Prediction of Protein Structure from Multiple Sequence Alignment

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 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.

In the given work it is shown, that at machining the technological dimensional structures are optimum if they are similar to the design dimensional structure of a detail. The conditions of the locating and fastening, and also some technical requirements interfere achievements of similarity. In these cases the directed change of the design dimensional structure of a detail is recommended due to recalculation of the sizes. From this point of view creation of technological process represents a compromise of mutual approche of the dimensional structures of a detail and of a technological system. Technological process may be built from operation elements, but they (the operation elements) do not have the necessary characteristics to create the dimensional structures. These characteristics are not formed during decomposition because the relevant details dimensional structure is not taken into account. The most appropriate entity as element of which may be built the technological process is a technological mounting. The mounting has a dimensional structure and includes the communication elements - the sizes up to locating surfaces Thus, the dimensional designing of technological processes results in optimality if it is possible to achieve the similarity of design and technological dimensional structures in the following order: creation of the blank's dimensional structure similar to the detail's dimensional structure; formation of optimum technological dimensional links of the first technological operations or mounting (roughing) by resizing if necessary of the blank and of the detail; formation of optimum technological dimensional links on the subsequent technological operations or mounting by resizing if necessary of the detail in progress; formation of optimum technological dimensional links on the final technological operations or mounting by resizing if necessary the detail; calculation of the minimal machining allowances; the dimensional analysis of technology with calculation of the operational sizes, their tolerances and limit deviations; formation of drawings of the blank.

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.

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/.

The abundance of different protein variants generated by alternative splicing (AS) is not fully represented in the Protein Structure Database (PDB). Among the already limited set of proteins with experimentally determined structures deposited in PDB, only a small fraction have structures ofmore than one splicing variant. In the absence of experimental data, computational methods can be used to predict the structure of protein isoforms. In this chapter, the established approaches to computational protein structure prediction will be briefly described, and their use for studying differences that result from AS will be detailed. In particular, it will be noted when a reliable structural model can be obtained for a given protein sequence. Topics of finding the best template structures for modeling individual protein domains, predicting the structure of long, multidomain proteins, and assessing the quality of theoretical models, together with error correction, will also be addressed. The suggested protocol will be illustrated by the structure prediction of isoform C of phosphotyrosine phosphatase (LMPTP-C). 54.1 Theoretical Background In 1961, based on studies with ribonuclease A, Christian Anfinsen hypothesized that the structure of a protein in its native environment would be determined by the protein’s amino acid sequence, and that this native conformation is the one in which the Gibbs free energy of the system is lowest [1]. Since then, the prediction of a protein’s structure from its amino acid sequence has become the “holy grail” of computational biology. However, despite great efforts having been made, a universal algorithm with which to infer protein structure has not yet been developed. Nonetheless, several methods have been developed that can provide useful models, depending on a variety of conditions. There are two major approaches for protein structure prediction. The first approach, termed “comparative modeling” or “template-based modeling,” is based on the experimental observation that evolutionarily related proteins usually retain similar structures, despite an accumulation of substitutions at the level of amino acid sequence, and that the structure changes very slowly compared to the sequence [2]. Thus, an experimentally determined structure of one protein can be used as a template to model the structure of another related protein (a modeling target) by simulating the process of evolution at the sequence level.Modification of the template to “transmute” it into the target requires only limited computation. Therefore, comparative modeling does not require special computer resources, and can be easily carried out on a personal workstation using computer programs that are simple to install and use. Template-based modeling requires that, for a given target sequence, a structurally similar template is identified, and a correct target–template sequence alignment is Alternative pre-mRNA Splicing: Theory and Protocols, First Edition. Edited by Stefan Stamm, Chris Smith, and Reinhard Luhrmann. 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA. j 583

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> .

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally for Protein. MSA has wide range of applications and to cite few of them such as phylogenetic analysis, protein pattern identification, protein domain identification, prediction of protein structure, structural similarity of amino acids and to get evolutionary similarity. ClustalW2 is a general purpose global multiple sequence alignment program for proteins. It produces biologically meaningful multiple sequence alignments of divergent sequences. The output from ClustalW2 shows the best match for the selected sequences and lines up them in such a way that the identities, similarities and differences can be easily understood. Evolutionary relationships can be seen by creating Cladograms or Phylograms. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. In this paper, protein sequences of few legume species from UNIPROT database were taken and focused on MSA for protein sequences for these tree species of family Leguminosae, where ClustalW2 tool have used to generate biological data. The results are discussed with the help of Cladograms and Phylograms for selected tree species.

We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end‐to‐end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modeling targets, and represents a significant improvement in the state of the art in protein structure prediction. We reported how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large‐scale structure prediction.

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.

Proteins in the Genome Browser: Integration of Phylogenies, Alignments, and Structures With Nucleotide-level Evidence in JBrowse 2.

An integrated approach to the analysis and modeling of protein sequences and structures. III. A comparative study of sequence conservation in protein structural families using multiple structural alignments

Bu çalışmada, her ikisi de otobiyografik roman olan, İngiliz modernist yazar Virginia Woolf’un Deniz Feneri (1927) ile Türk Edebiyatı’nın ilk Nobel ödüllü yazarı olan Orhan Pamuk’un ilk romanı olan Cevdet Bey ve Oğulları (1982) eserlerin oluşturulma yapıları bağlamında benzerlikleri ve farklılıkları mukayese edilmektedir. Deniz Feneri konu olarak, sekiz çocuklu Ramsey ailesinin İskoçya’daki yazlık evlerinden yakınlarında bulunan deniz fenerine gerçekleştirmek istedikleri ama bir türlü aile fertlerinin bir arada bulunduğu dönemde gerçekleştiremedikleri ziyaret ekseninde şekillenmektedir. Cevdet Bey ve Oğulları ise ilk bakışta İstanbul’da tüccarlık mesleğiyle uğraşmakta olan Işıkçı ailesinin üç nesil fertlerini ve onların hayat hikâyelerini ele almakla birlikte aslında aynı zamanda Osmanlı’nın son döneminden başlayarak Türkiye Cumhuriyeti’nin ilk dönemi ve 1970’lerdeki Türkiye tarihinden önemli kesitler sunmaktadır. Farklı konu, zaman ve mekanları ele alan bu iki eser ortak bir payda olarak yapısal açıdan üç bölümden oluşmaktadır ve ayrıca her iki eserde de her bir bölüm farklı anlatıcıların kişisel bakış açılarından aktarılmaktadır. Ancak Deniz Feneri’nde öncü modernist roman özelliklerine bağlı olarak bilinç akışı ve iç monolog teknikleri kullanılarak her bir bölüm kişilerin duygu ve izlenimleri üzerinden şekillenmekle birlikte Cevdet Bey ve Oğulları’nda ise bölümler daha çok geleneksel roman özelliklerine bağlı kalarak kişiler arası diyaloglardan oluşmaktadır. Çalışmada yöntem olarak öncelikle adı geçen romanlardaki bu üç bölümlü yapının eserlere konu/tema, karakter ve olay örgüsü yönünden etkileri tespit edilmekle birlikte daha sonra eserlerdeki bu üç bölümlü yapının romanların başkarakterlerinin hayatta kendilerine koydukları hedeflere ulaşmalarında nasıl bir rol oynadığı ortaya konmaktadır.

In science, the relationship between methods and discovery is symbiotic.As we discover more, we are able to construct more precise and sensitive tools and methods that enable further discovery.With better lens crafting came microscopes, and with them the discovery of living cells.In the last 40 years, advances in molecular biology, statistics, and computer science have ushered in the field of bioinformatics and the genomic era.Computational scientists enjoy developing new methods, and the community encourages them to do so.Indeed, the editorial guidelines for PLOS Computational Biology require manuscripts to apply novel methods.However, it is often confusing to know which method to choose: which method is best?And, in this context, what does "best" mean?To help choose an appropriate method for a particular task, scientists often form community-based challenges for the unbiased evaluation of methods in a given field.These challenges help evaluate existing and novel methods, while helping to coalesce a community and leading to new ideas and collaborations.In computational biology, the first of these challenges was arguably the Critical Assessment of protein Structure Prediction, or CASP [1], whose goal is to evaluate methods for predicting three-dimensional protein structure from amino acid sequence.The first CASP meeting was held in December of 1994, following a "prediction period" where members of the community were presented with protein amino acid sequences and asked to predict their three dimensional structures.The sequences that were chosen had recently been solved by X-ray crystallography but had not been not published or released until after the predictions from the community were made.Since the first CASP, we have seen many successful challenges, including Critical Assessment of Function Annotation (CAFA) for protein function prediction [2], Critical Assessment of Genome Interpretation (CAGI) (for genome interpretation) [3], Critical Assessment of Massive (originally "Microarray") Data Analysis (CAMDA) (for large-scale biological data) [4], BioCreative (for biomedical text mining) [5], the Assemblathon (for sequence assembly), and the NCI-DREAM Challenges (for various biomedical challenges), amongst others [6].Computational challenges also help solve new problems.While the original CASP experiment was developed to evaluate existing methods applied to current problems, other communities often look at other areas for which there are no existing tools.These challenges have spread successfully to industry, and companies such as Innocentive [7] and X-Prize [8] offer large prizes for solving novel questions.