DynDom1D_Python: An Open-Source Python tool for the analysis of domain movements in proteins

Rep3D : an algorithm to identify structurally similar motifs

This method enables rapid and accurate identification of structural repeats by calculating the distance between Cα atoms in the peptide backbone by calculating the distance between Cα atoms in the peptide backbone.

ResiRuler: A Toolkit for Visualizing Residue-Residue Distances and Structural Changes in Biomolecular Models

RPDynaFlow: Generating RNA–protein Conformation Ensembles by Atomic Conditional Flow Matching

Conformation ensembles of biomolecules provide the basis for understanding structural transformations and drug design. Deep-learning generative models have advanced protein and small molecule ensemble generation, while RNA–protein complexes remain unaddressed due to the chemical heterogeneity, limited dataset size and the different flexibility scales of RNA and protein components. We present RPDynaFlow, a flow-matching model to generate conformation ensembles of RNA–protein complexes, trained on 600 ns trajectories of molecular dynamics (MD) simulation. The results show our model extends the sampling range of the phase space compared to MD simulation, which could be treated as a rapid and efficient complement to MD trajectories for studying RNA–protein interactions.

Experimental data-guided parameterization and validation of an AMBER protein force field

An improved force field is developed, derived from its parent, Amber ff24EXP-GA, and its evaluation against Amber ff14SB and other contemporary force fields, such as CHARMM36m, in capturing the empirically determined conformational properties of unfolded systems: short peptides that serve as model systems for IDPs, and longer unfolded proteins.

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.

Chapter 4: A link between structural and functional plasticity in an evolvable peptide sequence-space

NMR structural and dynamical characterization of a two-step walk through peptide sequence space is presented; from the (cid:2) N peptide sequence, which functions to activate transcription antitermination, to a peptide sequence that inhibits antitermination.