Docking approaches for modeling multi-molecular assemblies

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.

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

Exploring the hypolipidemic effects of bergenin from Saxifraga melanocentra Franch: mechanistic insights and potential for hyperlipidemia treatment

OBJECTIVE

The goal of this study was to explore the hypolipidemic effects of bergenin extracted from Saxifraga melanocentra Franch (S. melanocentra), which is a frequently utilized Tibetan medicinal plant known for its diverse bioactivities. Establishing a quality control system for black stem saxifrage is crucial to ensure the rational utilization of its medicinal resources.

METHODS

A one-step polyamide medium-pressure liquid chromatography technique was applied to isolate and prepare bergenin from a methanol extract of S. melanocentra. A zebrafish model of hyperlipidemia was used to investigate the potential hypolipidemic effects of bergenin.

RESULTS

The results revealed that bergenin exhibited substantial hypo efficacy in vivo. Specifically, bergenin significantly reduced the levels of triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-c) while simultaneously increasing high-density lipoprotein cholesterol (HDL-c) levels. At the molecular level, bergenin exerted its effects by inhibiting the expression of FASN, SREBF1, HMGCRα, RORα, LDLRα, IL-1β, and TNF while promoting the expression of IL-4 at the transcriptional level. Molecular docking analysis further demonstrated the strong binding affinity of bergenin to proteins such as FASN, SREBF1, HMGCRα, RORα, LDLRα, IL-4, IL-1β, and TNF.

CONCLUSIONS

Findings indicate that bergenin modulates lipid metabolism by regulating lipid and cholesterol synthesis as well as inflammatory responses through signaling pathways associated with FASN, SREBF1, and RORα. These results position bergenin as a potential candidate for the treatment of hyperlipidemia.

Photophysical, photobiological, and biomolecule-binding properties of new tri-cationic meso-tri(2-thienyl)corroles with Pt(II) and Pd(II) polypyridyl derivatives

We report the synthesis and characterization of new tri-cationic corrole derivatives, containing Pt(II) or Pd(II) complexes attached at the peripheral position of thienyl moieties. Corrole derivatives were characterized through microanalysis, electrochemical, spectrometry and spectroscopy analysis. Singlet and triplet excited-states are investigated by photophysical/theoretical calculation methods and photobiological parameters were also evaluated spectroscopic techniques (UV-Vis and EPR). Also, the binding capacity of each corrole derivative with nucleic acids (DNA) and human serum albumin (HSA) was determined by UV-Vis, steady-state, and time-resolved fluorescence spectroscopy, combined with molecular docking analysis. Moreover, the new corroles containing peripheral complexes improve their interactions with biomacromolecules, generate reactive oxygen species under light source irradiation studied and has potential for application in photodynamic therapeutic processes.

Molecular Simulation Study on the Interaction between Porcine CR1-like and C3b

The molecular basis of porcine red blood cell immune adhesion function stems from the complement receptor type 1-like (CR1-like) on its cell membrane. The ligand for CR1-like is C3b, which is produced by the cleavage of complement C3; however, the molecular mechanism of the immune adhesion of porcine erythrocytes is still unclear. Here, homology modeling was used to construct three-dimensional models of C3b and two fragments of CR1-like. An interaction model of C3b-CR1-like was constructed by molecular docking, and molecular structure optimization was achieved using molecular dynamics simulation. A simulated alanine mutation scan revealed that the amino acids Tyr761, Arg763, Phe765, Thr789, and Val873 of CR1-like SCR 12-14 and the amino acid residues Tyr1210, Asn1244, Val1249, Thr1253, Tyr1267, Val1322, and Val1339 of CR1-like SCR 19-21 are key residues involved in the interaction of porcine C3b with CR1-like. This study investigated the interaction between porcine CR1-like and C3b using molecular simulation to clarify the molecular mechanism of the immune adhesion of porcine erythrocytes.

Novel hybrid virtual screening protocol based on pharmacophore and molecular docking for discovery of GSK-3β inhibitors

GSK-3β is a member of the GSKs subfamily and plays a major role in the regulation of transcriptional elongation, which has attracted widespread attention as a therapeutic target for AD. In this study, by combining pharmacophore-based virtual screening and kinase inhibition assays, we have successfully identified four small molecules that inhibit GSK-3β activity at micromolar potency. These hit compounds showed drug-like properties according to Lipinski's rule of five and ADMET. An inter-complex interaction study showed that all hit compounds adapted well to the ATP pocket of the GSK-3β protein. Among them, hits 2 and 4 displayed considerable inhibitory activities with IC₅₀ value of 0.74 ± 0.04 μM and 2.32 ± 0.84 μM respectively. Overall, the discovered GSK-3β inhibitors act as new chemical leads to develop improved inhibitors that block the interaction of GSK-3β, and the hybrid virtual screening strategy designed in this study provides an important reference for design and synthesis novel selective GSK-3β inhibitors.

Online bioinformatics teaching practice: Comparison of popular docking programs using SARS-CoV-2 spike RBD-ACE2 complex as a benchmark

In this information era, there is an urgent need for tighter integration of bioinformatics and experimental biology. The enormous amount of data generated by biological experiments calls for extensive computational analysis. Many bioinformatics textbooks at present mainly focus on theories, which hinders the vigorous development of scientific research. As a result, most students are simply familiar with the bioinformatics theories but lack the opportunity to put them into practice. Here, we present our bioinformatics docking project conducted during the self-isolation period of the COVID-19 pandemic. Five students used the RBD-ACE2 complex as a benchmark to conduct a systematic comparison of several open-source online molecular docking programs. The virus surface spike protein mediates the entry of the SARS-CoV-2 virus into human cells by binding to its receptor, angiotensin-converting enzyme 2 (ACE2), through its receptor-binding domain (RBD). Through docking and comparing predicted structures to the crystal structure, students gained the opportunity to practice different bioinformatics tools independently and conduct research collaboratively. It opens a window for students to reach out to the state-of-the-art bioinformatics techniques and to keep up with the research trends. The online workshop has also proven to be an innovative method for bioinformatics teaching. We hope our work can inspire other educators to develop strategies to expose undergraduate students to modern bioinformatics and turn every temporary difficulty into a possible learning opportunity.

InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps

The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

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.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.