Protein Side Chains Research Articles

Installation of an Indole on the BRCA1 Disordered Domain Using Triazine Chemistry

The functionalization of protein sidechains with highly water-soluble chlorotriazines (or derivatives thereof) using nucleophilic aromatic substitution reactions has been commonly employed to install various functional groups, including poly(ethylene glycol) tags or fluorogenic labels. Here, a poorly soluble dichlorotriazine with an appended indole is shown to react with a construct containing the disordered domain of BRCA1. Subsequently, this construct can undergo proteolytic cleavage to remove the SUMO-tag: the N-terminal poly(His) tag is still effective for purification. Steady-state fluorescence, circular dichroism spectroscopy, and isothermal titration calorimetry with the binding partner of BRCA1, PALB2, are used to characterize the indole-labeled BRCA1. Neither the reaction conditions nor the indole-tag appreciably alter the structure of the BRCA1. Mass spectrometry confirms that the target is modified once, although the location of modification cannot be determined by tandem mass spectrometry with collision-induced dissociation due to disadvantageous fragmentation patterns.

UCBShift 2.0: Bridging the Gap from Backbone to Side Chain Protein Chemical Shift Prediction for Protein Structures.

Chemical shifts are a readily obtainable NMR observable that can be measured with high accuracy, and because they are sensitive to conformational averages and the local molecular environment, they yield detailed information about protein structure in solution. To predict chemical shifts of protein structures, we introduced the UCBShift method that uniquely fuses a transfer prediction module, which employs sequence and structure alignments to select reference chemical shifts from an experimental database, with a machine learning model that uses carefully curated and physics-inspired features derived from X-ray crystal structures to predict backbone chemical shifts for proteins. In this work, we extend the UCBShift 1.0 method to side chain chemical shift prediction to perform whole protein analysis, which, when validated against well-defined test data shows higher accuracy and better reliability compared to the popular SHIFTX2 method. With the greater abundance of cleaned protein shift-structure data and the modularity of the general UCBShift algorithms, users can gain insight into different features important for residue-specific stabilizing interactions for protein backbone and side chain chemical shift prediction. We suggest several backward and forward applications of UCBShift 2.0 that can help validate AlphaFold structures and probe protein dynamics.

Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics.

All-atom constant pH molecular dynamics simulations offer a powerful tool for understanding pH-mediated and proton-coupled biological processes. As the protonation equilibria of protein side chains are shifted by electrostatic interactions and desolvation energies, pKa values calculated from the constant pH simulations may be sensitive to the underlying protein force field and water model. Here we investigated the force field dependence of the all-atom particle mesh Ewald (PME) continuous constant pH (PME-CpHMD) simulations of a mini-protein BBL. The replica-exchange titration simulations based on the Amber ff19sb and ff14sb force fields with the respective water models showed significantly overestimated pKa downshifts for a buried histidine (His166) and for two glutamic acids (Glu141 and Glu161) that are involved in salt-bridge interactions. These errors (due to undersolvation of neutral histidines and overstabilization of salt bridges) are consistent with the previously reported pKa's based on the CHARMM c22/CMAP force field, albeit in larger magnitudes. The pKa calculations also demonstrated that ff19sb with OPC water is significantly more accurate than ff14sb with TIP3P water, and the salt-bridge related pKa downshifts can be partially alleviated by the atom-pair specific Lennard-Jones corrections (NBFIX). Together, these data suggest that the accuracies of the protonation equilibria of proteins from constant pH simulations can significantly benefit from improvements of force fields.

DSDPFlex: Flexible-Receptor Docking with GPU Acceleration.

Molecular docking is an essential tool in structure-based drug discovery, widely utilized to model ligand-protein interactions and enrich potential hits. Among the different docking strategies, semiflexible docking (rigid-receptor and flexible-ligand model) is the most popular, benefiting from its balance of docking accuracy and speed. However, this approach ignores the conformational changes of proteins and hence demands suitable protein conformations as input. When the binding interaction adheres to an induced-fit model, flexible methods such as molecular dynamics simulation can be utilized, but they are computationally demanding. To balance between speed and accuracy, the flexible docking approach is an effective choice, as exemplified by AutoDock Vina and AutoDockFR, which treat selected protein side chains as flexible parts. However, the efficiency of flexible docking methods is yet to be improved for virtual screening usage. In this article, we introduce DSDPFlex, an improved flexible-receptor docking method accelerated by GPU parallelization. Beyond acceleration, optimizations with respect to sampling, scoring, and search space are implemented in DSDPFlex to further improve its capability in flexible tasks. In cross-docking evaluation, DSDPFlex demonstrates superior accuracy compared to AutoDock Vina and is 100 times faster than Vina in flexible-receptor tasks. We also show the advantage of flexible-receptor methods on suboptimal pockets and validate the advantage of DSDPFlex in screening on apo and AlphaFold2-predicted structures. With improvements in both efficiency and accuracy, DSDPFlex is expected to hold potential in future docking-based studies.

Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics.

All-atom constant pH molecular dynamics simulations offer a powerful tool for understanding pH-mediated and proton-coupled biological processes. As the protonation equilibria of protein sidechains are shifted by electrostatic interactions and desolvation energies, pKa values calculated from the constant pH simulations may be sensitive to the underlying protein force field and water model. Here we investigated the force field dependence of the all-atom particle mesh Ewald (PME) continuous constant pH (PME-CpHMD) simulations of a mini-protein BBL. The replica-exchange titration simulations based on the Amber ff19SB and ff14SB force fields with the respective water models showed significantly overestimated pKa downshifts for a buried histidine (His166) and for two glutamic acids (Glu141 and Glu161) that are involved in salt-bridge interactions. These errors (due to undersolvation of neutral histidines and overstabilization of salt bridges) are consistent with the previously reported \pka's based on the CHARMM c22/CMAP force field, albeit in larger magnitudes. The pKa calculations also demonstrated that ff19SB with OPC water is significantly more accurate than ff14SB with TIP3P water, and the salt-bridge related pKa downshifts can be partially alleviated by the atom-pair specific Lennard-Jones corrections (NBFIX). Together, these data suggest that the accuracies of the protonation equilibria of proteins from constant pH simulations can significantly benefit from improvements of force fields.

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks. OpenDock is publicly available at: https://github.com/guyuehuo/opendock.

Insight into the oxidation mechanism of coconut globulin by atmospheric cold plasma focusing on side chain amino acids

Atmospheric cold plasma (ACP), a novel non-thermal processing technology, generates active substances that stimulate protein oxidation in protein-based foods. Nevertheless, the precise mechanism through which ACP initiates amino acid oxidation on protein side chains remains ambiguous. This study primarily aimed to elucidate the mechanism of ACP-induced oxidation of coconut globulin, focusing on the process of amino acid oxidation. Analysis of protein oxidation products indicated a positive correlation between the extent of protein oxidation and the voltage and duration of ACP treatment. By analyzing the composition of amino acids and active ingredients, the study identified that the most significant changes amino acids were methionine, cysteine, and arginine, and •OH was the primary free radicals. The findings from oxidation kinetics and dynamic simulation indicated that •OH predominantly oxidized methionine, followed by L-cysteine and L-arginine. These results offer theoretical framework for understanding protein oxidation by ACP and suggest potential applications in protein-based food.

Modeling protein-small molecule conformational ensembles with ChemNet.

Modeling the conformational heterogeneity of protein-small molecule systems is an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state an entirely atomic level description could have advantages in speed and generality. We developed a graph neural network called ChemNet trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. ChemNet accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding, and given a description of the larger protein context, and builds up structures of small molecules and protein side chains for protein-small molecule docking. Because ChemNet is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using ChemNet to assess the accuracy and pre-organization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with a k cat / K M of 11000 M -1 min - 1 , considerably higher than any pre-deep learning design for this reaction. We anticipate that ChemNet will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems, and for designing higher activity preorganized enzymes.

Pitfalls in measurements of R1 relaxation rates of protein backbone 15N nuclei.

The dynamics of the backbone and side-chains of protein are routinely studied by interpreting experimentally determined 15N spin relaxation rates. R1(15N), the longitudinal relaxation rate, reports on fast motions and encodes, together with the transverse relaxation R2, structural information about the shape of the molecule and the orientation of the amide bond vectors in the internal diffusion frame. Determining error-free 15N longitudinal relaxation rates remains a challenge for small, disordered, and medium-sized proteins. Here, we show that mono-exponential fitting is sufficient, with no statistical preference for bi-exponential fitting up to 800MHz. A detailed comparison of the TROSY and HSQC techniques at medium and high fields showed no statistically significant differences. The least error-prone DD/CSA interference removal technique is the selective inversion of amide signals while avoiding water resonance. The exchange of amide with solvent deuterons appears to affect the rate R1 of solvent-exposed amides in all fields tested and in each DD/CSA interference removal technique in a statistically significant manner. In summary, the most accurate R1(15N) rates in proteins are achieved by selective amide inversion, without the addition of D2O. Importantly, at high magnetic fields stronger than 800MHz, when non-mono-exponential decay is involved, it is advisable to consider elimination of the shortest delays (typically up to 0.32s) or bi-exponential fitting.

Analysis of chi angle distributions in free amino acids via multiplet fitting of proton scalar couplings.

Scalar couplings are a fundamental aspect of nuclear magnetic resonance (NMR) experiments and provide rich information about electron-mediated interactions between nuclei. 3 couplings are particularly useful for determining molecular structure through the Karplus relationship, a mathematical formula used for calculating 3 coupling constants from dihedral angles. In small molecules, scalar couplings are often determined through analysis of one-dimensional proton spectra. Larger proteins have typically required specialized multidimensional pulse programs designed to overcome spectral crowding and multiplet complexity. Here, we present a generalized framework for fitting scalar couplings with arbitrarily complex multiplet patterns using a weak-coupling model. The method is implemented in FitNMR and applicable to one-dimensional, two-dimensional, and three-dimensional NMR spectra. To gain insight into the proton-proton coupling patterns present in protein side chains, we analyze a set of free amino acid one-dimensional spectra. We show that the weak-coupling assumption is largely sufficient for fitting the majority of resonances, although there are notable exceptions. To enable structural interpretation of all couplings, we extend generalized and self-consistent Karplus equation parameterizations to all angles. An enhanced model of side-chain motion incorporating rotamer statistics from the Protein Data Bank (PDB) is developed. Even without stereospecific assignments of the beta hydrogens, we find that two couplings are sufficient to exclude a single-rotamer model for all amino acids except proline. While most free amino acids show rotameric populations consistent with crystal structure statistics, beta-branched valine and isoleucine deviate substantially.

Guanidinium and spermidinium decavanadates: as small biomimetic models to understand non-covalent interactions between decavanadate and arginine and lysine side chains in proteins

During the last three decades, numerous investigations have been conducted on polyoxidovanadates to treat several illnesses and inhibit enzymes. Numerous decavanadate compounds have been proposed as potential therapies for Diabetes mellitus, Cancer, and Alzheimer’s disease. Only six relevant functional proteins interacting with decavanadate, V10, have been deposited in the PDB. These are acid phosphatase, tyrosine kinase, two ecto-nucleoside triphosphate diphosphohydrolases (NTPDases), the human transient receptor potential cation channel (TRPM4), and the human cell cycle protein CksHs1. The interaction sites in these proteins mainly consist of Arginine and Lysine, side chains binding to the decavanadate anion. To get further knowledge regarding non-covalent interactions of decavanadate in protein environments, guanidinium and spermidinium decavanadates were synthesized, crystallized, and subjected to analysis utilizing various techniques, including FTIR, Raman, 51V-NMR, TGA, and X-ray diffraction. The DFT calculations were employed to calculate the interaction energy between the decavanadate anion and the organic counterions. Furthermore, the Quantum Theory of Atoms in Molecules (QTAIM) and Non-covalent Interaction-Reduced Density Gradient (NCI-RDG) analyses were conducted to understand the non-covalent interactions present in these adducts. Decavanadate can engage in electrostatic forces, van der Waals, and hydrogen bond interactions with guanidinium and spermidinium, as shown by their respective interaction energies. Both compounds were highly stabilized by strong hydrogen bond interactions N−H···O and weak non-covalent interactions C−H···O. In addition, the interactions between guanidinium and spermidinium cations and decavanadate anion form several stable rings. This study provides new information on non-covalent intermolecular interactions between decavanadate and small biomimetic models of arginine and lysine lateral chains in protein environments.

Utilizing Thermal Shift Assay to Probe Substrate Binding to Selenoprotein O.

Defining the biological importance of proteins with unknown functions poses a significant obstacle in understanding cellular processes. Although bioinformatic and structural predictions have contributed to the study of unknown proteins, in vitro experimental validations are often hampered by the optimal conditions and cofactors required for biochemical activity. Cofactor binding is not only essential for the activity of some enzymes but may also enhance the thermal stability of the protein. One practical application of this phenomenon lies in utilizing the change in thermal stability, as measured by alterations in the protein's melting temperature, to probe ligand binding. Thermal shift assay (TSA) can be used to analyze the binding of different ligands to the protein of interest or find a stabilizing condition to perform experiments such as X-ray crystallography. Here we will describe a protocol for TSA utilizing the pseudokinase, Selenoprotein O (SelO), for a simple and high-throughput method for testing metal and nucleotide binding. In contrast to canonical kinases, SelO binds ATP in an inverted orientation to catalyze the transfer of AMP to the hydroxyl side chains of proteins in a posttranslational modification known as protein AMPylation. By leveraging the shift in the melting temperatures, we provide crucial insights into the molecular interactions underlying SelO function.

Top-down Proteomics for the Characterization and Quantification of Calreticulin Arginylation.

Arginylation installed by arginyltransferase 1 (ATE1) features an addition of arginine (Arg) to the reactive amino acids (e.g., Glu and Asp) at the protein N-terminus or side chain. Systemic removal of arginylation after ATE1 knockout (KO) in mouse models resulted in heart defects leading to embryonic lethality. The biological importance of arginylation has motivated the discovery of arginylation sites on proteins using bottom-up approaches. While bottom-up proteomics is powerful in localizing peptide arginylation, it lacks the ability to quantify proteoforms at the protein level. Here we developed a top-down proteomics workflow for characterizing and quantifying calreticulin (CALR) arginylation. To generate fully arginylated CALR (R-CALR), we have inserted an R residue after the signaling peptide (AA1-17). Upon overexpression in ATE1 KO cells, CALR and R-CALR were purified by affinity purification and analyzed by LCMS in positive mode. Both proteoforms showed charge states ranging from 27-68 with charge 58 as the most intense charge state. Their MS2 spectra from electron-activated dissociation (EAD) showed preferential fragmentation at the protein N-terminals which yielded sufficient c ions facilitating precise localization of the arginylation sites. The calcium-binding domain (CBD) gave minimum characteristic ions possibly due to the abundant presence of >100 D and E residues. Ultraviolet photodissociation (UVPD) compared with EAD and ETD significantly improved the sequence coverage of CBD. This method can identify and quantify CALR arginylation at absence, endogenous (low), and high levels. To our knowledge, our work is the first application of top-down proteomics in characterizing post-translational arginylation in vitro and in vivo.

Transverse relaxation optimized spectroscopy of NH2 groups in glutamine and asparagine side chains of proteins.

A transverse relaxation optimized spectroscopy (TROSY) approach is described for the optimal detection of NH2 groups in asparagine and glutamine side chains of proteins. Specifically, we have developed NMR experiments for isolating the slow-relaxing 15N and 1H components of NH2 multiplets. Although even modest sensitivity gains in 2D NH2-TROSY correlation maps compared to their decoupled NH2-HSQC counterparts can be achieved only occasionally, substantial improvements in resolution of the NMR spectra are demonstrated for asparagine and glutamine NH2 sites of a buried cavity mutant, L99A, of T4 lysozyme at 5ºC. The NH2-TROSY approach is applied to CPMG relaxation dispersion measurements at the side chain NH2 positions of the L99A T4 lysozyme mutant-a model system for studies of the role of protein dynamics in ligand binding.

Solid state NMR spectral editing of histidine, arginine and lysine using Hadamard encoding.

The NMR signals from protein sidechains are rich in information about intra- and inter-molecular interactions, but their detection can be complicated due to spectral overlap as well as conformational and hydrogen exchange. In this work, we demonstrate a protocol for multi-dimensional solid-state NMR spectral editing of signals from basic sidechains based on Hadamard matrix encoding. The Hadamard method acquires multi-dimensional experiments in such a way that both the backbone and under-sampled sidechain signals can be decoded for unambiguous editing in the 15 N spectral frequency dimension. All multi-dimensional 15 N-edited solid-state NMR experiments can be acquired using this strategy, thereby accelerating the acquisition of spectra spanning broad frequency bandwidth. Application of these methods to the ferritin nanocage, reveals signals from N atoms from His, Arg, Lys and Trp sidechains, as well as their tightly bound, ordered water molecules. The Hadamard approach adds to the arsenal of spectroscopic approaches for protein NMR signal detection.

Parsing Dynamics of Protein Backbone NH and Side-Chain Methyl Groups using Molecular Dynamics Simulations.

Experimental NMR spectroscopy and theoretical molecular dynamics (MD) simulations provide complementary insights into protein conformational dynamics and hence into biological function. The present work describes an extensive set of backbone NH and side-chain methyl group generalized order parameters for the Escherichia coli ribonuclease HI (RNH) enzyme derived from 2-μs microsecond MD simulations using the OPLS4 and AMBER-FF19SB force fields. The simulated generalized order parameters are compared with values derived from NMR 15N and 13CH2D spin relaxation measurements. The squares of the generalized order parameters, S2 for the N-H bond vector and Saxis2 for the methyl group symmetry axis, characterize the equilibrium distribution of vector orientations in a molecular frame of reference. Optimal agreement between simulated and experimental results was obtained by averaging S2 or Saxis2 calculated by dividing the simulated trajectories into 50 ns blocks (∼five times the rotational diffusion correlation time for RNH). With this procedure, the median absolute deviations (MAD) between experimental and simulated values of S2 and Saxis2 are 0.030 (NH) and 0.061 (CH3) for OPLS4 and 0.041 (NH) and 0.078 (CH3) for AMBER-FF19SB. The MAD between OPLS4 and AMBER-FF19SB are 0.021 (NH) and 0.072 (CH3). The generalized order parameters for the methyl group symmetry axis can be decomposed into contributions from backbone fluctuations, between-rotamer dihedral angle transitions, and within-rotamer dihedral angle fluctuations. Analysis of the simulation trajectories shows that (i) backbone and side chain conformational fluctuations exhibit little correlation and that (ii) fluctuations within rotamers are limited and highly uniform with values that depend on the number of dihedral angles considered. Low values of Saxis2, indicative of enhanced side-chain flexibility, result from between-rotamer transitions that can be enhanced by increased local backbone flexibility.

Discovery of ester-linked ubiquitylation of PARP10 mono-ADP-ribosylation in cells: a dual post-translational modification on Glu/Asp side chains.

The prevailing view on post-translational modifications (PTMs) is that amino acid side chains in proteins are modified with a single PTM at any given time. However, a growing body of work has demonstrated crosstalk between different PTMs, some occurring on the same residue. Such interplay is seen with ADP-ribosylation and ubiquitylation, where specialized E3 ligases ubiquitylate targets for proteasomal degradation in an ADP-ribosylation-dependent manner. More recently, the DELTEX family of E3 ligases was reported to catalyze ubiquitylation of the 3'- hydroxy group of the adenine-proximal ribose of free NAD + and ADP-ribose in vitro , generating a non-canonical ubiquitin ester-linked species. In this report, we show, for the first time, that this dual PTM occurs in cells on mono-ADP-ribosylated (MARylated) PARP10 on Glu/Asp sites to form a MAR ubiquitin ester (MARUbe). We term this process m ono- A DP-ribosyl ub iquit ylation or MARUbylation. Using chemical and enzymatic treatments, including a newly characterized bacterial deubiquitinase with esterase-specific activity, we discovered that PARP10 MARUbylation is extended with K11-linked polyubiquitin chains. Finally, mechanistic studies using proteasomal and ubiquitin-activating enzyme inhibitors demonstrated that PARP10 MARUbylation leads to its proteasomal degradation, providing a functional role for this new PTM in regulating protein turnover.

Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins.

A method is described to deconstruct the network of hydropathic interactions within and between a protein's sidechain and its environment into residue-based three-dimensional maps. These maps encode favorable and unfavorable hydrophobic and polar interactions, in terms of spatial positions for optimal interactions, relative interaction strength, as well as character. In addition, these maps are backbone angle-dependent. After map calculation and clustering, a finite number of unique residue sidechain interaction maps exist for each backbone conformation, with the number related to the residue's size and interaction complexity. Structures for soluble proteins (~749,000 residues) and membrane proteins (~387,000 residues) were analyzed, with the latter group being subdivided into three subsets related to the residue's position in the membrane protein: soluble domain, core-facing transmembrane domain, and lipid-facing transmembrane domain. This work suggests that maps representing residue types and their backbone conformation can be reassembled to optimize the medium-to-high resolution details of a protein structure. In particular, the information encoded in maps constructed from the lipid-facing transmembrane residues appears to paint a clear picture of the protein-lipid interactions that are difficult to obtain experimentally.

Protein Haptenation and Its Role in Allergy.

Humans are exposed to numerous electrophilic chemicals either as medicines, in the workplace, in nature, or through use of many common cosmetic and household products. Covalent modification of human proteins by such chemicals, or protein haptenation, is a common occurrence in cells and may result in generation of antigenic species, leading to development of hypersensitivity reactions. Ranging in severity of symptoms from local cutaneous reactions and rhinitis to potentially life-threatening anaphylaxis and severe hypersensitivity reactions such as Stephen-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), all these reactions have the same Molecular Initiating Event (MIE), i.e. haptenation. However, not all individuals who are exposed to electrophilic chemicals develop symptoms of hypersensitivity. In the present review, we examine common chemistry behind the haptenation reactions leading to formation of neoantigens. We explore simple reactions involving single molecule additions to a nucleophilic side chain of proteins and complex reactions involving multiple electrophilic centers on a single molecule or involving more than one electrophilic molecule as well as the generation of reactive molecules from the interaction with cellular detoxification mechanisms. Besides generation of antigenic species and enabling activation of the immune system, we explore additional events which result directly from the presence of electrophilic chemicals in cells, including activation of key defense mechanisms and immediate consequences of those reactions, and explore their potential effects. We discuss the factors that work in concert with haptenation leading to the development of hypersensitivity reactions and those that may act to prevent it from developing. We also review the potential harnessing of the specificity of haptenation in the design of potent covalent therapeutic inhibitors.

Invariant point message passing for protein side chain packing.

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ∼1400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.