The relationship between sequence and interaction divergence in proteins

StructMAn 2.0 Web: a web server for structural annotation of protein sequences and mutations

StructMAn is a method for protein structural annotation. It describes each position of a protein sequence or specific variants in it in terms of their importance for the three-dimensional (3D) structure of the protein and its interactions with other molecules. StructMAn maps, aligns, and aggregates data from experimentally resolved and predicted 3D structures of proteins and their homologs for any given protein sequence and/or a combination of mutations in it. The results provide structural annotation for every amino acid position allowing a detailed structural analysis. Furthermore, StructMAn enables generation of a wide variety of position-specific high-quality structural features that can be leveraged in machine learning applications. With the new web server StructMAn 2.0 Web, we provide a user-friendly way to use StructMAn offering an easy-to-use input interface and a comprehensive visualization for the various results of StructMAn. StructMAn 2.0 Web is available at https://tools.helmholtz-hips.de/structman.

Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues

Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.

The cap-binding complex modulates ABA-responsive transcript splicing during germination in barley (Hordeum vulgare)

To decipher the molecular bases governing seed germination, this study presents the pivotal role of the cap-binding complex (CBC), comprising CBP20 and CBP80, in modulating the inhibitory effects of abscisic acid (ABA) in barley. Using both single and double barley mutants in genes encoding the CBC, we revealed that the double mutant hvcbp20.ab/hvcbp80.b displays ABA insensitivity, in stark contrast to the hypersensitivity observed in single mutants during germination. Our comprehensive transcriptome and metabolome analysis not only identified significant alterations in gene expression and splicing patterns but also underscored the regulatory nexus among CBC, ABA, and brassinosteroid (BR) signaling pathways.

Guiding questions to avoid data leakage in biological machine learning applications

Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons for this is data leakage, which can be seen as the illicit sharing of information between the training data and the test data, resulting in performance estimates that are far better than the performance observed in the intended application scenario. Data leakage can be difficult to detect in biological datasets due to their complex dependencies. With this in mind, we present seven questions that should be asked to prevent data leakage when constructing machine learning models in biological domains. We illustrate the usefulness of our questions by applying them to nontrivial examples. Our goal is to raise awareness of potential data leakage problems and to promote robust and reproducible machine learning-based research in biology.

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.

In silico insights into the dimer structure and deiodinase activity of type III iodothyronine deiodinase from bioinformatics, molecular dynamics simulations, and QM/MM calculations

The homodimeric family of iodothyronine deiodinases (Dios) regioselectively remove iodine from thyroid hormones. Currently, structural data has only been reported for the monomer of the mus type III thioredoxin (Trx) fold catalytic domain (Dio3Trx), but the mode of dimerization has not yet been determined. Various groups have proposed dimer structures that are similar to the A-type and B-type dimerization modes of peroxiredoxins. Computational methods are used to compare the sequence of Dio3Trx to related proteins known to form A-type and B-type dimers. Sequence analysis and in silico protein-protein docking methods suggest that Dio3Trx is more consistent with proteins that adopt B-type dimerization. Molecular dynamics (MD) simulations of the refined Dio3Trx dimer constructed using the SymmDock and GalaxyRefineComplex databases indicate stable dimer formation along the β4α3 interface consistent with other Trx fold B-type dimers. Free energy calculations show that the dimer is stabilized by interdimer interactions between the β-sheets and α-helices. A comparison of MD simulations of the apo and thyroxine-bound dimers suggests that the active site binding pocket is not affected by dimerization. Determination of the transition state for deiodination of thyroxine from the monomer structure using QM/MM methods provides an activation barrier consistent with previous small model DFT studies.Communicated by Ramaswamy H. Sarma.

Characterization of epitranscriptome reader proteins experimentally and in silico: Current knowledge and future perspectives beyond the YTH domain

To date, over 150 chemical modifications to the four canonical RNA bases have been discovered, known collectively as the epitranscriptome. Many of these modifications have been implicated in a variety of cellular processes and disease states. Additional work has been done to identify proteins known as "readers" that selectively interact with RNAs that contain specific chemical modifications. Protein interactomes with N6-methyladenosine (m⁶A), N1-methyladenosine (m¹A), N5-methylcytosine (m⁵C), and 8-oxo-7,8-dihydroguanosine (8-oxoG) have been determined, mainly through experimental advances in proteomics techniques. However, relatively few proteins have been confirmed to bind directly to RNA containing these modifications. Furthermore, for many of these protein readers, the exact binding mechanisms as well as the exclusivity for recognition of modified RNA species remain elusive, leading to questions regarding their roles within different cellular processes. In the case of the YT-521B homology (YTH) family of proteins, both experimental and in silico techniques have been leveraged to provide valuable biophysical insights into the mechanisms of m⁶A recognition at atomic resolution. To date, the YTH family is one of the best characterized classes of readers. Here, we review current knowledge about epitranscriptome recognition of the YTH domain proteins from previously published experimental and computational studies. We additionally outline knowledge gaps for proteins beyond the well-studied human YTH domains and the current in silico techniques and resources that can enable investigation of protein interactions with modified RNA outside of the YTH-m⁶A context.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Data-driven design of orthogonal protein-protein interactions

Engineering protein-protein interactions to generate new functions presents a challenge with great potential for many applications, ranging from therapeutics to synthetic biology. To avoid unwanted cross-talk with preexisting protein interaction networks in a cell, the specificity and selectivity of newly engineered proteins must be controlled. Here, we developed a computational strategy that mimics gene duplication and the divergence of preexisting interacting protein pairs to design new interactions. We used the bacterial PhoQ-PhoP two-component system as a model system to demonstrate the feasibility of this strategy and validated the approach with known experimental results. The designed protein pairs are predicted to exclusively interact with each other and to be insulated from potential cross-talk with their native partners. Thus, our approach enables exploration of uncharted regions of the protein sequence space and the design of new interacting protein pairs.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

The crystal structure of iC3b-CR3 αI reveals a modular recognition of the main opsonin iC3b by the CR3 integrin receptor

Complement activation on cell surfaces leads to the massive deposition of C3b, iC3b, and C3dg, the main complement opsonins. Recognition of iC3b by complement receptor type 3 (CR3) fosters pathogen opsonophagocytosis by macrophages and the stimulation of adaptive immunity by complement-opsonized antigens. Here, we present the crystallographic structure of the complex between human iC3b and the von Willebrand A inserted domain of the α chain of CR3 (αI). The crystal contains two composite interfaces for CR3 αI, encompassing distinct sets of contiguous macroglobulin (MG) domains on the C3c moiety, MG1-MG2 and MG6-MG7 domains. These composite binding sites define two iC3b-CR3 αI complexes characterized by specific rearrangements of the two semi-independent modules, C3c moiety and TED domain. Furthermore, we show the structure of iC3b in a physiologically-relevant extended conformation. Based on previously available data and novel insights reported herein, we propose an integrative model that reconciles conflicting facts about iC3b structure and function and explains the molecular basis for iC3b selective recognition by CR3 on opsonized surfaces.

Complement activation on foreign cell surfaces leads to the generation of complement opsonins, which activate complement receptor type 3 (CR3) and pathogen clearance by macrophages. Here, the authors reveal structural basis of the interaction between human opsonin iC3b and the von Willebrand A inserted domain of the α chain of CR3.

On the role of phase separation in the biogenesis of membraneless compartments

Molecular mechanistic biology has ushered us into the world of life’s building blocks, revealing their interactions in macromolecular complexes and inspiring strategies for detailed functional interrogations. The biogenesis of membraneless cellular compartments, functional mesoscale subcellular locales devoid of strong internal order and delimiting membranes, is among mechanistic biology’s most demanding current challenges. A developing paradigm, biomolecular phase separation, emphasizes solvation of the building blocks through low‐affinity, weakly adhesive unspecific interactions as the driver of biogenesis of membraneless compartments. Here, I discuss the molecular underpinnings of the phase separation paradigm and demonstrate that validating its assumptions is much more challenging than hitherto appreciated. I also discuss that highly specific interactions, rather than unspecific ones, appear to be the main driver of biogenesis of subcellular compartments, while phase separation may be harnessed locally in selected instances to generate material properties tailored for specific functions, as exemplified by nucleocytoplasmic transport.

In vitro and in silico analysis reveals antifungal activity and potential targets of curcumin on Paracoccidioides spp

The search for new compounds with activity against Paracoccidioides, etiologic agents of Paracoccidioidomycosis (PCM), is extremely necessary due to the current scenario of the available therapeutic arsenal. Treatment is restricted to three classes of antifungals with side effects. Curcumin is a polyphenol with antifungal effects that is extracted from Curcuma longa. The present work aimed to evaluate the activity of curcumin in different species of Paracoccidioides and to evaluate the potential molecular targets of curcumin using computational strategies. In addition, interactions with classic antifungals used in the treatment of PCM were evaluated. Curcumin inhibits the growth of Paracoccidioides spp. exerting a fungicidal effect. The combination of curcumin with amphotericin B, co-trimoxazole, and itraconazole showed a synergistic or additive interaction. Molecular targets as superoxide dismutase, catalase, and isocitrate lyase were proposed based on in silico approaches. Curcumin affects the fungal plasma membrane and increases the production of reactive oxygen species. Therefore, curcumin is a good alternative for the treatment of PCM.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.

An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.

Robust and accurate prediction of protein-protein interactions by exploiting evolutionary information

Various biochemical functions of organisms are performed by protein-protein interactions (PPIs). Therefore, recognition of protein-protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

Modeling of protein complexes in CASP14 with emphasis on the interaction interface prediction

The goal of CASP experiments is to monitor the progress in the protein structure prediction field. During the 14th CASP edition we aimed to test our capabilities of predicting structures of protein complexes. Our protocol for modeling protein assemblies included both template‐based modeling and free docking. Structural templates were identified using sensitive sequence‐based searches. If sequence‐based searches failed, we performed structure‐based template searches using selected CASP server models. In the absence of reliable templates we applied free docking starting from monomers generated by CASP servers. We evaluated and ranked models of protein complexes using an improved version of our protein structure quality assessment method, VoroMQA, taking into account both interaction interface and global structure scores. If reliable templates could be identified, generally accurate models of protein assemblies were generated with the exception of an antibody‐antigen interaction. The success of free docking mainly depended on the accuracy of initial subunit models and on the scoring of docking solutions. To put our overall results in perspective, we analyzed our performance in the context of other CASP groups. Although the subunits in our assembly models often were not of the top quality, these models had, overall, the best‐predicted intersubunit interfaces according to several accuracy measures. We attribute our relative success primarily to the emphasis on the interaction interface when modeling and scoring.

A computational framework for modeling functional protein-protein interactions

Protein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three-dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein-complex structures utilizing GO-based semantic similarity technique and co-evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid-body docking and template-based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re-ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template-based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein-protein interactions.

Structural Insights into the Interaction of Filovirus Glycoproteins with the Endosomal Receptor Niemann-Pick C1: A Computational Study

Filoviruses, including marburgviruses and ebolaviruses, have a single transmembrane glycoprotein (GP) that facilitates their entry into cells. During entry, GP needs to be cleaved by host proteases to expose the receptor-binding site that binds to the endosomal receptor Niemann-Pick C1 (NPC1) protein. The crystal structure analysis of the cleaved GP (GPcl) of Ebola virus (EBOV) in complex with human NPC1 has demonstrated that NPC1 has two protruding loops (loops 1 and 2), which engage a hydrophobic pocket on the head of EBOV GPcl. However, the molecular interactions between NPC1 and the GPcl of other filoviruses remain unexplored. In the present study, we performed molecular modeling and molecular dynamics simulations of NPC1 complexed with GPcls of two ebolaviruses, EBOV and Sudan virus (SUDV), and one marburgvirus, Ravn virus (RAVV). Similar binding structures were observed in the GPcl–NPC1 complexes of EBOV and SUDV, which differed from that of RAVV. Specifically, in the RAVV GPcl–NPC1 complex, the tip of loop 2 was closer to the pocket edge comprising residues at positions 79–88 of GPcl; the root of loop 1 was predicted to interact with P116 and Q144 of GPcl. Furthermore, in the SUDV GPcl–NPC1 complex, the tip of loop 2 was slightly closer to the residue at position 141 than those in the EBOV and RAVV GPcl–NPC1 complexes. These structural differences may affect the size and/or shape of the receptor-binding pocket of GPcl. Our structural models could provide useful information for improving our understanding the differences in host preference among filoviruses as well as contributing to structure-based drug design.

Classification and prediction of protein-protein interaction interface using machine learning algorithm

Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein-protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein-protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein-protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/ .

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Protein cost allocation explains metabolic strategies in Escherichia coli

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.

Identification and Characterization of a Novel Thermostable and Salt-Tolerant β-1,3 Xylanase from Flammeovirga pacifica Strain WPAGA1

β-1,3 xylanase is an important enzyme in the biorefinery process for some algae. The discovery and characterization of new β-1,3 xylanase is a hot research topic. In this paper, a novel β-1,3 xylanase (Xyl88) is revealed from the annotated genome of Flammeovirga pacifica strain WPAGA1. Bioinformatic analysis shows that Xyl88 belongs to the glycoside hydrolase 26 (GH26) with a suspected CBM (carbohydrate-binding module) sequence. The activity of rXyl88 is 75% of the highest enzyme activity (1.5 mol/L NaCl) in 3 mol/L NaCl buffer, which suggests good salt tolerance of rXy188. The optimum reaction temperature in the buffer without NaCl and with 1.5 mol/L NaCl is 45 °C and 55 °C, respectively. Notably, the catalytic efficiency of rXyl88 (k_cat/K_m) is approximately 20 higher than that of the thermophilic β-1,3 xylanase that has the highest catalytic efficiency. Xyl88 in this study becomes the most efficient enzyme ever found, and it is also the first reported moderately thermophilic and salt-tolerant β-1,3 xylanase. Results of molecular dynamics simulation further prove the excellent thermal stability of Xyl88. Moreover, according to the predicted 3D structure of the Xyl88, the surface of the enzyme is distributed with more negative charges, which is related to its salt tolerance, and significantly more hydrogen bonds and Van der Waals force between the intramolecular residues, which is related to its thermal stability.

P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking

MOTIVATION

The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy.

RESULTS

In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html.

AVAILABILITY AND IMPLEMENTATION

www.rnabinding.com/P3DOCK/P3DOCK.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular and Structural Basis of Cross-Reactivity in M. tuberculosis Toxin-Antitoxin Systems

Mycobacterium tuberculosis genome encodes over 80 toxin-antitoxin (TA) systems. While each toxin interacts with its cognate antitoxin, the abundance of TA systems presents an opportunity for potential non-cognate interactions. TA systems mediate manifold interactions to manage pathogenicity and stress response network of the cell and non-cognate interactions may play vital roles as well. To address if non-cognate and heterologous interactions are feasible and to understand the structural basis of their interactions, we have performed comprehensive computational analyses on the available 3D structures and generated structural models of paralogous M. tuberculosis VapBC and MazEF TA systems. For a majority of the TA systems, we show that non-cognate toxin-antitoxin interactions are structurally incompatible except for complexes like VapBC15 and VapBC11, which show similar interfaces and potential for cross-reactivity. For TA systems which have been experimentally shown earlier to disfavor non-cognate interactions, we demonstrate that they are structurally and stereo-chemically incompatible. For selected TA systems, our detailed structural analysis identifies specificity conferring residues. Thus, our work improves the current understanding of TA interfaces and generates a hypothesis based on congenial binding site, geometric complementarity, and chemical nature of interfaces. Overall, our work offers a structure-based explanation for non-cognate toxin-antitoxin interactions in M. tuberculosis.

PRIME-3D2D is a 3D2D model to predict binding sites of protein-RNA interaction

Protein-RNA interaction participates in many biological processes. So, studying protein–RNA interaction can help us to understand the function of protein and RNA. Although the protein–RNA 3D3D model, like PRIME, was useful in building 3D structural complexes, it can’t be used genome-wide, due to lacking RNA 3D structures. To take full advantage of RNA secondary structures revealed from high-throughput sequencing, we present PRIME-3D2D to predict binding sites of protein–RNA interaction. PRIME-3D2D is almost as good as PRIME at modeling protein–RNA complexes. PRIME-3D2D can be used to predict binding sites on PDB data (MCC = 0.75/0.70 for binding sites in protein/RNA) and transcription-wide (MCC = 0.285 for binding sites in RNA). Testing on PDB and yeast transcription-wide data show that PRIME-3D2D performs better than other binding sites predictor. So, PRIME-3D2D can be used to predict the binding sites both on PDB and genome-wide, and it’s freely available.

Xie et al. report a new computational method PRIME-3D2D to predict binding sites of protein–RNA interaction by considering protein 3D structure and RNA 2D structure. It is freely available, performs better than other binding sites predictor and is as good as PRIME to model protein–RNA complex.

Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces

Protein functions can be predicted based on their three-dimensional structures. However, many multidomain proteins have unstable structures, making it difficult to determine the whole structure in biological experiments. Additionally, multidomain proteins are often decomposed and identified based on their domains, with the structure of each domain often found in public databases. Recent studies have advanced structure prediction methods of multidomain proteins through computational analysis. In existing methods, proteins that serve as templates are used for three-dimensional structure prediction. However, when no protein template is available, the accuracy of the prediction is decreased. This study was conducted to predict the structures of multidomain proteins without the need for whole structure templates.

We improved structure prediction methods by performing rigid-body docking from the structure of each domain and reranking a structure closer to the correct structure to have a higher value. In the proposed method, the score for the domain-domain interaction obtained without a structural template of the multidomain protein and score for the three-dimensional structure obtained during docking calculation were newly incorporated into the score function. We successfully predicted the structures of 50 of 55 multidomain proteins examined in the test dataset.

Interaction residue pair information of the protein-protein complex interface contributes to domain reorganizations even when a structural template for a multidomain protein cannot be obtained. This approach may be useful for predicting the structures of multidomain proteins with important biochemical functions.

HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA

Protein–protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction of the three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, a free and open accessed web server, was developed to predict and analyze the structures of PPIs. In the HawkDock server, the ATTRACT docking algorithm, the HawkRank scoring function developed in our group and the MM/GBSA free energy decomposition analysis were seamlessly integrated into a multi-functional platform. The structures of PPIs were predicted by combining the ATTRACT docking and the HawkRank re-scoring, and the key residues for PPIs were highlighted by the MM/GBSA free energy decomposition. The molecular visualization was supported by 3Dmol.js. For the structural modeling of PPIs, HawkDock could achieve a better performance than ZDOCK 3.0.2 in the benchmark testing. For the prediction of key residues, the important residues that play an essential role in PPIs could be identified in the top 10 residues for ∼81.4% predicted models and ∼95.4% crystal structures in the benchmark dataset. To sum up, the HawkDock server is a powerful tool to predict the binding structures and identify the key residues of PPIs. The HawkDock server is accessible free of charge at http://cadd.zju.edu.cn/hawkdock/.

Probing Protein Interaction Networks by Combining MS-Based Proteomics and Structural Data Integration

Protein-protein interactions play a major role in the molecular machinery of life, and various techniques such as AP-MS are dedicated to their identification. However, those techniques return lists of proteins devoid of organizational structure, not detailing which proteins interact with which others. Proposing a hierarchical view of the interactions between the members of the flat list becomes highly tedious for large data sets when done by hand. To help hierarchize this data, we introduce a new bioinformatics protocol that integrates information of the multimeric protein 3D structures available in the Protein Data Bank using remote homology detection, as well as information related to Short Linear Motifs and interaction data from the BioGRID. We illustrate on two unrelated use-cases of different complexity how our approach can be useful to decipher the network of interactions hidden in the list of input proteins, and how it provides added value compared to state-of-the-art resources such as Interactome3D or STRING. Particularly, we show the added value of using homology detection to distinguish between orthologs and paralogs, and to distinguish between core obligate and more facultative interactions. We also demonstrate the potential of considering interactions occurring through Short Linear Motifs.

Challenges and opportunities of automated protein-protein docking: HDOCK server vs human predictions in CAPRI Rounds 38-46

Protein-protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein-protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38-45 and CASP13-CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an "acceptable" or higher CAPRI-rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13-CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed.

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.

Template-based modeling of diverse protein interactions in CAPRI rounds 38-45

Structures of proteins complexed with other proteins, peptides, or ligands are essential for investigation of molecular mechanisms. However, the experimental structures of protein complexes of interest are often not available. Therefore, computational methods are widely used to predict these structures, and, of those methods, template-based modeling is the most successful. In the rounds 38-45 of the Critical Assessment of PRediction of Interactions (CAPRI), we applied template-based modeling for 9 of 11 protein-protein and protein-peptide interaction targets, resulting in medium and high-quality models for six targets. For the protein-oligosaccharide docking targets, we used constraints derived from template structures, and generated models of at least acceptable quality for most of the targets. Apparently, high flexibility of oligosaccharide molecules was the main cause preventing us from obtaining models of higher quality. We also participated in the CAPRI scoring challenge, the goal of which was to identify the highest quality models from a large pool of decoys. In this experiment, we tested VoroMQA, a scoring method based on interatomic contact areas. The results showed VoroMQA to be quite effective in scoring strongly binding and obligatory protein complexes, but less successful in the case of transient interactions. We extensively used manual intervention in both CAPRI modeling and scoring experiments. This oftentimes allowed us to select the correct templates from available alternatives and to limit the search space during the model scoring.

Topology independent structural matching discovers novel templates for protein interfaces

Motivation

Protein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner.

Results

We present InterComp, a method for topology and sequence-order independent structural comparisons. The method is general and can be applied to various structural comparison applications. By representing residues as independent points in space rather than as a sequence of residues, InterComp can be applied to a wide range of problems including interface-surface comparisons and interface-interface comparisons. We demonstrate a use-case by applying InterComp to find similar protein interfaces on the surface of proteins. We show that InterComp pinpoints the correct interface for almost half of the targets (283 of 586) when considering the top 10 hits, and for 24% of the top 1, even when no templates can be found with regular sequence-order dependent structural alignment methods.

Availability and implementation

The source code and the datasets are available at: http://wallnerlab.org/InterComp.

Supplementary information

Supplementary data are available at Bioinformatics online.

SWISS-MODEL: homology modelling of protein structures and complexes

Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

Unraveling the structural landscape of intra-chain domain interfaces: Implication in the evolution of domain-domain interactions

Intra-chain domain interactions are known to play a significant role in the function and stability of multidomain proteins. These interactions are mediated through a physical interaction at domain-domain interfaces (DDIs). With a motivation to understand evolution of interfaces, we have investigated similarities among DDIs. Even though interfaces of protein-protein interactions (PPIs) have been previously studied by structurally aligning interfaces, similar analyses have not yet been performed on DDIs of either multidomain proteins or PPIs. For studying the structural landscape of DDIs, we have used iAlign to structurally align intra-chain domain interfaces of domains. The interface alignment of spatially constrained domains (due to inter-domain linkers) showed that ~88% of these could identify a structural matching interface having similar C-alpha geometry and contact pattern despite that aligned domain pairs are not structurally related. Moreover, the mean interface similarity score (IS-score) is 0.307, which is higher compared to the average random IS-score (0.207) suggesting domain interfaces are not random. The structural space of DDIs is highly connected as ~84% of all possible directed edges among interfaces are found to have at most path length of 8 when 0.26 is IS-score threshold. At this threshold, ~83% of interfaces form the largest strongly connected component. Thus, suggesting that structural space of intra-chain domain interfaces is degenerate and highly connected, as has been found in PPI interfaces. Interestingly, searching for structural neighbors of inter-chain interfaces among intra-chain interfaces showed that ~86% could find a statistically significant match to intra-chain interface with a mean IS-score of 0.311. This implies that domain interfaces are degenerate whether formed within a protein or between proteins. The interface degeneracy is most likely due to limited possible ways of packing secondary structures. In principle, interface similarities can be exploited to accurately model domain interfaces in structure prediction of multidomain proteins.

Structural modeling of protein complexes: Current capabilities and challenges

Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.

Flexible Backbone Assembly and Refinement of Symmetrical Homomeric Complexes

Symmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries-starting from homology-modeled monomers-we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 17 out of 31 cyclic complexes and 3 out of 12 dihedral complexes.

NMR structure of CmPI-II, a non-classical Kazal protease inhibitor: Understanding its conformational dynamics and subtilisin A inhibition

Subtilisin-like proteases play crucial roles in host-pathogen interactions. Thus, protease inhibitors constitute important tools in the regulation of this interaction. CmPI-II is a Kazal proteinase inhibitor isolated from Cenchritis muricatus that inhibits subtilisin A, trypsin and elastases. Based on sequence analysis it defines a new group of non-classical Kazal inhibitors. Lacking solved 3D structures from this group prevents the straightforward structural comparison with other Kazal inhibitors. The 3D structure of CmPI-II, solved in this work using NMR techniques, shows the typical fold of Kazal inhibitors, but has significant differences in its N-terminal moiety, the disposition of the CysI-CysV disulfide bond and the reactive site loop (RSL) conformation. The high flexibility of its N-terminal region, the RSL, and the α-helix observed in NMR experiments and molecular dynamics simulations, suggest a coupled motion of these regions that could explain CmPI-II broad specificity. The 3D structure of the CmPI-II/subtilisin A complex, obtained by modeling, allows understanding of the energetic basis of the subtilisin A inhibition. The residues at the P2 and P2' positions of the inhibitor RSL were predicted to be major contributors to the binding free energy of the complex, rather than those at the P1 position. Site directed mutagenesis experiments confirmed the Trp14 (P2') contribution to CmPI-II/subtilisin A complex formation. Overall, this work provides the structural determinants for the subtilisin A inhibition by CmPI-II and allows the designing of more specific and potent molecules. In addition, the 3D structure obtained supports the existence of a new group in non-classical Kazal inhibitors.

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory

Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.

Protein-protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information

Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.

Evolutionary Morphing of Tryptophan Synthase: Functional Mechanisms for the Enzymatic Channeling of Indole

Tryptophan synthase (TrpS) is a heterotetrameric αββα enzyme that exhibits complex substrate channeling and allosteric mechanisms and is a model system in enzymology. In this work, we characterize proposed early and late evolutionary states of TrpS and show that they have distinct quaternary structures caused by insertions-deletions of sequence segments (indels) in the β-subunit. Remarkably, indole hydrophobic channels that connect α and β active sites have re-emerged in both TrpS types, yet they follow different paths through the β-subunit fold. Also, both TrpS geometries activate the α-subunit through the rearrangement of loops flanking the active site. Our results link evolutionary sequence changes in the enzyme subunits with channeling and allostery in the TrpS enzymes. The findings demonstrate that indels allow protein quaternary architectures to escape "minima" in the evolutionary landscape, thereby overcoming the conservational constraints imposed by existing functional interfaces and being free to morph into new mechanistic enzymes.

Evolutionary Interplay between Symbiotic Relationships and Patterns of Signal Peptide Gain and Loss

Can orthologous proteins differ in terms of their ability to be secreted? To answer this question, we investigated the distribution of signal peptides within the orthologous groups of Enterobacterales. Parsimony analysis and sequence comparisons revealed a large number of signal peptide gain and loss events, in which signal peptides emerge or disappear in the course of evolution. Signal peptide losses prevail over gains, an effect which is especially pronounced in the transition from the free-living or commensal to the endosymbiotic lifestyle. The disproportionate decline in the number of signal peptide-containing proteins in endosymbionts cannot be explained by the overall reduction of their genomes. Signal peptides can be gained and lost either by acquisition/elimination of the corresponding N-terminal regions or by gradual accumulation of mutations. The evolutionary dynamics of signal peptides in bacterial proteins represents a powerful mechanism of functional diversification.

DynBench3D, a Web-Resource to Dynamically Generate Benchmark Sets of Large Heteromeric Protein Complexes

Multi-protein machines are responsible for most cellular tasks, and many efforts have been invested in the systematic identification and characterization of thousands of these macromolecular assemblies. However, unfortunately, the (quasi) atomic details necessary to understand their function are available only for a tiny fraction of the known complexes. The computational biology community is developing strategies to integrate structural data of different nature, from electron microscopy to X-ray crystallography, to model large molecular machines, as it has been done for individual proteins and interactions with remarkable success. However, unlike for binary interactions, there is no reliable gold-standard set of three-dimensional (3D) complexes to benchmark the performance of these methodologies and detect their limitations. Here, we present a strategy to dynamically generate non-redundant sets of 3D heteromeric complexes with three or more components. By changing the values of sequence identity and component overlap between assemblies required to define complex redundancy, we can create sets of representative complexes with known 3D structure (i.e., target complexes). Using an identity threshold of 20% and imposing a fraction of component overlap of <0.5, we identify 495 unique target complexes, which represent a real non-redundant set of heteromeric assemblies with known 3D structure. Moreover, for each target complex, we also identify a set of assemblies, of varying degrees of identity and component overlap, that can be readily used as input in a complex modeling exercise (i.e., template subcomplexes). We hope that resources like this will significantly help the development and progress assessment of novel methodologies, as docking benchmarks and blind prediction contests did. The interactive resource is accessible at https://DynBench3D.irbbarcelona.org.

The Adaptor Protein ENY2 Is a Component of the Deubiquitination Module of the Arabidopsis SAGA Transcriptional Co-activator Complex but not of the TREX-2 Complex

The conserved nuclear protein ENY2 (Sus1 in yeast) is involved in coupling transcription and mRNA export in yeast and metazoa, as it is a component both of the transcriptional co-activator complex SAGA and of the mRNA export complex TREX-2. Arabidopsis thaliana ENY2 is widely expressed in the plant and it localizes to the nucleoplasm, but unlike its yeast/metazoan orthologs, it is not enriched in the nuclear envelope. Affinity purification of ENY2 in combination with mass spectrometry revealed that it co-purified with SAGA components, but not with the nuclear pore-associated TREX-2. In addition, further targeted proteomics analyses by reciprocal tagging established the composition of the Arabidopsis SAGA complex consisting of the four modules HATm, SPTm, TAFm and DUBm, and that several SAGA subunits occur in alternative variants. While the HATm, SPTm and TAFm robustly co-purified with each other, the deubiquitination module (DUBm) appears to associate with the other SAGA modules more weakly/dynamically. Consistent with a homology model of the Arabidopsis DUBm, the SGF11 protein interacts directly with ENY2 and UBP22. Plants depleted in the DUBm components, SGF11 or ENY2, are phenotypically only mildly affected, but they contain increased levels of ubiquitinated histone H2B, indicating that the SAGA-DUBm has histone deubiquitination activity in plants. In addition to transcription-related proteins (i.e., transcript elongation factors, Mediator), many splicing factors were found to associate with SAGA, linking the SAGA complex and ongoing transcription with mRNA processing.