Discovering structural motifs using a structural alphabet: Application to magnesium-binding sites

BACKGROUND: For many metalloproteins, sequence motifs characteristic of metal-binding sites have not been found or are so short that they would not be expected to be metal-specific. Striking examples of such metalloproteins are those containing Mg(2+), one of the most versatile metal cofactors in cellular biochemistry. Even when Mg(2+)-proteins share insufficient sequence homology to identify Mg(2+)-specific sequence motifs, they may still share similarity in the Mg(2+)-binding site structure. However, no structural motifs characteristic of Mg(2+)-binding sites have been reported. Thus, our aims are (i) to develop a general method for discovering structural patterns/motifs characteristic of ligand-binding sites, given the 3D protein structures, and (ii) to apply it to Mg(2+)-proteins sharing <30% sequence identity. Our motif discovery method employs structural alphabet encoding to convert 3D structures to the corresponding 1D structural letter sequences, where the Mg(2+)-structural motifs are identified as recurring structural patterns. RESULTS: The structural alphabet-based motif discovery method has revealed the structural preference of Mg(2+)-binding sites for certain local/secondary structures: compared to all residues in the Mg(2+)-proteins, both first and second-shell Mg(2+)-ligands prefer loops to helices. Even when the Mg(2+)-proteins share no significant sequence homology, some of them share a similar Mg(2+)-binding site structure: 4 Mg(2+)-structural motifs, comprising 21% of the binding sites, were found. In particular, one of the Mg(2+)-structural motifs found maps to a specific functional group, namely, hydrolases. Furthermore, 2 of the motifs were not found in non metalloproteins or in Ca(2+)-binding proteins. The structural motifs discovered thus capture some essential biochemical and/or evolutionary properties, and hence may be useful for discovering proteins where Mg(2+ )plays an important biological role. CONCLUSION: The structural motif discovery method presented herein is general and can be applied to any set of proteins with known 3D structures. This new method is timely considering the increasing number of structures for proteins with unknown function that are being solved from structural genomics incentives. For such proteins, which share no significant sequence homology to proteins of known function, the presence of a structural motif that maps to a specific protein function in the structure would suggest likely active/binding sites and a particular biological function

Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity

Background: Automatic identification of structure fingerprints from a group of diverse protein structures is challenging, especially for proteins whose divergent amino acid sequences may fall into the “twilight-” or “midnight– ” zones where pair-wise sequence identities to known sequences fall below 25 % and sequence-based functional annotations often fail. Results: Here we report a novel graph database mining method and demonstrate its application to protein structure pattern identification and structure classification. The biologic motivation of our study is to recognize common structure patterns in “immunoevasins”, proteins mediating virus evasion of host immune defense. Our experimental study, using both viral and non-viral proteins, demonstrates the efficiency and efficacy of the proposed method. Conclusions: We present a theoretic framework, offer a practical software implementation for incorporating prior domain knowledge, such as substitution matrices as studied here, and devise an efficient algorithm to identify approximate matched frequent subgraphs. By doing so, we significantly expanded the analytical power of sophisticated data mining algorithms in dealing with large volume of complicated and noisy protein structure data. And without loss of generality, choice of appropriate compatibility matrices allows our method to be easily employed in domains where subgraph labels have some uncertainty

Discovering structural motifs using a structural alphabet: Application to magnesium-binding sites

[[abstract]]Background: For many metalloproteins, sequence motifs characteristic of metal-binding sites have not been found or are so short that they would not be expected to be metal-specific. Striking examples of such metalloproteins are those containing Mg2+, one of the most versatile metal cofactors in cellular biochemistry. Even when Mg2+-proteins share insufficient sequence homology to identify Mg2+-specific sequence motifs, they may still share similarity in the Mg2+-binding site structure. However, no structural motifs characteristic of Mg2+-binding sites have been reported. Thus, our aims are (\i) to develop a general method for discovering structural patterns/motifs characteristic of ligand-binding sites, given the 3D protein structures, and (\ii) to apply it to Mg2+-proteins sharing < 30% sequence identity. Our motif discovery method employs structural alphabet encoding to convert 3D structures to the corresponding 1D structural letter sequences, where the Mg2+-structural motifs are identified as recurring structural patterns.[[fileno]]2020129010036[[fileno]]2010309010036[[department]]化學

The investigation of type-specific features of the copper coordinating AA9 proteins and their effect on the interaction with crystalline cellulose using molecular dynamics studies

AA9 proteins are metallo-enzymes which are crucial for the early stages of cellulose degradation. AA9 proteins have been suggested to cleave glycosidic bonds linking cellulose through the use of their Cu2+ coordinating active site. AA9 proteins possess different regioselectivities depending on the resulting cleavage they form and as result, are grouped accordingly. Type 1 AA9 proteins cleave the C1 carbon of cellulose while Type 2 AA9 proteins cleave the C4 carbon and Type 3 AA9 proteins cleave either C1 or C4 carbons. The steric congestion of the AA9 active site has been proposed to be a contributor to the observed regioselectivity. As such, a bioinformatics characterisation of type-specific sequence and structural features was performed. Initially AA9 protein sequences were obtained from the Pfam database and multiple sequence alignment was performed. The sequences were phylogenetically characterised and sequences were grouped into their respective types and sub-groups were identified. A selection analysis was performed on AA9 LPMO types to determine the selective pressure acting on AA9 protein residues. Motif discovery was then performed to identify conserved sequence motifs in AA9 proteins. Once type-specific sequence features were identified structural mapping was performed to assess possible effects on substrate interaction. Physicochemical property analysis was also performed to assess biochemical differences between AA9 LPMO types. Molecular dynamics (MD) simulations were then employed to dynamically assess the consequences of the discovered type-specific features on AA9-cellulose interaction. Due to the absence of AA9 specific force field parameters MD simulations were not readily applicable. As a result, Potential Energy Surface (PES) scans were performed to evaluate the force field parameters for the AA9 active site using the PM6 semi empirical approach and least squares fitting. A Type 1 AA9 active site was constructed from the crystal structure 4B5Q, encompassing only the Cu2+ coordinating residues, the Cu2+ ion and two water residues. Due to the similarity in AA9 active sites, the Type force field parameters were validated on all three AA9 LPMO types. Two MD simulations for each AA9 LPMO types were conducted using two separate Lennard-Jones parameter sets. Once completed, the MD trajectories were analysed for various features including the RMSD, RMSF, radius of gyration, coordination during simulation, hydrogen bonding, secondary structure conservation and overall protein movement. Force field parameters were successfully evaluated and validated for AA9 proteins. MD simulations of AA9 proteins were able to reveal the presence of unique type-specific binding modes of AA9 active sites to cellulose. These binding modes were characterised by the presence of unique type-specific loops which were present in Type 2 and 3 AA9 proteins but not in Type 1 AA9 proteins. The loops were found to result in steric congestion that affects how the Cu2+ ion interacts with cellulose. As a result, Cu2+ binding to cellulose was observed for Type 1 and not Type 2 and 3 AA9 proteins. In this study force field parameters have been evaluated for the Type 1 active site of AA9 proteins and this parameters were evaluated on all three types and binding. Future work will focus on identifying the nature of the reactive oxygen species and performing QM/MM calculations to elucidate the reactive mechanism of all three AA9 LPMO types

Rotamer-specific Statistical Potentials for Protein Structure Modeling.

Knowledge-based (or statistical) potentials are widely used as essential tools in protein structure modeling and quality assessment. They are derived from experimentally determined protein structures aiming to extract relevant structural features that characterize the tightly folded structures. Since the surrounding circumstances are inhomogeneous and anisotropic, multibody contributions are important for accurate account of cooperative effects of molecular interactions. On the other hand, protein residues have great flexibility. It is energetically favorable for residues to adopt only a limited number of staggered conformations, known as rotamers. Depending on the rotameric state, the residue conformation and intra-residue interaction vary significantly within protein structures, resulting in different solvent accessibility and different electric polarization effect as well as different steric effect on residue elements. The major goal of this thesis is the design and development of statistical potentials that take into account the rotamer-dependence of interactions. We hypothesized that the rotameric state of residues is related to the specificity of interactions within protein structures. We first investigated how amino acid residues in PDB structures show different interaction patterns with the environment depending on their rotameric states. Observed rotamer-specific environmental features were incorporated to a scoring function, ProtGrid for protein designs. Our tests demonstrated that the ProtGrid is superior to widely used Rosetta energy function in prediction of the native amino acid types and rotameric states. Next, we formulated a rotamer-specific atomic statistical potential, named ROTAS that extends an existing orientation-dependent atomic potential (GOAP) by including the influence of rotameric states of residues on the specificity of interactions. The results showed that ROTAS performs better than other competing potentials not only in native structure recognition, but also in best model selection and correlation coefficients between energy and model quality. Finally, we applied the ROTAS potential to the problem of side-chain prediction. Compared with the existing side-chain modeling programs, ROTAS achieved comparable or even better prediction accuracy. We expect that the effectiveness of our energy functions would provide insightful information for the development of many applications which require accurate side-chain modeling such as homology modeling, protein design, mutation analysis, protein-protein docking and flexible ligand docking.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102342/1/jungkap_1.pd

Knowledge discovery in biomedical research and drug design : The development and application of biological databases

Ph.DDOCTOR OF PHILOSOPH

Graph based pattern discovery in protein structures

The rapidly growing body of 3D protein structure data provides new opportunities to study the relation between protein structure and protein function. Local structure pattern of proteins has been the focus of recent efforts to link structural features found in proteins to protein function. In addition, structure patterns have demonstrated values in applications such as predicting protein-protein interaction, engineering proteins, and designing novel medicines. My thesis introduces graph-based representations of protein structure and new subgraph mining algorithms to identify recurring structure patterns common to a set of proteins. These techniques enable families of proteins exhibiting similar function to be analyzed for structural similarity. Previous approaches to protein local structure pattern discovery operate in a pairwise fashion and have prohibitive computational cost when scaled to families of proteins. The graph mining strategy is robust in the face of errors in the structure, and errors in the set of proteins thought to share a function. Two collaborations with domain experts at the UNC School of Pharmacy and the UNC Medical School demonstrate the utility of these techniques. The first is to predict the function of several newly characterized protein structures. The second is to identify conserved structural features in evolutionarily related proteins

Motif discovery in sequential data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (v. 2, leaves [435]-467).In this thesis, I discuss the application and development of methods for the automated discovery of motifs in sequential data. These data include DNA sequences, protein sequences, and real-valued sequential data such as protein structures and timeseries of arbitrary dimension. As more genomes are sequenced and annotated, the need for automated, computational methods for analyzing biological data is increasing rapidly. In broad terms, the goal of this thesis is to treat sequential data sets as unknown languages and to develop tools for interpreting an understanding these languages. The first chapter of this thesis is an introduction to the fundamentals of motif discovery, which establishes a common mode of thought and vocabulary for the subsequent chapters. One of the central themes of this work is the use of grammatical models, which are more commonly associated with the field of computational linguistics. In the second chapter, I use grammatical models to design novel antimicrobial peptides (AmPs). AmPs are small proteins used by the innate immune system to combat bacterial infection in multicellular eukaryotes. There is mounting evidence that these peptides are less susceptible to bacterial resistance than traditional antibiotics and may form the basis for a novel class of therapeutics.(cont.) In this thesis, I described the rational design of novel AmPs that show limited homology to naturally-occurring proteins but have strong bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis. These peptides were designed using a linguistic model of natural AmPs by treating the amino acid sequences of natural AmPs as a formal language and building a set of regular grammars to describe this language. is set of grammars was used to create novel, unnatural AmP sequences that conform to the formal syntax of natural antimicrobial peptides but populate a previously unexplored region of protein sequence space. The third chapter describes a novel, GEneric MOtif DIscovery Algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As I show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. These motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices, or any other model for sequential data.(cont.) I demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids and DNA sequences, and the discovery of conserved protein sub-structures. The final chapter is devoted to a series of smaller projects, employing tool methods indirectly related to motif discovery in sequential data. I describe the construction of a software tool, Biogrep that is designed to match large pattern sets against large biosequence databases in a parallel fashion. is makes biogrep well-suited to annotating sets of sequences using biologically significant patterns. In addition, I show that the BLOSUM series of amino acid substitution matrices, which are commonly used in motif discovery and sequence alignment problems, have changed drastically over time.The fidelity of amino acid sequence alignment and motif discovery tools depends strongly on the target frequencies implied by these underlying matrices. us, these results suggest that further optimization of these matrices is possible. The final chapter also contains two projects wherein I apply statistical motif discovery tools instead of grammatical tools.(cont.) In the first of these two, I develop three different physiochemical representations for a set of roughly 700 HIV-I protease substrates and use these representations for sequence classification and annotation. In the second of these two projects, I develop a simple statistical method for parsing out the phenotypic contribution of a single mutation from libraries of functional diversity that contain a multitude of mutations and varied phenotypes. I show that this new method successfully elucidates the effects of single nucleotide polymorphisms on the strength of a promoter placed upstream of a reporter gene. The central theme, present throughout this work, is the development and application of novel approaches to finding motifs in sequential data. The work on the design of AmPs is very applied and relies heavily on existing literature. In contrast, the work on Gemoda is the greatest contribution of this thesis and contains many new ideas.by Kyle L. Jensen.Ph.D