constitutes a high-risk factor, the underlying pathologic mechanism is still unknown. In the present study, we investigated the impact of physiological stress factors on SPINK1 protein structure and trypsin inhibitor function using biophysical methods. Our circular dichroism spectroscopy data revealed differences in the secondary structure of SPINK1 and N34S mutant suggesting protein structural changes induced by the mutation as an impairment that could be disease-relevant. We further confirmed that both SPINK1 (KD of 0.15 +/- 0.06 nM) and its N34S variant (KD of 0.08 +/- 0.02 nM) have similar binding affinity and inhibitory effect towards trypsin as shown by surface plasmon resonance and trypsin inhibition assay studies, respectively. We found that stress conditions such as altered ion concentrations (i.e. potassium, calcium), temperature shifts, as well as environmental pH lead to insignificant differences in trypsin inhibition between SPINK1 and N34S mutant. However, we have shown that the environmental pH induces structural changes in both SPINK1 constructs in a different manner. Our findings suggest protein structural changes in the N34S variant as an impairment of SPINK1 and environmental pH shift as a trigger that could play a role in disease progression of pancreatitis.

Explore related subjects

Keep this discovery

BibTeXRIS

Ina Buchholz, Felix Nagel, Annelie Klein, Preshit R. Wagh, Ujjwal M. Mahajan, Andreas Greinacher, Markus M. Lerch, Julia Mayerle, Mihaela Delcea. 2020-12-18. The impact of physiological stress conditions on protein structure and trypsin inhibition of serine protease inhibitor Kazal type 1 (SPINK1) and its N34S variant. https://doi.org/10.1016/j.bbapap.2019.140281

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