Direct Coupling Analysis Research Articles

Designing proteins: Mimicking natural protein sequence heterogeneity.

This study presents an enhanced protein design algorithm that aims to emulate natural heterogeneity of protein sequences. Initial analysis revealed that natural proteins exhibit a permutation composition lower than the theoretical maximum, suggesting a selective utilization of the 20-letter amino acid alphabet. By not constraining the amino acid composition of the protein sequence but instead allowing random reshuffling of the composition, the resulting design algorithm generates sequences that maintain lower permutation compositions in equilibrium, aligning closely with natural proteins. Folding free energy computations demonstrated that the designed sequences refold to their native structures with high precision, except for proteins with large disordered regions. In addition, direct coupling analysis showed a strong correlation between predicted and actual protein contacts, with accuracy exceeding 82% for a large number of top pairs (>4L). The algorithm also resolved biases in previous designs, ensuring a more accurate representation of protein interactions. Overall, it not only mimics the natural heterogeneity of proteins but also ensures correct folding, marking a significant advancement in protein design and engineering.

Preliminary Implementation of High-Resolution Multi-Scale coupling calculations for the entire pressure vessel based on OpenFOAM

Significant coupling effects exist among system components in nuclear pressure vessel. Due to the complex geometric structures, the nuclear industry primarily relies on system codes or sub-channel methods for core safety analysis. However, these methods suffer from low model accuracy and insufficient coupling capabilities. Additionally, the differences in model scales impede direct coupling analysis with the CFD calculations of the plenum system. To address these issues, this paper proposes a multi −scale coupling calculation method for the entire pressure vessel: For the plenum system, detailed CFD modeling is employed, while the core calculations are conducted using CorTAF, a high-resolution core Multiphysics-coupling analysis method developed by our team. A cross-resolution coupling model is utilized to integrate the two, achieving cross-resolution coupling simulations for the entire pressure vessel, encompassing both the plenum and core system. The above method was applied to the coupling calculations of a typical pressurized water reactor’s lower plenum and core, revealing detailed thermal–hydraulic phenomena under precise core flow inlet distribution conditions. The lateral flow at the core inlet exceeds 1 m/s, with the maximum and minimum fluid velocities in the subchannels deviating by up to 70 % from the average velocity of 2.42 m/s. The flow distribution only begins to stabilize after a height of 1.2 m in the core. The paper also includes inlet asymmetric flow reduction calculations. Overall, the method enables multi-scale and multi-physics coupling calculations, which provide significant reference value for improving the accuracy of current core safety analyses.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited. Thus, we conduct a family wide analysis of the B1 metallo-β-lactamases, combining experiments (deep mutational scanning, DMS) on two distant homologs (NDM-1 and VIM-2) and computational analyses (in silico DMS based on Direct Coupling Analysis, DCA) of 100 homologs. The methods jointly reveal and quantify prevalent epistasis, as ~1/3rd of equivalent mutations are epistatic in DMS. From DCA, half of the positions have a >6.5 fold difference in effective number of tolerated mutations across the entire family. Notably, both methods locate residues with the strongest epistasis in regions of intermediate residue burial, suggesting a balance of residue packing and mutational freedom in forming epistatic networks. We identify entrenched WT residues between NDM-1 and VIM-2 in DMS, which display statistically distinct behaviors in DCA from non-entrenched residues. Entrenched residues are not easily compensated by changes in single nearby interactions, reinforcing existing findings where a complex epistatic network compounds smaller effects from many interacting residues.

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico, significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein's fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.

The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes

Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.

Research on Modal Behavior of Large Francis Turbine Runner

The water-added-mass can affect the modal behavior of the Francis turbine runner largely. The sealing rings in the band-chamber and the crown-chamber outside of the runner are utilized to prevent leakage of water around the Francis turbine. The sealing rings and the chambers filled with water can also affect the modal behavior of the turbine. When the turbine runner operates in the power plant at various operating conditions, the seal clearances of the band-chamber and the crown-chamber filled with water may change the modal behavior of the large Francis turbine runner. Firstly, the 3D geometrical structure of the studied large-scale Francis turbine runner was constructed, and then the CAD model was meshed to establish the finite-element model for numerical analysis. Next, the modal shapes and the natural-frequencies of runners in the air were calculated, which is the traditional method used for the structural modal behavior analysis of the turbine runners. Finally, this paper investigates the modal behavior of a large-scale Francis runner prototype with the combined effects of sealing rings and water added-mass by direct water-structure coupled analysis via the finite element method (FEM) to obtain more realistic results. The results reveal that the effects of water-added-mass and sealing rings cause a significant decrease in the natural-frequency of the large Francis turbine runner, which may lead to an increase in the amplitude of vibrations and even cracks. Technical suggestions are proposed to improve the monitoring and maintenance in the power stations to maintain the safe and efficient operation of the large Francis turbine units.

Towards parsimonious generative modeling of RNA families.

Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the characteristics of RNA sequences, with a strong emphasis on simplicity, efficiency, and interpretability. eaDCA explicitly constructs sparse coevolutionary models for RNA families, achieving performance levels comparable to more complex methods while utilizing a significantly lower number of parameters. Our approach demonstrates efficiency in generating artificial RNA sequences that closely resemble their natural counterparts in both statistical analyses and SHAPE-MaP experiments, and in predicting the effect of mutations. Notably, eaDCA provides a unique feature: estimating the number of potential functional sequences within a given RNA family. For example, in the case of cyclic di-AMP riboswitches (RF00379), our analysis suggests the existence of approximately 1039 functional nucleotide sequences. While huge compared to the known <4000 natural sequences, this number represents only a tiny fraction of the vast pool of nearly 1082 possible nucleotide sequences of the same length (136 nucleotides). These results underscore the promise of sparse and interpretable generative models, such as eaDCA, in enhancing our understanding of the expansive RNA sequence space.

Thermal-Mechanical Analysis of a Power Module with Parametric Model Order Reduction

This paper presents parametric model order reduction (pMOR) by the Lagrange approach of matrix interpolation for the thermal-mechanical and reliability study of a power electronics module (PEM) with nonlinear behaviours. Most previous research in model order reduction (MOR) studies reports thermal-mechanical simulations using a sequentially coupled method. In this research, a direct-coupled thermal-mechanical analysis, which simultaneously solves the thermal and structural governing equations, has been used to obtain thermal and deformation results. Furthermore, for pMOR, the linear approach of matrix interpolation is limited to linear changes between sampled-parametric points. Hence, a new way of interpolating system matrices using the Lagrange interpolation method has been adopted to implement the matrix interpolation efficiently. The parametric reduced-order model (pROM) solution by the Lagrange approach of matrix interpolation agrees well with the full-order model (FOM) and takes similar computational time as the linear (bi-linear) approach of matrix interpolation. pROM simulations offer up to 85.5% reduction in computational time.

Residue coevolution and mutational landscape for OmpR and NarL: You can teach old dogs new tricks

The timeless question of whether old dogs can learn new tricks can also be asked for scientific methods. One such example can be found in the article "Residue coevolution and mutational landscape for OmpR and NarL response regulator subfamilies" by Mayu Shibata and coworkers. In their study the authors show some new tricks using an established method, Direct Coupling Analysis (DCA). So let us dive first into its history.

Enhancing protein dynamics studies with an automated FRET network design informed by direct coupling analysis

Enhancing protein dynamics studies with an automated FRET network design informed by direct coupling analysis

Pycofitness-Evaluating the fitness landscape of RNA and protein sequences.

The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue. We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability. https://github.com/KIT-MBS/pycofitness.

Coevolutionary Information Captures Catalytic Functions and Reveals Divergent Roles of Terpene Synthase Interdomain Connections.

Inferring the historical and biophysical causes of diversity within protein families is a complex puzzle. A key to unraveling this problem is characterizing the rugged topography of sequence-function adaptive landscapes. Using biochemical data from a 29 = 512 combinatorial library of tobacco 5-epi-aristolochene synthase (TEAS) mutants engineered to make the native major product of Egyptian henbane premnaspirodiene synthase (HPS) and a complementary 512 mutant HPS library, we address the question of how product specificity is controlled. These data sets reveal that HPS is far more robust and resistant to mutations than TEAS, where most mutants are promiscuous. We also combine experimental data with a sequence Potts Hamiltonian model and direct coupling analysis to quantify mutant fitness. Our results demonstrate that the Hamiltonian captures variation in product outputs across both libraries, clusters native family members based on their substrate specificities, and exposes the divergent catalytic roles of couplings between the catalytic and noncatalytic domains of TEAS versus HPS. Specifically, we found that the role of the interdomain connectivities in specifying product output is more important in TEAS than connectivities within the catalytic domain. Despite being 75% identical, this property is not shared by HPS, where connectivities within the catalytic domain are more important for specificity. By solving the X-ray crystal structure of HPS, we assessed structural bases for their interdomain network differences. Last, we calculate the product profile Shannon entropies of the two libraries, which showcases that site-site connectivities also play divergent roles in catalytic accuracy.

Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.

Homologous protein sequences are wonderfully diverse, indicating many possible evolutionary "solutions" to the encoding of function. Consequently, one can construct statistical models of protein sequence by analyzing amino acid frequency across a large multiple sequence alignment. A central premise is that covariance between amino acid positions reflects coevolution due to a shared functional or biophysical constraint. In this review, we describe the implementation and discuss the advantages, limitations, and recent progress on two coevolution-based modeling approaches: (1) Potts models of protein sequence (direct coupling analysis [DCA]-like), and (2) the statistical coupling analysis (SCA). Each approach detects interesting features of protein sequence and structure-the former emphasizes local physical contacts throughout the structure, while the latter identifies larger evolutionarily coupled networks of residues. Recent advances in large-scale gene synthesis and high-throughput functional selection now motivate additional work to benchmark model performance across quantitative function prediction and de novo design tasks.

Abstract B131: NXC03, an AI-designed, affinity-attenuated IL-21 mutein with half-life extension enhances antitumor immunity

Abstract Background:IL-21 is a pleotropic cytokine with actions including functional programming of CD8+ T cells towards a central memory phenotype with enhanced activation, expansion and survival. Indeed, recombinant IL-21 exhibited antitumor activity in preclinical and clinical studies, but its clinical development has been hindered due to short serum half-life and high dose-limiting toxicities such as hepatotoxicity and neutropenia. We hypothesized that an IL-21 mutein with weaker binding to IL-21 receptor (IL-21R) and increased stability and half-life may achieve an optimal therapeutic index for development. Here we describe the discovery and preclinical profile of such an AI-designed IL-21-Fc fusion mutein, NXC03. Methods:We computationally evaluated ~400,000 combinatorial mutations of IL-21 to look for those with desired affinity and stability profile via multiple techniques, including direct coupling analysis and physical energy functions. Following iterative in silico and in vitro screening, we selected NXC03-9804 out of the top-20 hits and fashioned it as an IgG4 Fc fusion protein (NXC03). We measured the affinity of NXC03 to human IL-21R by biolayer interferometry and its melting temperature (Tm) by label-free nano differential scanning fluorimetry. We determined the bioactivity of NXC03 by assessing human primary T-cell proliferation and STAT3 phosphorylation (pSTAT3) in huT78 cells. We evaluated the antitumor efficacy of NXC03 alone or in combination with a murine PD-1 mAb (aPD1) in MC38 and anti-PD-1 resistant B16-F10 syngeneic tumor models in hIL21R knock-in mice. We also studied the safety profiles and pharmacokinetics of NXC03 following subcutaneous (SC) administrations in monkeys. Results:NXC03 differs from wildtype (WT) IL-21 by 4 amino acid residues and displays a unique fast-on fast-off binding kinetics to IL-21R with a 1000-fold larger KD than that of WT IL-21. It has a Tm of 62.0 °C, notably higher than that of WT IL-21 (48.5 °C). Despite a ~10-fold reduction in potency in inducing pSTAT3 in huT78 cells, NXC03 was comparable to WT IL-21 in enriching CCR7+CD45RO-CD8+ stem memory T cells (TSCM) in PBMC culture. In both MC38 and B16-F10 mouse models, NXC03 monotherapy demonstrated strong, dose-dependent antitumor activity and was more potent than WT IL-21. Combination treatment of NXC03 with aPD1 further increased efficacy compared to either alone. Fusion of NXC03 and aPD1 had similar effect. Moreover, NXC03 treatment produced lasting memory response that protected against tumor rechallenge following drug cessation. NXC03 exhibited a half-life in monkeys (14.1 h, IV; 55.5h, SC) much greater than that of WT IL-21 (0.8 h, IV). NXC03 was well tolerated with no neutropenia or hepatotoxicity observed in monkeys. Conclusion:NXC03, an AI-designed attenuated IL-21 mutein, exhibits strong antitumor activity and holds promise as an immunostimulatory agent for combination with checkpoint inhibitors. Given its excellent drug developability profile, NXC03 is being fast-tracked for development towards clinical testing. Citation Format: Puwei Yuan, Junli Liu, Fei Zhang, Ying Lei, Yanan Zhu, Songsong Gao, Fan Liu, Taylor B Guo. NXC03, an AI-designed, affinity-attenuated IL-21 mutein with half-life extension enhances antitumor immunity [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B131.

Co-evolution at protein-protein interfaces guides inference of stoichiometry of oligomeric protein complexes by de novo structure prediction.

The quaternary structure with specific stoichiometry is pivotal to the specific function of protein complexes. However, determining the structure of many protein complexes experimentally remains a major bottleneck. Structural bioinformatics approaches, such as the deep learning algorithm Alphafold2-multimer (AF2-multimer), leverage the co-evolution of amino acids and sequence-structure relationships for accurate de novo structure and contact prediction. Pseudo-likelihood maximization direct coupling analysis (plmDCA) has been used to detect co-evolving residue pairs by statistical modeling. Here, we provide evidence that combining both methods can be used for de novo prediction of the quaternary structure and stoichiometry of a protein complex. We achieve this by augmenting the existing AF2-multimer confidence metrics with an interpretable score to identify the complex with an optimal fraction of native contacts of co-evolving residue pairs at intermolecular interfaces. We use this strategy to predict the quaternary structure and non-trivial stoichiometries of Bacillus subtilis spore germination protein complexes with unknown structures. Co-evolution at intermolecular interfaces may therefore synergize with AI-based de novo quaternary structure prediction of structurally uncharacterized bacterial protein complexes.

Latent generative landscapes as maps of functional diversity in protein sequence space

Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.

Evolutionary Couplings and Molecular Dynamic Simulations Highlight Details of GPCRs Heterodimers' Interfaces.

A growing body of evidence suggests that only a few amino acids ("hot-spots") at the interface contribute most of the binding energy in transient protein-protein interactions. However, experimental protocols to identify these hot-spots are highly labor-intensive and expensive. Computational methods, including evolutionary couplings, have been proposed to predict the hot-spots, but they generally fail to provide details of the interacting amino acids. Here we showed that unbiased evolutionary methods followed by biased molecular dynamic simulations could achieve this goal and reveal critical elements of protein complexes. We applied the methodology to selected G-protein coupled receptors (GPCRs), known for their therapeutic properties. We used the structure-prior-assisted direct coupling analysis (SP-DCA) to predict the binding interfaces of A2aR/D2R, CB1R/D2R, A2aR/CB1R, 5HT2AR/D2R, and 5-HT2AR/mGluR2 receptor heterodimers, which all agreed with published data. In order to highlight details of the interactions, we performed molecular dynamic (MD) simulations using the newly developed AWSEM energy model. We found that these receptors interact primarily through critical residues at the C and N terminal domains and the third intracellular loop (ICL3). The MD simulations showed that these residues are energetically necessary for dimerization and revealed their native conformational state. We subsequently applied the methodology to the 5-HT2AR/5-HTR4R heterodimer, given its implication in drug addiction and neurodegenerative pathologies such as Alzheimer's disease (AD). Further, the SP-DCA analysis showed that 5-HT2AR and 5-HTR4R heterodimerize through the C-terminal domain of 5-HT2AR and ICL3 of 5-HT4R. However, elucidating the details of GPCR interactions would accelerate the discovery of druggable sites and improve our knowledge of the etiology of common diseases, including AD.

Chasing long-range evolutionary couplings in the AlphaFold era.

Coevolution between protein residues is normally interpreted as direct contact. However, the evolutionary record of a protein sequence contains rich information that may include long-range functional couplings, couplings that report on homo-oligomeric states or even conformational changes. Due to the complexity of the sequence space and the lack of structural information on various members of a protein family, it has been difficult to effectively mine the additional information encoded in a multiple sequence alignment (MSA). Here, taking advantage of the recent release of the AlphaFold (AF) database we attempt to identify coevolutionary couplings that cannot be explained simply by spatial proximity. We propose a simple computational method that performs direct coupling analysis on a MSA and searches for couplings that are not satisfied in any of the AF models of members of the identified protein family. Application of this method on 2012 protein families suggests that ~12% of the total identified coevolving residue pairs are spatially distant and more likely to be disordered than their contacting counterparts. We expect that this analysis will help improve the quality of coevolutionary distance restraints used for structure determination and will be useful in identifying potentially functional/allosteric cross-talk between distant residues.

High-throughput droplet-based analysis of influenza A virus genetic reassortment by single-virus RNA sequencing

The segmented RNA genome of influenza A viruses (IAVs) enables viral evolution through genetic reassortment after multiple IAVs coinfect the same cell, leading to viruses harboring combinations of eight genomic segments from distinct parental viruses. Existing data indicate that reassortant genotypes are not equiprobable; however, the low throughput of available virology techniques does not allow quantitative analysis. Here, we have developed a high-throughput single-cell droplet microfluidic system allowing encapsulation of IAV-infected cells, each cell being infected by a single progeny virion resulting from a coinfection process. Customized barcoded primers for targeted viral RNA sequencing enabled the analysis of 18,422 viral genotypes resulting from coinfection with two circulating human H1N1pdm09 and H3N2 IAVs. Results were highly reproducible, confirmed that genetic reassortment is far from random, and allowed accurate quantification of reassortants including rare events. In total, 159 out of the 254 possible reassortant genotypes were observed but with widely varied prevalence (from 0.038 to 8.45%). In cells where eight segments were detected, all 112 possible pairwise combinations of segments were observed. The inclusion of data from single cells where less than eight segments were detected allowed analysis of pairwise cosegregation between segments with very high confidence. Direct coupling analysis accurately predicted the fraction of pairwise segments and full genotypes. Overall, our results indicate that a large proportion of reassortant genotypes can emerge upon coinfection and be detected over a wide range of frequencies, highlighting the power of our tool for systematic and exhaustive monitoring of the reassortment potential of IAVs.

Computational mutation prediction to enhance or recover enzymatic activity.

Atomistic computational simulations have become a useful tool for biomolecular research. These tools are now becoming sufficiently accurate to predict single point mutations that can enhance or recover enzymatic activity. In this talk I will present the use of computational approaches to perform these predictions, and examples coupled with experimental validation. Two examples of applications of these methods to DNA transaction enzymes will be discussed. The first example involves an enzyme from the APOBEC3 family, which are host restriction factors that inhibit lentiviruses, such as HIV-1. For APOBEC3C (A3C), the chimpanzee and gorilla orthologues are more active than human A3C, and the Old World Monkey A3C from rhesus macaque (rh) is not active against HIV-1. Using a combination of molecular dynamics (MD), direct coupling analysis (DCA) and experimental techniques we have investigated the key residues for antivirala ctivities. Overall, our results determine the basis for why rhA3C is less active than human A3C and establish the amino acid network for dimerization and increased activity. The second example involves the methyltransferase (MTase) from Mycoplasma penetrans, which has recently been shown to synthesize the modified nucleotide 5-carboxymethylcytosine (5cxmC), formed as a trace byproduct of cytidine methylation by M.MpeI with the secondary metabolite carboxy-S-adenosyl-L-methionine (CxSAM) in place of the normal reaction cofactor, SAM. The use of computational simulations to investigate this new selectivity and predict additional mutations to enhance this new function, and experimental confirmation of these predictions will be presented and discussed.