Proteins-Structure Function and Bioinformatics最新文献

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.

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.

Transport and Golgi Organization 2 Homolog (TANGO2) protein deficiency disorder (TDD) is a rare autosomal recessive disorder characterized by multi-systemic abnormalities and significant phenotypic variability including neurodevelopmental delay, seizures, intermittent ataxia, hypothyroidism, rhabdomyolysis, life-threatening metabolic derangements, and cardiac arrhythmias. Mutations in TANGO2 result in mitochondrial dysfunction, abnormal lipid homeostasis with cardiolipin deficiency, and impaired Golgi-ER trafficking in TANGO2 patient-derived cells. Despite the wide recognition of the clinical manifestations of TDD and numerous molecular studies, the precise function of TANGO2 and the pathophysiology of TDD remain poorly understood. A computationally derived three-dimensional structure model suggested that TANGO2 adopts an αββα-fold, similar to the N-terminal nucleophile aminohydrolase (Ntn) superfamily of proteins, but the experimentally verified structure has not been available thus far. Here, we present the first crystal structure of the recombinant human TANGO2, determined at 1.70 Å resolution. The X-ray structure data confirmed its predicted tertiary fold with similarity to the Ntn-hydrolase family of proteins, and the comparative analysis of the active site architecture, including residues involved in catalysis and putative ligand binding site, suggests a potential hydrolase function. Additional examination of the common mutation sites found in TDD patients provides insight regarding their potential effect on protein structure integrity.

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.

Network clustering is employed in bioinformatics and data mining studies to investigate the structural and functional properties of protein-protein interaction (PPI) networks. In multiple studies over the past two decades, network clustering has proven valuable for uncovering functional modules and elucidating the functions of previously undiscovered proteins. Protein complexes are vital cellular components that play a crucial role in generating biological activity. Experimental techniques have inherent limitations in inferring protein complexes. Given these constraints, numerous computational methods have emerged over the past decade for predicting protein complexes. Typically, these methods take the input PPI data and generate predicted protein complexes as output subnetworks. Most of these methods have shown encouraging outcomes in predicting protein complexes. Prediction is challenging for sparse, small, and overlapping complexes. New strategies should include explicit knowledge about the biological characteristics of proteins to increase performance. Furthermore, specific issues should be considered more effectively in the future while developing new complex prediction algorithms. The bioinformatics community has developed various techniques for clustering PPI networks, which we identified, analyzed, and compared in this paper. This review evaluates various graph clustering algorithms for protein complex identification, facilitating the benchmarking of existing methods, identifying limitations, motivating the development of novel computational tools, and ultimately improving biological insight and therapeutic progress. Through the assessment of strengths and limitations, researchers may develop efficient and scalable algorithms designed explicitly for biological data, integrating graph-based methodologies with machine learning and deep learning approaches. This study is an invaluable tool for new researchers in the area to recognize upcoming trends, including dynamic PPI networks and temporal complex identification.

AlphaFold (AF) is a valuable tool for generating protein 3D structures, but its application in structure-based drug design is limited. In this study, we introduce AF Optimizer-a new deep learning-assisted approach that refines binding site geometry based on neural network scores and calculated free binding energy. We refined TTK protein geometry using AF Optimizer and performed virtual screening using the optimized version of the AF-generated protein model. The application of the model showed a decrease in steric clashes with ligands from known crystal complexes, more precise results of molecular docking and virtual screening, and hits enrichment during a prospective in vitro study.

Metamorphic proteins (MPs) can fold into two or more distinct spatial structures. Increasing interest in MPs has spurred the search for computational tools to predict proteins fold-switching potential and model their refolding pathways. Here we address this problem by using the recently reported generative diffusion predictor UFConf, based on the AlphaFold2 network. We have developed a new UFConf-driven algorithm dubbed IMSD (iterative modeling via structural diffusion) to model the MP's path from one conformational state to another. In brief, we begin with the experimental structure of state A, perturb it through the "noising" process, and infer a number of models (replicas) through the reverse diffusion or "denoising" process. From this set of models, we choose the one that is closest to the alternative structure B; then we use it as a starting point to perform another round of noising/denoising and thus generate the next batch of replicas. Repeating this process in an iterative fashion, we have been able to map the entire path from state A to state B for metamorphic proteins GA98, SA1 V90T, and the C-terminal domain of RfaH. The obtained representation of the fold-switching pathways in these MPs is consistent with the dual-funnel energy landscape observed in the previous modeling studies and shows good agreement with the available experimental data. The new UFConf-based IMSD protocol can be viewed as a part of the emerging generation of modeling tools aiming to model protein dynamics by means of deep learning technology.

Expansins loosen plant cell wall networks through disrupting non-covalent bonds between cellulose microfibrils and matrix polysaccharides. Whereas expansins were first discovered in plants, expansin-related proteins have since been identified in bacteria and fungi. The biological function of microbial expansins remains unclear; however, several studies have shown distinct binding preferences toward different structural polysaccharides. Earlier studies of bacterial expansin-related proteins uncovered sequence and structural features that correlate to substrate binding. Herein, 20 fungal expansin-related sequences were recombinantly produced in Komagataella phaffii, and the purified proteins were compared in terms of substrate binding to cellulosic and chitinous substrates. The impact of pH on the zeta potential of prioritized substrates was also measured, and Principal Component Analysis was performed to uncover correlations between protein characteristics (e.g., pI, hydrophobicity, surface charge distribution) and measured substrate binding preferences. Whereas acidic proteins with a predicted pI less than 5.0 preferentially bound to chitin, basic proteins with pI greater than 8.0 preferentially bound to xylan and xylan-containing fiber. Similar to many cellulases, binding to cellulose was correlated to relatively high aromatic amino acid content in the protein sequence and presence of a carbohydrate binding module (CBM), which in the case of expansins is a C-terminal CBM63. Whereas overall sequence characteristics could be correlated to substrate binding preference, the identity of amino acids occupying conserved positions that impact protein activity was better correlated with loosenin versus expansin classifications.

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.