Integrative modeling of membrane-associated protein assemblies

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

Investigating SARS-CoV-2 virus-host interactions and mRNA expression: Insights using three models of D. melanogaster

Responsible for COVID-19, SARS-CoV-2 is a coronavirus in which contagious variants continue to appear. Therefore, some population groups have demonstrated greater susceptibility to contagion and disease progression. For these reasons, several researchers have been studying the SARS-CoV-2/human interactome to understand the pathophysiology of COVID-19 and develop new pharmacological strategies. D. melanogaster is a versatile animal model with approximately 90 % human protein orthology related to SARS-CoV-2/human interactome and is widely used in metabolic studies. In this context, our work assessed the potential interaction between human proteins (ZNF10, NUP88, BCL2L1, UBC9, and RBX1) and their orthologous proteins in D. melanogaster (gl, Nup88, Buffy, ubc9, and Rbx1a) with proteins from SARS-CoV-2 (nsp3, nsp9, E, ORF7a, N, and ORF10) using computational approaches. Our results demonstrated that all the proteins have the potential to interact, and we compared the binding sites between humans and fruit flies. The stability and consistency in the structure of the gl_nsp3 complex, specifically, could be crucial for its specific biological functions. Lastly, to enhance the understanding of the influence of host factors on coronavirus infection, we also analyse the mRNA expression of the five genes (mbo, gl, lwr, Buffy, and Roc1a) responsible for encoding the fruit fly proteins. Briefly, we demonstrated that those genes were differentially regulated according to diets, sex, and age. Two groups showed higher positive gene regulation than others: females in the HSD group and males in the aging group, which could imply a higher virus-host susceptibility. Overall, while preliminary, our work contributes to the understanding of host defense mechanisms and potentially identifies candidate proteins and genes for in vivo viral studies against SARS-CoV-2.

The HADDOCK2.4 web server for integrative modeling of biomolecular complexes

Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server's various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody-antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types.

Discovery of Kinetin in inhibiting colorectal cancer progression via enhancing PSMB1-mediated RAB34 degradation

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Understanding the underlying mechanism driving CRC progression and identifying potential therapeutic drug targets are of utmost urgency. We previously utilized LC-MS-based proteomic profiling to identify proteins associated with postoperative progression in stage II/III CRC. Here, we revealed that proteasome subunit beta type-1 (PSMB1) is an independent predictor for postoperative progression in stage II/III CRC. Mechanistically, PSMB1 binds directly to onco-protein RAB34 and promotes its proteasome-dependent degradation, potentially leading to the inactivation of the MEK/ERK signaling pathway and inhibition of CRC progression. To further identify potential anticancer drugs, we screened a library of 2509 FDA-approved drugs using computer-aided drug design (CADD) and identified Kinetin as a potentiating agent for PSMB1. Functional assays confirmed that Kinetin enhanced the interaction between PSMB1 and RAB34, hence facilitated the degradation of RAB34 protein and decreased the MEK/ERK phosphorylation. Kinetin suppresses CRC progression in patient-derived xenograft (PDX) and liver metastasis models. Conclusively, our study identifies PSMB1 as a potential biomarker and therapeutic target for CRC, and Kinetin as an anticancer drug by enhancing proteasome-dependent onco-protein degradation.

Optimizing the Martini 3 Force Field Reveals the Effects of the Intricate Balance between Protein-Water Interaction Strength and Salt Concentration on Biomolecular Condensate Formation

Condensation/dissolution has become a widely acknowledged biological macromolecular assembly phenomenon in subcellular compartmentalization. The MARTINI force field offers a coarse-grained protein model with a resolution that preserves molecular details with an explicit (CG) solvent. Despite its relatively higher resolution, it can still achieve condensate formation in a reasonable computing time with explicit solvent and ionic species. Therefore, it is highly desirable to tune this force field to be able to reproduce the experimentally observed properties of the condensate formation. In this work, we studied the condensate formation of the low-sequence complexity domain of fused in sarcoma protein using a MARTINI 3 force field by systematically modifying (increasing) the protein-water interaction strength and varying the salt concentration. We found that the condensate formation is sensitive both to the protein-water interaction strength and the presence of salt. While the unmodified MARTINI force field yields a complete collapse of proteins into one dense phase (i.e., no dilute phase), we reported a range of modified protein-water interaction strength that is capable of capturing the experimentally found transfer free energy between dense and dilute phases. We also found that the condensates lose their spherical shape upon the addition of salt, especially when the protein-water interactions are weak. Interchain amino acid contact map analysis showed one explanation for this observation: the protein-protein contact fraction reduces as salt is added to systems (when the protein-water interactions are weak), consistent with electrostatic screening effects. This reduction might be responsible for the condensates becoming nonspherical upon the addition of salt by reducing the need for minimizing the interfacial area. However, as the protein-water interactions become stronger to the extent that makes the transfer free energy agree well with experimentally observed transfer free energy, we found an increase in the protein-protein contact fraction upon the addition of salt, consistent with the salting-out effects. Therefore, we concluded that there is an intricate balance between screening effects and salting-out effects upon the addition of salt and this balance is highly sensitive to the strength of protein-water interactions.

Pannexin-3 stabilizes the transcription factor Bcl6 in a channel-independent manner to protect against vascular oxidative stress

Targeted degradation regulates the activity of the transcriptional repressor Bcl6 and its ability to suppress oxidative stress and inflammation. Here, we report that abundance of endothelial Bcl6 is determined by its interaction with Golgi-localized pannexin 3 (Panx3) and that Bcl6 transcriptional activity protects against vascular oxidative stress. Consistent with data from obese, hypertensive humans, mice with an endothelial cell-specific deficiency in Panx3 had spontaneous systemic hypertension without obvious changes in channel function, as assessed by Ca2+ handling, ATP amounts, or Golgi luminal pH. Panx3 bound to Bcl6, and its absence reduced Bcl6 protein abundance, suggesting that the interaction with Panx3 stabilized Bcl6 by preventing its degradation. Panx3 deficiency was associated with increased expression of the gene encoding the H2O2-producing enzyme Nox4, which is normally repressed by Bcl6, resulting in H2O2-induced oxidative damage in the vasculature. Catalase rescued impaired vasodilation in mice lacking endothelial Panx3. Administration of a newly developed peptide to inhibit the Panx3-Bcl6 interaction recapitulated the increase in Nox4 expression and in blood pressure seen in mice with endothelial Panx3 deficiency. Panx3-Bcl6-Nox4 dysregulation occurred in obesity-related hypertension, but not when hypertension was induced in the absence of obesity. Our findings provide insight into a channel-independent role of Panx3 wherein its interaction with Bcl6 determines vascular oxidative state, particularly under the adverse conditions of obesity.

Rational Prediction of PROTAC-Compatible Protein-Protein Interfaces by Molecular Docking

Proteolysis targeting chimeras (PROTACs) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex, whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACs currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on the minimal solvent-accessible surface distance to produce PROTAC-compatible PPIs suitable for when there is no a priori known PROTAC ligand. In a benchmark employing a manually curated data set of 13 ternary complex crystals, we achieved an accuracy of 92% when starting from bound structures and 77% when starting from unbound structures, respectively. Our method only requires that the ligand-bound structures of the monomeric forms of the E3 ligase and target proteins be given to run, making it general, accurate, and highly efficient, with the ability to impact early-stage PROTAC-based drug design campaigns where no structural information about the ternary complex structure is available.

The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions

Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at: https://server.lightdock.org/.

Comprehensive classification of proteins based on structures that engage lipids by COMPOSEL

Structures can now be predicted for any protein using programs like AlphaFold and Rosetta, which rely on a foundation of experimentally determined structures of architecturally diverse proteins. The accuracy of such artificial intelligence and machine learning (AI/ML) approaches benefits from the specification of restraints which assist in navigating the universe of folds to converge on models most representative of a given protein's physiological structure. This is especially pertinent for membrane proteins, with structures and functions that depend on their presence in lipid bilayers. Structures of proteins in their membrane environments could conceivably be predicted from AI/ML approaches with user-specificized parameters that describe each element of the architecture of a membrane protein accompanied by its lipid environment. We propose the Classification Of Membrane Proteins based On Structures Engaging Lipids (COMPOSEL), which builds on existing nomenclature types for monotopic, bitopic, polytopic and peripheral membrane proteins as well as lipids. Functional and regulatory elements are also defined in the scripts, as shown with membrane fusing synaptotagmins, multidomain PDZD8 and Protrudin proteins that recognize phosphoinositide (PI) lipids, the intrinsically disordered MARCKS protein, caveolins, the β barrel assembly machine (BAM), an adhesion G-protein coupled receptor (aGPCR) and two lipid modifying enzymes - diacylglycerol kinase DGKε and fatty aldehyde dehydrogenase FALDH. This demonstrates how COMPOSEL communicates lipid interactivity as well as signaling mechanisms and binding of metabolites, drug molecules, polypeptides or nucleic acids to describe the operations of any protein. Moreover COMPOSEL can be scaled to express how genomes encode membrane structures and how our organs are infiltrated by pathogens such as SARS-CoV-2.

Molecular dynamic simulations identifying the mechanism of holoenzyme formation by O-GlcNAc transferase and active p38α

O-N-Acetylglucosamine transferase (OGT) can catalyze the O-GlcNAc modification of thousands of proteins. The holoenzyme formation of OGT and adaptor protein is the precondition for further recognition and glycosylation of the target protein, while the corresponding mechanism is still open. Here, static and dynamic schemes based on statistics can successfully screen the feasible identifying, approaching, and binding mechanism of OGT and its typical adaptor protein p38α. The most favorable interface, energy contribution of hotspots, and conformational changes of fragments were discovered. The hydrogen bond interactions were verified as the main driving force for the whole process. The distinct characteristic of active and inactive p38α is explored and demonstrates that the phosphorylated tyrosine and threonine will form strong ion-pair interactions with Lys714, playing a key role in the dynamic identification stage. Multiple method combinations from different points of view may be helpful for exploring other systems of the protein-protein interactions.

Chitosan and HPMCAS double-coating as protective systems for alginate microparticles loaded with Ctx(Ile21)-Ha antimicrobial peptide to prevent intestinal infections

The incorrect use of conventional drugs for both prevention and control of intestinal infections has contributed to a significant spread of bacterial resistance. In this way, studies that promote their replacement are a priority. In the last decade, the use of antimicrobial peptides (AMP), especially Ctx(Ile²¹)-Ha AMP, has gained strength, demonstrating efficient antimicrobial activity (AA) against pathogens, including multidrug-resistant bacteria. However, gastrointestinal degradation does not allow its direct oral application. In this research, double-coating systems using alginate microparticles loaded with Ctx(Ile²¹)-Ha peptide were designed, and in vitro release assays simulating the gastrointestinal tract were evaluated. Also, the AA against Salmonella spp. and Escherichia coli was examined. The results showed the physicochemical stability of Ctx(Ile²¹)-Ha peptide in the system and its potent antimicrobial activity. In addition, the combination of HPMCAS and chitosan as a gastric protection system can be promising for peptide carriers or other low pH-sensitive molecules, adequately released in the intestine. In conclusion, the coated systems employed in this study can improve the formulation of new foods or biopharmaceutical products for specific application against intestinal pathogens in animal production or, possibly, in the near future, in human health.

Cellulose synthesis in land plants

All plant cells are surrounded by a cell wall that provides cohesion, protection, and a means of directional growth to plants. Cellulose microfibrils contribute the main biomechanical scaffold for most of these walls. The biosynthesis of cellulose, which typically is the most prominent constituent of the cell wall and therefore Earth's most abundant biopolymer, is finely attuned to developmental and environmental cues. Our understanding of the machinery that catalyzes and regulates cellulose biosynthesis has substantially improved due to recent technological advances in, for example, structural biology and microscopy. Here, we provide a comprehensive overview of the structure, function, and regulation of the cellulose synthesis machinery and its regulatory interactors. We aim to highlight important knowledge gaps in the field, and outline emerging approaches that promise a means to close those gaps.

Absolute binding free energies of mucroporin and its analog mucroporin-M1 with the heptad repeat 1 domain and RNA-dependent RNA polymerase of SARS-CoV-2

The peptide Mucroporin and its analog Mucroporin-M1 were studied using the molecular docking and molecular dynamics simulation of their complexation with two protein targets, the Heptad Repeat 1 (HR1) domain and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2. The molecular docking of the peptide-protein complexes was performed using the glowworm swarm optimization algorithm. The lowest energy poses were submitted to molecular dynamics simulation. Then, the binding free energies of Mucroporin and its analog Mucroporin-M1 with these two protein targets were calculated using the Multistate Bennett Acceptance Ratio (MBAR) method. It was verified that the peptides/HR1 domain complex showed stability in the interaction site determined by molecular docking. It was also found that Mucroporin-M1 has a much higher affinity than Mucroporin to the HR1 protein target. The peptides showed similar stability and affinity at the NTP binding site in the RdRp protein. Additional experimental studies are needed to confirm the antiviral activity of Mucroporin-M1 and a possible mechanism of action against SARS-CoV-2. However, here we indicate that Mucroporin-M1 may have potential antiviral activity against the HR1 domain with the possibility for further peptide optimization.Communicated by Ramaswamy H. Sarma.

Potential Common Mechanisms of Cytotoxicity Induced by Amide Herbicides via TRPA1 Channel Activation

The “Multi-Threat Medical Countermeasure (MTMC)” strategy was proposed to develop a single drug with therapeutic efficacy against multiple pathologies or broad-spectrum protection against various toxins with common biochemical signals, molecular mediators, or cellular processes. This study demonstrated that cytotoxicity, expression of transient receptor potential cation channel subfamily A member 1 (TRPA1) mRNA, and intracellular calcium influx were increased in A549 cells exposed to amide herbicides (AHs), in which the order of cytotoxicity was metolachlor > acetochlor > propisochlor > alachlor > butachlor > propanil > pretilachlor, based on IC50 values of 430, 524, 564, 565, 619, 831, and 2333 μM, respectively. Inhibition/knockout of TRPA1 efficiently protected against cytotoxicity, decreased TRPA1 mRNA expression, and reduced calcium influx. The results suggested that the TRPA1 channel could be a key common target for AHs poisoning. The order of TRPA1 affinity for AHs was propanil > pretilachlor > metolachlor > (propiso/ala/aceto/butachlor), based on KD values of 16.2, 309, and 364 μM, respectively. The common molecular mechanisms of TRPA1-AHs interactions were clarified, including toxicity-effector groups (benzene ring, nitrogen/oxygen-containing functional groups, halogen) and residues involved in interactions (Lys787, Leu982). This work provides valuable information for the development of TRPA1 as a promising therapeutic target for broad-spectrum antitoxins.

Interface refinement of low- to medium-resolution Cryo-EM complexes using HADDOCK2.4

A wide range of cellular processes requires the formation of multimeric protein complexes. The rise of cryo-electron microscopy (cryo-EM) has enabled the structural characterization of these protein assemblies. The density maps produced can, however, still suffer from limited resolution, impeding the process of resolving structures at atomic resolution. In order to solve this issue, monomers can be fitted into low- to medium-resolution maps. Unfortunately, the models produced frequently contain atomic clashes at the protein-protein interfaces (PPIs), as intermolecular interactions are typically not considered during monomer fitting. Here, we present a refinement approach based on HADDOCK2.4 to remove intermolecular clashes and optimize PPIs. A dataset of 14 cryo-EM complexes was used to test eight protocols. The best-performing protocol, consisting of a semi-flexible simulated annealing refinement with centroid restraints on the monomers, was able to decrease intermolecular atomic clashes by 98% without significantly deteriorating the quality of the cryo-EM density fit.

Dependence of Protein Structure on Environment: FOD Model Applied to Membrane Proteins

The natural environment of proteins is the polar aquatic environment and the hydrophobic (amphipathic) environment of the membrane. The fuzzy oil drop model (FOD) used to characterize water-soluble proteins, as well as its modified version FOD-M, enables a mathematical description of the presence and influence of diverse environments on protein structure. The present work characterized the structures of membrane proteins, including those that act as channels, and a water-soluble protein for contrast. The purpose of the analysis was to verify the possibility that an external force field can be used in the simulation of the protein-folding process, taking into account the diverse nature of the environment that guarantees a structure showing biological activity.

Protein-Protein Docking: Past, Present, and Future

The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.

VARIDT 2.0: structural variability of drug transporter

The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs' variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT (https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.

Moving pictures: Reassessing docking experiments with a dynamic view of protein interfaces

The modeling of protein assemblies at the atomic level remains a central issue in structural biology, as protein interactions play a key role in numerous cellular processes. This problem is traditionally addressed using docking tools, where the quality of the models is based on their similarity to a single reference experimental structure. However, using a static reference does not take into account the dynamic quality of the protein interface. Here, we used all-atom classical Molecular Dynamics simulations to investigate the stability of the reference interface for three complexes that previously served as targets in the CAPRI competition. For each one of these targets, we also ran MD simulations for ten models that are distributed over the High, Medium and Acceptable accuracy categories. To assess the quality of these models from a dynamic perspective, we set up new criteria which take into account the stability of the reference experimental protein interface. We show that, when the protein interfaces are allowed to evolve along time, the original ranking based on the static CAPRI criteria no longer holds as over 50% of the docking models undergo a category change (which can be either toward a better or a lower accuracy group) when reassessing their quality using dynamic information.

Transmembrane Protein Docking with JabberDock

Transmembrane proteins act as an intermediary for a broad range of biological process. Making up 20% to 30% of the proteome, their ubiquitous nature has resulted in them comprising 50% of all targets in drug design. Despite their importance, they make up only 4% of all structures in the PDB database, primarily owing to difficulties associated with isolating and characterizing them. Membrane protein docking algorithms could help to fill this knowledge gap, yet only few exist. Moreover, these existing methods achieve success rates lower than the current best soluble proteins docking software. We present and test a pipeline using our software, JabberDock, to dock membrane proteins. JabberDock docks shapes representative of membrane protein structure and dynamics in their biphasic environment. We verify JabberDock's ability to yield accurate predictions by applying it to a benchmark of 20 transmembrane dimers, returning a success rate of 75.0%. This makes our software very competitive among available membrane protein-protein docking tools.