Effects of β-TCP Ceramics on Intracellular Ca^(2+) Concentration,Mineralization of Osteoblast and Protein Structure

β-TCP, as one of calcium phosphates ceramics, exerts perfect biocompatibility and osteoconductivity, and is clinically used as a bone graft substitute for decades. Consequently, the effects of β-TCP ceramics on intracellular Ca2＋ concentration, mineralization of osteoblast and BSA protein structure were studied. Results showed that β-TCP could increase the intracelluar Ca2＋ concentration and mineralization of osteoblast, indicating that β-TCP ceramics could take part in the organic metabolism and the degradation product had no detrimental effect on osteoblast in vitro. Furthermore, β-TCP ceramics could increase the content of α-helix and β-pleated sheet and change BSA into more ordering structure, those changes might be favorable for the biomineralization after β-TCP ceramics implanted.

The Evolutionary Computation Techniques for Protein Structure Prediction:A Survey

In this paper, the applications of evolutionary algorithm in prediction of protein secondary structure and tertiary structures are introduced, and recent studies on solving protein structure prediction problems using evolutionary algorithms are reviewed, and the challenges and prospects of EAs applied to protein structure modeling are analyzed and discussed.

Heuristic Quasi-physical Algorithm for Protein Structure Prediction

A three-dimensional off-lattice protein model with two species of monomers, hydrophobic and hydrophilic, is studied. Enligh- tened by the law of reciprocity among things in the physical world, a heuristic quasi-physical algorithm for protein structure prediction problem is put forward. First, by elaborately simulating the movement of the smooth elastic balls in the physical world, the algorithm finds low energy configurations for a given monomer chain. An ＂off-trap＂ strategy is then proposed to get out of local minima. Experimental results show promising performance. For all chains with lengths 13≤n ≤55, the proposed algorithm finds states with lower energy than the putative ground states reported in literatures. Furthermore, for chain lengths n = 21, 34, and 55, the algorithm finds new low energy configurations different from those given in literatures.

Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.

PHYLETIC RELATIONSHIP OF PROTEIN STRUCTURES BASED ON SPATIAL PREFERENCE OF RESIDUES

A distance measure that infers to indicate the evolutionary relationship of protein structures has been developed based on spatial preference factors of residues. The spatial preference factor is a reflection of the environment of residues in tertiary structure. Compared with the phyletic relationships derived from sequence homologies and three-dimensional structures, we find that the two lines of evolution are similar in general. This approach is applied to a group of glins here.

Tuning Metallic Co0.85Se Quantum Dots/Carbon Hollow Polyhedrons with Tertiary Hierarchical Structure for High-Performance Potassium Ion Batteries

Potassium-ion batteries(KIBs)are a potential candidate to lithium-ion batteries(LIBs)but possess unsatisfactory capacity and rate properties.Herein,the metallic cobalt selenide quantum dots(Co0.85Se-QDs)encapsulated in mesoporous carbon matrix were designed via a direct hydrothermal method.Specifically,the cobalt selenide/carbon composite(Co0.85Se-QDs/C)possesses tertiary hierarchical structure,which is the primary quantum dots,the secondary petals flake,and the tertiary hollow micropolyhedron framework.Co0.85Se-QDs are homogenously embedded into the carbon petals flake,which constitute the hollow polyhedral framework.This unique structure can take the advantages of both nanoscale and microscale features:Co0.85Se-QDs can expand in a multidimensional and ductile carbon matrix and reduce the K-intercalation stress in particle dimensions;the micropetals can restrain the agglomeration of active materials and promote the transportation of potassium ion and electron.In addition,the hollow carbon framework buffers volume expansion,maintains the structural integrity,and increases the electronic conductivity.Benefiting from this tertiary hierarchical structure,outstanding K-storage performance(402 mAh g?1 after 100 cycles at 50 mA g?1)is obtained when Co0.85Se-QDs/C is used as KIBs anode.More importantly,the selenization process in this work is newly reported and can be generally extended to prepare other quantum dots encapsulated in edge-limited frameworks for excellent energy storage.

Insights into tertiary lymphoid structures in the solid tumor microenvironment: anti-tumor mechanism, functional regulation, and immunotherapeutic strategies

Tertiary lymphoid structures(TLSs)are ectopic immune cell aggregations that develop in peripheral tissues in response to a wide range of chronic inflammatory conditions,including infection,autoimmune disease,and cancer.In the tumor microenvironment(TME),the structures of TLSs,including B-cell-and T-cell-enriched areas indicate that the TLSs might be the local site during the initiation and maintenance of humoral and cellular immune responses against cancers.Numerous studies have evaluated the expression of TLSs in different cancer patients and their association with prognoses of cancer patients.It was shown that welldeveloped TLSs characterized by mature B cells synthesized tumor specific antibodies,which were considered as specific markers for a good prognosis.However,there are still some immunosuppressive factors existing in the TLSs that may affect anti-tumor responses.These factors include dysfunctional B cells,regulatory T cells,and T follicular regulatory cells.The complexity and heterogeneity of the TLS composition may affect the function and activity of TLSs;it is therefore essential to fully understand the function and influencing factors in TLSs.It has been reported that checkpoint inhibitors and vaccines are currently being developed to reprogram the TME by establishing mature TLSs to improve cancer immunotherapies.In this review,we focused on recent advances in TLSs in human solid tumors,including structural characteristics and classes,antitumor mechanisms,immunosuppressive factors,and TLSbased therapeutic approaches.

Discrete Differential Geometry of <i>n</i>-Simplices and Protein Structure Analysis

This paper proposes a novel discrete differential geometry of n-simplices. It was originally developed for protein structure analysis. Unlike previous works, we consider connection between space-filling n-simplices. Using cones of an integer lattice, we introduce tangent bundle-like structure on a collection of n-simplices naturally. We have applied the mathematical framework to analysis of protein structures. In this paper, we propose a simple encoding method which translates the conformation of a protein backbone into a 16-valued sequence.

The Earliest Discovery of the Role of Magnesium Ions on Stabilizing the Tertiary Structure of the Transfer RNA and Its Biological Significance —A Short Memoir

In early of 1960s, I was a graduate student studying on tRNA biochemistry. In the course of the research, the magnesium ions stabilized the tertiary structure of tRNA, resulting in its resistance to enzymatic degradation was discovered independently. The experiment of deaminated (denatured) tRNA obtained from native tRNA was designed and conducted and further proved the validity of this finding. It was found that magnesium ions could stabilize the tertiary structure of the natrive tRNA but could not stabilize structure of the deaminated tRNA. In term of the methodology, this stabilization technique has been widely applied in sequencing analysis of RNA and has greatly promoted the progress in the study of primary structure of RNA. More importantly, the stabilization of the tertiary structure of RNA by magnesium ions plays a key role both in the processing of messenger RNAs and the ribozyme activity. After our first article in Chinese was published in 1963, a paper of Nishimura & Novelli came into our note. The received date of their paper was March 22 of 1963, only 4 days earlier than that of our first paper. Thus, we and Nishimura & Novelli made almost at the same time the earliest discovery of the role of magnesium ions on stabilizing the tertiary structure of the transfer RNA and thus resulted in resistance of tRNA degradation by enzymes. However, this discovery was not initially appreciated for a period of time but was finally “visualized” and proved by X-ray crystal structure of yeast phenylalanine tRNA, which has provided more accurate information on the geometry of the magnesium-binding sites in tRNA.

Broadening environmental research in the era of accurate protein structure determination and predictions

The deep-learning protein structure prediction method AlphaFold2 has garnered enormous attention beyond the realm of structural biology,for its groundbreaking contribution to solving the"protein foiding problem"In this perspective,we explore the connection between protein structure studies and environmental research,delving into the potential for addressing specific environmental challenges.Proteins are promising for environmental applications because of the functional diversity endowed by their structural complexity.However,structural studies on proteins with environmental significance remain scarce.Here,we present the opportunity to study proteins by advancing experimental determination and deep-learning prediction methods.Specifically,the latest progress in environmental research via cryogenic electron microscopy is highlighted.It allows us to determine the structure of protein complexes in their native state within cells at molecular resolution,revealing environmentally-associated structural dynamics.With the remarkable advancements in computational power and experimental resolution,the study of protein structure and dynamics has reached unprecedented depth and accuracy.These advancements will undoubtedly accelerate the establishment of comprehensive environmental protein structural and functional databases.Tremendous opportunities for protein engineering exist to enable innovative solutions for environmental applications,such as the degradation of persistent contaminants,and the recovery of valuable metals as well as rare earth elements.

Non-covalent interaction between almond protein and sinapic acid: impact on protein structure and antioxidant activity

Plant phenolic acids are good sources of antioxidants and sinapic acid(SA)is one of them that can be applied in protein-based food system.However,little research is available regarding interactions between almond protein(AP)and SA.In this study,structure-affinity interaction between SA and AP,structure and antioxidant activity of proteins were investigated.Different mathematical models showed that Ka of binding SA and AP were 3.27×10^4 L/mol and 3.08×10^4 L/mol.CD(Circular dichroism)spectroscopy and FT-IR(Fourier transform infrared)spectroscopy showed that the amount of random coil andα-helix decreased whileβ-sheet increased in AP-SA complex.In combination,the interaction model of AP-SA complex was static quenching and attributed to hydrophobic interaction.Further,AP-SA complex exerted better DPPH radical scavenging ability(36.97±0.78%),ABTS+radical scavenging ability(47.26±0.45%),and higher ORAC value(2.41±0.23 M trolox/g)compared to AP.In the further,SA can be applied in protein matrix to improve film stability,gel strength and restraining fat oxidation degradation.

Impact of Preparation Process on the Protein Structure and on the Volatile Compounds in <i>Eisenia foetida</i>Protein Powders

Protein powders from Eisenia foetida were prepared using different drying processes and fractionation. Differential scanning calorimetry was used to show that heat denaturation occurred during the drying process above 42°C. Protein solubility was also studied. The addition of dissociating reagents allowed concluding that solubility was decreased during oven drying due to thermo denaturation including hydrogen bonds. The volatile compounds of the different powders were extracted by solid phase micro-extraction and identified by mass spectrometry. Volatile compounds were related to lipid oxidation and Maillard reactions occurring during the preparation of the powders. High drying temperatures led to more volatile compounds resulting from Maillard reactions. In the protein powder preparation process, a fractionation step led to a “pulp fraction” and a “juice fraction” of earthworms. The “pulp fraction” contained less odorant volatile compounds resulting from Maillard reactions than the “juice fraction” did.

A Low-Memory-Requiring and Fast Approach to Cluster Large-Scale Decoy Protein Structures

This work demonstrates the so-called PCAC (Protein principal Component Analysis Clustering) method, which clusters large-scale decoy protein structures in protein structure prediction based on principal component analysis (PCA), is an ultra-fast and low-memory-requiring clustering method. It can be two orders of magnitude faster than the commonlyused pairwise rmsd-clustering (pRMSD) when enormous of decoys are involved. Instead of N(N – 1)/2 least-square fitting of rmsd calculations and N2 memory units to store the pairwise rmsd values in pRMSD, PCAC only requires N rmsd calculations and N × P memory storage, where N is the number of structures to be clustered and P is the number of preserved eigenvectors. Furthermore, PCAC based on the covariance Cartesian matrix generates essentially the identical result as that from the reference rmsd-clustering (rRMSD). From a test of 41 protein decoy sets, when the eigenvectors that contribute a total of 90% eigenvalues are preserved, PCAC method reproduces the results of near-native selections from rRMSD.

Hierarchy of Protein Loop-Lock Structures: A New Server for the Decomposition of a Protein Structure into a Set of Closed Loops

HoPLLS (Hierarchy of protein loop-lock structures) (http://leah.haifa.ac.il/~skogan/Apache/mydata1/main.html) is a web server that identifies closed loops-a structural basis for protein domain hierarchy. The server is based on the loop-and-lock theory for structural organisation of natural proteins. We describe this web server, the algorithms for the decomposition of a 3D protein into loops and the results of scientific investigations into a structural “alphabet” of loops and locks.

Visualization of protein structure relationships using constrained twin kernel embedding

In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.

A GASVM Algorithm for Predicting Protein Structure Classes

The research methods of protein structure prediction mainly focus on finding effective features of protein sequences and developing suitable machine learning algorithms. But few people consider the importance of weights of features in classification. We propose the GASVM algorithm (classification accuracy of support vector machine is regarded as the fitness value of genetic algorithm) to optimize the coefficients of these 16 features (5 features are proposed first time) in the classification, and further develop a new feature vector. Finally, based on the new feature vector, this paper uses support vector machine and 10-fold cross-validation to classify the protein structure of 3 low similarity datasets (25PDB, 1189, FC699). Experimental results show that the overall classification accuracy of the new method is better than other methods.

Changes of structure and functional properties of rice protein in the fresh edible rice during the seed development

It has been reported that fresh edible rice has more bioactive compounds and its protein is easier to digest and has lower hypoallergenic than mature rice. In this paper, the changes in structure and functional properties of proteins at five different stages, including early milky stage(EMS), middle milky stage(MMS), late milky stage(LMS), waxy ripe stage(WS)and ripening stage(RS), during the seed development were investigated. It was found that with the seed developing, the molecular weight of fresh rice protein gradually become larger while the secondary structure changed from the highest content of disordered structure at MMS to the highest content of ordered structure at RS, which affect the surface hydrophobicity and then the functional properties of proteins, including foaming properties, emulsifying properties and oil holding capacity. Fresh rice protein at MMS has the strongest surface hydrophobicity while fresh edible rice protein at RS has the strongest oil holding capability. The results of our study can provide a theoretical basis for the application of fresh rice protein in the food industry and help to develop new fresh edible rice food.

Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

Detection and quantitative analysis of tumor-associated tertiary lymphoid structures

Tumor-associated tertiary lymphoid structures(TLSs)are ectopic lymphoid formations within tumor tissue,with mainly B and T cell populations forming the organic aggregates.The presence of TLSs in tumors has been strongly associated with patient responsiveness to immunotherapy regimens and improving tumor prognosis.Researchers have been motivated to actively explore TLSs due to their bright clinical application prospects.Various studies have attempted to decipher TLSs regarding their formation mechanism,structural composition,induction generation,predictive markers,and clinical utilization.Meanwhile,the scientific approaches to qualitative and quantitative descriptions are crucial for TLS studies.In terms of detection,hematoxylin and eosin(H&E),multiplex immunohistochemistry(mIHC),multiplex immunofluorescence(mIF),and 12-chemokine gene signature have been the top approved methods.However,no standard methods exist for the quantitative analysis of TLSs,such as absolute TLS count,analysis of TLS constituent cells,structural features,TLS spatial location,density,and maturity.This study reviews the latest research progress on TLS detection and quantification,proposes new directions for TLS assessment,and addresses issues for the quantitative application of TLSs in the clinic.

Unraveling structure and performance of protein a ligands at liquid–solid interfaces: A multi-techniques analysis

Oriented ligand immobilization is one of the most effective strategies used in the design and construction of a high-capacity protein A chromatography. In this work, cysteine was introduced as anchoring sites by substituting a specific residue on Helix Ⅰ, Ⅱ, and at C-terminus of antibody binding domain Z from protein A, respectively, to investigate structural evolution and binding behavior of protein A ligands at liquid-solid interfaces. Among the three affinity dextran-coated Fe_(3)O_(4) magnetic nanoparticles(Fe_(3)O_(4)@Dx MNPs), affinity MNPs with the immobilized ligand via N11C on Helix Ⅰ(Fe_(3)O_(4)@Dx-Z_(1) MNPs) had the highest helical content, and MNPs with the immobilized ligand via G29C on Helix Ⅱ(Fe_(3)O_(4)@Dx-Z_(2) MNPs) had the lowest helical content at the same pHs. It was attributed to less electrostatic attraction of ligand to negatively charged surface on Fe_(3)O_(4)@Dx-Z_(1) MNPs because of less positive charged residues on Helix Ⅰ(K6) than Helix Ⅱ(R27/K35). Among the three affinity MNPs, moreover, the highest affinity to immunoglobulin G(IgG) binding was observed on Fe_(3)O_(4)@Dx-Z_(1) MNPs in isothermal titration calorimetry measurement, further validating greater structural integrity of the ligand on Fe_(3)O_(4)@Dx-Z_(1) MNPs. Finally,the study of IgG binding on MNPs and 96-well plates showed that anchoring sites for ligand immobilization had distinct influences on IgG binding and IgG-mediated antigen binding. This work illustrated that anchoring sites of the ligands had a striking significance for the molecular structure of the ligand at liquid-solid interfaces and raised an important implication for the design and optimization of protein A chromatography and protein A-based immunoassay analysis.