378 research outputs found

    DISCRETE APPROACHES FOR SOLVING MOLECULAR DISTANCE GEOMETRY PROBLEMS USING NMR DATA

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    Euclidean distance geometry and applications

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    Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in Euclidean space that realizes the given distances. We survey some of the theory of Euclidean distance geometry and some of the most important applications: molecular conformation, localization of sensor networks and statics.Comment: 64 pages, 21 figure

    SCREENING INTERACTIONS BETWEEN PROTEINS AND DISORDERED PEPTIDES BY A NOVEL COMPUTATIONAL METHOD

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    Concerted interactions between proteins in cells form the basis of most biological processes. Biophysicists study protein–protein association by measuring thermodynamic and kinetic properties. Naively, strong binding affinity should be preferred in protein–protein binding to conduct certain biological functions. However, evidence shows that regulatory interactions, such as those between adapter proteins and intrinsically disordered proteins, communicate via low affinity but high complementarity interactions. PDZ domains are one class of adapters that bind linear disordered peptides, which play key roles in signaling pathways. The misregulation of these signals has been implicated in the progression of human cancers. To understand the underlying mechanism of protein-peptide binding interactions and to predict new interactions, in this thesis I have developed: (a) a unique biophysical-derived model to estimate their binding free energy; (b) a novel semi-flexible structure-based method to dock disordered peptides to PDZ domains; (c) predictions of the peptide binding landscape; and, (d) an automated algorithm and web-interface to predict the likelihood that a given linear sequence of amino acids binds to a specific PDZ domain. The docking method, PepDock, takes a peptide sequence and a PDZ protein structure as input, and outputs docked conformations and their corresponding binding affinity estimation, including their optimal free energy pathway. We have applied PepDock to screen several PDZ protein domains. The results not only validated the capabilities of PepDock to accurately discriminate interactions, but also explored the underlying binding mechanism. Specifically, I showed that interactions followed downhill free energy pathways, reconciling a relatively fast association mechanism of intrinsically disordered peptides. The pathways are such that initially the peptide’s C-terminal motif binds non-specifically, forming a weak intermediate, whereas specific binding is achieved only by a subsequent network of contacts (7–9 residues in total). This mechanism allows peptides to quickly probe PDZ domains, rapidly releasing those that do not attain sufficient affinity during binding. Further kinetic analysis indicates that disorder enhanced the specificity of promiscuous interactions between proteins and peptides, while achieving association rates comparable to interactions between ordered proteins

    Exploration of the Disambiguation of Amino Acid Types to Chi-1 Rotamer Types in Protein Structure Prediction and Design

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    A protein’s global fold provide insight into function; however, function specificity is often detailed in sidechain orientation. Thus, determining the rotamer conformations is often crucial in the contexts of protein structure/function prediction and design. For all non-glycine and non-alanine types, chi-1 rotamers occupy a small number of discrete number of states. Herein, we explore the possibility of describing evolution from the perspective of the sidechains’ structure versus the traditional twenty amino acid types. To validate our hypothesis that this perspective is more crucial to our understanding of evolutionary relationships, we investigate its uses as evolutionary, substitution matrices for sequence alignments for fold recognition purposes and computational protein design with specific focus in designing beta sheet environments, where previous studies have been done on amino acid-types alone. Throughout this study, we also propose the concept of the “chi-1 rotamer sequence” that describes the chi-1 rotamer composition of a protein. We also present attempts to predict these sequences and real-value torsion angles from amino acid sequence information. First, we describe our developments of log-odds scoring matrices for sequence alignments. Log-odds substitution matrices are widely used in sequence alignments for their ability to determine evolutionary relationship between proteins. Traditionally, databases of sequence information guide the construction of these matrices which illustrates its power in discovering distant or weak homologs. Weak homologs, typically those that share low sequence identity (< 30%), are often difficult to identify when only using basic amino acid sequence alignment. While protein threading approaches have addressed this issue, many of these approaches include sequenced-based information or profiles guided by amino acid-based substitution matrices, namely BLOSUM62. Here, we generated a structural-based substitution matrix born by TM-align structural alignments that captures both the sequence mutation rate within same protein family folds and the chi-1 rotamer that represents each amino acid. These rotamer substitution matrices (ROTSUMs) discover new homologs and improved alignments in the PDB that traditional substitution matrices, based solely on sequence information, cannot identify. Certain tools and algorithms to estimate rotamer torsions angles have been developed but typically require either knowledge of backbone coordinates and/or experimental data to help guide the prediction. Herein, we developed a fragment-based algorithm, Rot1Pred, to determine the chi-1 states in each position of a given amino acid sequence, yielding a chi-1 rotamer sequence. This approach employs fragment matching of the query sequence to sequence-structure fragment pairs in the PDB to predict the query’s sidechain structure information. Real-value torsion angles were also predicted and compared against SCWRL4. Results show that overall and for most amino-acid types, Rot1Pred can calculate chi-1 torsion angles significantly closer to native angles compared to SCWRL4 when evaluated on I-TASSER generated model backbones. Finally, we’ve developed and explored chi-1-rotamer-based statistical potentials and evolutionary profiles constructed for de novo computational protein design. Previous analyses which aim to energetically describe the preference of amino acid types in beta sheet environments (parallel vs antiparallel packing or n- and c-terminal beta strand capping) have been performed with amino acid types although no explicit rotamer representation is given in their scoring functions. In our study, we construct statistical functions which describes chi-1 rotamer preferences in these environments and illustrate their improvement over previous methods. These specialized knowledge-based energy functions have generated sequences whose I-TASSER predicted models are structurally-alike to their input structures yet consist of low sequence identity.PHDChemical BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145951/1/jarrettj_1.pd

    Automated Ligand Design in Simulated Molecular Docking

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    The drug discovery process broadly follows the sequence of high-throughput screening, optimisation, synthesis, testing, and finally, clinical trials. We investigate methods for accelerating this process with machine learning algorithms that can automatically design novel ligands for biological targets. Recent work has demonstrated the viability of deep reinforcement learning, generative adversarial networks and auto-encoders. Here, we extend state-of-the-art deep reinforcement learning molecular modification algorithms and, through the integration of molecular docking simulations, apply them to automatically design novel antagonists for the adenosine triphosphate binding site of Plasmodium falciparum phosphatidylinositol 4-kinase, an enzyme essential to the malaria parasite’s development within an infected host. We demonstrated that such an algorithm was capable of designing novel molecular graphs with better DSs than the best DSs in a set of reference molecules. There reference set here was a set of 1,011 structural analogues of napthyridine, imidazopyridazine, and aminopyradine

    A Dance with Protein Assemblies : Analysis, Structure Prediction, and Design

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    Protein assemblies are some of the most complex molecular machines in nature. They facilitate many cellular functions, from DNA replication to molecular motion, energy production, and even the production of other proteins. In a series of 3 papers, we analyzed the structure, developed structure prediction tools, and design tools, for different protein assemblies. Many of the studies were centered around viral protein capsids. Viral capsids are protein coats found inside viruses that contain and protect the viral genome. In one paper, we studied the interfaces of these capids and their energy landscapes. We found that they differ from regular homomers in terms of the amino acid composition and size, but not in the quality of interactions. This contradicts existing experimental and theoretical studies that suggest that the interactions are weak. We hypothesise that the occlusion by our models of electrostatic and entropic contributions might be at play. In another paper, we developed methods to predict large cubic symmetrical protein assemblies, such as viral capsids, from sequence. This method is based upon AlphaFold, a new AI tool that has revolutionized protein structure prediction. We found that we can predict up to 50% of the structures of these assemblies. The method can quickly elucidate the structure of many relevant proteins for humans, and for understanding structures relevant to disease, such as the structures of viral capsids. In the final paper, we developed tools to design capsid-like proteins called cages – structures that can be used for drug delivery and vaccine design. A fundamental problem in designing cage structures is achieving different architectures and low porosity, goals that are important for vaccine design and the delivery of small drug molecules. By explicitly modelling the shapes of the subunits in the cage and matching the shapes with proteins from structural databases, we find that we can create structures with many different sizes, shapes, and porosities - including low porosities. While waiting for experimental validation, the design strategy described in the paper must be extended, and more designs must be tested

    Comparative modeling of mainly-beta proteins by profile wrapping

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 61-67).The ability to predict structure from sequence is particularly important for toxins, virulence factors, allergens, cytokines, and other proteins of public heath importance. Many such functions are represented in the parallel [beta]-helix fold class. Structure prediction for this fold is a challenging computational problem because there exists very little sequence similarity (less than 15%) across the SCOP family. This thesis introduces BetaWrapPro, a program for comparative modeling of the parallel -helix fold. By estimating pairwise [beta]-strand interaction probabilities, a profile of the target sequence is aligned, or "wrapped," onto al abstract supersecondary structural template. This wrapping procedure may capture folding processes that have al initiation stage' followed by processive, interaction between the unfolded region and the already-formed substructure. This wrap is then placed on a known structure and side-chains are modeled to produce a three-dimensional structure prediction. We demonstrate that wrapping onto an abstract template produces accurate structure predictions for this fold (ill cross-validation: average C0 RMSD of 1.55 A in accurately wrapped regions, with 88% of the residues accurately aligned). In addition, BetaWrapPro outperforms other fold recognition methods, recognizing the .l-helix fold( with 1]00% sensitivity at 99.7% specificity in cross-validation on the PDB. One striking result has been the prediction of an unexpected parallel -helix structure for a. pollen allergen, and its recent confirmation through solution of its structure.by Nathan Patrick Palmer.S.M

    Protein Structure Prediction

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    Práce popisuje prostorovou strukturu molekul bílkovin a databází uchovávajících representace této struktury, či její hierarchické klasifikace. Je poskytnut přehled současných metod výpočetní predikce struktury bílkovin, přičemž největší pozornost je soustředěna na komparativní modelování. Tato metoda je rovněž v základní podobě implementována a na závěr její implementace analyzována.This work describes the three dimensional structure of protein molecules and biological databases used to store information about this structure or its hierarchical classification. Current methods of computational structure prediction are overviewed with an emphasis on comparative modeling. This particular method is also implemented in a proof-of-concept program and finally, the implementation is analysed.
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