Screening of world approved drugs against highly dynamical spike glycoprotein of SARS-CoV-2 using CaverDock and machine learning

Caver Web 2.0: analysis of tunnels and ligand transport in dynamic ensembles of proteins

Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at https://loschmidt.chemi.muni.cz/caverweb.

Assessing the role of deep eutectic solvents in Yarrowia lipolytica inhibition

Yarrowia lipolytica has gained recognition as a microorganism with biological relevance and extensive biotechnological applications. Some of its features include a high enzyme secretion capacity and a high cell-density fermentation mode. Hexokinase (YlHxk) is a vital enzyme in Y. lipolytica growth since it catalyzes glucose metabolism through phosphorylation in the glycolytic pathway. Given the potential application of deep eutectic solvents (DES) as novel solvents in biotechnological processes, this study evaluated the influence of eighteen DES on the growth of Y. lipolytica. Furthermore, this work examined the effects of individual ions on the YlHxk enzyme by analyzing its enzymatic tunnel structure, molecule transport, and molecular docking. The results revealed a significant reduction in yeast growth in the presence of most DES compared to the control (medium without DES), with the exception of the [N8881]Cl: hexanoic acid (1:1) DES. The growth varied between 11.95 ± 0.60 and 0.68 ± 0.17 g dry cell weight L-1. According to the enzymatic tunnel analysis, DES components associated with the lowest microbial growth values were transported through tunnel 1. On the other hand, DES components had their pathway facilitated through tunnel 2 ([N8881]+ and hexanoic acid) and showed growth values close to the control. Molecular docking analysis identified a similarity between all the ligands in this tunnel (including substrate and product), presenting binding interactions with the ASN273 amino acid of the YlHxk active site. Combining experimental results with computational tools provided promising insights at the molecular level, while also potentially reducing analysis costs and time, paving the way for similar approaches in broad biocatalytic reactions.

Identifying potential inhibitors against galectin-3-binding protein (LGALS3BP) for therapeutic targeting of glioma

Glioblastoma is one of the most lethal types of Gliomas, and its treatment greatly depends on the stage of its detection. Galactin-3-binding protein (LGALS3BP) serves as a novel circulatory biomarker for detecting glioma at an early stage. This protein is also responsible for metastasis, proliferative signaling, angiogenesis, and immune system evasion in the case of brain tumors. Inhibition of LGALS3BP can help in the reduction of metastasis and progression of the disease. Currently, no effective drug is available that can completely treat glioma. In this study, we have virtually screened the National Cancer Institute (NCI) drug databank to discover potential inhibitors of LGALS3BP. Based on the binding free energy calculations using MMPBSA, three compounds, 627861 (-16.69 kcal/mol), 329090 (-13.66 kcal/mol), and 627855 (-10.01 kcal/mol), were selected as potent inhibitors. 200 ns MD simulation studies further complemented this study. Finally, we recommend three molecules, 627861, 329090, and 627855, can be potential inhibitors of LGAL3SBP. The structural scaffolds of these molecules can also lead to the optimization of better inhibitors of LGALS3BP and be implicated in the therapeutic management of glioma after desired experimental validations.

Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme-ligand complexes

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.Scientific contributionsThe pipeline introduced in this work allows for the detailed analysis of a large set of protein-ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git . The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/ .

pyCaverDock: Python implementation of the popular tool for analysis of ligand transport with advanced caching and batch calculation support

SUMMARY

Access pathways in enzymes are crucial for the passage of substrates and products of catalysed reactions. The process can be studied by computational means with variable degrees of precision. Our in-house approximative method CaverDock provides a fast and easy way to set up and run ligand binding and unbinding calculations through protein tunnels and channels. Here we introduce pyCaverDock, a Python3 API designed to improve user experience with the tool and further facilitate the ligand transport analyses. The API enables users to simplify the steps needed to use CaverDock, from automatizing setup processes to designing screening pipelines.

AVAILABILITY AND IMPLEMENTATION

pyCaverDock API is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://loschmidt.chemi.muni.cz/caverdock/.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

Fast approximative methods for study of ligand transport and rational design of improved enzymes for biotechnologies

Acceleration of chemical reactions by the enzymes optimized using protein engineering represents one of the key pillars of the contribution of biotechnology towards sustainability. Tunnels and channels of enzymes with buried active sites enable the exchange of ligands, ions, and water molecules between the outer environment and active site pockets. The efficient exchange of ligands is a fundamental process of biocatalysis. Therefore, enzymes have evolved a wide range of mechanisms for repetitive conformational changes that enable periodic opening and closing. Protein-ligand interactions are traditionally studied by molecular docking, whereas molecular dynamics is the method of choice for studying conformational changes and ligand transport. However, computational demands make molecular dynamics impractical for screening purposes. Thus, several approximative methods have been recently developed to study interactions between a protein and ligand during the ligand transport process. Apart from identifying the best binding modes, these methods also provide information on the energetics of the transport and identify problematic regions limiting the ligand passage. These methods use approximations to simulate binding or unbinding events rapidly (calculation times from minutes to hours) and provide energy profiles that can be used to rank ligands or pathways. Here we provide a critical comparison of available methods, showcase their results on sample systems, discuss their practical applications in molecular biotechnologies and outline possible future developments.

Evaluation of lipase access tunnels and analysis of substance transport in comparison with experimental data

Lipases (E.C. 3.1.1.3) have buried active sites and used access tunnels in the transport of substrates and products for biotransformation processes. Computational methods are used to predict the trajectory and energy profile of ligands through these tunnels, and they complement the experimental methodologies because they filter data, optimizing laboratory time and experimental costs. Access tunnels of Burkholderia cepacia lipase (BCL), Candida rugosa lipase (CRL), and porcine pancreas lipase (PPL) and the transport of fatty acids, alcohols and esters through the tunnels were evaluated using the online server CaverWeb V1.0, and server calculation results were compared with experimental data (productivity). BCL showed higher productivity with palmitic acid-C16:0 (4029.95 µmol/h mg); CRL obtained productivity for oleic acid-C18:1 (380.80 µmol/h mg), and PPL achieved productivity for lauric acid-C12:0 (71.27 µmol/h mg). The highest probability of transport for BCL is through the tunnels 1 and 2, for CRL through the tunnel 1, and for PPL through the tunnels 1, 2, 3 and 4. Thus, the best in silico result was the transport of the substrates palmitic acid and ethanol and product ethyl palmitate in tunnel 1 of BCL. This result corroborates with the best result for the productivity data (higher productivity for BCL with palmitic acid-4029.95 µmol/h mg). The combination of in silico evaluation and experimental data gave similar results, demonstrating that in silico approaches are a promising alternative for reducing screening tests and minimizing laboratory time in the bio-catalysis area by identifying the lipases with the greatest reaction potential, as in the case of this proposal.

Co-crystallization and structure determination: An effective direction for anti-SARS-CoV-2 drug discovery

Safer and more-effective drugs are urgently needed to counter infections with the highly pathogenic SARS-CoV-2, cause of the COVID-19 pandemic. Identification of efficient inhibitors to treat and prevent SARS-CoV-2 infection is a predominant focus. Encouragingly, using X-ray crystal structures of therapeutically relevant drug targets (PLpro, Mpro, RdRp, and S glycoprotein) offers a valuable direction for anti-SARS-CoV-2 drug discovery and lead optimization through direct visualization of interactions. Computational analyses based primarily on MMPBSA calculations have also been proposed for assessing the binding stability of biomolecular structures involving the ligand and receptor. In this study, we focused on state-of-the-art X-ray co-crystal structures of the abovementioned targets complexed with newly identified small-molecule inhibitors (natural products, FDA-approved drugs, candidate drugs, and their analogues) with the assistance of computational analyses to support the precision design and screening of anti-SARS-CoV-2 drugs.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.