RosettaSurf—A surface-centric computational design approach

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.     

Computational study of proton and methyl cation affinities of imidazole-based highly energetic ionic liquids

The present study deals with the evaluation of gas phase proton and methyl cation affinities for alkyl- and nitrosubstituted imidazoles using DFT (B3LYP)/6-31 + G(d) and MP2 methods in the Gaussian 03 software package. The extent of charge delocalization of these cations is correlated with proton affinity. The study reveals that weakly electron-donating alkyl groups at position 1 of the imidazole enhance its proton affinity, which also increases with increasing alkyl chain length. This is expected to result in an increased tendency to form salts. In contrast, the presence of strongly electron-withdrawing nitro groups lowers proton affinity, which decreases as the number of nitro groups on the ring increases. The same trend is observed for the methyl cation affinity, but to a lower degree. These trends in the proton and methyl cation affinities were analyzed to study the effects of these substituents on the basicity of the energetic imidazole moieties and their tendency to form salts. This, in turn, should aid searches for better highly energetic ionic liquids. In addition, calculations performed on different isomers of mono and dinitroimidazoles show that 5-nitro-1H-imidazole and 2,4-dinitro-1H-imidazole are more stable than the other isomers. Amongst the many nitro derivatives of imidazoles considered in the present study, cations resulting from these two would be the best choice for creating highly energetic ionic liquids when coupled with appropriate energetic anions.     

A computational approach to motion perception

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.     

A computational approach to animal breeding

We propose a computational model of mating strategies for controlled animal breeding programs. A mating strategy in a controlled breeding program is a heuristic with some optimization criteria as a goal. Thus, it is appropriate to use the computational tools available for analysis of optimization heuristics. In this paper, we propose the first discrete model of the controlled animal breeding problem and analyse heuristics for two possible objectives: (1) breeding for maximum diversity and (2) breeding a target individual. These two goals are representative of conservation biology and agricultural livestock management, respectively. We evaluate several mating strategies and provide upper and lower bounds for the expected number of matings. While the population parameters may vary and can change the actual number of matings for a particular strategy, the order of magnitude of the number of expected matings and the relative competitiveness of the mating heuristics remains the same. Thus, our simple discrete model of the animal breeding problem provides a novel viable and robust approach to designing and comparing breeding strategies in captive populations.     

A computational and cellular solids approach to the stiffness-based design of bone scaffolds

We derive a cellular solids approach to the design of bone scaffolds for stiffness and pore size. Specifically, we focus on scaffolds made of stacked, alternating, orthogonal layers of hydroxyapatite rods, such as those obtained via micro-robotic deposition, and aim to determine the rod diameter, spacing and overlap required to obtain specified elastic moduli and pore size. To validate and calibrate the cellular solids model, we employ a finite element model and determine the effective scaffold moduli via numerical homogenization. In order to perform an efficient, automated execution of the numerical studies, we employ a geometry projection method so that analyses corresponding to different scaffold dimensions can be performed on a fixed, non-conforming mesh. Based on the developed model, we provide design charts to aid in the selection of rod diameter, spacing and overlap to be used in the robotic deposition to attain desired elastic moduli and pore size.     

A computational approach to the polymerizabilities of diallylamines

Some selected diallylamine monomers have been studied with the semiempirical PM3 method as model compounds for N,N-dialkyl-N-2-(alkoxycarbonyl)allylammonium salts, in order to build up a quantitative and qualitative relationship between the experimental cyclopolymerizabilities of the monomers and calculated parameters such as charge, energy, geometrical features, bond orders, local softness values and HOMO-LUMO gaps. The charges on nitrogen, vinyl and allyl carbons, the activation barriers, the local softness values and the HOMO-LUMO gaps are found to represent the polymerizability trend of the monomers in general. Three-dimensional structures have been proposed for the reactants and their transition states by geometry optimizations with PM3.     

A computational framework to empower probabilistic protein design

MOTIVATION: The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult. RESULTS: In this paper, we develop a computational framework for probabilistic protein design following the structural paradigm. We formulate the distribution of sequences for a structure using the Boltzmann distribution over their free energies. The corresponding probabilistic graphical model is constructed, and we apply belief propagation (BP) to calculate marginal amino acid probabilities. We test this method on a large structural dataset and demonstrate the superiority of BP over previous methods. Nevertheless, since the results obtained by BP are far from optimal, we thoroughly assess the paradigm using high-quality experimental data. We demonstrate that, for small scale sub-problems, BP attains identical results to those produced by exact inference on the paradigmatic model. However, quantitative analysis shows that the distributions predicted significantly differ from the experimental data. These findings, along with the excellent performance we observed using BP on the smaller problems, suggest potential shortcomings of the paradigm. We conclude with a discussion of how it may be improved in the future.     

A computational approach to the synthesis of dirithromycin

Dirithromycin is a macrolide antibiotic derived from erythromycin A. Dirithromycin is synthesized by the condensation of 9(S)-erythromycylamine with 2-(2-methoxyethoxy)-acetaldehyde. To gain insight into the synthesis, the condensation mechanism has been analyzed computationally by the AM1 method in the gas phase. First, the formation of the Schiff bases of dirithromycin and epidirithromycin from 9(S)-erythromycylamine and 2-(2-methoxyethoxy)-acetaldehyde were modeled. Then, the tautomerization of the Schiff bases to dirithromycin and epidirithromycin were considered. Finally, the epimerization of the Schiff base of epidirithromycin to the Schiff base of dirithromycin was investigated. Our results show that, even though carbinolamine forms faster for epidirithromycin than the corresponding structure for dirithromycin, dirithromycin is the major product of the synthesis. Figure Synthesis of dirithromycin     

A solvated ligand rotamer approach and its application in computational protein design

The structure-based design of protein–ligand interfaces with respect to different small molecules is of great significance in the discovery of functional proteins. By statistical analysis of a set of protein–ligand complex structures, it was determined that water-mediated hydrogen bonding at the protein–ligand interface plays a crucial role in governing the binding between the protein and the ligand. Based on the novel statistic results, a solvated ligand rotamer approach was developed to explicitly describe the key water molecules at the protein–ligand interface and a water-mediated hydrogen bonding model was applied in the computational protein design context to complement the continuum solvent model. The solvated ligand rotamer approach produces only one additional solvated rotamer for each rotamer in the ligand rotamer library and does not change the number of side-chain rotamers at each protein design site. This has greatly reduced the total combinatorial number in sequence selection for protein design, and the accuracy of the model was confirmed by two tests. For the water placement test, 61 % of the crystal water molecules were predicted correctly in five protein-ligand complex structures. For the sequence recapitulation test, 44.7 % of the amino acid identities were recovered using the solvated ligand rotamer approach and the water-mediated hydrogen bonding model, while only 30.4 % were recovered when the explicitly bound waters were removed. These results indicated that the developed solvated ligand rotamer approach is promising for functional protein design targeting novel protein–ligand interactions.     

A novel approach to local similarity of protein binding sites substantially improves computational drug design results

We present a novel notion of binding site local similarity based on the analysis of complete protein environments of ligand fragments. Comparison of a query protein binding site (target) against the 3D structure of another protein (analog) in complex with a ligand enables ligand fragments from the analog complex to be transferred to positions in the target site, so that the complete protein environments of the fragment and its image are similar. The revealed environments are similarity regions and the fragments transferred to the target site are considered as binding patterns. The set of such binding patterns derived from a database of analog complexes forms a cloud-like structure (fragment cloud), which is a powerful tool for computational drug design. It has been shown on independent test sets that the combined use of a traditional energy-based score together with the cloud-based score responsible for the quality of embedding of a ligand into the fragment cloud improves the self-docking and screening results dramatically. The usage of a fragment cloud as a source of positioned molecular fragments fitting the binding protein environment has been validated by reproduction of experimental ligand optimization results.     

咪唑离子液体对四尾栅藻的毒性

按照藻类生长抑制试验标准方法,以四尾栅藻为受试生物,研究了离子液体1-丁基-3-甲基咪唑氯盐([C4mim][Cl])和1-丁基-3-甲基咪唑四氟硼酸盐([C4mim][BF4])对四尾栅藻的影响,测定了半数有效浓度(EC50)和叶绿素含量。结果表明:[C4mim][Cl]和[C4mim][BF4]对四尾栅藻的生长和叶绿素的产生均具有抑制作用,毒性效应随着浓度的增大和培养时间的增长而增加,96 h的EC50分别为57.8和38.8 mg/L。离子液体质量浓度为200mg/L时,四尾栅藻的叶绿素总量仅为对照组的32%和26%。此结果可以说明离子液体对生态系统存在潜在的负面效应。     

A biological approach to computational models of proteomic networks

Computational modeling is useful as a means to assemble and test what we know about proteins and networks. Models can help address key questions about the measurement, definition and function of proteomic networks. Here, we place these biological questions at the forefront in reviewing the computational strategies that are available to analyze proteomic networks. Recent examples illustrate how models can extract more information from proteomic data, test possible interactions between network proteins and link networks to cellular behavior. No single model can achieve all these goals, however, which is why it is critical to prioritize biological questions before specifying a particular modeling approach.     

A fast and accurate computational approach to protein ionization

We report a very fast and accurate physics-based method to calculate pH-dependent electrostatic effects in protein molecules and to predict the pK values of individual sites of titration. In addition, a CHARMm-based algorithm is included to construct and refine the spatial coordinates of all hydrogen atoms at a given pH. The present method combines electrostatic energy calculations based on the Generalized Born approximation with an iterative mobile clustering approach to calculate the equilibria of proton binding to multiple titration sites in protein molecules. The use of the GBIM (Generalized Born with Implicit Membrane) CHARMm module makes it possible to model not only water-soluble proteins but membrane proteins as well. The method includes a novel algorithm for preliminary refinement of hydrogen coordinates. Another difference from existing approaches is that, instead of monopeptides, a set of relaxed pentapeptide structures are used as model compounds. Tests on a set of 24 proteins demonstrate the high accuracy of the method. On average, the RMSD between predicted and experimental pK values is close to 0.5 pK units on this data set, and the accuracy is achieved at very low computational cost. The pH-dependent assignment of hydrogen atoms also shows very good agreement with protonation states and hydrogen-bond network observed in neutron-diffraction structures. The method is implemented as a computational protocol in Accelrys Discovery Studio and provides a fast and easy way to study the effect of pH on many important mechanisms such as enzyme catalysis, ligand binding, protein-protein interactions, and protein stability.     

A computational approach to the functional screening of genomes

Comparative genomics usually involves managing the functional aspects of genomes, by simply comparing gene-by-gene functions. Following this approach, Mushegian and Koonin proposed a hypothetical minimal genome, Minimal Gene Set (MGS), aiming for a possible oldest ancestor genome. They obtained MGS by comparing the genomes of two simple bacteria and eliminating duplicated or functionally identical genes. The authors raised the fundamental question of whether a hypothetical organism possessing MGS is able to live or not. We attacked this viability problem specifying in silico the metabolic pathways of the MGS-based prokaryote. We then performed a dynamic simulation of cellular metabolic activities in order to check whether the MGS-prokaryote reaches some equilibrium state and produces the necessary biomass. We assumed these two conditions to be necessary for a living organism. Our simulations clearly show that the MGS does not express an organism that is able to live. We then iteratively proceeded with functional replacements in order to obtain a genome composition that gives rise to equilibrium. We ruled out 76 of the original 254 genes in the MGS, because they resulted in duplication from a functional point of view. We also added seven genes not present in the MGS. These genes encode for enzymes involved in critical nodes of the metabolic network. These modifications led to a genome composed of 187 elements expressing a virtually living organism, Virtual Cell (ViCe), that exhibits homeostatic capabilities and produces biomass. Moreover, the steady-state distribution of the concentrations of virtual metabolites that resulted was similar to that experimentally measured in bacteria. We conclude then that ViCe is able to “live in silico.”     

A matrix computational approach to kinesin neck linker extension

Kinesin stepping requires both tethered diffusion of the free head and conformational changes driven by the chemical state of the motor. We present a numerical method using matrix representations of approximating Markov chains and renewal theory to compute important experimental quantities for models that include both tethered diffusion and chemical transitions. Explicitly modeling the tethered diffusion allows for exploration of the model under perturbation of the neck linker; comparisons are made between the computed models and in vitro assays.     

Enzyme catalysis in ionic liquids

Ionic liquids offer new possibilities for the application of solvent engineering to biocatalytic reactions. Although in many cases ionic liquids have simply been used to replace organic solvents, they have often led to improved process performance. Unlike conventional organic solvents, ionic liquids possess no vapor pressure, are able to dissolve many compounds, and can be used to form two-phase systems with many solvents. To date, reactions involving lipases have benefited most from the use of ionic liquids, but the use of ionic liquids with other enzymes and in whole-cell processes has also been described. In some cases, remarkable results with respect to yield, (enantio)selectivity or enzyme stability were observed.     

Tyrosinase activity in ionic liquids

Activity of mushroom tyrosinase was studied in three ionic liquids, 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIm][PF₆]), 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIm][BF₄]) and 1-butyl-3-methylimidazolium methylsulfate ([BMIm][MeSO₄]), and was compared to that in chloroform. Kinetic parameters of the enzyme were determined and the results indicate that the enzyme in ionic liquids basically follows the same catalytic mechanism as in water, and that the ionic liquids may affect the enzyme activity by direct interacting with the enzyme and thus hindering the E–S binding due to their high hydrophilicity and polarity.     

Biocatalytic transformations in ionic liquids

Room temperature ionic liquids are non-volatile, thermally stable and highly polar; they are also moderately hydrophilic solvents. Here, we discuss their use as reaction media for biocatalysis. Enzymes of widely diverging types are catalytically active in ionic liquids or aqueous biphasic ionic liquid systems. Lipases, in particular, maintain their activity in anhydrous ionic liquid media; the (enantio)selectivity and operational stability are often better than in traditional media. The unconventional solvent properties of ionic liquids have been exploited in biocatalyst recycling and product recovery schemes that are not feasible with traditional solvent systems.     

A recyclable enzymatic biodiesel production process in ionic liquids

Immobilized Candida antarctica lipase B suspended in ionic liquids containing long alkyl-chain cations showed excellent synthetic activity and operational stability for biodiesel production. The interest of this process lies in the possibility of recycling the biocatalyst and the easy separation of the biodiesel from the reaction mixture. The ionic liquids used, 1-hexadecyl-3-methylimidazolium triflimide ([C(16)MIM][NTf(2)]) and 1-octadecyl-3-methylimidazolium triflimide ([C(18)MIM][NTf(2)]), produced homogeneous systems at the start of the reaction and, at the end of the same, formed a three-phase system, allowing the selective extraction of the products using straightforward separation techniques, and the recycling of both the ionic liquid and the enzyme. These are very important advantages which may be found useful in environmentally friendly production conditions.