Protein Structure Prediction

Recently, predicting proteins three-dimensional (3D) structure from its sequence information has made a significant progress due to the advances in computational techniques and the growth of experimental structures. However, selecting good models from a structural model pool is an important and challenging task in protein structure prediction. In this study, we present the first application of random forest based model quality assessment (RFMQA) to rank protein models using its structural features and knowledge-based potential energy terms. The method predicts a relative score of a model by using its secondary structure, solvent accessibility and knowledge-based potential energy terms. We trained and tested the RFMQA method on CASP8 and CASP9 targets using 5-fold cross-validation. The correlation coefficient between the TMscore of the model selected by RFMQA (TM RF ) and the best server model (TM best ) is 0.945. We benchmarked our method on recent CASP10 targets by using CASP8 and 9 server models as a training set. The correlation coefficient and average difference between TM RF and TM best over 95 CASP10 targets are 0.984 and 0.0385, respectively. The test results show that our method works better in selecting top models when compared with other top performing methods. RFMQA is available for download from .

Praktikum ini bertujuan untuk mengenal sifat-sifat protein serta memahami perubahan kimia yang terjadi akibat berbagai perlakuan fisik dan kimia, melalui beberapa uji yaitu uji Biuret, koagulasi oleh alkohol, pengendapan oleh garam, pengendapan oleh logam berat, dan denaturasi protein.

Praktikum ini bertujuan untuk mengenal sifat-sifat protein serta memahami perubahan kimia yang terjadi akibat berbagai perlakuan fisik dan kimia, melalui beberapa uji yaitu uji Biuret, koagulasi oleh alkohol, pengendapan oleh garam, pengendapan oleh logam berat, dan denaturasi protein

The assumption that cellulose degradation and assimilation can only be carried out by heterotrophic organisms was shattered in 2012 when it was discovered that the unicellular green alga, (Cr), can utilize cellulose for growth under CO₂-limiting conditions. Publications of genomes/transcriptomes of the colonial microalgae, (Gp) and (Vc), between 2010⁻2016 prompted us to look for cellulase genes in these algae and to compare them to cellulases from bacteria, fungi, lower/higher plants, and invertebrate metazoans. Interestingly, algal catalytic domains (CDs), belonging to the family GH9, clustered separately and showed the highest (33⁻42%) and lowest (17⁻36%) sequence identity with respect to cellulases from invertebrate metazoans and bacteria, respectively, whereas the identity with cellulases from plants was only 27⁻33%. Based on comparative multiple alignments and homology models, the domain arrangement and active-site architecture of algal cellulases are described in detail. It wa...

‘Inner mitochondrial membrane peptidase 2 like’ (IMMP2L) is a nuclear-encoded mitochondrial peptidase that has been conserved through evolutionary history, as has its target enzyme, ‘mitochondrial glycerol phosphate dehydrogenase 20 (GPD2). IMMP2L is known to cleave the mitochondrial transit peptide from GPD2 and another nuclear-encoded mitochondrial respiratory-related protein, cytochrome C1 (CYC1). However, it is not known whether IMMP2L peptidase activates or alters the activity or respiratory-related functions of GPD2 or CYC1. Previous investigations
found compelling evidence of behavioural change in the Immp2lKD−/− KO mouse, and in this study, EchoMRI analysis found that the organs of the Immp2lKD−/− KO mouse were smaller and that the KO mouse had significantly less lean mass and overall body weight compared with wildtype littermates (p < 0.05). Moreover, all organs analysed from the Immp2lKD−/− KO had lower relative levels of mitochondrial reactive oxygen species (mitoROS). The kidneys of the Immp2lKD−/− KO mouse
displayed the greatest decrease in mitoROS levels that were over 50% less compared with wildtype litter mates. Mitochondrial respiration was also lowest in the kidney of the Immp2lKD−/− KO mouse compared with other tissues when using succinate as the respiratory substrate, whereas respiration was similar to the wildtype when glutamate was used as the substrate. When glycerol-3-phosphate (G3P) was used as the substrate for Gpd2, we observed ~20% and ~7% respective decreases in respiration in female and male Immp2lKD−/− KO mice over time. Together, these findings indicate that the respiratory-related functions of mGpd2 and Cyc1 have been compromised to different degrees in different tissues and genders of the Immp2lKD−/− KO mouse. Structural analyses using AlphaFold2-Multimer further predicted that the interaction between Cyc1 and mitochondrial-encoded cytochrome b (Cyb) in Complex III had been altered, as had the homodimeric structure of the mGpd2 enzyme
within the inner mitochondrial membrane of the Immp2lKD−/− KO mouse. mGpd2 functions as an integral component of the glycerol phosphate shuttle (GPS), which positively regulates both mitochondrial respiration and glycolysis. Interestingly, we found that nonmitochondrial respiration
(NMR) was also dramatically lowered in the Immp2lKD−/− KO mouse. Primary mouse embryonic fibroblast (MEF) cell lines derived from the Immp2lKD−/− KO mouse displayed a ~27% decrease in total respiration, comprising a ~50% decrease in NMR and a ~12% decrease in total mitochondrial respiration, where the latter was consistent with the cumulative decreases in substrate-specific mediated mitochondrial respiration reported here. This study is the first to report the role of Immp2l in enhancing Gpd2 structure and function, mitochondrial respiration, nonmitochondrial respiration, organ size and homeostasis

The assumption that cellulose degradation and assimilation can only be carried out by heterotrophic organisms was shattered in 2012 when it was discovered that the unicellular green alga, Chlamydomonas reinhardtii (Cr), can utilize cellulose for growth under CO 2-limiting conditions. Publications of genomes/transcriptomes of the colonial microalgae, Gonium pectorale (Gp) and Volvox carteri (Vc), between 2010-2016 prompted us to look for cellulase genes in these algae and to compare them to cellulases from bacteria, fungi, lower/higher plants, and invertebrate metazoans. Interestingly, algal catalytic domains (CDs), belonging to the family GH9, clustered separately and showed the highest (33-42%) and lowest (17-36%) sequence identity with respect to cellulases from invertebrate metazoans and bacteria, respectively, whereas the identity with cellulases from plants was only 27-33%. Based on comparative multiple alignments and homology models, the domain arrangement and active-site architecture of algal cellulases are described in detail. It was found that all algal cellulases are modular, consisting of putative novel cysteine-rich carbohydrate-binding modules (CBMs) and proline/serine-(PS) rich linkers. Two genes were found to encode a protein with a putative Ig-like domain and a cellulase with an unknown domain, respectively. A feature observed in one cellulase homolog from Gp and shared by a spinach cellulase is the existence of two CDs separated by linkers and with a C-terminal CBM. Dockerin and Fn-3-like domains, typically found in bacterial cellulases, are absent in algal enzymes. The targeted gene expression analysis shows that two Gp cellulases consisting, respectively, of a single and two CDs were upregulated upon filter paper addition to the medium.

Radio wave propagation of millimeter wave is highly influenced by the movements of objects (humans); simple movements of the objects can changes entire propagation paths of transmitted waves inside the buildings. Ray tracing is one of the easiest methods to predict these propagation paths. To do this, it requires extremely accurate information about the objects of the realistic environments. Moreover, calculations of ray paths can be computationally expensive due to the large number of rays that must be traced. Therefore, a new ray tracing method based on object distribution along with Red-Black tree data structure is presented in this paper, which enabling the proposed method easy to the implementation, and will make the characterization of wave propagation more flexible, reliable and efficient. The results show that the proposed method is an accurate and powerfully tools for the prediction of propagated rays in presence of human movements.

Recent trends show that it is a major challenge to predict the exact propagation path accurately and efficiently for three-dimensional (3D) indoor environments. Therefore, this study introduces a new 3D ray tracing algorithm based on a red-black tree with facet mining and object bouncing techniques. The red-black tree data structure offers a faster object searching operation while repeatedly performing intersection tests. On the other hand, extensive ray-facet intersection tests can be eliminated by using object and facet bouncing techniques, whereas the facet mining makes it easier to trace the transmitted ray paths accurately. The proposed method is compared with ray launching, bidirectional path tracing, modified angular Z-buffer, and binary space partitioning-based modified wavefront decomposition techniques and it is found that it obtains better prediction results in terms of computation time, the number of predicted rays, and radio propagation path loss.

Rift Valley fever virus (RVFV) is a bunyavirus endemic to Africa and the Arabian Peninsula that infects humans and livestock. The virus encodes two glycoproteins, Gn and Gc, which represent the major structural antigens and are responsible for host cell receptor binding and fusion. Both glycoproteins are organized on the virus surface as cylindrical hollow spikes that cluster into distinct capsomers with the overall assembly exhibiting an icosahedral symmetry. Currently, no experimental three-dimensional structure for any entire bunyavirus glycoprotein is available. Using fold recognition, we generated molecular models for both RVFV glycoproteins and found significant structural matches between the RVFV Gn protein and the influenza virus hemagglutinin protein and a separate match between RVFV Gc protein and Sindbis virus envelope protein E1. Using these models, the potential interaction and arrangement of both glycoproteins in the RVFV particle was analyzed, by modeling their placement within the cryo-electron microscopy density map of RVFV. We identified four possible arrangements of the glycoproteins in the virion envelope. Each assembly model proposes that the ectodomain of Gn forms the majority of the protruding capsomer and that Gc is involved in formation of the capsomer base. Furthermore, Gc is suggested to facilitate intercapsomer connections. The proposed arrangement of the two glycoproteins on the RVFV surface is similar to that described for the alphavirus E1-E2 proteins. Our models will provide guidance to better understand the assembly process of phleboviruses and such structural studies can also contribute to the design of targeted antivirals.

This paper presents a novel learning algorithm for structured classification, where the task is to predict multiple and interacting labels (multilabel) for an input object. The problem of finding a large-margin separation between correct multilabels and incorrect ones is formulated as a linear program. Instead of explicitly writing out the entire problem with an exponentially large constraint set, the linear program is solved iteratively via column generation. In this case, the process of generating most violated constraints is equivalent to searching for highest-scored misclassified incorrect multilabels, which can be easily achieved by decoding the structure based on current estimations. In addition, we also explore the integration of column generation and an extragradient method for linear programming to gain further efficiency. The proposed method has the advantages that it can handle arbitrary structures and larger-scale problems. Experimental results on part-of-speech tagging and statistical machine translation tasks are reported, demonstrating the competitiveness of our approach.

High-pressure phase transformations of Ca are studied using the metadynamics method to explore the anharmonic free-energy surface, together with a genetic algorithm structural search method to identify lowest enthalpy structures. Disagreement between theory and experiment regarding the structure of Ca in the pressure range 32-119 GPa is partially resolved by the demonstration of different phase transition behavior at 300 K from that at low temperatures. A new lowest enthalpy I4 1 =amd structure is obtained with both methods with an estimated superconducting critical temperature in agreement with experiment.

The prediction of Compound-Protein Interactions (CPI) is an essential step in drugtarget analysis for developing new drugs. Therefore, it needs a good incentive to develop a faster and more effective method to predicting the interaction between compound and protein. Predicting the unobserved link of CPI can be done with Ant Colony Optimization for Link Prediction (ACO_LP) algorithms. Each ant selects its path according to the pheromone value and the heuristic information in the link. The path passed by the ant is evaluated and the pheromone information on each link is updated according to the quality of the path. The pheromones on each link are used as the final value of similarity between nodes. The ACO_LP are tested on benchmark CPI data: Nuclear Receptor, G-Protein Coupled Receptor (GPCR), Ion Channel, and Enzyme. Result show that the accuracy values for Nuclear Receptor, GPCR, Ion Channel, and Enzyme dataset are 0.62, 0.62, 0.74, and 0.79 respectively. The results indicate that ACO_LP has good accuracy for prediction of CPI.

The first and probably the most important step in predicting the tertiary structure of proteins from its primary structure is to predict as many as possible secondary structures in a protein chain. Secondary structure prediction problem has been known for almost a quarter of century. In this paper, new machine learning methods such as LAD, LEM2, and MODLEM have been used for secondary protein structure prediction with the aim to choose the best among them which will be later parallelized in order to handle a huge amount of data sets.

Biological membrane channels and pore-forming proteins display a level of sophistication in managing molecular scale transport that is typically unmatched by inorganic analogs, and efforts to develop nanopores that approach the transport efficiency of biological molecules often run into fabrication or synthetic difficulties. A different approach is exploiting nanomaterial scaffolds that provide near-atomic level of control over the structure and surface properties of these structures. Carbon nanotubes provide an especially interesting system for such studies due to their inherently smooth hydrophobic pore walls that support extremely high transport rates, which are comparable to biological channels. We describe the preparation of nanotube-based membrane nanopores, their assembly into biological membranes, and single-channel measurements of molecular transport in these assemblies.

Since enzymes are essential for the ripening of fruit and have a physiological role in that process, in this study, the most significant factors that contribute to the roles of invertase and cellulase in the ripening process were determined. The enzyme invertase is well-known for promoting the breakdown of sucrose into reduced sugars to enhance the quality of fruits. Fruit moisture content isn't always the main factor in determining freshness (ripeness) as well as the efficiency of various hydrolysis enzymes presenting during the last stages of ripening such as invertase and it's thought to play an important part in controlling sugar levels to support all aspects of plant growth. Numerous enzymes are known to have an impact on the fruit's softness. It is known that the cellulase enzyme is active when the cell wall of cells is soft, it aids in the softening and ripening of fruits, which is one of the essential factors that affect the level of quality of dates and the quantity of their consumption. Researchers came to different conclusions about the primary causes of fruit ripening, according to the variety, stage, and chemical structure, so the true causes of enzymes' role in the ripening stage remained unknown. In this study, invertase and cellulase from several species of date palm fruits and an understanding of the ripeness of the fruit were used to identify the physiological mechanisms behind the variations in the pattern of enzyme alterations, The importance of enzymes, and their function in the ripening of the several date palm species were highlighted, the procedure about which, there is still a lot that is unknown.

Using information-theoretic concepts, we examine the role of the reference state, a crucial component of empirical potential functions, in protein fold recognition. We derive an informationbased connection between the probability distribution functions of the reference state and those that characterize the decoy set used in threading. In examining commonly used contact reference states, we find that the quasi-chemical (QC) approximation is informatically superior to other variant models designed to include characteristics of real protein chains, such as finite length and variable amino acid composition from protein to protein. We observe that in these variant models, the total divergence, the operative function that quantifies discrimination, descreases along with threading performance. We find that any amount of nativeness encoded in the reference state model does not significantly improve threading performance. A promising avenue for the development of better potentials is suggested by our information-theoretic analysis of the action of contact potentials on individual protein sequences. Our results show that contact potentials perform better when the compositional properties of the data set used to derive the score function probabilities are similar to the properties of the sequence of interest. Results also suggest to use only sequences of similar composition in deriving contact potentials, to tailor the contact potential specifically for a test sequence.

Managing text-based information is crucial when trying to extract valuable information from documents. Assigning a numerical value to the text-based (unstructured) information is one of the ways to extract value. This research studied the quantification of unstructured text and its forecasting power. In order to examine unstructured information that related to predictive models, the Beige Books were utilized to investigate and predict changes in the U.S. economy. The Beige Books describe current economic conditions and discuss fluctuations in real Gross Domestic Product (GDP). To quantify the text-based unstructured information, the Direct Scoring Algorithm (DSA) was proposed. It utilized the keywords in the document and their subjectivelydetermined numerical weights to score individual sentence. Statistical analyses were then conducted to verify which sections of the Beige Books contributed the most significant information to the prediction of GDP. Utilizing the significant sections, a linear regression model was constructed to predict future GDP growth. The adjusted-R 2 values of the DSA model were compared to the scoring of the same documents by an economic expert. The comparison demonstrated that the DSA model using the Beige Book significantly contributed to the prediction of GDP, and it explained similar amounts of variance compared to the scores created by an economic expert. Also, a comparison between a structured predictive model and the DSA model was conducted to again prove the significance of text-based information. This positive result implies that there is more room for research in this area and that the ideas have future potential in the quantification of unstructured information.

Background There is slight evidence on the effectiveness of relaxation techniques to improve quality of life of the old people, and no comparative studies have particularly investigated this population. Hence, the present study was conducted to examine the effect of Mitchell relaxation versus Benson relaxation technique to improve quality of life of the old people. In the present quasi-experimental study, 96 eligible old people in a nursing home were selected by available sampling method. Afterwards, they were assigned to three groups: Mitchell's Relaxation Technique, Benson Relaxation Technique, and control (each of 32 participants) using the random block sampling method. The intervention groups received relaxation for 8 weeks and 3 sessions of 20 min each week. However, the control group did not receive any relaxation. Data was gathered by questionnaires (SF-36) and (CASP-19) before (week 0) and after the intervention (week 8) and were analyzed using the SPSS software version 26. The results indicated that both Benson and Mitchell relaxation had improved the quality of life (SF-36) and (CASP-19) and their sub-scales in the participants compare to the control group (P < 0.001). Accordingly, the median (quartile 25, 75) of the specific quality of life of the participants before the intervention was 21 (18.25, 25.75) in the Benson group, 20.5 (16, in the Michel group, and 21 (16.25, in the control group. However, after the intervention they reached 35(26.25, 38.75), 34.5(26.75, 42.25), and 17 (14, 21) respectively. There was no statistically significant difference between the Benson and Michel relaxation groups. Conclusions Based on the results, Benson and Mitchell relaxation techniques improve the quality of life of the old people. If the results be confirmed in other studies, the education of each of them, especially for the old people living in nursing homes and their caregivers, is suggested as routine care.

This paper introduces a groundbreaking computational paradigm for protein structure prediction through novel OmegaFold architecture that fundamentally transforms traditional multiple sequence alignment dependent methodologies. The research establishes an unprecedented theoretical framework incorporating transformer-based attention mechanisms, geometric deep learning principles, and evolutionary language modeling to achieve universal folding prediction capabilities. The methodology demonstrates exceptional performance metrics including Template Modeling scores exceeding 0.85 for diverse protein families while maintaining computational efficiency superior to conventional approaches. Experimental validation across comprehensive benchmarks reveals remarkable accuracy improvements of approximately 15-20 percent compared to existing state-of-the-art methods, establishing new performance standards for structural biology applications.

Mycobacterium tuberculosis (M.tb) remains a formidable global health threat. The increasing drug resistance among M.tb clinical isolates is exacerbating the current tuberculosis (TB) burden. In this study we focused on identifying novel repurposed drugs that could be further investigated as potential anti-TB drugs. We utilized M.tb RNA methyltransferase Rv3366 (spoU) as a potential drug target due to its imperative activity in RNA modification and no structural homology with human proteins. Using computational modeling approaches the structure of Rv3366 was determined followed by high throughput virtual screening of Food and Drug Administration (FDA) approved drugs to screen potential binders of Rv3366. Molecular dynamics (MD) simulations were performed to assess the drug-protein binding interactions, complex stability and rigidity. Through this multi-step structure-based drug repurposing workflow two promising inhibitors of Rv3366 were identified, namely, Levodopa and Droxidopa. This study highlights the significance of targeting M.tb RNA methyltransferases to combat drug-resistant M.tb. and proposes Levodopa and Droxidopa as promising inhibitors of Rv3366 for future preclinical investigations.

Currently, the data mining and machine learning fields are facing new challenges because of the amount of information that is collected and needs processing. Many sophisticated learning approaches cannot simply cope with large and complex domains, because of the unmanageable execution times or the loss of prediction and generality capacities that occurs when the domains become more complex. Therefore, to cope with the volumes of information of the current realworld problems there is a need to push forward the boundaries of sophisticated data mining techniques. This thesis is focused on improving the efficiency of Evolutionary Learning systems in large scale domains. Specifically the objective of this thesis is improving the efficiency of the Bioinforma tic Hierarchical Evolutionary Learning (BioHEL) system, a system designed with the purpose of handling large domains. This is a classifier system that uses an Iterative Rule Learning approach to generate a set of rules one by one using consecutive Genetic Algorithms. This system have shown to be very competitive so far in large and complex domains. In particular, BioHEL has obtained very important results when solving protein structure prediction problems and has won related merits, such as being placed among the best algorithms for this purpose at the Critical Assessment of Techniques for Protein Structure Prediction (CASP) in 2008 and 2010, and winning the bronze medal at the HUMIES Awards for Human-competitive results in 2007. However, there is still a need to analyse this system in a principled way to determine how the current mechanisms work together to solve larger domains and determine the aspects of the system that can be improved towards this aim. To fulfil the objective of this thesis, the work is divided in two parts. In the first part of the thesis exhaustive experimentation was carried out to determine ways in which the system could be improved. From this exhaustive analysis three main weaknesses are pointed out: a) the problem-dependancy of parameters in BioHEL's fitness function, which results in having a system difficult to set up and which requires an extensive preliminary experimentation to determine the adequate values for these parameters; b) the execution time of the learning process, which at the moment does not use any parallelisation techniques and depends on the size of the training sets; and c) the lack of global supervision over the generated solutions which comes from the usage of the Iterative Rule Learning paradigm and produces larger rule sets in which there is no guarantee of minimality or maximal generality. The second part of the thesis is focused on tackling each one of the weaknesses abovementioned to have a system capable of handling larger domains. First a heuristic approach to v set parameters within BioHEL's fitness function is developed. Second a new parallel evaluation process that runs on General Purpose Graphic Processing Units was developed. Finally, post-processing operators to tackle the generality and cardinality of the generated solutions are proposed. By means of these enhancements we managed to improve the BioHEL system to reduce both the learning and the preliminary experimentation time, increase the generality of the final solutions and make the system more accessible for end-users. Moreover, as the techniques discussed in this thesis can be easily extended to other Evolutionary Learning systems we consider them important additions to the research in this field towards tackling large scale domains. vi

Protein secondary structure prediction from its amino acids is purposely used to evaluate and improve the accuracy of performance as well as drug design and cell functionality. Various approaches for predicting protein secondary structure have been used, each with varying accuracy, vulnerabilities, and strengths. In view of this, this paper is aimed at training a deep neural network with particle swarm optimization and comparing the results with the state of accuracy. Also, the methodology used is basic particle swarm optimization for training a 20-15-15-15-3 deep neural network. The Java programming language and the Spring Boot framework were employed to implement the various application programming interfaces of the model. The dataset acquired after the training of JPred Server 1.2, which included 1349 training sets and 149 test sets, was used in training the model. Following the training, it was discovered that the model had a highest accuracy of 53.18 percent on epoch 140, indicating that this model is not a best fit or an alternative to the current state of the art for the prediction of protein secondary structure.

Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this paper, the most successful groups in CASP12 describe their latest methods for Estimates of Model Accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the three top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). While the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcombdomain and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact based model quality measures (CAD, lDDT) the single model quality methods perform relatively better.

This supplement extends the VPR (Vortex-Pattern-Resonance) model by applying its informational energy framework to resonant phenomena observed in biological macromolecules and astrophysical signals. We introduce VPR+-a focused extension addressing stabilization of proteins (e.g., insulin) and the temporal structure of fast radio bursts (FRB). Through theoretical adaptation and predictive calculations, the model links internal resonance cycles, informational energy, and system complexity. The results demonstrate cross-scale applicability and propose new interpretations of empirical anomalies. This work opens pathways for experimental validation and interdisciplinary exploration.

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated...

Six empirical force fields were tested for applicability to calculations for automated carbohydrate database filling. They were probed on eleven disaccharide molecules containing representative structural features from widespread classes of carbohydrates. The accuracy of each method was queried by predictions of nuclear Overhauser effects (NOEs) from conformational ensembles obtained from 50 to 100 ns molecular dynamics (MD) trajectories and their comparison to the published experimental data. Using various ranking schemes, it was concluded that explicit solvent MM3 MD yielded non-inferior NOE accuracy with newer GLYCAM-06, and ultimately PBE0-D3/def2-TZVP (Triple-Zeta Valence Polarized) Density Functional Theory (DFT) simulations. For seven of eleven molecules, at least one empirical force field with explicit solvent outperformed DFT in NOE prediction. The aggregate of characteristics (accuracy, speed, and compatibility) made MM3 dynamics with explicit solvent at 300 K the most favorable method for bulk generation of disaccharide conformation maps for massive database filling.

Six empirical force fields were tested for applicability to calculations for automated carbohydrate database filling. They were probed on eleven disaccharide molecules containing representative structural features from widespread classes of carbohydrates. The accuracy of each method was queried by predictions of nuclear Overhauser effects (NOEs) from conformational ensembles obtained from 50 to 100 ns molecular dynamics (MD) trajectories and their comparison to the published experimental data. Using various ranking schemes, it was concluded that explicit solvent MM3 MD yielded non-inferior NOE accuracy with newer GLYCAM-06, and ultimately PBE0-D3/def2-TZVP (Triple-Zeta Valence Polarized) Density Functional Theory (DFT) simulations. For seven of eleven molecules, at least one empirical force field with explicit solvent outperformed DFT in NOE prediction. The aggregate of characteristics (accuracy, speed, and compatibility) made MM3 dynamics with explicit solvent at 300 K the most fav...

Genetic defects on 6-pyruvoyl-tetrahydropterin synthase (PTPS) are the most prevalent cause of hyperphenylalaninaemia not due to phenylalanine hydrolyase deficiency (phenylketonuria). PTPS catalyses the second step of tetrahydrobiopterin (BH4) cofactor biosynthesis, and its deficiency represents the most common form of BH4 deficiency. Untreated PTPS deficiency results in depletion of the neurotransmitters dopamine, catecholamine and serotonin causing neurological symptoms. We archived reported missense variants of the PTS gene. Common in silico algorithms were used to predict the effects of such variants, and substantial proportions (up to 19%) of the variants were falsely classified as benign or uncertain. We have determined the crystal structure of the human PTPS hexamer, allowing another level of interpretation to understand the potential deleterious consequences of the variants from a structural perspective. The in silico and structure approaches appear to be complimentary and may provide new insights that are not available from each alone. Information from the protein structure suggested that the variants affecting amino acid residues required for interaction between monomeric subunits of the PTPS hexamer were those misclassified as benign by in silico algorithms. Our findings illustrate the important utility of 3D protein structure in interpretation of variants and also current limitations of in silico prediction algorithms. However, software to analyse mutation in the perspective of 3D protein structure is far less readily available than other in silico prediction tools.

We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)-a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions, and sequence motifs in non-structurally conserved regions. b) NEST-a fast model building program that applies an "artificial evolution" algorithm to construct a model from a given template and alignment. c) GRASP2-a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical-chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided.

In this study we investigate the extent to which techniques for homology modeling that were developed for water-soluble proteins are appropriate for membrane proteins as well. To this end we present an assessment of current strategies for homology modeling of membrane proteins, and introduce a benchmark data set of homologous membrane protein structures, called HOMEP. First, we use HOMEP to reveal the relationship between sequence identity and structural similarity in membrane proteins. This analysis indicates that homology modeling is at least as applicable to membrane proteins as it is to water-soluble proteins, and that acceptable models (with Cα-RMSD values to the native of 2 Å or less in the transmembrane regions) may be obtained for template sequence identities of 30% or higher, if an accurate alignment of the sequences is used. Second, we show that secondary-structure prediction algorithms that were developed for water-soluble proteins perform approximately as well for membrane proteins. Third, we provide a comparison of a set of commonly-used sequence alignment algorithms as applied to membrane proteins. We find that high-accuracy alignments of membrane protein sequences can be obtained using state-of-the-art profile-to-profile methods that were developed for water-soluble proteins. Improvements are observed when weights derived from the secondary structure of the query and the template are used in the scoring of the alignment, a result which relies on the accuracy of the secondary-structure prediction of the query sequence. The most accurate alignments were obtained using template profiles constructed with the aid of structural alignments. In contrast, a simple sequence-to-sequence alignment algorithm, using a membrane protein-specific substitution matrix, shows no improvement in alignment accuracy. We suggest that profile-to-profile alignment methods should be adopted in order to maximize the accuracy of homology models of membrane proteins.

Background The TlyA protein has a controversial function as a virulence factor in Mycobacterium tuberculosis (M. tuberculosis). At present, its dual activity as hemolysin and RNA methyltransferase in M. tuberculosis has been indirectly proposed based on in vitro results. There is no evidence however for TlyA relevance in the survival of tubercle bacilli inside host cells or whether both activities are functionally linked. A thorough analysis of structure prediction for this mycobacterial protein in this study shows the need for reevaluating TlyA&#39;s function in virulence. Results Bioinformatics analysis of TlyA identified a ribosomal protein binding domain (S4 domain), located between residues 5 and 68 as well as an FtsJ-like methyltranferase domain encompassing residues 62 and 247, all of which have been previously described in translation machinery-associated proteins. Subcellular localization prediction showed that TlyA lacks a signal peptide and its hydrophobicity profile show...

Using an information theoretic formalism, we optimize classes of amino acid substitution to be maximally indicative of local protein structure. Our statistically-derived classes are loosely identifiable with the heuristic constructions found in previously published work. However, while these other methods provide a more rigid idealization of physicochemically constrained residue substitution, our classes provide substantially more structural information with many fewer parameters. Moreover, these substitution classes are consistent with the paradigmatic view of the sequence-to-structure relationship in globular proteins which holds that the three-dimensional architecture is predominantly determined by the arrangement of hydrophobic and polar side chains with weak constraints on the actual amino acid identities. More specific constraints are imposed on the placement of prolines, glycines, a n d the charged residues. These substitution classes have been used in highly accurate predictions of residue solvent accessibility. They could also be used in the identification of homologous proteins, the construction and refinement of multiple sequence alignments, and as a means of condensing and codifying the information in multiple sequence alignments for secondary structure prediction and tertiary fold recognition.

We present a new method for multiple sequence alignment (MSA), which we call MSACSA. The method is based on the direct application of a global optimization method called the conformational space annealing (CSA) to a consistency-based score function constructed from pairwise sequence alignments between constituting sequences. We applied MSACSA to two MSA databases, the 82 families from the BAliBASE reference set 1 and the 366 families from the HOMSTRAD set. In all 450 cases, we obtained well optimized alignments satisfying more pairwise constraints producing, in consequence, more accurate alignments on average compared with a recent alignment method SPEM. One of the advantages of MSACSA is that it provides not just the global minimum alignment but also many distinct low-lying suboptimal alignments for a given objective function. This is due to the fact that conformational space annealing can maintain conformational diversity while searching for the conformations with low energies. This characteristics can help us to alleviate the problem arising from using an inaccurate score function. The method was the key factor for our success in the recent blind protein structure prediction experiment.

We present the sixth report evaluating the performance of methods for predicting the atomic resolution structures of protein complexes offered as targets to the community-wide initiative on the Critical Assessment of Predicted Interactions (CAPRI). The evaluation is based on a total of 20,670 predicted models for 8 protein-peptide complexes, a novel category of targets in CAPRI, and 12 protein-protein targets in CAPRI prediction Rounds held during the years 2013-2016. For two of the protein-protein targets, the focus was on the prediction of side-chain conformation and positions of interfacial water molecules. Seven of the protein-protein targets were particularly challenging owing to their multicomponent nature, to conformational changes at the binding site, or to a combination of both. Encouragingly, the very large multiprotein complex with the nucleosome was correctly predicted, and correct models were submitted for the protein-peptide targets, but not for some of the challenging...

A structured folding pathway, which is a time ordered sequence of folding events, plays an important role in the protein folding process and hence, in the conformational search. Pathway prediction, thus gives more insight into the folding process and is a valuable guiding tool to search the conformation space. In this paper, we propose a novel 'unfolding' approach to predict the folding pathway. We apply graph-based methods on a weighted secondary structure graph of a protein to predict the sequence of unfolding events. When viewed in reverse this yields the folding pathway. We demonstrate the success of our approach on several proteins whose pathway is partially known.

Summary: A structured folding pathway, which is a time ordered sequence of folding events, plays an important role in the protein folding process and hence, in the conformational search. Pathway prediction, thus gives more insight into the folding process and is a valuable guiding tool to search the conformation space. In this paper, we propose a novel ‘unfolding’ approach to predict the folding pathway. We apply graph-based methods on a weighted secondary structure graph of a protein to predict the sequence of unfolding events. When viewed in reverse this yields the folding pathway. We demonstrate the success of our approach on several proteins whose pathway is partially known.

This study presents a unified machine learning-based system for predicting multiple diseases diabetes, Parkinson's disease, and heart disease through a single interface. Support Vector Machine (SVM) is used for diabetes and Parkinson's prediction, while Logistic Regression handles heart disease classification. The models are trained on publicly available datasets and integrated into an interactive web app using Streamlit. This approach enhances diagnostic efficiency, supports early detection, and provides a scalable solution to assist clinical decision-making.

Dimethyl gold complexes bonded to partially dehydroxylated MgO powder calcined at 673 K were synthesized by adsorption of Au(CH3)2(acac) (acac is C5H7O2) from n-pentane solution. The synthesis and subsequent decomposition of the complexes by treatment in He or H2 were characterized with diffuse reflectance Fourier transform infrared (DRIFT), X-ray absorption near edge structure (XANES), and extended X-ray absorption fine structure (EXAFS) spectroscopies. The XANES results identify Au(III) in the supported complexes, and the EXAFS and DRIFTS data indicate mononuclear dimethyl gold complexes as the predominant surface gold species, consistent with the lack of Au-Au contributions in the EXAFS spectrum and the presence of ν as(CH3) and νs(CH3) bands in the IR spectrum. EXAFS data show that each complex is bonded to two oxygen atoms of the MgO surface at an Au-O distance of 2.16 Å. The DRIFT spectra show that reaction of Au(CH3)2(acac) with MgO at room temperature also formed Mg(acac)2 and H(acac) species on the support. Treatment of the dimethyl gold complexes in He or H2 at increasing temperatures varying from 373 to 573 K removed CH3 ligands and caused aggregation forming zerovalent gold nanoclusters of increasing size, ultimately with an average diameter of about 30 Å. Analysis of the gas-phase products during the genesis of the gold clusters indicated formation of CH 4 (consistent with removal of CH3 groups) and CO2 at 473-573 K, associated with decomposition of the organic ligands derived from acac species. O2 and CO2 were also formed in the decomposition of ubiquitous carbonates present on the surface of the MgO support.

Bacterial cell division is driven by the divisome, a ring-shaped protein complex organized by the bacterial tubulin homolog FtsZ. Although most of the division proteins in Escherichia coli have been identified, how they assemble into the divisome and synthesize the septum remains poorly understood. Recent studies suggest that the bacterial actin homolog FtsA plays a critical role in divisome assembly and acts synergistically with the FtsQLB complex to regulate the activity of the divisome. FtsEX, an ATP-binding cassette transporter-like complex, is also necessary for divisome assembly and inhibits division when its ATPase activity is inactivated. However, its role in division is not clear. Here, we find that FtsEX acts on FtsA to regulate both divisome assembly and activity. FtsX interacts with FtsA and this interaction is required for divisome assembly and inhibition of divisome function by ATPase mutants of FtsEX. Our results suggest that FtsEX antagonizes FtsA polymerization to p...

One of the main challenges in bioinformatics is predicting the structures of macromolecules, particularly nucleic acids and proteins. In this study, we propose a hybrid approach integrating K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN) algorithms. We perform an in-depth analysis using various metrics, including accuracy, Q3 score, ROC, and precision-recall curves. Based on the RS126 dataset, we compared our hybrid model with individual approaches, revealing that our model achieves an accuracy of 80% and a Q3 score of 86%, outperforming each of the algorithms separately. These results validate the effectiveness of combining models for protein secondary structure prediction (PSSP). We show that the hybrid model outperforms the other models for this task. We also discuss the implications of these results and propose future work to further improve the accuracy and robustness of the model. This approach could have important implications for protein structure modeling, in particular for understanding their three-dimensional structure and function.

As for secondary structure prediction, we believe that tertiary structure prediction should also be automatic and hence renroduc&. A j&lly automatic protocol HOM-FOLD for modelling proteins by homology, using a fragment based approach, is described. The present report describes the performance of the basic algorithm. Written in the GLOBAL language, it is a simple matter to add further knowledge and expertise as new rules in the protocol. The protocol was intended to be, and is normally used as, a first step only, with more elaborate algorithms for energy refinement to be applied subsequently in the context of a higher l&Tel protocol. However, structures in agreement within 1.80 A X-ray crystallographic data can be achieved at least as good as that obtained by interactive modelling and refinement.

The computational design and simulation of the properties of proteins requires powerful computers, colour graphics and interactive software. A brief description of such a system will be presented. One of the key requirements in the design of novel proteins is the ability to construct a three-dimensional model of the target protein from a knowledge of its amino-acid sequence and the X-ray crystal structure of a homologous protein. Most of the models constructed in this fashion have used the computer as an electronic Dreiding model; where the inserted, deleted, and substituted amino acids are placed in three-dimensional space by subjective, visually oriented procedures (R. J. Read, G. D. Brayer, L. Jurasek & M. N. G. James, Biochemistry 23, 6570 (1984)). An objective computational procedure has been developed whereby the insertions, deletions, and substitutions are performed automatically by making use of a list of rules and a hierarchical energy minimization scheme. The procedure is ...

age' secondary structure prediction for the family. The prediction of Benner et al. was in the event disappointing in some respects 8 . But other predictions have been better. The recent publication of tertiary structures for SH2 domains from src (ref. 9), abl (ref. 10) and p85CY (ref. 11), for example, has confirmed our previously published analysis of this family 7 in which we aligned 67 SH2domain sequences and used this information to predict the secondary structure and position of buried residues. The prediction was performed by combining two-turn and three secondary-structure prediction algorithms, and was augmented by the identification of residue conservation patterns characteristic of helix, loop and strand. The experimentally determined structures confirm our secondary-structure prediction within the conserved core, although we failed to identify a small surface sheet in a variable part of the domain. The overall accuracy of the prediction on a residue-by-residue basis was 78, 76 and 76% when compared to the secondary structures of src, abl and p85CY SH2domains, respectively. Furthermore, analysis of the src SH2-domain coordinates shows that the 22 positions predicted to be buried are inaccessible to solvent. We, in common with most people developing methods for protein-structure prediction, welcome challenges to predict the structures of other proteins that are soon to be experimentally determined.