Investigating direct current potentials that affect native protein conformation during trapped ion mobility spectrometry-mass spectrometry.

Trapped ion mobility spectrometry-time-of-flight mass spectrometry (TIMS-TOFMS) has emerged as a tool to study protein conformational states. In TIMS, gas-phase ions are guided across the IM stages by applying direct current (DC) potentials (D1-6), which, however, might induce changes in protein structures through collisional activation. To define conditions for native protein analysis, we evaluated the influence of these DC potentials using the metalloenzyme bovine carbonic anhydrase (BCA) as primary test compound. The variation of DC potentials did not change BCA-ion charge and heme content but affected (relative) charge-state intensities and adduct retention. Constructed extracted-ion mobilograms and corresponding collisional cross-section (CCS) profiles gave useful insights in (alterations of) protein conformational state. For BCA, the D3 and D6 potential (which are applied between the deflection transfer and funnel 1 [F1] and the accumulation exit and the start of the ramp, respectively) had most profound effects, showing multimodal CCS distributions at higher potentials indicating gradual unfolding. The other DC potentials only marginally altered the CCS profiles of BCA. To allow for more general conclusions, five additional proteins of diverse molecular weight and conformational stability were analyzed, and for the main protein charge states, CCS profiles were constructed. Principal component analysis (PCA) of the obtained data showed that D1 and D3 exhibit the highest degree of correlation with the ratio of folded and unfolded protein (F/U) as extracted from the mobilograms obtained per set D potential. The correlation of D6 with F/U and protein charge were similar, and D2, D4, and D5 showed an inverse correlation with F/U but were correlated with protein charge. Although DC boundary values for induced conformational changes appeared protein dependent, a set of DC values could be determined, which assured native analysis of most proteins.

Orientational Pathways during Protein Translocation through Polymer-Modified Nanopores.

Protein translocation through nanopores holds significant promise for applications in biotechnology, biomolecular analysis, and medicine. However, the interpretation of signals generated by the translocation of the protein remains challenging. In this way, it is crucial to gain a comprehensive understanding on how macromolecules translocate through a nanopore and to identify what are the critical parameters that govern the process. In this study, we investigate the interplay between protein charge regulation, orientation, and nanopore surface modifications using a theoretical framework that allows us to explicitly take into account the acid-base reactions of the titrable amino acids in the proteins and in the polyelectrolytes grafted to the nanopore surface. Our goal is to thoroughly characterize the translocation process of different proteins (GFP, ß-lactoglobulin, lysozyme, and RNase) through nanopores modified with weak polyacids. Our calculations show that the charge regulation mechanism exerts a profound effect on the translocation process. The pH-dependent interactions between proteins and charged polymers within the nanopore lead to diverse free energy landscapes with barriers, wells, and flat regions dictating translocation efficiency. Comparison of different proteins allows us to identify the significance of protein isoelectric point, size, and morphology in the translocation behavior. Taking advantage of these insights, we propose pH-responsive nanopores that can load proteins at one pH and release them at another, offering opportunities for controlled protein delivery, separation, and sensing applications.

Differential Interactions of the Proteasome Inhibitor PI31 with Constitutive and Immuno-20S Proteasomes.

PI31 (Proteasome Inhibitor of 31,000 Da) is a 20S proteasome binding protein originally identified as an in vitro inhibitor of 20S proteasome proteolytic activity. Recently reported cryo-electron microscopy structures of 20S-PI31 complexes have revealed that the natively disordered proline-rich C-terminus of PI31 enters the central chamber in the interior of the 20S proteasome and interacts directly with the proteasome's multiple catalytic threonine residues in a manner predicted to inhibit their enzymatic function while evading its own proteolysis. Higher eukaryotes express an alternative form of the 20S proteasome (termed "immuno-proteasome") that features genetically and functionally distinct catalytic subunits. The effect of PI31 on immuno-proteasome function is unknown. We examine the relative inhibitory effects of PI31 on purified constitutive (20Sc) and immuno-(20Si) 20S proteasomes in vitro and show that PI31 inhibits 20Si hydrolytic activity to a significantly lesser degree than that of 20Sc. Unlike 20Sc, 20Si hydrolyzes the carboxyl-terminus of PI31 and this effect contributes to the reduced inhibitory activity of PI31 toward 20Si. Conversely, loss of 20Sc inhibition by PI31 point mutants leads to PI31 degradation by 20Sc. These results demonstrate unexpected differential interactions of PI31 with 20Sc and 20Si and document their functional consequences.

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures.

MOTIVATION: The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. RESULTS: To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. AVAILABILITY AND IMPLEMENTATION: https://github.com/vicruiser/3Dmapper.

De novo design of drug-binding proteins with predictable binding energy and specificity.

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Production of Isotopically Labeled KRAS4b.

Isotopically labelled proteins are important reagents in structural biology as well as in targeted drug development. The field continues to advance with complex multi-isotope labeling. We have combined our experience in high-level soluble KRAS4b expression with protocols for isotope incorporation, to achieve reliable and efficient approaches for several labeling strategies. Typical experiments achieve nearly 100% 15N incorporation, with yields in the range of 1.3-24.6 mg/L (median = 6.4 mg/L, n = 53). Improvements in the growth parameters in the presence of deuterium reduce the standard time of fermentation from 5 days to 3 days by modifying the medium used during the weaning process. The methods described are compatible with multi-isotope labeling and site-specific labeling.

Luminescence-Based Techniques for KRAS Thermal Stability Monitoring.

Various biochemical methods have been introduced to detect and characterize KRAS activity and interactions, from which the vast majority is based on luminescence detection in its varying forms. Among these methods, thermal stability assays, using luminophore-conjugated proteins or external environment sensing dyes, are widely used. In this chapter, we describe methods enabling KRAS stability monitoring in vitro, with an emphasis on ligand-induced stability. This chapter focuses mainly on luminescence-based techniques utilizing external dye molecules and fluorescence detection.

Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Multilevel superposition for deciphering the conformational variability of protein ensembles.

The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.

Diverse crystalline protein scaffolds through metal-dependent polymorphism.

As protein crystals are increasingly finding diverse applications as scaffolds, controlled crystal polymorphism presents a facile strategy to form crystalline assemblies with controllable porosity with minimal to no protein engineering. Polymorphs of consensus tetratricopeptide repeat proteins with varying porosity were obtained through co-crystallization with metal salts, exploiting the innate metal ion geometric requirements. A single structurally exposed negative amino acid cluster was responsible for metal coordination, despite the abundance of negatively charged residues. Density functional theory calculations showed that while most of the crystals were the most thermodynamically stable assemblies, some were kinetically trapped states. Thus, crystalline porosity diversity is achieved and controlled with metal coordination, opening a new scope in the application of proteins as biocompatible protein-metal-organic frameworks (POFs). In addition, metal-dependent polymorphic crystals allow direct comparison of metal coordination preferences.

From covalent transition states in chemistry to noncovalent in biology: from ß- to Φ-value analysis of protein folding.

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the ß-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of ß- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.

MOTIVATION: Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS: To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION: https://github.com/lixiaokun2020/DrugMGR.

Single-Molecule Diffusivity Quantification Unveils Ubiquitous Net Charge-Driven Protein-Protein Interaction.

Recent microscopy and nuclear magnetic resonance (NMR) studies have noticed substantial suppression of intracellular diffusion for positively charged proteins, suggesting an overlooked role of electrostatic attraction in nonspecific protein interactions in a predominantly negatively charged intracellular environment. Utilizing single-molecule detection and statistics, here, we quantify in aqueous solutions how protein diffusion, in the limit of low diffuser concentration to avoid aggregate/coacervate formation, is modulated by differently charged interactor proteins over wide concentration ranges. We thus report substantially suppressed diffusion when oppositely charged interactors are added at parts per million levels, yet unvaried diffusivities when same-charge interactors are added beyond 1%. The electrostatic attraction-driven suppression of diffusion is sensitive to the protein net charge states, as probed by varying the solution pH and ionic strength or chemically modifying the proteins and is robust across different diffuser-interactor pairs. By converting the measured diffusivities to diffuser diameters, we further show that in the limit of excess interactors, a positively charged diffuser molecule effectively drags along just one monolayer of negatively charged interactors, where further interactions stop. We thus unveil ubiquitous, net charge-driven protein-protein interactions and shed new light on the mechanism of charge-based diffusion suppression in living cells.

G Protein Activation Occurs via a Largely Universal Mechanism.

Understanding how signaling proteins like G proteins are allosterically activated is a long-standing challenge with significant biological and medical implications. Because it is difficult to directly observe such dynamic processes, much of our understanding is based on inferences from a limited number of static snapshots of relevant protein structures, mutagenesis data, and patterns of sequence conservation. Here, we use computer simulations to directly interrogate allosteric coupling in six G protein α-subunit isoforms covering all four G protein families. To analyze this data, we introduce automated methods for inferring allosteric networks from simulation data and assessing how allostery is conserved or diverged among related protein isoforms. We find that the allosteric networks in these six G protein α subunits are largely conserved and consist of two pathways, which we call pathway-I and pathway-II. This analysis predicts that pathway-I is generally dominant over pathway-II, which we experimentally corroborate by showing that mutations to pathway-I perturb nucleotide exchange more than mutations to pathway-II. In the future, insights into unique elements of each G protein family could inform the design of isoform-specific drugs. More broadly, our tools should also be useful for studying allostery in other proteins and assessing the extent to which this allostery is conserved in related proteins.

Assessing Weak Anion Binding to Small Peptides.

Since Hofmeister's seminal studies in the late 19th century, it has been known that salts and buffers can drastically affect the properties of peptides and proteins. These Hofmeister effects can be conceived of in terms of three distinct phenomena/mechanisms: water-salt interactions that indirectly induce the salting-out of a protein by water sequestration by the salt, and direct salt-protein interactions that can either salt-in or salt-out the protein. Unfortunately, direct salt-protein interactions responsible for Hofmeister effects are weak and difficult to quantify. As such, they are frequently construed of as being nonspecific. Nevertheless, there has been considerable effort to better specify these interactions. Here, we use pentapeptides to demonstrate the utility of the H-dimension of nuclear magnetic resonance (NMR) spectroscopy to assess anion binding using N-H signal shifts. We qualify binding using these, demonstrating the upfield shifts induced by anion association and revealing how they are much larger than the corresponding downfield shifts induced by magnetic susceptibility and other ionic strength change effects. We also qualify binding in terms of how the pattern of signal shifts changes with point mutations. In general, we find that the observed upfield shifts are small compared with those induced by anion binding to amide-based hosts, and MD simulations suggest that this is so. Thus, charge-diffuse anions associate mostly with the nonpolar regions of the peptide rather than directly interacting with the amide N-H groups. These findings reveal the utility of 1H NMR spectroscopy for qualifying affinity to peptides─even when affinity constants are very low─and serve as a benchmark for using NMR spectroscopy to study anion binding to more complex systems.

Maturation and Conformational Switching of a De Novo Designed Phase-Separating Polypeptide.

Cellular compartments formed by biomolecular condensation are widespread features of cell biology. These organelle-like assemblies compartmentalize macromolecules dynamically within the crowded intracellular environment. However, the intermolecular interactions that produce condensed droplets may also create arrested states and potentially pathological assemblies such as fibers, aggregates, and gels through droplet maturation. Protein liquid-liquid phase separation is a metastable process, so maturation may be an intrinsic property of phase-separating proteins, where nucleation of different phases or states arises in supersaturated condensates. Here, we describe the formation of both phase-separated droplets and proteinaceous fibers driven by a de novo designed polypeptide. We characterize the formation of supramolecular fibers in vitro and in bacterial cells. We show that client proteins can be targeted to the fibers in cells using a droplet-forming construct. Finally, we explore the interplay between phase separation and fiber formation of the de novo polypeptide, showing that the droplets mature with a post-translational switch to largely ß conformations, analogous to models of pathological phase separation.

Tertiary Amine Coupling by Oxidation for Selective Labeling of Dimethyl Lysine Post-Translational Modifications.

Lysine dimethylation (Kme2) is a crucial post-translational modification (PTM) that regulates biological processes and is implicated in diseases. There is significant interest in globally identifying these methylation marks. Unfortunately, this remains challenging due to the lack of robust technologies for selectively labeling Kme2. To address this, we present a chemical method named tertiary amine coupling by oxidation (TACO). This method selectively modifies Kme2 to aldehydes using Selectfluor and a base. The resulting aldehydes from Kme2 were then functionalized using reductive amination, thiolamine, and oxime chemistry. We successfully demonstrated the versatility of TACO in selectively labeling Kme2 peptides and proteins in complex cell lysate mixtures with varying payloads, including affinity tags and fluorophores. We further showed the application of TACO chemistry for the identification of Kme2 sites at a single-molecule level by fluorosequencing. We discovered novel 30 Kme2 sites, in addition to previously known 5 Kme2 sites, by proteomics analysis of TACO-modified nuclear extracts. Our work establishes a unique strategy for covalently modifying Kme2, facilitating the global identification of low-abundance Kme2-PTMs and their sites within complex cell lysate mixtures.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.