Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.Comment: JGG and SMK contributed equally to the wor

A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates

Development and Application of Computational Biology tools for Biomedicine

Biomolecular simulation can be considered as a virtual microscope for molecular biology, allowing to gain insights into the sub-cellular mechanisms of biological relevance at spatial and temporal scales that are difficult to observe experimentally. It provides a powerful tool to link the laws of physics with the complex behavior of biological systems. Dramatic recent advancements in achievable simulation speed and the underlying physical models will increasingly lead to molecular views of large systems. These improvements may largely affect biological sciences. In this thesis, I have applied computational molecular biology approaches to different biological systems using state of the art structural bioinformatics and computational biophysics tools (Chapter 3). My principal focus was on the computational design of molecular imprinted polymers (MIPs), which have recently attracted significant attention as cost effective substitutes for natural antibodies and receptors in chromatography, sensors and assays. I have used molecular modelling in the optimization of polymer compositions to make high affinity synthetic receptors based on Molecular Imprinting. In particular, I developed a new free of charge protocol that can be performed within just few hours that outputs a list of candidate monomers which are capable of strong binding interactions with the template. Furthermore, I have produced a new computational method for the calculation of the ideal monomer: template stoichiometric ratio to be used in the lab for the MIPs synthesis. These protocols have been implemented as a webserver that is available at http://mirate.di.univr.it/. In parallel, I have also investigated the modelling of much more complex MIPs systems by the introduction of some factors e.g. solvent and cross-linker molecules that are also essential in the polymerisation process. A novel algorithm, which mimics a radical polymerization mechanism, has been written for application in the rational design of MIPs (Chapter 4). Moreover, I have been involved in the field of computational molecular biomedicine. Indeed, in Chapters 5 and 6 I describe the work done in collaboration with two labs at the Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona. In Chapter 5, starting from unpublished experimental data I have computationally characterized the interaction of ACOT8 with HIV-1 Nef accessory protein. I have performed a detailed structural and functional characterization of these two proteins in order to infer any possible functional details about their interactions. The bioinformatics predictions were then confirmed by wet-lab experiments. I have also carried out a detailed structural and functional characterization of two pathogenic mutations of AGT-Mi (Chapter 6). In particular, I have used classical molecular dynamics (MD) simulations to study the possible interference with the dimerization process of AGT-Mi exerted by I244T-Mi and F152I-Mi mutants. Those variants are associated with Primary Hyperoxaluria type 1 disease. In Chapter 7, I present the coarse-grained MD simulations of Membrane/Human ileal bile-acid-binding protein Interactions. This study was carried out in collaboration with the NMR group at the University of Verona and it is a part of an extensive research aimed at better understanding of the main biomolecular interactions in crowded cellular environments. MD simulations results were in agreement with experimental findings

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection

<p>Abstract</p> <p>Background</p> <p>Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection.</p> <p>Results</p> <p>In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed.</p> <p>Conclusion</p> <p>This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA.</p

Comparative modelling of protein structure and its impact on microbial cell factories

Comparative modeling is becoming an increasingly helpful technique in microbial cell factories as the knowledge of the three-dimensional structure of a protein would be an invaluable aid to solve problems on protein production. For this reason, an introduction to comparative modeling is presented, with special emphasis on the basic concepts, opportunities and challenges of protein structure prediction. This review is intended to serve as a guide for the biologist who has no special expertise and who is not involved in the determination of protein structure. Selected applications of comparative modeling in microbial cell factories are outlined, and the role of microbial cell factories in the structural genomics initiative is discussed

Structural investigation of the Bacillus subtilis morphogenic factor RodZ

A thesis to obtain a Master degree in Structural and Functional BiochemistryRodZ is a protein widely conserved in bacteria and a core component of the morphogenic apparatus of the cell. It is known to be required for assembly of the bacterial actin homologue, MreB, that controls cell wall synthesis and cell shape. The domain organization of RodZ consists of a well-conserved N-terminal (RodZn) with helix-turn-helix motif (HTH), a conserved transmembrane domain, and a conserved C-terminal domain (RodZc). RodZn, located in the cytoplasm, has been shown to interact with MreB actin-homologue by x-ray studies in T. maritima. However, the structure of RodZn from gram-positive B. subtilis showed low homology with the published one from gram-negative T. maritima. Here we present the solution structure of RodZn from B. subtilis determined for the first time, by NMR spectroscopy. Compared to previous structural data obtained from the crystallized RodZn from T. maritima and more recently from S. aureus, several differences could be observed, namely the length of the alpha-helices and the presence of an extended coil. Interaction studies were preformed between RodZn domain and MreB from which no significant results could be extrapolated. Since HTH motif is frequently associated with DNA interaction, the involvement of RodZn in DNA organization is being investigated. At the same time, RodZc domain, which structure has never been reported, was subject of study. Bioinformatic, biophysical and biochemical methodologies were employed to study this domain. A model based in a pseudo-ab initio methodology was built, revealing an Ig-like fold. The Ig superfamily is a large group of cell surface and soluble proteins that are involved in the recognition, binding, or adhesion processes of cells. Therefore, RodZ is thought to be a protein that establishes a link between the inner side of the cell membrane and the outer side, promoting spatiotemporal coordination between peptidoglycan synthesis and cell division