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Isotactic polypropylene (iPP) is a frequently used polymer in industrial processing and scientific research, used as a model material. This review highlights some current and forefront issues in polymer science and engineering. This review alludes to classical deformation schemes to interpret the complicated deformation in isotactic polypropylene as a semicrystalline polymer. Also, due to the complex hierarchical structures of crystallized polymers, the progress on the multiscale and multistage microstructural evolution covering each deformation stage from the initiation of plasticity until failure is mainly concerned, particularly in terms of crystalline morphology changes. Specifically, the characterization of the microstructure evolution of a single spherulite, lamella, and chain of isotactic polypropylene will be emphasized and mentioned. Here, the authors are most interested in the results obtained from stretching (strain‐induced) orientation and crystallization studies accompanied by the in situ scattering and spectroscopy techniques offering a detection window from nano to micrometer scale, which should be able to reflect the microstructural changes at different length scales of interest. During polymer processing, the melts are frequently subjected to shear or/and elongation flow fields, producing flow‐induced molecular chain orientation in the melt. The oriented molecular chains crystallize differently than those encountered under quiescent conditions. Thus, orientation‐induced crystallization has long been an object of intense interest. A vast body of research is reported about the effects of preorientation on the crystallization of various polymers. Among them, iPP is most frequently studied since its diversified structure and morphology are very sensitive to changes in processing conditions and molecular parameters. [ABSTRACT FROM AUTHOR]

An analytical solution to the master equation governing the conformational dynamics of linear polymer chains is formulated. Symmetric chains with N bonds subject to independent rotational potentials are considered. The eigenvalues of the transition rate matrix, which characterize the frequencies of the various relaxation modes, and the corresponding eigenvectors and eigenrows are obtained in closed form. A simple recurrence equation permits one to express the eigenvalues of the N-bond motion in terms of the nonzero eigenvalues associated with the isomeric transitions of single bonds. This leads to a clear understanding of the increase in conformational mobility with N. [ABSTRACT FROM AUTHOR]

We have studied the freezing of hard spheres with a Yukawa attractive or repulsive potential in the mean spherical approximation by using the density functional theory proposed by Ramakrishnan and Yussouff and extended later by Haymet and Oxtoby. We have calculated the densities of the liquid and solid phases at the phase transition as a function of temperature for two different parameters of the range of Yukawa potential (z=1 and z=5). [ABSTRACT FROM AUTHOR]

Background: Sequence matching is extremely important for applications throughout biology, particularly for discovering information such as functional and evolutionary relationships, and also for discriminating between unimportant and disease mutants. At present the functions of a large fraction of genes are unknown; improvements in sequence matching will improve gene annotations. Universal amino acid substitution matrices such as Blosum62 are used to measure sequence similarities and to identify distant homologues, regardless of the structure class. However, such single matrices do not take into account important structural information evident within the different topologies of proteins and treats substitutions within all protein folds identically. Others have suggested that the use of structural information can lead to significant improvements in sequence matching but this has not yet been very effective. Here we develop novel substitution matrices that include not only general sequence information but also have a topology specific component that is unique for each CATH topology. This novel feature of using a combination of sequence and structure information for each protein topology significantly improves the sequence matching scores for the sequence pairs tested. We have used a novel multi-structure alignment method for each homology level of CATH in order to extract topological information. Results: We obtain statistically significant improved sequence matching scores for 73 % of the alpha helical test cases. On average, 61 % of the test cases showed improvements in homology detection when structure information was incorporated into the substitution matrices. On average z-scores for homology detection are improved by more than 54 % for all cases, and some individual cases have z-scores more than twice those obtained using generic matrices. Our topology specific similarity matrices also outperform other traditional similarity matrices and single matrix based structure methods. When default amino acid substitution matrix in the Psi-blast algorithm is replaced by our structure-based matrices, the structure matching is significantly improved over conventional Psi-blast. It also outperforms results obtained for the corresponding HMM profiles generated for each topology. Conclusions: We show that by incorporating topology-specific structure information in addition to sequence information into specific amino acid substitution matrices, the sequence matching scores and homology detection are significantly improved. Our topology specific similarity matrices outperform other traditional similarity matrices, single matrix based structure methods, also show improvement over conventional Psi-blast and HMM profile based methods in sequence matching. The results support the discriminatory ability of the new amino acid similarity matrices to distinguish between distant homologs and structurally dissimilar pairs. [ABSTRACT FROM AUTHOR]

Tyrosinase is a key enzyme target to design new chemical ligands against melanogenesis. In the current review, different chemical derivatives are explored which have been used as anti-melanogenic compounds. These are different chemical compounds naturally present in plants and semi-synthetic and synthetic compounds inspired by these natural products, such as kojic acid produced by several species of fungi; arbutin—a glycosylated hydroquinone extracted from the bearberry plant; vanillin—a phenolic aldehyde extracted from the vanilla bean, etc. After enzyme inhibition screening, various chemical compounds showed different therapeutic effects as tyrosinase inhibitors with different values of the inhibition constant and IC50. We show how appropriately designed scaffolds inspired by the structures of natural compounds are used to develop novel synthetic inhibitors. We review the results of numerous studies, which could lead to the development of effective anti-tyrosinase agents with increased efficiency and safety in the near future, with many applications in the food, pharmaceutical and cosmetics industries. [ABSTRACT FROM AUTHOR]

Background: The ability to generate, visualize, and analyze motions of biomolecules has made a significant impact upon modern biology. Molecular Dynamics has gained substantial use, but remains computationally demanding and difficult to setup for many biologists. Elastic network models (ENMs) are an alternative and have been shown to generate the dominant equilibrium motions of biomolecules quickly and efficiently. These dominant motions have been shown to be functionally relevant and also to indicate the likely direction of conformational changes. Most structures have a small number of dominant motions. Comparing computed motions to the structure's conformational ensemble derived from a collection of static structures or frames from an MD trajectory is an important way to understand functional motions as well as evaluate the models. Modes of motion computed from ENMs can be visualized to gain functional and mechanistic understanding and to compute useful quantities such as average positional fluctuations, internal distance changes, collectiveness of motions, and directional correlations within the structure. Results: Our new software, MAVEN, aims to bring ENMs and their analysis to a broader audience by integrating methods for their generation and analysis into a user friendly environment that automates many of the steps. Models can be constructed from raw PDB files or density maps, using all available atomic coordinates or by employing various coarse-graining procedures. Visualization can be performed either with our software or exported to molecular viewers. Mixed resolution models allow one to study atomic effects on the system while retaining much of the computational speed of the coarse-grained ENMs. Analysis options are available to further aid the user in understanding the computed motions and their importance for its function. Conclusion: MAVEN has been developed to simplify ENM generation, allow for diverse models to be used, and facilitate useful analyses, all on the same platform. This represents an integrated approach that incorporates all four levels of the modeling process - generation, evaluation, analysis, visualization - and also brings to bear multiple ENM types. The intension is to provide a versatile modular suite of programs to a broader audience. MAVEN is available for download at http://maven.sourceforge.net. [ABSTRACT FROM AUTHOR]

Background: Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is. Methods: In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built -- the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction. Results: The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported. Conclusions: Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes. [ABSTRACT FROM AUTHOR]

Background: Knowledge-based potentials have been widely used in the last 20 years for fold recognition, protein structure prediction from amino acid sequence, ligand binding, protein design, and many other purposes. However generally these are not readily accessible online. Results: Our new knowledge-based potential server makes available many of these potentials for easy use to automatically compute the energies of protein structures or models supplied. Our web server for protein energy estimation uses four-body potentials, short-range potentials, and 23 different two-body potentials. Users can select potentials according to their needs and preferences. Files containing the coordinates of protein atoms in the PDB format can be uploaded as input. The results will be returned to the user's email address. Conclusions: Our Potentials 'R'Us server is an easily accessible, freely available tool with a web interface that collects all existing and future protein coarse-grained potentials and computes energies of multiple structural models. [ABSTRACT FROM AUTHOR]

Abstract: Monte Carlo simulations based on rotational isomeric state models were carried out to determine how amorphous polyethylene chains are deformed by the presence of spherical filler particles. The resulting distributions of the chain end-to-end distances were then employed to calculate mechanical properties of the cross-linked chains. The basic goals were (i) to refine previous simulations of this type carried out on this system to remove possible sampling biases, and (ii) to document the circumstances under which the particles stretch the chains to larger end-to-end distances, rather than compress them to smaller distances, and (iii) to study in more detail the flattening of the chains near particle surfaces. Comparisons with experiments include filler-induced changes in chain dimensions measured by neutron scattering, and general features of the stress–strain isotherms widely observed for reinforced elastomers in simple elongation. [Copyright &y& Elsevier]

Abstract: We derived the analytical solution of the eigenvalue problem for stereo-regular vinyl chains, such as stereo-regular polypropylene chains. The solution is applicable to all stereo-regular polymers of the type (AB) x which do not have symmetry between gauche + and gauche − states, and to polymers, such as polyoxymethylene or polydimethylsiloxane, for which symmetry between the gauche + and gauche − states does exist. For symmetric chains, the general solution of the eigenvalue problem is reduced to the known solution for polyoxymethylene chains. To illustrate the method the calculations have been performed for the three rotational states (trans, gauche + and gauche −) model, but the general algebraic solution is applicable for any ν rotational states models of polymer chains. We used the analytical solution of the eigenvalue problem to calculate numerically elastic properties of stereo-regular polypropylene chains within the framework of Mark–Curro theory (J Chem Phys, 79, 5705, 1983). [Copyright &y& Elsevier]

The transfer matrix method for generating lattice conformations of proteins is explained and applied to lattice proteins having high-level cooperativity to represent hydrophobic interactions. The main advantage of the method is the extremely efficient attrition-free generation and enumeration of compact conformations. We review the application of the method for the generation and complete, exact enumeration of all conformation for linear and cyclic chains in 2D on the square lattice and in 3D on the cubic lattice. We show for compact conformations that the growth of the chain in a piecewise way, cross-section by cross-section, is much more efficient than the traditional linear chain growth. We discuss an extension of the method by including information about the amino acid sequence. We develop a Zimm–Bragg [J Chem Phys 31 (1959) 476–85]-like theory of hydrophobic cluster formation by using the transfer matrix method. We show that the transfer matrix approach to the generation and averaging over chain conformations can be formulated as an algebraic problem. We show also how the transfer matrix method can be extended to off-lattice proteins. [Copyright &y& Elsevier]

Background: Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Results: In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. Conclusions: We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems. [ABSTRACT FROM AUTHOR]

We studied the refolding free energy landscape of 26 proteins using the Go-like model. The distance between the denaturated state and the transition state, XF, was calculated using the Bell theory and the nonlinear Dudko-Hummer-Szabo theory, and its relation to the geometrical properties of the native state was considered in detail. We showed that none of the structural parameters, such as the contact order, protein length, and radius of cross section, correlate with XF for all classes of proteins. To overcome this problem, we have introduced the nematic order parameter P02, which describes the ordering of the structured elements of the native state. Due to its topologically global nature, P02 is better than other structural parameters in describing the folding free energy landscape. In particular, P02 displays a good correlation with XF extracted from the nonlinear theory for all three classes of proteins. Therefore, this parameter can be used to predict XF for any protein, if its native structure is known. [ABSTRACT FROM AUTHOR]

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment. [ABSTRACT FROM AUTHOR]

Mushrooms are new potential sources of valuable medicines, long neglected because of difficulties experienced in their cultivation. There is a large variety of medicinal mushrooms which possess significant therapeutic properties and are used as medications for various diseases because they contain several novel highly bioactive components. Medicinal mushrooms can be identified based on their morphology, size, mass, and the color of the stalk, cap and spore, and attachment to the stalk. Medicinal mushrooms possess a variety of important biological activities and are used as antioxidants, hepatoprotectors, anticancer, antidiabetic, anti-inflammatory, antiaging, antiviral, antiparasitic, and antimicrobial agents, among others. This review provides a basic overview of the chemical scaffolds present in mushrooms and their therapeutic implications in the human body. [ABSTRACT FROM AUTHOR]

Charcot–Marie–Tooth disease (CMT) is a heritable neurodegenerative disease of peripheral nervous system diseases in which more than 100 genes and their mutations are associated. Two consanguineous families Dera Ghazi Khan (PAK-CMT1-DG KHAN) and Layyah (PAK-CMT2-LAYYAH) with multiple CMT-affected subjects were enrolled from Punjab province in Pakistan. Basic epidemiological data were collected for the subjects. Nerve conduction study (NCS) and electromyography (EMG) were performed for the patients. Whole-exome sequencing (WES) followed by Sanger sequencing was applied to report the genetic basic of CMT. The NCS findings revealed that sensory and motor nerve conduction velocities for both families were <38 m/s. EMG presented denervation, neuropathic motor unit potential, and reduced interference pattern of peripheral nerves. WES identified that a novel nonsense mutation (c. 226 G>T) in GADP1 gene and a previously known missense mutation in MFN2 gene (c. 334 G>A) cause CMT4A (Charcot–Marie–Tooth disease type 4A) in the PAK-CMT1-DG KHAN family and CMT2A (Charcot–Marie–Tooth disease type 2A) in the PAK-CMT2-LAYYAH family, respectively. Mutations followed Mendelian pattern with autosomal recessive mode of inheritance. Multiple sequence alignment by Clustal Omega indicated that mutation-containing domain in both genes is highly conserved, and in situ analysis revealed that both mutations are likely to be pathogenic. We reported that a novel nonsense mutation and a previously known missense mutation in GAPD1 gene and MFN2 gene, respectively, cause CMT in consanguineous Pakistani families. [ABSTRACT FROM AUTHOR]

A matrix method is used to determine fluctuations of junctions and points along the polymer chains making up a phantom Gaussian network that has the topology of an infinite, symmetrically grown tree. The functionalities of the junctions alternates between [lowercase_phi_synonym]1 and [lowercase_phi_synonym]2, such that one end of each network chain has functionality [lowercase_phi_synonym]1, while the opposite end has functionality [lowercase_phi_synonym]2. Quantities calculated include fluctuations of [lowercase_phi_synonym]1-functional and [lowercase_phi_synonym]2-functional junctions, and fluctuations of points along network chains, as well as correlations of these fluctuations. This was done for points and junctions along any path in the network, where these points and junctions were separated by no junctions or several junctions, Fluctuations have also been calculated for the distances between points and junctions. The present results represent significant generalizations of earlier work in this area [Kloczkowski et al., Macromolecules 22, 1423 (1989)]. These generalizations and extensions should be very useful in a number of contexts, such as interpreting small-angle neutron scattering results on labeled paths in polymer networks, or fluctuations of loops in the Gaussian network model of proteins. [ABSTRACT FROM AUTHOR]

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment. [ABSTRACT FROM AUTHOR]

Ewing's sarcoma is a rare type of cancer that forms in bones and soft tissues in the body, affecting mostly children and young adults. Current treatments for ES are limited to chemotherapy and/or radiation, followed by surgery. Recently, microRNAs have shown favourable results as latent diagnostic and prognostic biomarkers in various cancers. Furthermore, microRNAs have shown to be a good therapeutic agent due to their involvement in the dysregulation of various molecular pathways linked to tumour progression, invasion, angiogenesis, and metastasis. In this review, comprehensive data mining was employed to explore various microRNAs that might have therapeutic potential as target molecules in the treatment of ES. [ABSTRACT FROM AUTHOR]

In this study, a series of 1,3,4-thiadiazole-thiazolidinone derivatives (7a–j) were synthesized as carbonic anhydrase inhibitors. The in vitro carbonic anhydrase inhibitory activity of these synthesized compounds was determined and the results revealed that compound 7i (IC50 = 0.402 ± 0.017 μM) is more potent than the standard reference acetazolamide (IC50 = 0.998 ± 0.046 μM). Moreover, kinetic analysis was also performed to see the mode of binding with the target enzyme, and it was shown that compound 7i bound with the target in a competitive manner. All compounds were screened for antioxidant activity against DPPH and anticancer activity against A549 (human lung cancer cells), and the MTT assay results indicated that compounds 7a and 7d had more inhibitory growth. Moreover, compound 7i exhibited a good conformational state with hydrogen bond interactions within the receptor binding pocket according to molecular docking studies. Therefore, the novel compound 7i might be employed as a promising pharmacophore for potent and selective carbonic anhydrase inhibitors. [ABSTRACT FROM AUTHOR]

The availability of many DNA-protein structures makes their classification timely and important. In this issue of Structure, the method of Akinori Sarai and his collaborators () utilizes aspects of the binding interactions and DNA properties to identify seven clusters of structures with a classification scheme that differs significantly from previous approaches. [Copyright &y& Elsevier]

Dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1 (DAX1) is an orphan nuclear receptor encoded by the NR0B1 gene. The functional study showed that DAX1 is a physiologically significant target for EWS/FLI1-mediated oncogenesis, particularly Ewing Sarcoma (ES). In this study, a three-dimensional DAX1 structure was modeled by employing a homology modeling approach. Furthermore, the network analysis of genes involved in Ewing Sarcoma was also carried out to evaluate the association of DAX1 and other genes with ES. Moreover, a molecular docking study was carried out to check the binding profile of screened flavonoid compounds against DAX1. Therefore, 132 flavonoids were docked in the predicted active binding pocket of DAX1. Moreover, the pharmacogenomics analysis was performed for the top ten docked compounds to evaluate the ES-related gene clusters. As a result, the five best flavonoid-docked complexes were selected and further evaluated by Molecular Dynamics (MD) simulation studies at 100 ns. The MD simulation trajectories were evaluated by generating RMSD, hydrogen bond plot analysis, and interaction energy graphs. Our results demonstrate that flavonoids showed interactive profiles in the active region of DAX1 and can be used as potential therapeutic agents against DAX1-mediated augmentation of ES after in-vitro and in-vivo evaluations. [ABSTRACT FROM AUTHOR]

A well‐known key enzyme in melanogenesis and hyperpigmentation is tyrosinase. The present study introduces a novel series of thiophenyl‐pyrazolylthiazole‐coumarin hybrids (6a–6h) as tyrosinase inhibitors. The in‐vitro tyrosinase inhibition results indicated that all compounds have strong tyrosinase inhibitory activity, particularly compound 6g (IC50 = 0.043 ± 0.006 μM), was identified as the most active compound compared to the positive control (kojic acid, IC50 = 18.521 ± 1.162 μM). Lineweaver‐Burk plots were employed to analyze the kinetic mechanism, and compound 6g formed an enzyme‐inhibitor complex by inhibiting tyrosinase non‐competitively. Furthermore, all compounds demonstrated excellent antioxidant activity against DPPH. MTT assay was used to screen the cytotoxicity of all compounds on B16F10 melanoma cells, and they had no toxic effect on the cells. The binding affinity of compounds with tyrosinase was also investigated using molecular docking, and the ligands displayed good binding energy values. These molecules could be a promising lead for skin pigmentation and associated diseases as nontoxic pharmacological scaffolds. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst's, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health). [ABSTRACT FROM AUTHOR]

Background: Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of SSP methods are applied as input to higher-level structure prediction pipelines, even small errors may have large perturbations in final models. Previous works relied on cross validation as an estimate of classifier accuracy. However, training on large numbers of protein chains compromises the classifier ability to generalize to new sequences. This prompts a novel approach to training and an investigation into the possible structural factors that lead to poor predictions. Here, a small group of 55 proteins termed the compact model is selected from the CB513 dataset using a heuristics-based approach. In a prior work, all sequences were represented as probability matrices of residues adopting each of Helix, Sheet and Coil states, based on energy calculations using the C-Alpha, C-Beta, Side-chain (CABS) algorithm. The functional relationship between the conformational energies computed with CABS force-field and residue states is approximated using a classifier termed the Fully Complex-valued Relaxation Network (FCRN). The FCRN is trained with the compact model proteins. Results: The performance of the compact model is compared with traditional cross-validated accuracies and blind-tested on a dataset of G Switch proteins, obtaining accuracies of ∼81 %. The model demonstrates better results when compared to several techniques in the literature. A comparative case study of the worst performing chain identifies hydrogen bond contacts that lead to Coil↔Sheet misclassifications. Overall, mispredicted Coil residues have a higher propensity to participate in backbone hydrogen bonding than correctly predicted Coils. Conclusions: The implications of these findings are: (i) the choice of training proteins is important in preserving the generalization of a classifier to predict new sequences accurately and (ii) SSP techniques sensitive in distinguishing between backbone hydrogen bonding and side-chain or water-mediated hydrogen bonding might be needed in the reduction of Coil↔Sheet misclassifications. [ABSTRACT FROM AUTHOR]

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES. [ABSTRACT FROM AUTHOR]

MicroRNAs are hidden players in complex psychophysical phenomena such as depression and anxiety related disorders though the activation and deactivation of multiple proteins in signaling cascades. Depression is classified as a mood disorder and described as feelings of sadness, loss, or anger that interfere with a person's everyday activities. In this review, we have focused on exploration of the significant role of miRNAs in depression by affecting associated target proteins (cellular and synaptic) and their signaling pathways which can be controlled by the attachment of miRNAs at transcriptional and translational levels. Moreover, miRNAs have potential role as biomarkers and may help to cure depression through involvement and interactions with multiple pharmacological and physiological therapies. Taken together, miRNAs might be considered as promising novel therapy targets themselves and may interfere with currently available antidepressant treatments. [ABSTRACT FROM AUTHOR]

To develop new alkaline phosphatase inhibitors (ALP), a series of pyrazolo-oxothiazolidine derivatives were synthesized and biologically assessed, and the results showed that all of the synthesized compounds significantly inhibited ALP. Specifically, compound 7g displayed the strongest inhibitory activity (IC50 = 0.045 ± 0.004 μM), which is 116-fold more active than monopotassium phosphate (IC50 = 5.242 ± 0.472 μM) as a standard reference. The most potent compound among the series (7g) was checked for its mode of binding with the enzyme and shown as non-competitively binding with the target enzyme. The antioxidant activity of these compounds was examined to investigate the radical scavenging effect. Moreover, the MTT assay method was performed to evaluate their toxic effects on the viability of MG-63 human osteosarcoma cells, and all compounds have no toxic effect on the cells at 4 μM. Computational research was also conducted to examine the binding affinity of the ligands with alkaline phosphatase, and the results revealed that all compounds showed good binding energy values within the active site of the target. Therefore, these novel pyrazolo-oxothiazolidine derivatives might be employed as promising pharmacophores for potent and selective alkaline phosphatase inhibitors. [ABSTRACT FROM AUTHOR]

Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equations to have an imperfect solution. In addition, in the case of highly underdetermined parameterization, noise can be absorbed by the model, generating spurious solutions. This is a very undesirable situation that might lead to incorrect conclusions. We presented mathematical formalism based on the inverse problem theory combined with artificial intelligence methodologies to perform an enhanced sampling of noisy biomedical data to improve the finding of meaningful solutions. Random sampling methods fail for high-dimensional biomedical problems. Sampling methods such as smart model parameterizations, forward surrogates, and parallel computing are better suited for such problems. We applied these methods to several important biomedical problems, such as phenotype prediction and a problem related to predicting the effects of protein mutations, i.e., if a given single residue mutation is neutral or deleterious, causing a disease. We also applied these methods to de novo drug discovery and drug repositioning (repurposing) through the enhanced exploration of huge chemical space. The purpose of these novel methods that address the problem of noise and uncertainty in biomedical data is to find new therapeutic solutions, perform drug repurposing, and accelerate and optimize drug discovery, thus reestablishing homeostasis. Finding the right target, the right compound, and the right patient are the three bottlenecks to running successful clinical trials from the correct analysis of preclinical models. Artificial intelligence can provide a solution to these problems, considering that the character of the data restricts the quality of the prediction, as in any modeling procedure in data analysis. The use of simple and plain methodologies is crucial to tackling these important and challenging problems, particularly drug repositioning/repurposing in rare diseases. [ABSTRACT FROM AUTHOR]

Highly designable structures can be distinguished based on certain geometric graphical features of the interactions, confirming the fact that the topology of a protein structure and its residue-residue interaction network are important determinants of its designability. The most designable structures and least designable structures obtained for sets of proteins having the same number of residues are compared. It is shown that the most designable structures predicted by the graph features of the contact diagrams are more densely packed, whereas the poorly designable structures are more open structures or structures that are loosely packed. Interestingly enough, it can also be seen that the highly designable identified are also common structural motifs found in nature. [ABSTRACT FROM AUTHOR]

Fibril formation resulting from protein misfolding and aggregation is a hallmark of several neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases. Despite much progress in the understanding of the protein aggregation process, the factors governing fibril formation rates and fibril stability have not been fully understood. Using lattice models, we have shown that the fibril formation time is controlled by the kinetic stability of the fibril state but not by its energy. Having performed all-atom explicit solvent molecular dynamics simulations with the GROMOS43a1 force field for full-length amyloid beta peptides Aβ40 and Aβ42 and truncated peptides, we demonstrated that kinetic stability can be accessed via mechanical stability in such a way that the higher the mechanical stability or the kinetic stability, the faster the fibril formation. This result opens up a new way for predicting fibril formation rates based on mechanical stability that may be easily estimated by steered molecular dynamics. [ABSTRACT FROM AUTHOR]

Hematopoietic stem cells (HSCs) reside in a specialized microenvironment in a peculiar anatomic location which regulates the maintenance of stem cells and controls its functions. Recent scientific progress in experimental technologies have enabled the specific detection of epigenetic factors responsible for the maintenance and quiescence of the hematopoietic niche, which has improved our knowledge of regulatory mechanisms. The aberrant role of RNA-binding proteins and their impact on the disruption of stem cell biology have been reported by a number of recent studies. Despite recent modernization in hematopoietic microenvironment research avenues, our comprehension of the signaling mechanisms and interactive pathways responsible for integration of the hematopoietic niche is still limited. In the past few decades, zebrafish usage with regards to exploratory studies of the hematopoietic niche has expanded our knowledge for deeper understanding of novel cellular interactions. This review provides an update on the functional roles of different genetic and epigenetic factors and molecular signaling events at different sections of the hematopoietic microenvironment. The explorations of different molecular approaches and interventions of latest web-based tools being used are also outlined. This will help us to get more mechanistic insights and develop therapeutic options for the malignancies. [ABSTRACT FROM AUTHOR]

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics. [ABSTRACT FROM AUTHOR]

A series of nine novel 1,2,4-triazole based compounds were synthesized through a multistep reaction pathway and their structures were scrutinized by using spectral methods such as FTIR, LC-MS, 1H NMR, and 13C NMR. The synthesized derivatives were screened for inhibitory activity against the mushroom tyrosinase and we found that all the synthesized compounds demonstrated decent inhibitory activity against tyrosinase. However, among the series of compounds, N-(4- fluorophenyl)-2-(5-(2-fluorophenyl)-4-(4-fluorophenyl)-4H-1,2,4-triazol-3-ylthio) acetamide exhibited more prominent activity when accompanied with the standard drug kojic acid. Furthermore, the molecular docking studies identified the interaction profile of all synthesized derivatives at the active site of tyrosinase. Based on these results, N-(4-fluorophenyl)-2-(5-(2-fluorophenyl)-4-(4-fluorophenyl)- 4H-1,2,4-triazol-3-ylthio) acetamide could be used as a novel scaffold to design some new drugs against melanogenesis. [ABSTRACT FROM AUTHOR]

Background The comparative modeling approach to protein structure prediction inherently relies on a template structure. Before building a model such a template protein has to be found and aligned with the query sequence. Any error made on this stage may dramatically affects the quality of result. There is a need, therefore, to develop accurate and sensitive alignment protocols. Results BioShell threading software is a versatile tool for aligning protein structures, protein sequences or sequence profiles and query sequences to a template structures. The software is also capable of suboptimal alignment generation. It can be executed as an application from the UNIX command line, or as a set of Java classes called from a script or a Java application. The implemented Monte Carlo search engine greatly facilitates the development and benchmarking of new alignment scoring schemes even when the functions exhibit non-deterministic polynomial-time complexity. Conclusions Numerical experiments indicate that the new threading application offers template detection abilities and provides much better alignments than other methods. The package along with documentation and examples is available at: http://bioshell.pl/threading3d. [ABSTRACT FROM AUTHOR]

Protein structure prediction and protein–proteindockingare important and widely used tools, but methods to confidently evaluatethe quality of a predicted structure or binding pose have had limitedsuccess. Typically, either knowledge-based or physics-based energyfunctions are employed to evaluate a set of predicted structures (termed“decoys” in structure prediction and “poses”in docking), with the lowest energy structure being assumed to bethe one closest to the native state. While successful for many cases,failures are still common. Thus, improvements to structure evaluationmethods are essential for future improvements. In this work, we combinemultibody statistical potentials with dynamics models, evaluatingfluctuation-based entropies that include contributions from the entirestructure. This leads to enhanced selection of native-like structuresfor CASP9 decoys, refined ClusPro docking poses, as well as largesets of docking poses from the Benchmark 3.0 and Dockground data sets.The data used include both bound and unbound docking, and positiveresults are found for each type. Not only does this method yield improvedaverage results, but for high quality docking poses, we often pickthe best pose. [ABSTRACT FROM AUTHOR]

Abstract: A new method for predicting protein secondary structure from amino acid sequence has been developed. The method is based on multiple sequence alignment of the query sequence with all other sequences with known structure from the protein data bank (PDB) by using BLAST. The fragments of the alignments belonging to proteins from the PBD are then used for further analysis. We have studied various schemes of assigning weights for matching segments and calculated normalized scores to predict one of the three secondary structures: α-helix, β-sheet, or coil. We applied several artificial intelligence techniques: decision trees (DT), neural networks (NN) and support vector machines (SVM) to improve the accuracy of predictions and found that SVM gave the best performance. Preliminary data show that combining the fragment mining approach with GOR V (Kloczkowski et al, Proteins 49 (2002) 154–166) for regions of low sequence similarity improves the prediction accuracy. [Copyright &y& Elsevier]

The prediction of the dynamics of the COVID-19 outbreak and the corresponding needs of the health care system (COVID-19 patients' admissions, the number of critically ill patients, need for intensive care units, etc.) is based on the combination of a limited growth model (Verhulst model) and a short-term predictive model that allows predictions to be made for the following day. In both cases, the uncertainty analysis of the prediction is performed, i.e., the set of equivalent models that adjust the historical data with the same accuracy. This set of models provides the posterior distribution of the parameters of the predictive model that adjusts the historical series. It can be extrapolated to the same analyzed time series (e.g., the number of infected individuals per day) or to another time series of interest to which it is correlated and used, e.g., to predict the number of patients admitted to urgent care units, the number of critically ill patients, or the total number of admissions, which are directly related to health needs. These models can be regionalized, that is, the predictions can be made at the local level if data are disaggregated. We show that the Verhulst and the Gompertz models provide similar results and can be also used to monitor and predict new outbreaks. However, the Verhulst model seems to be easier to interpret and to use. [ABSTRACT FROM AUTHOR]

Proteins are tiny players involved in the activation and deactivation of multiple signaling cascades through interactions in cells. The TNFR1 and MADD interact with each other and mediate downstream protein signaling pathways which cause neuronal cell death and Alzheimer's disease. In the current study, a molecular docking approach was employed to explore the interactive behavior of TNFR1 and MADD proteins and their role in the activation of downstream signaling pathways. The computational sequential and structural conformational results revealed that Asp400, Arg58, Arg59 were common residues of TNFR1 and MADD which are involved in the activation of downstream signaling pathways. Aspartic acid in negatively charged residues is involved in the biosynthesis of protein. However, arginine is a positively charged residue with the potential to interact with oppositely charged amino acids. Furthermore, our molecular dynamic simulation results also ensured the stability of the backbone of TNFR1 and MADD death domains (DDs) in binding interactions. This DDs interaction mediates some conformational changes in TNFR1 which leads to the activation of mediators proteins in the cellular signaling pathways. Taken together, a better understanding of TNFR1 and MADD receptors and their activated signaling cascade may help treat Alzheimer's disease. The death domains of TNFR1 and MADD could be used as a novel pharmacological target for the treatment of Alzheimer's disease by inhibiting the MAPK pathway. [ABSTRACT FROM AUTHOR]

We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive–regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set. [ABSTRACT FROM AUTHOR]

Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab experiments to get a better understanding of protein mutations is costly and time consuming, and because of huge number of possible mutations the need of computational methods that could accurately predict effects of amino acid mutations is of greatest importance. In this research, we present a robust methodology to predict the energy changes of a proteins upon mutations. The proposed prediction scheme is based on two step algorithm that is a Holdout Random Sampler followed by a neural network model for regression. The Holdout Random Sampler is utilized to analysis the energy change, the corresponding uncertainty, and to obtain a set of admissible energy changes, expressed as a cumulative distribution function. These values are further utilized to train a simple neural network model that can predict the energy changes. Results were blindly tested (validated) against experimental energy changes, giving Pearson correlation coeffcients of 0.66 for Single Point Mutations and 0.77 for Multiple Point Mutations. These results confirm the successfulness of our method, since it outperforms majority of previous studies in this field. [ABSTRACT FROM AUTHOR]

It is well recognized that thermal motions of atoms in the protein native state, the fluctuations about the minimum of the global free energy, are well reproduced by the simple elastic network models (ENMs) such as the anisotropic network model (ANM). Elastic network models represent protein dynamics as vibrations of a network of nodes (usually represented by positions of the heavy atoms or by the Cα atoms only for coarse-grained representations) in which the spatially close nodes are connected by harmonic springs. These models provide a reliable representation of the fluctuational dynamics of proteins and RNA, and explain various conformational changes in protein structures including those important for ligand binding. In the present paper, we study the problem of protein structure refinement by analyzing thermal motions of proteins in non-native states. We represent the conformational space close to the native state by a set of decoys generated by the I-TASSER protein structure prediction server utilizing template-free modeling. The protein substates are selected by hierarchical structure clustering. The main finding is that thermal motions for some substates, overlap significantly with the deformations necessary to reach the native state. Additionally, more mobile residues yield higher overlaps with the required deformations than do the less mobile ones. These findings suggest that structural refinement of poorly resolved protein models can be significantly enhanced by reduction of the conformational space to the motions imposed by the dominant normal modes. [ABSTRACT FROM AUTHOR]

Protein structures are evolutionarily more conserved than sequences, and sequences with very low sequence identity frequently share the same fold. This leads to the concept of protein designability. Some folds are more designable and lots of sequences can assume that fold. Elucidating the relationship between protein sequence and the three-dimensional (3D) structure that the sequence folds into is an important problem in computational structural biology. Lattice models have been utilized in numerous studies to model protein folds and predict the designability of certain folds. In this study, all possible compact conformations within a set of two-dimensional and 3D lattice spaces are explored. Complementary interaction graphs are then generated for each conformation and are described using a set of graph features. The full HP sequence space for each lattice model is generated and contact energies are calculated by threading each sequence onto all the possible conformations. Unique conformation giving minimum energy is identified for each sequence and the number of sequences folding to each conformation (designability) is obtained. Machine learning algorithms are used to predict the designability of each conformation. We find that the highly designable structures can be distinguished from other non-designable conformations based on certain graphical geometric features of the interactions. This finding confirms the fact that the topology of a conformation is an important determinant of the extent of its designability and suggests that the interactions themselves are important for determining the designability. [ABSTRACT FROM AUTHOR]

We enumerated all compact conformations within simple geometries on the two-dimensional (2D) triangular and three-dimensional (3D) face centered cubic (fcc) lattice. These compact conformations correspond mathematically to Hamiltonian paths and Hamiltonian circuits and are frequently used as simple models of proteins. The shapes that were studied for the 2D triangular lattice included m×n parallelograms, regular equilateral triangles, and various hexagons. On the 3D fcc lattice we generated conformations for a limited class of skewed parallelepipeds. Symmetries of the shape were exploited to reduce the number of conformations. We compared surface to volume ratios against protein length for compact conformations on the 3D cubic lattice and for a selected set of real proteins. We also show preliminary work in extending the transfer matrix method, previously developed by us for the 2D square and the 3D cubic lattices, to the 2D triangular lattice. The transfer matrix method offers a superior way of generating all conformations within a given geometry on a lattice by completely avoiding attrition and reducing this highly complicated geometrical problem to a simple algebraic problem of matrix multiplication. [ABSTRACT FROM AUTHOR]

• An efficient one-pot synthesis of dihydropyrimidinones was performed with cysteine as a bio-organic catalyst. • These compounds (4a – 4j) displayed significant in vitro urease inhibitory activity. • MTT assay was used to evaluate the cytotoxicity of all compounds in HT1080 human fibroblast cell lines. • The kinetic mechanism was investigated by Lineweaver-Burk plots. • In silico molecular docking studies supported the experimental results. The aim of the present research was to develop a one-pot, three-component method for the synthesis of some 3,4-dihydro pyrimidine-2-(1 H)-one (DHPM) derivatives using aldehydes, urea, and ethyl acetoacetate in the presence of cysteine as a green, bio-organic catalyst. This procedure has several key advantages, including a facile and convenient method, green and low-cost catalyst, high yield, short reaction time, and avoidance of the use of environmentally harmful solvents. It is important to note that the synthesized compounds (4a – j) were evaluated for in vitro urease inhibitory activity, and all were found to be more effective than the standard thiourea (IC 50 = 4.745 ± 0.054 µM), with urease inhibitory potentials between IC 50 = 0.058 ± 0.006 µM to 0.297 ± 0.066 µM. Lineweaver-Burk plots were used to study the kinetics of the most potent compound (4e), and the results showed that the compound non-competitively inhibited urease by forming an enzyme-inhibitor complex. In addition, HT1080 human fibroblast cell lines were used to test the toxicity of the synthesized compounds on cell viability using the MTT assay method, and all compounds except 4g, 4i, and 4j exhibited no significant toxic effects on cells even at high concentrations. Moreover, molecular docking was used to determine the binding interactions between the molecules (ligands) and the active site of the urease enzyme, and the ligands indicated acceptable binding energy values. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • A series of 1,3,4-thiadiazol-1,3,4-oxadiazol-acetamide derivatives (7a – 7j) were synthesized as elastase inhibitors. • The in-vitro elastase inhibitory activity of all synthesized compounds was determined successfully. • The kinetic mechanism was investigated by Lineweaver-Burk plots. • MTT assay was used to evaluate the cytotoxicity of all compounds on B16F10 melanoma cell lines. • Molecular docking was applied to investigate the binding affinity of compounds with elastase and the ligands showed good binding energy values. Considering the biological significance of 1,3,4-thiadiazole/oxadiazole heterocyclic scaffolds, a novel series of 1,3,4-thiadiazole-1,3,4-oxadiazole-acetamide derivatives (7a – j) was designed and synthesized using molecular hybridization. The inhibitory effects of the target compounds on elastase were evaluated, and all of these molecules were found to be potent inhibitors compared to the standard reference oleanolic acid. Compound 7f exhibited the excellent inhibitory activity (IC 50 = 0.06 ± 0.02 μM), which is 214-fold more active than oleanolic acid (IC 50 = 12.84 ± 0.45 μM). Kinetic analysis was also performed on the most potent compound (7f) to determine the mode of binding with the target enzyme, and it was discovered that 7f inhibits the enzyme in a competitive manner. Furthermore, the MTT assay method was used to assess their toxicity on the viability of B16F10 melanoma cell lines, and all compounds did not display any toxic effect on the cells even at high concentrations. The molecular docking studies of all compounds also justified with their good docking score and among them, compound 7f had a good conformational state with hydrogen bond interactions within the receptor binding pocket, which is consistent with the experimental inhibition studies. [ABSTRACT FROM AUTHOR]

We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. This term is aimed at providing high frequency details in the energy optimization. The goal of this research is to analyze how the dimensionality reduction affects the prediction capability of the PSO procedure. For that purpose, different proteins from the Critical Assessment of Techniques for Protein Structure Prediction experiments were modeled. In all the cases, both the energy of the best decoy and the distance to the native structure have decreased. Our analysis also shows how the predicted backbone structure of native conformation and of alternative low energy states varies with respect to the PCA dimensionality. Generally speaking, the reconstruction can be successfully achieved with 10 principal components and the high frequency term. We also provide a computational analysis of protein energy landscape for the inverse problem of reconstructing structure from the reduced number of principal components, showing that the dimensionality reduction alleviates the ill-posed character of this high-dimensional energy optimization problem. The procedure explained in this paper is very fast and allows testing different PCA expansions. Our results show that PSO improves the energy of the best decoy used in the PCA when the adequate number of PCA terms is considered. [ABSTRACT FROM AUTHOR]