Proteins: Structure, Function, and Bioinformatics

Glutamate racemase (MurI) catalyzes the stereochemical interconversion of L-glutamate to D-glutamate, a key element of bacterial peptidoglycan biosynthesis. In this study, we present the crystal structure of Helicobacter pylori glutamate racemase at 1.43 [A] and in monoclinic symmetry, as previously reported models, but different unit-cell parameters. The present model contains a single dimer and retains the previously described head-to-head dimer arrangement. The differences between the models arise from variations in unit-cell parameters, which lead to altered crystal packing interactions rather than changes in the quaternary assembly. The monomeric fold and active-site architecture remain conserved and are consistent with the catalytic features described for bacterial glutamate racemases. This structure provides an updated, high-resolution structural model for H. pylori glutamate racemase and highlights the variability in crystal packing within the same space group.

Avian phototransduction and magnetoreception have been proposed to involve shared retinal proteins, including interactions between long-wavelength opsin (LWO), the cone-specific heterotrimeric G protein (Gt), and cryptochrome 4a (Cry4a), yet structural information on avian phototransduction complexes is lacking. Here we present and critically assess two atomistic models of the European robin LWO-Gt complex generated by distinct modelling strategies. A full-complex prediction using AlphaFold3 yields a tightly packed, structurally stable interface but exhibits pronounced activation-like conformational features of the Gt-subunit that persist in simulations of the isolated protein, revealing a strong bias toward the active state. In contrast, a template-guided assembly based on single-chain predictions and an experimental rhodopsin-Gt reference structure forms a weaker interface and shows no intrinsic activation bias, while still displaying subtle activation-related dynamics. These results demonstrate that machine-learned complex prediction can encode functional states independently of the local interaction environment, thereby limiting its interpretability for signalling mechanisms that hinge on activation equilibria. Our findings highlight the need for explicit assessment of conformational-state bias when modelling regulatory protein assemblies and provide a structural framework for future studies of Cry4a-dependent modulation of retinal G-protein signalling in avian magnetoreception.

A variant of the U1A protein containing four substitutions to ionizable residues was generated serendipitously due to a miscommunication. Biophysical measurements show that this variant has at least twice as much helical structure as the wild-type U1A and is trimeric in solution, in contrast to the monomeric wild type. In sharp contrast, structures predicted by deep-learning AI tools (AlphaFold2 and RoseTTAFold2) and transformer-based tools (OmegaFold and ESMFold) are all highly similar to the wild-type U1A (backbone RMSD < 1 [A]). Even more surprising, two of the substituted ionizable residues are predicted to be fully buried in the non-polar core of the protein, an outcome that contradicts well-established physico-chemical principles, as ionizable residues are normally located on the protein surface. To explore this effect further, we generated sequences containing up to all twelve residues that make up the non-polar core of U1A. Across thousands of sequences, and depending on the AI model used, the majority of predicted structures contained fully buried ionizable residues while still maintaining the overall U1A fold. We then examined two additional proteins of comparable size, acylphosphatase and the de novo-designed TOP7 fold, and observed the same phenomenon: AI models frequently predicted structures with buried ionizable residues that nevertheless retained the parent fold. When these AI-predicted structures were subjected to short (50 ns) molecular dynamics simulations using physics-based force fields such as CHARMM or AMBER, the structures rapidly relaxed into ensembles that exposed ionizable residues. We conclude that while AI-based structure prediction tools perform extremely well on naturally occurring sequences, they do not reliably encode the physico-chemical principles governing the placement of ionizable residues. A straightforward remedy is to include a brief molecular dynamics simulation as a final validation step for AI-generated structures.

Despite progress in predicting protein structures, how proteins arrive at their native state remains a subject of continuous debate. We present a single molecule force spectroscopy study of the unfolding and refolding intermediates of the conserved, diverse, and ancient Rossmann2x2 fold ({beta}12{beta}34{beta}56{beta}78). By inserting glycines at different locations in the protein, we can follow in real time and annotate its unfolding and refolding intermediates. This protein folds along a single reversible pathway involving the ordered and sequential organization of discrete and cooperative folding units or foldons: unfolded {rightleftarrows} {beta}12{beta}3 {rightleftarrows} {beta}12{beta}34{beta}5 {rightleftarrows} {beta}12{beta}34{beta}56{beta}7 {rightleftarrows} {beta}12{beta}34{beta}56{beta}78. This strict order results from the formation of an autonomously folding unit (primary foldon) and the subsequent organization of elements (secondary foldons) whose stability depends on their interactions with previously organized ones.

Computational protein design demands generally applicable models that reliably predict or generate unmeasured variants with superior functional properties. Although protein language models (pLMs) have been used in zero-shot and transfer-learning design studies, they have generally not been assessed in benchmarks that explicitly test combinatorial extrapolation from lower- to higher-order variants. Here we benchmark widely used pLMs against conventional baseline methods in recently described dense, experimentally validated multi-mutant landscapes. We find that regardless of architecture and parameter count, pLMs are statistically similar to one another, and none consistently outperforms conventional baseline methods. Furthermore, their ability to distinguish functional from non-functional variants in zero-shot prediction is comparable to that of conventional homology-based methods. We suggest that to contribute significantly to the design of protein function, pLMs may need to encode biophysical and structural priors or be combined with structure-based approaches.

CO dehydrogenases (CODH) are metalloenzymes that reversibly oxidize CO to CO2, at a buried NiFe4S4 active site. The substrates, CO and CO2, need therefore to be transported through the protein matrix to reach the active site. The most likely pathway for intra-protein diffusion is the hydrophobic channel identified in the crystal structures. Here, we use site-directed mutagenesis to study the highly conserved isoleucine 563 of Thermococcus sp. AM4 CODH2. Mutations at this position change the biochemical properties (KM for CO, product inhibition constant, catalytic bias...), and increase the resistance of the enzyme to the inhibitor O2, showing that isoleucine 563 indeed lines the gas channel. The I563F mutation decreases the bimolecular rate constant of inhibition by O2 15-fold, and increases the IC50 20-fold, which is the strongest improvement in O2 resistance reported so far. We show that the size of the introduced amino acids is less important than their flexibility - along with the size of the cavity formed near the active site in the channel. We also conclude that O2 access to the active site cannot be slowed down without also affecting CO diffusion. This tradeoff will have to be considered in further attempts to use site-directed mutagenesis to make CODHs more O2 tolerant.

CASP16 provided a community-wide benchmark for assessing RNA structure prediction, including the first large-scale blind assessment of RNA-RNA multimer prediction. The results showed that achieving high atomic precision remains a major challenge across the field. In this work, we use the performance of our group (LCBio) as a diagnostic case study to examine the current limits of RNA structure prediction. Our workflow ranked first in the RNA multimer category and remained competitive for monomers. We combine hierarchical analysis with representative case studies to identify a pattern of predictive breakdown, in which modeling fidelity degrades from reliable local features to increasingly speculative global architectures. Multi-helix junctions appear to mark a major transition boundary where 2D topological success often fails to translate into 3D geometric realism, leading to cascading errors in global architecture. This hierarchical breakdown is especially pronounced in RNA multimers, where limitations in the recovery of junction geometry and tertiary interactions propagate directly into errors in higher-order assembly, making multimer prediction increasingly speculative. By placing benchmark performance in a direct structural context, this case study helps define the current limits of RNA structure prediction and highlights priorities for improving predictive accuracy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/720187v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1c3fe1aorg.highwire.dtl.DTLVardef@5f80beorg.highwire.dtl.DTLVardef@1fd5e0aorg.highwire.dtl.DTLVardef@128f708_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIRNA structure prediction in CASP16 shows a hierarchical decline in accuracy, from relatively reliable local secondary structure to increasingly uncertain global architecture and multimer assembly. C_LIO_LIPrediction accuracy declines markedly at the level of multi-helix junctions, where correct 2D topology often does not translate into realistic 3D geometry. C_LIO_LINon-canonical interactions, stacking geometry, and specialized tertiary motifs remain major sources of error in current RNA modeling pipelines. C_LIO_LIHigh relative performance in RNA-RNA multimer prediction can be achieved despite limited atomic accuracy, highlighting the importance of expert-guided assembly and model curation. C_LIO_LIMany current multimer models are better interpreted as coarse-grained organizational hypotheses than as precise atomic structures. C_LI

AlphaFold3 introduces a unified framework for predicting the structures and interaction of several biomolecules including single-chain protein monomers, multi-chain protein multimers, and protein-ligand complexes. While it achieves the state-of-the-art performance in most predictions, its prediction accuracy depends on the quality of multiple sequence alignment (MSA) and structural template inputs. There are few works of using customized MSAs and templates to improve AlphaFold3. In this work, we systematically investigate how diverse and carefully engineered MSAs and templates can be leveraged to improve AlphaFold3 predictions. We evaluate our methods on protein monomers, multimers, and protein-ligand complexes, and observe consistent, sizable gains in structure prediction accuracy for monomers (TM-score 0.937 vs 0.882), for multimers (DockQ score 0.550 vs 0.525), and for protein-ligand complexes (ligand RMSD 3.258 [A] vs 4 [A]) compared to the default AlphaFold3. Moreover, for the first time, we demonstrate that AlphaFold3 performs significantly better than AlphaFold2 when both use the same customized MSA and template inputs. The results highlight the importance and effectiveness of using diverse MSAs and templates to improve AlphaFold3.

Machine learning continues to accelerate peptide and protein design through the rapid prediction and generation of sequences with desired characteristics. Many applications focus on predicting properties, functions, and structures, as well as generating point mutations and de novo designs. Nevertheless, many models prove less generalizable than initially claimed. Most predictors and generators are trained on sequential datasets, where imbalances can be addressed during preprocessing. In contrast, structural bias, a subtype of algorithmic bias arising from uneven representation of structural classes in training datasets, and the limitations of early protein structure predictors have frequently remained undetected and uncorrected. The recent surge in powerful protein structure prediction tools, such as the AlphaFold and RosettaFold series and their variants, now presents opportunities to mitigate this issue. We hypothesize that such structural sampling biases influence the downstream performance of ML models. Using antimicrobial peptides as a case study, we audited the structural biases in 16 state-of-the-art predictors for antimicrobial activity and tested whether structural information constrains their predictions. Our analysis revealed that models explicitly trained on sequential data still produce predictions biased by uneven fold representations and data leakage. These findings highlight the importance of integrating balanced structural data or implementing bias-mitigating strategies to develop agnostic models that maximize bioactive protein discovery and multi-objective optimization.

Proteins are highly diverse functional polymers where the specific sequence of amino acids, selected from a standard genetically-encoded alphabet of twenty (C20), determines the structure and ultimately the function of the resulting folded protein. This standard alphabet has been identified to be non-randomly distributed in physicochemical properties crucial to both structure-formation and function, often referred to as coverage theory. While machine learning models have drastically improved protein structure prediction, protein design has yet to have similar development. Here we therefore bridge contemporary biological theory with recent advancements in artificial intelligence (AI) to develop and evaluate a generative AI protein design model, trained on hundreds of thousands of proteins within the RSCB PDB, for custom secondary structure motifs using reduced amino acid alphabets. Results indicate an overall success in designing novel proteins with desired secondary structure motifs for a broad range of amino acid alphabets. Interestingly this tool often captures the full three-dimensional tertiary structure of a target protein despite training only on physicochemical sequence space and DSSP secondary structure. The development of this model advances research across multiple disciplines, from general scientific AI/ML architecture development to protein design for biotechnology, astrobiology, and early-Earth evolutionary biology.

Nicotinic acetylcholine receptors (nAChRs) belong to the pentameric ligand-gated ion channel superfamily (pLGICs). Among them, the neuronal homomeric 7 nAChR is highly permeable to calcium and plays critical roles in synaptic transmission, cell signaling, and inflammation modulation. The biogenesis of 7 nAChRs is enhanced by the chaperone proteins RIC-3 and NACHO. Previously, we reported a motif in the 5-HT3A receptor, another pLGIC, involved in RIC-3 modulation. Residues in this motif are conserved and also found within the L1-MX segment of the 7 nACh subunit. We therefore explored the regulatory roles of these conserved residues in the biogenesis of 7 nAChRs using multiple approaches, including heterologous expression in Xenopus laevis oocytes, mutagenesis, pull-down assays, cell-surface labeling, and two-electrode voltage-clamp (TEVC) recordings. We find that synthetic 7 L1-MX peptide interacts with both RIC-3 and NACHO. In particular, conserved residues W330, R332, and L336 in the L1-MX positively regulates the assembly of 7 oligomers and the biogenesis of 7nAChR. In presence of residues W330, R332, and L336, NACHO promotes an assembly of an 7 pentamer which is resistant to strong denaturing conditions. NACHO-promoted 7 pentamer is also resistant to Endo H enzyme. Sensitivity of the pentamer to moderate temperatures (37 {degrees}C, 45 {degrees}C, and 50 {degrees}C) suggests that NACHO stabilizes the pentamer via non-covalent interactions. In contrast, Ala replacements at these residues disrupt the biogenesis and abolish 7 current. NACHO and RIC-3 co-expression yields partial rescue of functional expression for some Ala replacement constructs. SUMMARYThis work identifies regulatory roles of conserved residues W330, R332, and L336 in the biogenesis of 7 nAChR. This discovery positions MX subdomain as a promising target for future drug development that can minimize adverse effects.

Multidrug efflux pumps transport antibiotics across the cellular membrane resulting in resistance conferred to the host organism. Efflux pump inhibitors (EPIs) potentiate the efficacy of antibiotics by blocking drug efflux and hold promise as adjuvant therapeutics in the fight against multidrug resistant pathogenic bacteria. A hurdle in the field has been the lack of selectivity of small molecule EPIs which often display off-target toxicity due to non-specific binding. To tackle this specificity challenge, we aimed to maximize an inhibitors binding surface area to efflux pumps by designing miniprotein EPIs using computational protein design and an E. coli co-expression assay to screen inhibition in cells. We used S. aureus NorA as a model efflux transporter since it confers drug resistance to fluoroquinolones, puromycin, and other cytotoxic compounds. Starting from a focused miniprotein library of only 86 members, we identified inhibitors in the screen that blocked NorA transport under active efflux conditions in vitro. Our most promising inhibitor I-23 was validated by solving a cryo-EM structure of the miniprotein in complex with NorA, which stabilized the transporter in the outward-open conformation. I-23 has a ferredoxin-like fold with one of its {beta}-hairpins inserted into the substrate binding pocket of NorA and other parts of the globular fold occupying the shallow pocket and making extensive intermolecular contacts with NorA. An arginine residue on the tip of the hairpin loop was positioned near an anionic patch required for NorA antibiotic efflux. The identified structural motifs in this work could be employed to explore the molecular properties of peptidoglycan penetration; full realization of the therapeutic potential of the designed miniprotein inhibitors will require determining the principles for facilitating passage of [~]7 to 8 kDa miniproteins across the peptidoglycan bacterial cell wall.

Serine-aspartate repeat-containing protein D (SdrD) is a Staphylococcus aureus cell wall-anchored, calcium-binding adhesin member of the MSCRAMM Sdr subfamily that may contribute to bacterial adhesion and virulence. S. aureus is the most common cause of periprosthetic joint infection (PJI). Population-level distribution and sequence diversity of SdrD among clinical PJI isolates have not been systematically characterized, and the SdrD binding mechanism is still not well understood. To address these gaps, sdrD alleles were queried across 156 newly sequenced PJI isolates and compared to publicly available S. aureus genomes, and nucleotide- and protein-level phylogenies of the sdrCDE locus constructed. The SdrD crystal structure from S. aureus JH1 was determined, with solution small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations, and assessment of conformational changes with calcium depletion. Three dominant sdrD subtypes were defined, associating with USA300, JH1, and TCH60; the JH1 sdrD subtype was predominant among PJI isolates. Structural studies showed that the conformation of individual domains and interdomain organization of the multidomain SdrD have limited flexibility in solution, and that the calcium-binding B domain retains its core fold under conditions of calcium depletion. Together, the findings presented support functional diversification among Sdr family members in mediating host attachment and inform a re-evaluation of the ligand-binding mechanism previously proposed for SdrD. AUTHOR SUMMARYStaphylococcus aureus is the leading cause of infections that develop around joint implants (periprosthetic joint infection, PJI). This bacterium has a large arsenal of surface proteins that allow it to stick to human tissues and implanted devices. This work focused on one such protein, SdrD, which has been linked to implant-associated infections but the structure and diversity of which among patients with PJI had not been well characterized. The genetic sequences of SdrD were analyzed across thousands of bacterial genomes, including those from patients with PJI. Distinct genetic variants of the protein were found, one of which was particularly common with PJI. The three-dimensional structure of SdrD was determined at atomic resolution and solution small-angle X-ray scattering (SAXS) and molecular dynamics used to study how it moves and responds to changes in its environment. Contrary to what was previously described, SdrD was shown to be relatively rigid. These findings change how SdrDs mechanism of action should be considered, potentially informing design strategies to block bacterial attachment before infection takes hold.

Molecular docking aims to predict the binding conformation of a small molecule to its protein target. Recent work has proposed diffusion models for this task, from rigid-body docking that diffuses over ligand degrees of freedom to co-folding approaches that jointly generate protein structure and ligand pose. However, diffusion-based docking models have been shown to frequently produce physically implausible poses and fail to consistently recover key protein-ligand interactions. To address this, we introduce a reinforcement learning framework for training diffusion-based docking models directly on non-differentiable objectives. Fine-tuning DiffDock-Pocket for physical validity with our approach substantially increases the number of generated poses that are physically valid and interaction-preserving, with no increase in inference-time compute. Importantly, this comes without sacrificing structural accuracy; in fact, our approach increases the proportion of structures with near-native poses. These effects are most pronounced for protein targets that are dissimilar to the training data. Our fine-tuned DiffDock-Pocket model outperforms both classical docking algorithms and machine learning-based approaches on the PoseBusters set. Our results demonstrate that reinforcement learning can teach diffusion-based docking models to better respect physical constraints and recover key interactions, without the requirement to rely on inference-time corrections.

Conformational plasticity of RNAs plays important roles in recognizing RNA-binding proteins, and is often modulated by their binding partners. Here, we investigate RNA conformational preferences in a non-redundant dataset of 263 protein-RNA complexes to characterize the structural landscape associated with protein recognition. RNA dinucleotide segments are analyzed using seven backbone torsion angles ({delta}1, {varepsilon}1, {zeta}1, 2, {beta}2, {gamma}2, and {delta}2), two glycosidic torsion angles ({chi}1 and {chi}2) and the pseudo-torsion angle . Focusing on dinucleotide steps present in both interface and non-interface regions, we performed density-based clustering using selected backbone torsion angles to identify recurrent conformational states. We identify 28 distinct RNA dinucleotide conformers containing at least ten members each. Among these, eight conformers represent previously unreported nucleotide conformers (NtCs), including the transitional and the non-canonical states AB06, AB07, BB21, BB22, OP32, OP33, IC08 and IC09. Several of these conformers are preferentially enriched at protein-binding interfaces, suggesting their involvement in local conformational adaptation during protein-RNA recognition. The newly identified conformers span transitional A-B geometries, distorted B-like states, open conformations and compact intercalated structures, highlighting the remarkable structural plasticity of RNA in ribonucleoprotein complexes. Overall, this study expands the current understanding of RNA conformational space and provides a refined RNA dinucleotide conformer library for protein-RNA complexes. These findings will facilitate the identification of novel RNA structural motifs and improved RNA structural modeling, docking protein-RNA complexes and deep learning-based prediction frameworks for describing RNA tertiary structures.

Soluble oligomers and transmembrane channels formed by the 42-residue variant of amyloid beta (A{beta}42) play key roles in Alzheimers disease. Unfortunately, detailed structures of these assemblies have not been determined. Our group addresses this problem by developing atomic scale models. Previously we proposed that both soluble A{beta}42 oligomers and transmembrane channels have symmetric concentric {beta}-barrel structures. Here we expand this hypothesis to include GM1 gangliosides and sometimes cholesterol and lattice models of channel assemblies. The presence of GM1 gangliosides increases the toxicity of A{beta}42, enhances its ability to penetrate liposome membranes, and facilitates interactions between adjacent liposomes. Although the conformations of numerous model assemblies vary, in these models the carboxyl group of GM1 always binds to side-chains of histidine 13 and/or histidine 14. Our soluble oligomer models are consistent with electron microscopy images of beaded annular protofibrils. Our models of membrane-bound assemblies are consistent with the following: freeze-fracture and atomic force microscopy images of A{beta}42 in lipid bilayers, secondary structure results, the calcium hypothesis of Alzheimers Disease, effects of lithium depletion on AD, established {beta}-barrel theory, and energetic criteria.

AO_SCPLOWBSTRACTC_SCPLOWProteins are built from 20 canonical amino acids. It is interesting to explore whether proteins can be formed from significantly reduced amino acid alphabets. Our bioinformatics survey of UniProt (more than 250 M sequences) revealed that proteins composed of reduced amino acid alphabets (< 10) are extremely rare among existing proteins. Next, we used computational protein design to design proteins composed of all 1,013 possible alphabets of 2-10 early amino acids (Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr, and Val). The length of all proteins was 100 amino acid residues. Small amino acid alphabets preferred simple helices or helix bundles. Larger amino acid alphabets allowed for the design of more complex structures. A protein composed of 8 amino acids (Ala, Asp, Gly, Leu, Val, Ser, Thr, and Pro) was successfully experimentally verified. It belongs to fibronectin type III domain {beta}-sheet-rich architecture. Attempts to experimentally verify designs composed of 6 and 4 amino acids were unsuccessful. We show by a computational experiment without an experimental validation that inverse folding programs, namely ProteinMPNN, can stabilize designed proteins within the same amino acid alphabet. Our results show that globular proteins may have formed early in evolution. Furthermore, we show that it is possible to design proteins with interesting properties for biotechnology and synthetic biology.

GABAA receptors (GABAARs) are pentameric ligand-gated ion channels (pLGICs) essential for inhibitory synaptic transmission throughout the central nervous system. Despite progress in understanding their three-dimensional structure, the molecular basis for how neurotransmitter binding is transduced to ion channel gating remains poorly understood. Furthermore, relatively little is known about the contributions of distinct subunits to this coupling within typical heteromeric receptors. A highly conserved proline (site 1) in the M2-M3 linker of pLGIC subunits is involved in channel gating - e.g., P273 in the GABAAR {beta}2 subunit. In GABAARs, only the {beta} subunits have an additional proline in the M2-M3 linker (site 2) - e.g., {beta}2(P276) - whereas all other subunits have a non-proline at the homologous site 2 position. Here, we investigate the functional contribution of proline at site 2 in distinct subunits of 1{beta}2{gamma}2 GABAARs. We expressed wild type or mutant 1{beta}2{gamma}2 GABAARs in Xenopus laevis oocytes and used two-electrode voltage clamp electrophysiology to record channel currents in response to GABA and/or other ligands. First, we introduced a proline at site 2 in 1 or {gamma}2 subunits: 1(A280P) and {gamma}2(S291P). Second, we replaced the site 2 proline in the {beta}2 subunit with its homologous non-proline residue from 1 or {gamma}2 subunits: {beta}2(P276A) or {beta}2(P276S). We show that 1(A280P) confers enhanced GABA-sensitivity and spontaneous unliganded channel activity, whereas {gamma}2(S291P) has minor effects on channel activation. In contrast, {beta}2(P276A) or {beta}2(P276S) either had no effect or enhanced GABA-activation, respectively, indicating complex functional dependence on the side chain at site 2 in the {beta}2 subunit. When in combination with other substitutions, the presence or absence of 1(A280P) was consistently correlated with enhanced GABA-sensitivity and spontaneous activity. Thus, introduction of a proline at site 2 in the 1 M2-M3 linker biases the channel towards an activated state and prevents it from remaining closed at rest.

The three conventional isoforms of the Ras G-protein (H-, K-, N-Ras) function as molecular on-off switches that regulate a wide array of signaling pathways, including the Ras-PI3K-PIP3-PDK1-AKT pathway that is central to innate immunity and normal cell growth, and is dysregulated in many disease states. Activation of the pathway by Ras requires adequate Ras-PI3K binding affinity. Here we focus on the interface of known structure in the H-Ras:PI3K{gamma} co-complex essential to multiple pathways including directed pseudopod growth in leukocyte chemotaxis. At this interface 10 H-Ras residues, all 100% conserved between the H-, K- and N-Ras isomers, contact the Ras binding domain of PI3K{gamma} (PI3K{gamma}RBD). To investigate the degree to which the native H-Ras:PI3K{gamma}RBD interface is optimized by evolution for maximal binding affinity, 8 interfacial Ras mutations selected from the COSMIC database and the literature were introduced at the contact positions. All 8 Ras mutations were observed to alter the H-Ras:PI3K{gamma}RBD binding affinity, with 4 mutations yielding significant affinity increases and 4 yielding significant affinity decreases. These findings indicate that the native H-Ras:PI3K{gamma}RBD interface provides intermediate, rather than maximal, binding affinity. Such intermediate affinity is consistent with the substantial binding plasticity of the conserved H-, N-, K-Ras effector docking surface, which has evolved to bind a diverse array of effectors. Furthermore, the findings provide evidence that COSMIC-linked mutations at the H-Ras:PI3K{gamma}RBD interface frequently generate affinity increases as well as decreases, with potential implications for molecular mechanisms of disease and for tool development in cell biology.

Metabolic pathways are often hypothesized to benefit from the spatial organization of enzymes, facilitating substrate transfer through mechanisms such as metabolic channeling or metabolon formation. However, it remains unclear whether the spatial proximity of catalytic sites represents a general organizational principle of metabolism or is restricted to specific pathways. Here, we investigate whether consecutive enzymes in metabolic pathways, when physically interacting, exhibit structurally optimized arrangements that minimize distances between their catalytic sites, thereby increasing metabolite transfer efficiency from one enzyme to the next. We first evaluated the ability of current protein-protein interaction prediction methods, including AlphaFold2, AlphaFold3, ESMFold, and HDOCK, to model weak and transient interactions using a benchmark dataset of 112 low-affinity protein dimers from PDBbind. AlphaFold-based approaches performed best in recovering correct interaction geometries, while ESMFold showed limited performance. We further assessed several confidence metrics and identified ipTM, ipSAE, and VoroIF-GNN as the most informative predictors of correct interaction conformations. In addition to simple Euclidean distance metrics, we developed a computational procedure to estimate shortest accessible space paths between catalytic sites in predicted enzyme-enzyme complexes. Applying this framework to 107 consecutive enzyme pairs in E.coli revealed an increased tendency for consecutive enzymes to interact, but no systematic evidence that interacting enzymes position their catalytic sites in spatially optimized configurations. In the predicted complex conformations, catalytic sites tend not to be positioned closer than expected at random. The developed computational workflow provides a general framework for analyzing structural aspects of metabolic organization.