Manipulating and Visualizing Proteins

Proteins are essential parts of our life and participate in virtually every process within a cell. The understanding of protein structures is vital to determine the function of a protein. Protein structure prediction (PSP) from amino acid sequence is one of the high focus problems in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its three dimensional structure. Thus, protein structure prediction is a fundamental area of computational biology. Its importance is intensed by large amounts of sequence data coming from PDB (Protein Data Bank) and the fact that experimentally methods such as X-ray crystallography or Nuclear Magnetic Resonance (NMR)which are used to determining protein structures remains very expensive and time consuming. For minimizing the time, computational methods are used for protein folding and structure prediction problem. In this paper results of protein p53 are discussed.

Protein adsorption at oil-water interfaces interfacial structure and function of proteins in emulsions

Protein structure prediction (PSP) from amino acid sequence is one of the high focus problems in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its three dimensional structure. The understanding of protein structures is vital to determine the function of a protein and its interaction with DNA, RNA and enzyme. Thus, protein structure is a fundamental area of computational biology. Its importance is intensed by large amounts of sequence data coming from PDB (Protein Data Bank) and the fact that experimentally methods such as X-ray crystallography or Nuclear Magnetic Resonance (NMR)which are used to determining protein structures remains very expensive and time consuming. In this paper, different types of protein structures and methods for its prediction are described.

Protein is one of the giant biomolecules that act as the main component of the organism. Protein is formed from building blocks namely amino acids. Hierarchically, the structure of protein is divided into four levels: primary, secondary, tertiary, and quaternary structure. Protein secondary structure is formed by amino acid sequences that would form three-dimensional structures and have information about the tertiary structure and function of proteins. This study used 277,389 protein residue data from enzyme categories. Position-specific scoring matrix (PSSM) profile and physicochemical are used for features. This study developed support vector machine models to predict the protein secondary structure by recognizing patterns of amino acid sequences. The Q3 results showed that the best scores obtained are 93.16% from the dataset that has 260 features with the radial kernel. Combining PSSM and physicochemical feature additions can be used for prediction.

More than sixty years have passed since it has disclosed that protein can be developed from amino acid sequence. Structures of protein have proven to be a key piece of information for designing a drug, synthetic biology, and diagnosis of diseases. By knowing the protein structure, one can predict its function, find out how molecules or drugs skillfully bind with proteins and structural behavior of protein can help us to diagnose diseases and to invent new drugs. Due to the fast growth of protein sequence from the amino acid, it finds to be much more difficult with experimental techniques to obtain three-dimensional protein structures. Hence protein structure prediction (PSP) is still seen to be more important. Here in this paper, we are presenting a review about the formation of protein, different protein structure, factors affecting the tertiary structure, the importance of protein structure prediction, structural database, experimental and recent computational approaches for PSP, and similarity measures.

One of the main research problems in Structural Bioinformatics is related to the prediction of three-dimensional structures (3-D) of polypeptides or proteins. The rate at which amino acid sequences are identified is increasing faster than the 3-D protein structure determination by experimental methods. Computational prediction methods have been developed during the last years, but the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. In this article we present a hybrid genetic algorithm for the Protein Structure Prediction (PSP) Problem. A genetic algorithm is combined with a structured population, and it is hybridized with a path-relinking procedure that helps the algorithm to scape from local minima. We perform a set of experiments and show that the proposed hybrid genetic algorithm is effective in finding good quality solutions for the PSP Problem.

Protein folding and de novo protein design for biotechnological applications

Background and Objectives: Changes in the secondary and tertiary structures of protein molecules during thermal denaturation lead to changes in their vibrational spectra. Vibrations corresponding to elements of the primary and secondary structure of the protein are manifested in the fingerprint range, while vibrational modes of large fragments corresponding to higher levels of the protein structural hierarchy can be observed in the low-frequency (40–500 cm−1) spectral range. The purpose of this work is to reveal changes in the vibrational spectra of proteins resulting from denaturation that can be used to characterize changes in the protein structure. Materials and Methods: Two proteins (collagen and chymotrypsin) having different secondary and tertiary structures are studied using Raman spectroscopy. The experimental data are analyzed using bandpass filtering of the Fourier transforms of the spectral curves. Results: Vibrational spectra of two proteins (collagen and chymotrypsin) having different secondary and tertiary structures, as well as the spectra of thermally denatured samples of these proteins have been measured in the fingerprint and low-frequency ranges. Several low-frequency spectral features that can be used to characterize structural changes of protein molecules have been considered. A few bands may correspond to vibrations of tertiary structure elements (both in the low-frequency range and in the “fingerprint” range). Conclusion: A comparison of the vibrational spectra of native and denatured (superhelical) collagen, as well as native and denatured (globular) chymotrypsin shows that changes in the amide I and amide III bands sensitive to the secondary structure are supplemented with spectral changes in bands that are not assigned to elements of the secondary structure and can be related to changes at higher levels of structural hierarchy. Comparison of the low-frequency vibrational spectra indicates a lower sensitivity of presumably tertiary structure of the globular protein to denaturation.

By computing strain energies of peptide fragments within protein structures and their intramolecular interaction energies, we attempt to reveal general biophysical trends behind the secondary structure formation in the context of protein evolution. Our "protein basis set" consisted of 1143 representatives of different folds obtained from curated SCOPe database, and for each member of the set, the strain and intramolecular energy was calculated on the "rolling tripeptide" basis, employing the DFT-D3/COSMO-RS method for the former and the QM-calibrated force field method (MM) for the latter. The calculated data, strain and interactions, were correlated with the conservation of amino acid residues in secondary structure elements and also with the level of the residue burial within the protein three-dimensional structure. It allowed us to formulate several observations concerning fundamental differences between two main secondary structure motifs: α-helices and β-strands. We have shown that a strong interaction is one of the determining characteristics of the β-sheet formation, at least at the level of tripeptides (and likely penta- or heptapeptides, too), and that the β-strand is a prevailing secondary structure in the strongly-interacting regions of the protein folds conserved by evolution. On the other hand, low strain was neither proven to be an important physicochemical property conserved by evolution nor does it correlate with the propensity for the α-helix and β-strand. Finally, it has been demonstrated that the strong interaction has a certain level of connection with residue burial; however, we demonstrate that these two characteristics should be rather regarded as two complementary factors. These findings represent an important contribution to understanding protein folding from first principles, which is a complementary approach to ongoing efforts to solve the protein folding problem by knowledge-based approaches and machine-learning.

Chapter 1 of this thesis provides an overview of the structure and function of proteins as well as their investigation via NMR spectroscopy. Proteins are one of the most important building blocks of life. They are part of every organism and fulfill a variety of functions including, for example, the catalysis of many chemical reactions, which is the reason various processes in an organism become possible in the first place. Proteins are made up of amino acids, which are linked together via peptide bonds during the protein biosynthesis. The 22 proteinogenic amino acids result in an almost unlimited number of sequential combinations that give proteins their versatility. The sequence of amino acids in a protein is also called the primary structure. From this, locally limited structural motifs such as α-helices and β-sheets are formed, representing the secondary structure. Further intramolecular interactions create the tertiary structure of a protein, which can be seen as the overall three-dimensional structure of the protein. Furthermore, several individual proteins can assemble into a larger complex to form a quaternary structure. There are various methods for investigating the structure of proteins, including nuclear magnetic resonance (NMR) spectroscopy. In NMR spectroscopy, atomic nuclei with a non-zero nuclear spin are polarized by a constant magnetic field and then excited using radio frequency pulses. The emitted resonance frequencies are detected in the form of a free induction decay (FID) and converted into an NMR spectrum by a Fourier transformation. Many NMR experiments are available for researching proteins. By default, a two-dimensional 1H15N-HSQC spectrum is measured, which provides signals for each N-H bond in a protein meaning that one signal is obtained for each N-H in the backbone of the amino acids. Using 3D NMR experiments, these signals can then be assigned to the respective amino acids in the protein sequence. This makes it possible to investigate the dynamics of proteins as well as the formation of complexes with a ligand or another biomacromolecule if they interact. Further 3D NMR experiments also make it possible to assign the side-chains of the protein. With the help of the determined resonance assignments and spatial information from various NOESY spectra, a protein structure can then be calculated using a suitable computer program. Recently, the prediction of protein structures using AI (artificial intelligence)-supported programs has also become more prevalent. In particular, the artificial neural network AlphaFold has been extremely successful in predicting correct structures without any experimental data provided by the user. Chapter 2 of this thesis introduces the research field of small proteins. These are proteins with a length of usually less than 100 amino acids, although definitions in literature vary and upper limits of, for example, 50 or 200 amino acids exist as well. Various other names are also used to describe small proteins, such as μ-proteins or sORF-encoded peptides. The field of small proteins is still relatively new, as they were overlooked in research for many years. One reason for this was the lower limit of 100 codons that was set for the identification of open reading frames (ORFs) in genome projects. This served to exclude numerous biologically irrelevant ORFs from genome annotation. However, this meant that functional ORFs coding for small proteins were also ignored. Another reason was that experimental methods were not designed for the analysis of small proteins, meaning they could often not be detected at all. However, new analytical methods such as ribosome profiling and a generally increased focus on the detection of small proteins have led to their detection in all domains of life. In the research group of Prof. Dr. Schwalbe, small proteins of various prokaryotes were structurally characterized as part of a special priority program by the DFG. NMR spectroscopy was primarily used for this purpose, as it is the method of choice for the investigation of small proteins. The results of this research were published in several articles, two of which are part of this dissertation.

Summary form only given. The understanding of protein structures is vital to determine the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein structures from protein sequences using computer programs is an important step to unveil proteins' three dimensional conformation and functions. As a result, prediction of protein structures has profound theoretical and practical influence over biological study. In this talk, we would show how to use machine learning methods with various advanced encoding schemes and classifiers improve the accuracy of protein structure prediction. The explanation of how a decision is made is also important for improving protein structure prediction. The reasonable interpretation is not only useful to guide the "wet experiments", but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. Some preliminary results using SVM and decision tree for rule extraction and prediction interpretation would also be presented.

Protein structure prediction is one of the most important problems in modern computational biology. Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machine (SVM) etc., to focus on the prediction of the secondary structures. In this paper, a new method was proposed based on SVM. Different from the existing methods, this method takes into account of the physical-chemical properties and structure properties of amino acids. When tested on the most popular dataset CB513, it achieved a Q(3) accuracy of 0.7844, which illustrates that it is one of the top range methods for protein of secondary structure prediction.

Protein structure prediction (PSP) problems are a major biocomputing challenge, owing to its scientific intrinsic that assists researchers to understand the relationship between amino acid sequences and protein structures, and to study the function of proteins. Although computational resources increased substantially over the last decade, a complete solution to PSP problems by computational methods has not yet been obtained. Using only one energy function is insufficient to characterize proteins because of their complexity. Diverse protein energy functions and evolutionary computation algorithms have been extensively studied to assist in the prediction of protein structures in different ways. Such algorithms are able to provide a better protein with less computational resources requirement than deep learning methods. For the first time, this study proposes a many-objective PSP (MaOPSP) problem with four types of objectives to alleviate the impact of imprecise energy functions for predicting protein structures. A many-objective evolutionary algorithm (MaOEA) is utilized to solve MaOPSP. The proposed method is compared with existing methods by examining 34 proteins. An analysis of the objectives demonstrates that our generated conformations are more reasonable than those generated by single/multiobjective optimization methods. Experimental results indicate that solving a PSP problem as an MaOPSP problem with four objectives yields better PSPs, in terms of both accuracy and efficiency. The source code of the proposed method can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://toyamaailab.github.io/sourcedata.html</uri> .

A major challenge in structural bioinformatics is the prediction of protein structure and function from primary amino acid sequences. The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. In this paper, we developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression with parametric insensitive model.KeywordsRoot Mean Square ErrorSupport Vector RegressionPrimary Amino Acid SequenceSequence SeparationProtein Secondary Structure PredictionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

BackgroundAs genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.ResultsTo meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.ConclusionsThese tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.