Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.

The Synergistic Effect of N2 and N7 Modifications on the Inhibitory Efficacy of mRNA Cap Analogues.

In the fight against cancer, researchers have turned their attention to the eukaryotic initiation factor eIF4E, a protein whose increased level is strongly correlated with the development and progression of various types of cancer. Among the numerous strategies devised to tackle eIF4E overexpression, the use of 5' end mRNA cap analogues has emerged as a promising approach. Here, we present new candidates as potent m7GMP analogues for inhibiting translation and interfacing with eIF4E. By employing an appropriate strategy, we synthesized doubly modified mono- and dinucleotide cap analogues, introducing simultaneous substituents at both the N7 and N2 positions of the guanine ring. This approach was identified as an effective and promising combination. Our findings reveal that these dual modifications increase the potency of the dinucleotide analogue, marking a significant advancement in the development of cancer therapeutics targeting the eIF4E pathway.

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation.

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

Structure prediction of linear and cyclic peptides using CABS-flex.

The structural modeling of peptides can be a useful aid in the discovery of new drugs and a deeper understanding of the molecular mechanisms of life. Here we present a novel multiscale protocol for the structure prediction of linear and cyclic peptides. The protocol combines two main stages: coarse-grained simulations using the CABS-flex standalone package and an all-atom reconstruction-optimization process using the Modeller program. We evaluated the protocol on a set of linear peptides and two sets of cyclic peptides, with cyclization through the backbone and disulfide bonds. A comparison with other state-of-the-art tools (APPTEST, PEP-FOLD, ESMFold and AlphaFold implementation in ColabFold) shows that for most cases, AlphaFold offers the highest resolution. However, CABS-flex is competitive, particularly when it comes to short linear peptides. As demonstrated, the protocol performance can be further improved by combination with the residue-residue contact prediction method or more efficient scoring. The protocol is included in the CABS-flex standalone package along with online documentation to aid users in predicting the structure of peptides and mini-proteins.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms.

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

A3DyDB: exploring structural aggregation propensities in the yeast proteome.

BACKGROUND: The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation. RESULTS: In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast . CONCLUSION: The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast.

Efficient improvement of the proliferation, differentiation, and anti-arthritic capacity of mesenchymal stem cells by simply culturing on the immobilized FGF2 derived peptide, 44-ERGVVSIKGV-53.

INTRODUCTION: The stem cell microenvironment has been evidenced to robustly affect its biological functions and clinical grade. Natural or synthetic growth factors, especially, are essential for modulating stem cell proliferation, metabolism, and differentiation via the interaction with specific extracellular receptors. Fibroblast growth factor-2 (FGF-2) possesses pleiotropic functions in various tissues and organs. It interacts with the FGF receptor (FGFR) and activates FGFR signaling pathways, which involve numerous biological functions, such as angiogenesis, wound healing, cell proliferation, and differentiation. OBJECTIVES: Here, we aim to explore the molecular functions, mode of action, and therapeutic activity of yet undetermined function, FGF-2-derived peptide, FP2 (44-ERGVVSIKGV-53) in promoting the proliferation, differentiation, and therapeutic application of human Wharton's jelly mesenchymal stem cells (hWJ-MSCs) in comparison to other test peptides, canofin1 (FP1), hexafin2 (FP3), and canofin3 (FP4) with known functions. METHODS: The immobilization of test peptides that are fused with mussel adhesive proteins (MAP) on the culture plate was carried out via EDC/NHS chemistry. Cell Proliferation assay, colony-forming unit, western blotting analysis, gene expression analysis, RNA-Seq. analysis, osteogenic, and chondrogenic differentiation capacity were applied to test the activity of the test peptides. We additionally utilized three-dimensional (3D) structural analysis and artificial intelligence (AI)-based AlphaFold2 and CABS-dock programs for receptor interaction prediction of the peptide receptor. We also verified the in vivo therapeutic capacity of FP2-cultured hWJ-MSCs using an osteoarthritis mice model. RESULTS: Culture of hWJ-MSC onto an FP2-immobilized culture plate showed a significant increase in cell proliferation (n = 3; *p < 0.05, **p < 0.01) and the colony-forming unit (n = 3; *p < 0.05, **p < 0.01) compared with the test peptides. FP2 showed a significantly upregulated phosphorylation of FRS2α and FGFR1 and activated the AKT and ERK signaling pathways (n = 3; *p < 0.05, **p < 0.01, ***p < 0.001). Interestingly, we detected efficient FP2 receptor binding that was predicted using AI-based tools. Treatment with an AKT inhibitor significantly abrogated the FP2-mediated enhancement of cell differentiation (n = 3; *p < 0.05, **p < 0.01, ***p < 0.001). Intra-articular injection of FP2-cultured MSCs significantly mitigated arthritis symptoms in an osteoarthritis mouse model, as shown through the functional tests (n = 10; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001), modulation of the expression level of the pro-inflammatory and anti-inflammatory genes, and improved osteochondral regeneration as demonstrated by tissue sections. CONCLUSION: Our study identified the FGF-2-derived peptide FP2 as a promising candidate peptide to improve the therapeutic potential of hWJ-MSCs, especially in bone and cartilage regeneration.

Menthol- and thymol-based ciprofloxacin derivatives against Mycobacterium tuberculosis: in vitro activity, lipophilicity, and computational studies.

In this work, we investigated the antitubercular properties of Ciprofloxacin derivatives conjugated with menthol and thymol moieties. For the sixteen derivatives, we established minimal inhibitory concentrations (MIC) using isolates of Mycobacterium tuberculosis that were resistant or susceptible to other antibiotics. For the most potent compound 1-cyclopropyl-6-fluoro-7-{4-[6-((1R,2S,5R)-2-isopropyl-5-methylcyclohexyloxy)-6-oxohexyl]piperazin-1-yl}-4-oxo-1,4-dihydroquinoline-3-carboxylic acid (6), we determined fractional inhibitory concentration index (FICI) values to confirm antibacterial susceptibility and synergistic effects with other reference drugs. In addition, chromatographic studies of all the derivatives demonstrated a significant three to four-fold increase in lipophilicity and affinity to phospholipids compared to Ciprofloxacin. Finally, we conducted structure-based studies of the investigated compounds using molecular docking and taking into account protein target mutations associated with fluoroquinolone resistance. In summary, our findings indicate that the investigated compounds possess tuberculostatic properties, with some showing similar or even better activity against resistant strains compared to reference drugs. Increased lipophilicity and affinity to phospholipids of the new derivatives can offer several advantages for new drug candidates, beyond just improved cell membrane penetration. However, further studies are needed to fully understand their safety, efficacy, and mechanism of action.

Fatty Acids Reverse the Supramolecular Chirality of Insulin Fibrils.

Long-chain unsaturated and polyunsaturated fatty acids (LCUFAs and LCPUFAs, respectively) are the essential components of phospholipids and sphingolipids, major building blocks of plasma and organelle membranes. These molecules are also involved in cell signaling and energy metabolism. Hence, both LCUFAs and LCPUFAs are broadly used as food supplements. However, the role of these fatty acids (FAs) in the self-assembly of misfolded proteins remains unclear. In this study, we investigated the effect of LCUFAs and LCPUFAs, as well as their saturated analogue, on insulin aggregation. Using vibrational circular dichroism, we found that all analyzed FAs reversed the supramolecular chirality of insulin fibrils. Molecular dynamics simulations showed that strong hydrophobic interactions were formed between the long aliphatic tails of FAs and hydrophobic amino acid residues of insulin. We infer that such insulin:FA complexes had different self-assembly mechanisms compared to that of insulin alone, which resulted in the observed reversal of the supramolecular chirality of the amyloid fibrils.

N-Acylated Ciprofloxacin Derivatives: Synthesis and In Vitro Biological Evaluation as Antibacterial and Anticancer Agents.

A novel series of N-acylated ciprofloxacin (CP) conjugates 1-21 were synthesized and screened as potential antimicrobial agents. Conjugates 1 and 2 were 1.25-10-fold more potent than CP toward all Staphylococci (minimal inhibitory concentration 0.05-0.4 µg/mL). Most of the chloro- (3-7), bromo- (8-11), and CF3-alkanoyl (14-16) derivatives expressed higher or comparable activity to CP against selected Gram-positive strains. A few CP analogues (5, 10, and 11) were also more effective toward the chosen clinical Gram-negative rods. Conjugates 5, 10, and 11 considerably influenced the phases of the bacterial growth cycle over 18 h. Additionally, compounds 2, 4-7, 9-12, and 21 exerted stronger tuberculostatic action against three Mycobacterium tuberculosis isolates than the first-line antitubercular drugs. Amides 1, 2, 5, 6, 10, and 11 targeted gyrase and topoisomerase IV at 2.7-10.0 µg/mL, which suggests a mechanism of antibacterial action related to CP. These findings were confirmed by molecular docking studies. In addition, compounds 3 and 15 showed high antiproliferative activities against prostate PC3 cells (IC50 2.02-4.8 µM), up to 6.5-2.75 stronger than cisplatin. They almost completely reduced the growth and proliferation rates in these cells, without a cytotoxic action against normal HaCaT cell lines. Furthermore, derivatives 3 and 21 induced apoptosis/necrosis in PC3 cells, probably by increasing the intracellular ROS amount, as well as they diminished the IL-6 level in tumor cells.

The Effect of Conjugation of Ciprofloxacin and Moxifloxacin with Fatty Acids on Their Antibacterial and Anticancer Activity.

Novel conjugates (CP) of moxifloxacin (MXF) with fatty acids (1m-16m) were synthesized with good yields utilizing amides chemistry. They exhibit a more pronounced cytotoxic potential than the parent drug. They were the most effective for prostate cancer cells with an IC50 below 5 µM for respective conjugates with sorbic (2m), oleic (4m), 6-heptenoic (10m), linoleic (11m), caprylic (15m), and stearic (16m) acids. All derivatives were evaluated against a panel of standard and clinical bacterial strains, as well as towards mycobacteria. The highest activity towards standard isolates was observed for the acetic acid derivative 14m, followed by conjugates of unsaturated crotonic (1m) and sorbic (2m) acids. The activity of conjugates tested against an expanded panel of clinical coagulase-negative staphylococci showed that the compound (14m) was recognized as a leading structure with an MIC of 0.5 µg/mL denoted for all quinolone-susceptible isolates. In the group of CP derivatives, sorbic (2) and geranic (3) acid amides exhibited the highest bactericidal potential against clinical strains. The M. tuberculosis Spec. 210 strain was the most sensitive to sorbic (2m) conjugate and to conjugates with medium- and long-chain polyunsaturated acids. To establish the mechanism of antibacterial action, selected CP and MXF conjugates were examined in both topoisomerase IV decatenation assay and the DNA gyrase supercoiling assay, followed by suitable molecular docking studies.

Design and Synthesis of Menthol and Thymol Derived Ciprofloxacin: Influence of Structural Modifications on the Antibacterial Activity and Anticancer Properties.

Sixteen new Ciprofloxacin derivatives were designed and successfully synthesized. In an in silico experiment, lipophilicity was established for obtained compounds. All compounds were screened for antimicrobial activity using standard and clinical strains. As for Gram-positive hospital microorganisms, all tested derivatives were active. Measured MICs were in the range 1-16 µg/mL, confirming high antimicrobial potency. Derivative 12 demonstrated activity against all standard Gram-positive Staphylococci, within the range of 0.8-1.6 µg/mL and was confirmed as the leading structure with MICs 1 µg/mL for S. pasteuri KR 4358 and S. aureus T 5591 (clinical strains). All compounds were screened for their in vitro cytotoxic properties via the MTT method. Three of the examined compounds (3, 11 and 16) showed good activity against cancer cells, and in parallel were found not to be cytotoxic toward normal cells. Doxorubicin SI ranged 0.14-1.11 while the mentioned three ranged 1.9-3.4. Selected Ciprofloxacin derivatives were docked into the crystal structure of topoisomerase II (DNA gyrase) in complex with DNA (PDB ID: 5BTC). In summary, leading structures were established (3, 11, 12 and 16). We have observed poor results in preformed studies for disubstituted derivatives, suggesting that 3-oxo-4-carboxylic acid core is the active DNA-gyrase binding site, and when structural changes were made in this fragment, there was an observed decrease in antibacterial potency.

MAPIYA contact map server for identification and visualization of molecular interactions in proteins and biological complexes.

Correct identification and effective visualization of interactions in biomolecular structures facilitate understanding of their functions and molecular design. In response to the practical needs of structure-based analysis, we have created a Mapiya web server. The Mapiya integrates four main functionalities: (i) generation of contact maps - intramolecular and intermolecular-for proteins, nucleic acids, and their complexes; (ii) characterization of the interactions physicochemical nature, (iii) interactive visualization of biomolecular conformations with automatic zoom on selected contacts using Molstar and (iv) additional sequence- and structure-based analyses performed with third-party software and in-house algorithms combined into an easy-to-use interface. Thus, Mapiya offers a highly customized analysis of the molecular interactions' in various biological systems. The web server is available at: http://mapiya.lcbio.pl/.

A3D database: structure-based predictions of protein aggregation for the human proteome.

SUMMARY: Protein aggregation is associated with many human disorders and constitutes a major bottleneck for producing therapeutic proteins. Our knowledge of the human protein structures repertoire has dramatically increased with the recent development of the AlphaFold (AF) deep-learning method. This structural information can be used to understand better protein aggregation properties and the rational design of protein solubility. This article uses the Aggrescan3D (A3D) tool to compute the structure-based aggregation predictions for the human proteome and make the predictions available in a database form. In the A3D database, we analyze the AF-predicted human protein structures (for over 20.5 thousand unique Uniprot IDs) in terms of their aggregation properties using the A3D tool. Each entry of the A3D database provides a detailed analysis of the structure-based aggregation propensity computed with A3D. The A3D database implements simple but useful graphical tools for visualizing and interpreting protein structure datasets. It also enables testing the influence of user-selected mutations on protein solubility and stability, all integrated into a user-friendly interface. AVAILABILITY AND IMPLEMENTATION: A3D database is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/hproteome. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protocols for Rational Design of Protein Solubility and Aggregation Properties Using Aggrescan3D Standalone.

Protein aggregation is a major hurdle in the development and manufacturing of protein-based therapeutics. Development of aggregation-resistant and stable protein variants can be guided by rational redesign using computational tools. Here, we describe the architecture and functionalities of the Aggrescan3D (A3D) standalone package for the rational design of protein solubility and aggregation properties based on three-dimensional protein structures. We present the case studies of the three therapeutic proteins, including antibodies, exploring the practical use of the A3D standalone tool. The case studies demonstrate that protein solubility can be easily improved by the A3D prediction of non-destabilizing amino acid mutations at the protein surfaces.

A3D 2.0 Update for the Prediction and Optimization of Protein Solubility.

Protein aggregation propensity is a property imprinted in protein sequences and structures, being associated with the onset of human diseases and limiting the implementation of protein-based biotherapies. Computational approaches stand as cost-effective alternatives for reducing protein aggregation and increasing protein solubility. AGGRESCAN 3D (A3D) is a structure-based predictor of aggregation that takes into account the conformational context of a protein, aiming to identify aggregation-prone regions exposed in protein surfaces. Here we inspect the updated 2.0 version of the algorithm, which extends the application of A3D to previously inaccessible proteins and incorporates new modules to assist protein redesign. Among these features, the new server includes stability calculations and the possibility to optimize protein solubility using an experimentally validated computational pipeline. Finally, we employ defined examples to navigate the A3D RESTful service, a routine to handle extensive protein collections. Altogether, this chapter is conceived to train and assist A3D non-experts in the study of aggregation-prone regions and protein solubility redesign.

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.

Phosphorylation of the conserved C-terminal domain of ribosomal P-proteins impairs the mode of interaction with plant toxins.

The ribosome is subjected to post-translational modifications, including phosphorylation, that affect its biological activity. Among ribosomal elements, the P-proteins undergo phosphorylation within the C terminus, the element which interacts with trGTPases or ribosome-inactivating proteins (RIPs); however, the role of phosphorylation has never been elucidated. Here, we probed the function of phosphorylation on the interaction of P-proteins with RIPs using the ribosomal P1-P2 dimer. We determined the kinetic parameters of the interaction with the toxins using biolayer interferometry and microscale thermophoresis. The results present the first mechanistic insight into the function of P-protein phosphorylation, showing that introduction of a negative charge into the C terminus of P1-P2 proteins promotes α-helix formation and decreases the affinity of the P-proteins for the RIPs.

Molecular Dynamics Scoring of Protein-Peptide Models Derived from Coarse-Grained Docking.

One of the major challenges in the computational prediction of protein-peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein-peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor-ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein-peptide complexes show that the proposed MD-based scoring approach can be used to identify protein-peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

Synthetic Transition from Thiourea-Based Compounds to Tetrazole Derivatives: Structure and Biological Evaluation of Synthesized New N-(Furan-2-ylmethyl)-1H-tetrazol-5-amine Derivatives.

Twelve novel derivatives of N-(furan-2-ylmethyl)-1H-tetrazol-5-amine were synthesized. For obtained compound 8, its corresponding substrate single crystals were isolated and X-ray diffraction experiments were completed. In the initial stage of research, in silico structure-based pharmacological prediction was conducted. All compounds were screened for their antibacterial and antimycobacterial activities using standard and clinical strains. The cytotoxic activity was evaluated against a panel of human cancer cell lines, in contrast to normal (HaCaT) cell lines, by using the MTT method. All examined derivatives were found to be noncytotoxic against normal cell lines. Within the studied group, compound 6 showed the most promising results in antimicrobial studies. It inhibited four hospital S. epidermidis rods' growth, when applied at the amount of 4 µg/mL. However, the most susceptible to the presence of compound 6 was S. epidermidis T 5501 851/19 clinical strain, for which the MIC value was only 2 µg/mL. Finally, a pharmacophore model was established based on lead compounds from this and our previous work.