Proteins-Structure Function and Bioinformatics最新文献

Amino acid L-serine (L-Ser) is a precursor of various biomolecules, including other amino acids, glutathione, and nucleotides. The metabolism of this amino acid is crucial in diseases such as brucellosis. Previous studies have revealed that the enzymes involved in L-Ser biosynthesis are essential for Brucella replication, making them potential targets for the development of new drugs. Here, we focus on Brucella melitensis phosphoserine phosphatase (BmPSP), which catalyzes the dephosphorylation of phosphoserine in L-Ser. The enzyme is characterized through enzymatic and structural studies, leading to the discovery of its first crystallographic structures. The interactions of BmPSP with different ligands are also investigated. We demonstrate that the substitution of its Mg²⁺ cofactor with Ca²⁺ inhibits the enzyme and results in a slight movement of catalytic residues in the active site. Crystallographic structures of BmPSP in complex with substrate, reaction products, and substrate analogs are also detailed, revealing the interaction between these molecules and the active site residues. This structural study provides a better understanding of phosphoserine phosphatases, highlighting the involvement of two highly conserved residues in the mechanism of substrate entry into the active site.

The exponential growth of biomedical and life sciences literature, including research on amyloid biology, has made it increasingly challenging to track new discoveries and gain a comprehensive understanding of the evolution of specific research fields. Advances in natural language models (NLM) and artificial intelligence (AI) approaches now enable large-scale analysis of scientific publications, uncovering hidden patterns and facilitating data-driven insights. Here, a two-dimensional mapping of the global amyloid research landscape is presented, using the transformer-based large language model PubMedBERT, in combination with t-SNE and Latent Dirichlet Allocation (LDA), to analyze more than 140 000 abstracts from the PubMed database. This analysis provides a comprehensive visualization of the amyloid field, capturing key trends such as the historical progression of amyloid research, the emergence of dominant subfields, the distribution of contributing authors and their respective countries, and the identification of latent research topics over time, including chemicals and small molecules. By integrating AI-driven text analysis with large-scale bibliometric data, this study offers a novel perspective on the evolution of amyloid research, facilitating a deeper interdisciplinary understanding. This work serves as a valuable interactive resource for researchers while highlighting the potential of machine learning-driven literature mapping in identifying knowledge gaps and guiding future investigations.

Cysteine cathepsins have been suggested as attractive therapeutic targets due to their critical role in several pathologies. In particular, inhibitors of cysteine cathepsins reduce the viability of tumor cells. The present study uses enzyme kinetics to characterize the interaction of human cathepsins L and S with their peptide substrate acetyl-QLLR-7-amino-4-methylcoumarin (Ac-QLLR-AMC) and peptide inhibitors with anti-tumor activity: FFSFGGAL (CS-PEP1) and acetyl-PLVE-fluoromethyl-ketone (Ac-PLVE-fmk). Due to multiple cellular locations of cathepsins, our study is conducted under different pH conditions, simulating lysosomal and cytosolic environments (pH 4.6 and 6.5-7.0). Catalytic activities of both cathepsins are higher at pH 6.5-7.0 compared to pH 4.6. Affinities for the substrate or inhibitor CS-PEP1 are higher for cathepsin L than S independent of pH, but show different pH sensitivities, reciprocating different pI's of the cathepsins. Mixed inhibition by CS-PEP1 is demonstrated for both cathepsins. While preincubation of cathepsins with CS-PEP1 does not enhance the inhibition, Ac-PLVE-fmk inactivates both cathepsins in the preincubation medium. A strong increase in the inactivation rate is observed with increasing pH in the interval including pK _a of the active site cysteine residues of cathepsins, in agreement with the irreversible modification by mono-fluoromethyl ketones of the catalytic thiolate anion. At pH 4.6, cathepsin L has a higher affinity for Ac-PLVE-fmk, but a slower rate of the irreversible modification compared to cathepsin S. Our findings highlight opportunities for differential targeting of L and S cathepsins by peptide inhibitors in different cellular compartments, providing directions for cathepsin- and location-specific drug design.

Bromo-DragonFLY (BDF), a potent designer psychedelic drug with hallucinogenic properties, has recently emerged as a significant recreational substance. Named for its dragonfly-like molecular structure, BDF induces prolonged psychedelic effects, with hallucinations lasting several days. Clinical reports highlight severe toxicity, including confusion, tachycardia, hypertension, seizures, renal failure, and, in extreme cases, death. BDF acts as a potent agonist of the 5-HT2A serotonin receptor subtype, which mediates the behavioral and psychedelic effects of hallucinogens. Despite its increasing prevalence and associated clinical implications, the precise molecular mechanisms underlying BDF's interaction with 5-HT2A remain inadequately characterized, particularly from an in silico perspective. This study addresses this gap by employing a comprehensive in silico framework to investigate the molecular interactions of BDF with the 5-HT_2A receptor. Molecular docking was used to identify binding sites, while all-atom molecular dynamics (MD) simulations provided insights into the stability of the protein-ligand complex, assessing deviations, local flexibility, and time-dependent gyration patterns. The results revealed stable and compact complex formation between BDF and 5-HT_2A, characterized by minimal per-residue fluctuations and high hydrogen bond occupancy, suggesting a highly stable interaction as shown experimentally. Additionally, principal component analysis, leveraging machine learning algorithms, demonstrated consistent motion, while free energy profiles highlighted stable energy basins with minimal variations for the BDF-5-HT_2A complex. These findings suggest strong binding affinities of BDF with the serotonin receptor, leading to highly stable complex formation. This study provides a foundational understanding of BDF's molecular interactions, offering critical insights into its role as a potent psychedelic agent and laying the groundwork for future investigations into the risks posed by novel designer drugs.

Metallothioneins (MTLs) are small, cysteine-rich proteins known for their ability to bind metal ions and exhibit flexible, disordered structures. The structural and functional characteristics of metallothionein I (MTL-1) from Caenorhabditis elegans were investigated, focusing on its behavior in both metal free (MTL-1 Apo) and metal-bond states with Zn²⁺, Cd²⁺, Cu²⁺, Hg²⁺, and Pb²⁺ divalent metal ions. Using molecular dynamics simulations and 3D modeling via AlphaFold, we characterized the flexibility and stability of MTL. The MTL-1 Apo form displayed high flexibility, aligning with its intrinsically disordered protein (IDP) nature, with 89.3% of its structure composed of coils, bends, and turns. Metal binding significantly enhanced the protein's stability, particularly with Zn²⁺, Cd²⁺, Cu²⁺, and Hg²⁺, reducing root mean square deviation (RMSD), root mean square fluctuation (RMSF), accessible surface area (SASA) and radius of gyration (R _g) values, indicating structural compaction. Conversely, Pb²⁺ showed a weaker stabilizing effect, with a more dynamic and less stable structure. Structural analysis revealed that conserved cysteine residues coordinate the metal through strong thiolate interactions, with additional contributions from non-cysteine residues, such as Glu and Lys. The study underscores the importance of incorporating intrinsically disordered protein models in MD simulations to provide deeper insights into how metallothionein's flexibility and stability vary in response to different metal ions, offering a structural perspective on their biological interactions and behavior under diverse environmental conditions. While thermodynamic aspects were not directly assessed, the results reveal consistent conformation trends across different metal coordination states.

The mechanisms driving amyloid assembly have long intrigued structural biologists, as they offer insights into systemic fibrotic changes and the dynamic behavior of transthyretin (TTR) aggregation, crucial for developing amyloid-targeted therapies. In TTR-associated amyloidosis, amyloid fibrils form via destabilization of the tetramer into dimers and monomers. While many TTR mutations have been studied, the atomistic impact of multiple mutations on amyloid transthyretin (ATTR) self-assembly remains underexplored. To the best of our knowledge, this is the first computational analysis reporting the impact of the L110M mutation on ATTR peptide aggregation. Using triplicate 1 μs all-atom molecular dynamics (MD) simulations, totaling 18 μs, the conformational dynamics of cross-β amyloid fibrils in the ATTR(105-115) segment were examined for both wild-type and L110M mutant TTR. The L110M mutation consistently enhanced the β-sheet content in all oligomers, with increases of ~1%, ~5%, and ~4% over the wild-type in the 2-, 4-, and 8-peptide systems, respectively. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations revealed higher effective binding free energy for the L110M mutant, with residue M110 contributing significantly to stabilization. These results suggest that L110M modestly enhances conformational order and stability in the TTR peptide assemblies without major structural disruption, deepening our understanding of amyloidogenesis in TTR-related disorders.

The sodium-pumping ubiquinone oxidoreductase sodium pumping quinone reductase (NQR) is an important enzyme in the respiratory chain of multiple pathogenic gram-negative bacteria. NQR has been proposed as a viable antibiotic target due to its importance in supporting energy-consuming reactions and its absence in human cells. In this study, molecular dynamics simulations were conducted to characterize the interactions between the ubiquinone binding pocket of Vibrio cholerae NQR with its substrate analogue ubiquinone-4 and three potent inhibitors: HQNO, aurachin-D42, and korormicin-A. Through interaction fingerprinting, distance calculations, and clustering analysis, important binding motifs for each of these ligands were identified. Subunit B residues K54, F137, E144, V145, V155, E157, G158, F159, and F160 were frequently identified as establishing either hydrogen bonding interactions or hydrophobic interactions with these three ligands. The findings of this in silico study are interpreted in view of mutagenesis analyses previously published in the literature. The elucidation of important binding interactions associated with the inhibitors is critical as it informs structure-activity relationships, which are essential for the development of novel antibiotics targeting NQR.

Kinetics of intramolecular disulphide pairing in a six-cysteine containing plant toxin peptide cycloviolacin O1 (CyO1) having a cyclic backbone and a cyclic cystine knot (CCK) is studied using a Hidden Markov Model (HMM) created from molecular dynamics simulation trajectories. Starting from a fully reduced form of CyO1 (peptide-D), the kinetic model is created to track the peptide's evolution to a native-like state (peptide-N) where all three correct pairs of S-S linkages are most likely to be observed. The structural evolution and fluctuation of peptide-D through many partially folded S-S intermediates and the associated propensity, along with the timescale of formation of a single or simultaneously two or three S-S pairs, is studied using this Markov chain. The phenomenon of intramolecular S-S pairing, as observed in proteins and peptides, is fast, with a computed rate constant of ~10⁶ s^-1 in line with experimental observations in the bacterial disulphide bond redox protein DsbD. Rate networks and transition path theory analysis are used to find the most probable pathway for peptide-D to evolve into peptide-N.

The advancement of T cell engineering has significantly transformed the field of cancer immunotherapy. In particular, T cells equipped with modified T cell receptors present a promising therapeutic strategy, especially for addressing solid tumors. Nonetheless, critical obstacles, including suboptimal clinical response rates, off-target toxicity, and the immunosuppressive nature of the tumor microenvironment, have impeded the full clinical implementation of this approach. Understanding the molecular determinants governing the interaction between T-cell receptors and major histocompatibility complex molecules is pivotal not only for designing TCRs capable of selectively and effectively recognizing MHC on cancer cells but also for minimizing off-target toxicity, thereby improving the safety profile of TCR-based therapies. In this study, we used a test case involving a natural TCR (c728) and its affinity-enhanced variant (c796), which differ by a single conservative mutation in the $\beta \text{CDR1}$ region. Through molecular dynamics simulations, MM/PBSA binding energy and Free Energy Perturbation calculations, residue-specific energy decomposition, and correlation analyses, we dissected the molecular basis of the engineered TCR's six-fold increase in binding affinity for the peptide-MHC complex compared to its parental counterpart. Interestingly, our results indicate that this affinity enhancement is not directly attributable to the mutation itself but rather to the dynamic interplay of both proximal and distal residues that are either directly correlated with the mutation or connected via allosteric pathways. Our findings, which align with experimental data, highlight the nuanced role of structural flexibility and allosteric communication in shaping TCR-pMHC interactions. By demonstrating the utility of combining computational techniques to unravel these dynamics, this work emphasizes how similar approaches can guide the rational design of engineered TCRs with improved efficacy and specificity, advancing their application in cancer immunotherapy.

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.