In recent years, the search for novel anticancer agents has increasingly focused on monocarboxylate transporters (MCTs) because of their involvement in tumor metabolism. Ganoderic acid A (GAA), a triterpenoid derived from the medicinal mushroom Ganoderma lucidum, has demonstrated anticancer potential; however, its interaction with MCT isoforms remains insufficiently characterized. In this study, we examined the interaction between GAA and MCT1 and MCT4 using complementary computational and experimental approaches. Full-length structures of MCT1 and MCT4 were predicted using AlphaFold2 and validated with the SAVES server. Molecular docking and molecular dynamics simulations using the known dual inhibitor syrosingopine as a reference indicated that GAA can associate with both MCT1 and MCT4. Cellular thermal shift assay (CETSA) and isothermal dose-response fingerprinting (ITDRF) showed that GAA thermally destabilized both MCT1 and MCT4, supporting a direct protein-compound interaction. Notably, ITDRF analysis revealed enhanced stability of a higher-molecular-weight MCT4, suggesting a biphasic binding behavior. Together, these findings indicate that GAA directly interacts with MCT1 and MCT4, and uncovers a biphasic binding pattern associated with MCT4.

Snakebite envenomation is a major public health concern, particularly in low- and middle-income regions where access to safe and effective antivenoms is limited. Traditional antivenoms, derived from immunization with crude venom, often trigger adverse reactions and lack specificity against key venom components. This study presents a bioinformatics-driven approach to design, construct, and evaluate a novel panel of recombinant multi-epitope toxin-derived immunogens targeting the most clinically significant snake venom toxin families. Five principal toxin families-three-finger toxins (3FT), phospholipase A2 (PLA2), snake venom metalloproteinases (SVMP), snake venom serine proteases (SVSP), and dendrotoxins (DDT)-were computationally analyzed to identify conserved, antigenic, non-toxic, and non-allergenic B-cell and T-cell epitopes. Multi-epitope recombinant constructs were designed using linker systems, a TLR4 agonist adjuvant, and an MITD-signal sequence to enhance immunogenicity. Structural modeling, refinement, and validation were performed using AlphaFold 3 and GalaxyRefine. Protein-TLR interactions were assessed using molecular docking with ClusPro, selecting the top-ranked pose based on the lowest energy score for initial analysis, as these often correspond to the most stable configurations. Normal mode analysis (NMA), molecular dynamics simulation (MDS), and post-simulation analysis were used to evaluate the stability of the complexes. While in silico immune simulations evaluated the immunogenic potential of the constructs. The designed multi-epitope toxin-derived immunogens were predicted to have favorable physicochemical properties, including molecular weights ranging from 33.7 to 56.37 kDa, basic isoelectric points, high thermostability (aliphatic index: 66.72-77.08), and hydrophilicity (negative GRAVY scores). Refined 3D models exhibited more than 93% residues in Ramachandran favored regions, suggesting structural reliability. Molecular docking revealed strong and stable interactions with TLR2 and TLR4, particularly in the SVMP-TLR4 complex (ΔG = -21.0 kcal/mol; Kd = 3.7 × 10-16 M). NMA, MDS, and post-simulation analysis collectively showed that 3FT immunogen complexes with TLR2/4 were the most stable and compact with coordinated motions, PLA2 and DDT displayed moderate flexibility with maintained integrity, SVSP showed intermediate instability, and SVMP, particularly with TLR2, exhibited pronounced conformational instability and dynamic disorder, highlighting clear receptor- and toxin-dependent differences in stability and collective behavior. Immune simulations theoretically predicted robust humoral and cellular immune responses, with early IgM/IgG production, expansion of B-cell and T-cell populations, and balanced cytokine profiles indicative of a safe immunogenic response. This study provides in silico evidence suggesting the potential of recombinant multi-epitope toxin-derived immunogens as a next-generation therapeutic strategy for snakebite management. The designed constructs may offer improvements in specificity, safety, and manufacturability over traditional antivenoms, providing a promising foundation for further experimental validation and clinical translation. Future refinements could incorporate cluster overlap evaluation to mitigate bias from single-pose selection, particularly for poses with overlapping scores.

Metastatic castration resistance prostate cancer (mCRPC) is the advanced state of prostate cancer where majority of patients succumb to ineffective treatment perspectives like androgen deprivation alongside salvage therapies. mCRPC is predominantly orchestrated by androgen receptor (AR)-dependent gene expression. On the account of AR being a "potentially attractive immunological target", an immunoinformatics pipeline was built to identify and screen mutation-independent, broad MHC covering, potential antigenic, non-allergenic, non-toxic and soluble epitopes. The filtered epitopes required assembly into a unified construct with interconnecting linkers and adjuvant and further TLR3-docking. We chose TLR3 because of its pro-apoptotic activity in prostate cancer, to check active immune response by the vaccine. Our study was not confined to the use of a conventional adjuvant like human beta defensin-3 but it extended to the scope of utilization of a protein-based TLR3-specific agonist as an adjuvant for assembling the second composite construct. Our path was guided by the discovery of TLR3-specific agonist minibinder 8.6 (a small, hyperstable protein) by Adams etal. An extensively comparative molecular dynamics study of the free state and bound states of the two constructs unveiled a more stable interaction, complex stability and immune response attributing to the specificity of the minibinder-based construct towards TLR3. Our work circumscribes a multi-headed approach beginning with peptide subunit multi-epitope vaccine construct design for mitigating mCRPC; secondarily, endorsing the advocacy of TLR3-agonizing minibinders as vaccine adjuvants for enhanced immunity and finally posing a comparative framework of minibinder 8.6 over HBD3, as a more potential adjuvant, apprehending wet-lab proof.

The control and modulation of protein-protein interactions (PPIs) is of central importance for the majority of biological processes and most biomedical applications. Stabilization of PPIs, besides inhibition, is of growing pharmaceutical interest. Due to their small size, drug-like organic molecules may not provide sufficient interaction surfaces to allow for high-affinity dual binding to both partners of a protein-protein complex. Cyclic peptides offer larger interaction surfaces, making them a promising class of stabilizers of PPIs. We have developed a computational protocol to rapidly and systematically design cyclic peptides that optimize not only the interaction with one target protein but simultaneously optimize the dual binding to two protein partners. The cyclic peptide generation is based on a modified AlphaFold2-based peptide design approach and combines confidence scoring with force field-based scoring using Molecular Dynamics simulations. The performance of the method is tested on protein-protein complexes with known cyclic peptide binders and stabilizers. In addition, the approach is used to design cyclic peptides that can act as bifunctional molecules, recruiting the cellular protein degradation system to a target protein. The designed cyclic peptides achieve similar or better calculated interaction scores than known binders and exhibit well-balanced interactions with both protein partners. The design protocol is generally applicable to cyclic peptide design for modulating or inducing protein-protein association and could be useful for many biomedical design efforts.

Developing allosteric drugs to treat pathogenic diseases can offer a promising alternative to orthosteric drugs that may bind to conserved motifs in human homologs. The allosteric drugs bind to allosteric sites, induce changes in the target protein's active site, and modulate its function with high selectivity, reduced adverse effects, and low toxicity. While identifying allosteric sites is costly and labor-intensive with experimental approaches, computational methods utilizing three-dimensional protein structures offer a cost-effective solution for discovering potential allosteric sites and predicting the effects of ligand binding. This study evaluates the effectiveness of two network models, the residue interaction network (RIN) model and the mixed coarse-grained anisotropic network model (mcgANM), in identifying putative allosteric regions, predicting the structural response of the protein to ligand binding, and elucidating allosteric mechanisms while maintaining computational efficiency. The SARS-CoV-2 main protease (Mpro) is employed as an allosteric protein model due to a rich experimental and computational data available since the COVID-19 pandemic. The findings of the methods are assessed with statistical analysis, all-atom molecular dynamics simulations, and other elastic network models, namely Essential Site Scanning Analysis and Gaussian Network Model using a dataset of 15 ligand-bound and 4 ligand-free structures. RIN predicted the known drug binding sites of Mpro with high statistics, up to 80.0% sensitivity, 89.7% specificity, 29.6% precision, and 89.2% accuracy. RIN suggested an allosteric mechanism of Mpro that facilitates the allosteric communication of the allosteric and active sites through residue fluctuations. RIN was able to decompose the enzyme structure to dynamic domains, showing the organization of structural components to form a functional viral protease. mcgANM suggested the changes in residue fluctuations after ligand binding. The findings underscore the utility of the network models in advancing allosteric drug design.

Proteolysis Targeting Chimeras (PROTACs) represent a transformative approach to drug development by leveraging the intracellular ubiquitin-proteasome system (UPS) for the selective degradation of target proteins. A PROTAC molecule comprises three essential components: a ligand that binds to the E3 ubiquitin ligase, a ligand that targets the protein of interest, and a flexible linker that connects the two. This distinctive structure enables the PROTAC to simultaneously engage with both the target protein and the E3 ligase, facilitating their interaction. Such proximity initiates the ubiquitination of the target protein, marking it for recognition and subsequent degradation. In this study, we benchmark ternary complexes based on PROTACs using four recently employed predictive tools: Chai-1, AlphaFold2, AlphaFold3, and Protenix. Comparative analysis indicated that the ternary complexes predicted by the four prediction tools demonstrated satisfactory accuracy (Cα-RMSD < 10 Å). Among the evaluated tools, three-Chai-1, AlphaFold3, and Protenix-demonstrated superior performance in over half of the tests, while AlphaFold2 exhibited comparatively lower performance. However, significant challenges remained in accurately predicting the orientation of POI and the E3 ligase (Cα-RMSD < 10 Å when POI or E3 ligase were used as reference), as well as the position of the small molecule PROTAC (RMSD < 5 Å). By benchmarking these tools, we underscore recent advancements in protein structure prediction, enhance our understanding of the mechanisms underpinning PROTAC complexes, and provide a valuable reference for evaluating the binding conformations of other ternary complexes, as well as for the development of future predictive tools.

Although the phenotypes and functions of nonessential proteins can be studied by deletion of their coding sequences (both gene copies in diploid organisms), essential genes cannot be deleted unless loss of the encoded protein can be bypassed. Bypass is often achieved by supplementation with the product of the enzyme. However, supplementation cannot bypass loss of essential genes such as those encoding enzymes of DNA or RNA synthesis. To study proteins encoded by essential genes that cannot be bypassed, the mutations must be conditional in nature. The mutant cells must be able to grow under a permissive condition, but fail to grow under a different condition, the nonpermissive condition. Several methods have been developed to obtain conditional mutations in essential genes. Mutations that result in proteins abnormally sensitive to high temperatures are called temperature-sensitive (Ts) mutants and are a widely used type of conditional mutation. An alternative to Ts mutants is the "degron" system to target proteins for destruction by cellular proteases. Approaches to conditionally control the functions of proteins encoded by essential genes, plus the advantages and disadvantages of these and other approaches, will be considered.

Brucellosis (Malta fever) is a zoonotic disease that affects both humans and animals, including cattle, sheep, and goats. Brucella melitensis is the most virulent and clinically significant species in humans. It is a gram-negative bacterium with three groups of outer membrane proteins (OMPs): minor OMPs (Group 1) and major OMPs (Groups 2 and 3). OMPs with β-barrel architecture play important roles in nutrient transport, efflux, adhesion, and membrane biogenesis. Despite their importance, the structure, function, and interaction dynamics of several B. melitensis β-barrel OMPs and associated protein complexes remain mostly unexplored. In this study, we conducted a comprehensive in silico analysis to characterize known outer membrane β-barrel (OMBB) proteins and identify novel OMBBs in B. melitensis 16 M. Proteins were modeled using five computational tools: AlphaFold 3, ESMFold, SWISS-MODEL, RoseTTAFold, and TrRosetta. Outer-membrane insertion of 12 novel OMBBs was confirmed using PPM 3.0, Protein GRAVY, DREAMM, and MemProtMD_Insane. Putative functions were predicted using structure- and sequence-based annotations. Sequence variation across 46 B. melitensis strains was identified and mapped onto the structural models. OMBB-associated protein complexes-the RND (Resistance-Nodulation-Division) efflux pumps, the lipopolysaccharide transport (Lpt) complex, and the β-barrel assembly machinery (BAM) complex-were modeled, and protein-protein interactions (PPIs) were analyzed to confirm thermodynamically stable assemblies. This study presents a robust in silico strategy for exploring OMP architecture and provides valuable structural insights to support the development of diagnostics, targeted therapeutics, and vaccines against B. melitensis.

Phytoplasmas are highly destructive phloem-restricted pathogens, acting as obligate plant parasites transmitted by sap-feeding insect vectors. They infect over 1000 plant species, including critical crops, leading to severe agricultural losses globally. Evolving from Gram-positive bacteria, phytoplasmas underwent extreme genome reduction, resulting in some of the smallest known bacterial genomes. Despite their minimal genetic content, they effectively manipulate host and vector cellular processes through effector proteins. These virulence factors are thought to be secreted via signal peptide (SP)-dependent cleavage by signal peptidase I (SPase I). Since phytoplasmas remain unculturable invitro, identification of these effectors relies heavily on in silico SP and cleavage site (CS) prediction methods, which often produce unreliable results, leading to misidentified effector candidates. In this study, to improve prediction accuracy, we applied a structural modeling approach that complements sequence-based methods by assessing SPs through 3D modeling of SP-SPase I hetero-oligomer complexes. We analyzed reference virulence proteins (RVPs) with experimentally validated SPs, identifying potential errors in their annotated CSs. Through structural characterization, we classified phytoplasma SPase Is as eukaryotic ER-type-a rare trait in bacteria-and modeled SP-SPase I hetero-oligomers using ColabFold. Our findings reveal structural determinants governing cleavable SP binding to SPase I, enabling more accurate SP/CS predictions. This work underscores the unique molecular adaptations of phytoplasmas and provides insights for targeting their effector secretion mechanisms in disease control.