Candidate composite biomarker to inform drug treatments for diabetic kidney disease.

Introduction: Current guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy. Results: Using data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment. Discussion: Higher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment.

Search for relevant subsets of binary predictors in high dimensional regression for discovering the lead molecule.

One of the main problems that the drug discovery research field confronts is to identify small molecules, modulators of protein function, which are likely to be therapeutically useful. Common practices rely on the screening of vast libraries of small molecules (often 1-2 million molecules) in order to identify a molecule, known as a lead molecule, which specifically inhibits or activates the protein function. To search for the lead molecule, we investigate the molecular structure, which generally consists of an extremely large number of fragments. Presence or absence of particular fragments, or groups of fragments, can strongly affect molecular properties. We study the relationship between molecular properties and its fragment composition by building a regression model, in which predictors, represented by binary variables indicating the presence or absence of fragments, are grouped in subsets and a bi-level penalization term is introduced for the high dimensionality of the problem. We evaluate the performance of this model in two simulation studies, comparing different penalization terms and different clustering techniques to derive the best predictor subsets structure. Both studies are characterized by small sets of data relative to the number of predictors under consideration. From the results of these simulation studies, we show that our approach can generate models able to identify key features and provide accurate predictions. The good performance of these models is then exhibited with real data about the MMP-12 enzyme.

Designing lead optimisation of MMP-12 inhibitors.

The design of new molecules with desired properties is in general a very difficult problem, involving heavy experimentation with high investment of resources and possible negative impact on the environment. The standard approach consists of iteration among formulation, synthesis, and testing cycles, which is a very long and laborious process. In this paper we address the so-called lead optimisation process by developing a new strategy to design experiments and modelling data, namely, the evolutionary model-based design for optimisation (EDO). This approach is developed on a very small set of experimental points, which change in relation to the response of the experimentation according to the principle of evolution and insights gained through statistical models. This new procedure is validated on a data set provided as test environment by Pickett et al. (2011), and the results are analysed and compared to the genetic algorithm optimisation (GAO) as a benchmark. The very good performance of the EDO approach is shown in its capacity to uncover the optimum value using a very limited set of experimental points, avoiding unnecessary experimentation.

Do natural proteins differ from random sequences polypeptides? Natural vs. random proteins classification using an evolutionary neural network.

Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a set of 762 natural proteins with an average length of 70 amino acids and an equal number of completely random ones of comparable length on the basis of their structural features. We use an ad hoc Evolutionary Neural Network Algorithm (ENNA) in order to assess whether and to what extent natural proteins are edited from random polypeptides employing 11 different structure-related variables (i.e. net charge, volume, surface area, coil, alpha helix, beta sheet, percentage of coil, percentage of alpha helix, percentage of beta sheet, percentage of secondary structure and surface hydrophobicity). The ENNA algorithm is capable to correctly distinguish natural proteins from random ones with an accuracy of 94.36%. Furthermore, we study the structural features of 32 random polypeptides misclassified as natural ones to unveil any structural similarity to natural proteins. Results show that random proteins misclassified by the ENNA algorithm exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. Altogether, our results suggest that natural proteins are significantly edited from random polypeptides and evolutionary editing can be readily detected analyzing structural features. Furthermore, we also show that the ENNA, employing simple structural descriptors, can predict whether a protein chain is natural or random.

A stochastic model of autocatalytic reaction networks.

Autocatalytic cycles are rather widespread in nature and in several theoretical models of catalytic reaction networks their emergence is hypothesized to be inevitable when the network is or becomes sufficiently complex. Nevertheless, the emergence of autocatalytic cycles has been never observed in wet laboratory experiments. Here, we present a novel model of catalytic reaction networks with the explicit goal of filling the gap between theoretical predictions and experimental findings. The model is based on previous study of Kauffman, with new features in the introduction of a stochastic algorithm to describe the dynamics and in the possibility to increase the number of elements and reactions according to the dynamical evolution of the system. Furthermore, the introduction of a temporal threshold allows the detection of cycles even in our context of a stochastic model with asynchronous update. In this study, we describe the model and present results concerning the effect on the overall dynamics of varying (a) the average residence time of the elements in the reactor, (b) both the composition of the firing disk and the concentration of the molecules belonging to it, (c) the composition of the incoming flux.

Method for optimizing coating properties based on an evolutionary algorithm approach.

In industry as well as many areas of scientific research, data collected often contain a number of responses of interest for a chosen set of exploratory variables. Optimization of such multivariable multiresponse systems is a challenge well suited to genetic algorithms as global optimization tools. One such example is the optimization of coating surfaces with the required absolute and relative sensitivity for detecting analytes using devices such as sensor arrays. High-throughput synthesis and screening methods can be used to accelerate materials discovery and optimization; however, an important practical consideration for successful optimization of materials for arrays and other applications is the ability to generate adequate information from a minimum number of experiments. Here we present a case study to evaluate the efficiency of a novel evolutionary model-based multiresponse approach (EMMA) that enables the optimization of a coating while minimizing the number of experiments. EMMA plans the experiments and simultaneously models the material properties. We illustrate this novel procedure for materials optimization by testing the algorithm on a sol-gel synthetic route for production and optimization of a well studied amino-methyl-silane coating. The response variables of the coating have been optimized based on application criteria for micro- and macro-array surfaces. Spotting performance has been monitored using a fluorescent dye molecule for demonstration purposes and measured using a laser scanner. Optimization is achieved by exploring less than 2% of the possible experiments, resulting in identification of the most influential compositional variables. Use of EMMA to optimize control factors of a product or process is illustrated, and the proposed approach is shown to be a promising tool for simultaneously optimizing and modeling multivariable multiresponse systems.

Selection dynamic of Escherichia coli host in M13 combinatorial peptide phage display libraries.

Phage display relies on an iterative cycle of selection and amplification of random combinatorial libraries to enrich the initial population of those peptides that satisfy a priori chosen criteria. The effectiveness of any phage display protocol depends directly on library amino acid sequence diversity and the strength of the selection procedure. In this study we monitored the dynamics of the selective pressure exerted by the host organism on a random peptide library in the absence of any additional selection pressure. The results indicate that sequence censorship exerted by Escherichia coli dramatically reduces library diversity and can significantly impair phage display effectiveness.

Design and dynamic simulation of minimal metallo-proteins.

Ab initio in silico design of proteins and enzymes has emerged as a powerful tool to design application-tailored proteins and catalysts for a wide range of applications. Several enzymes exploit the unique features of metal cofactors to achieve catalytic activity otherwise unattainable through the use of only natural amino acid residues. One of the major bottlenecks in ab initio design of novel proteins relies on long-range and epistatic effects that severely limit the possibility of a rational design. Within this framework there is an ongoing effort to reduce protein length and complexity to unlock the full potential of in silico protein design. In this work we specifically address this problem designing and investigating the dynamic features of 10 in silico designed minimal metallo-proteins. In particular, in this paper we investigate whether and to what extent it is possible to design a minimal metallo-enzyme made of only residues involved in metal binding. In this research we address these questions by investigating the ability of 10 different "mini-proteins" with a length shorter than 15 residues. Molecular dynamics studies clearly show that it is possible to design a minimal protein able to bind a metal atom with the correct geometry. It is noteworthy that designed mini-proteins cannot achieve the formation of a canonical hydrophobic core, rather the metal ion provides a "metal core" around which the entire protein is organized. This opens the possibility of designing synthetic enzymes composed of only functional residues organized around a "metal core" which acts as both structural and functional determinat.

Massive non-natural proteins structure prediction using grid technologies.

BACKGROUND: The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of proteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is their ability to form a well defined three-dimensional structure. Thus, the structural study of NBPs can help to understand if natural protein sequences were selected for their peculiar properties or if they are just one of the possible stable and functional ensembles. METHODS: The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 x 10(4) sequences) was generated, discarding amino acid sequences with significant similarity to natural proteins, and the corresponding structures were predicted using Rosetta. Given the highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc. RESULTS: The vast majority of NBPs, according to the Rosetta model, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in alpha helix-beta strands relative content and in hydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins. CONCLUSION: The results obtained suggest that the ability to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to adopt alpha helical folds indicate that all-alpha proteins may have emerged early in pre-biotic evolution. Further, the lower percentage of aromatic residues observed in natural proteins has important evolutionary implications as far as tolerance to mutations is concerned.

Synchronization phenomena in surface-reaction models of protocells.

A class of generic models of protocells is introduced, which are inspired by the Los Alamos bug hypothesis but which, due to their abstraction level, can be applied to a wider set of detailed protocell hypotheses. These models describe the coupled growth of the lipid container and of the self-replicating molecules. A technique to analyze the dynamics of populations of such protocells is described, which couples a continuous-time formalism for the growth between two successive cell divisions, and a discrete map that relates the quantity of self-replicating molecules in successive generations. This technique allows one to derive several properties in an analytical way. It is shown that, under fairly general assumptions, the two growth rates synchronize, so that the lipid container doubles its size when the number of self-replicating molecules has also doubled--thus giving rise to exponential growth of the population of protocells. Such synchronization had been postulated a priori in previous models of protocells; here it is an emergent property. We also compare the rate of duplication of two populations generated by two different protocells with different kinds of self-replicating molecules, considering the interesting case where the rate of self-replication of one kind is higher than that of the other, but its contribution to the container growth rate is smaller. It is shown that in this case the population of offspring of the protocell with the faster-replicating molecule will eventually grow faster than the other. The case where two different types of self-replicating monomers are present in the same protocell is also analyzed, and it is shown that, if the replication follows a first-order kinetic equation, then the faster replicator eventually displaces the slower one, whereas if the growth is sublinear the two coexist. It is also proven by an appropriate rescaling of time that the results that concern the system asymptotic dynamics hold both for micelles and vesicles.

A use of Ramachandran potentials in protein solution structure determinations.

A strategy is developed to use database-derived phi-psi constraints during simulated annealing procedures for protein solution structure determination in order to improve the Ramachandran plot statistics, while maintaining the agreement with the experimental constraints as the sole criterion for the selection of the family. The procedure, fully automated, consists of two consecutive simulated annealing runs. In the first run, the database-derived phi-psi constraints are enforced for all amino acids (but prolines and glycines). A family of structures is then selected on the ground of the lowest violations of the experimental constraints only, and the phi-psi values for each residue are examined. In the second and final run, the database-derived phi-psi constraints are enforced only for those residues which in the first run have ended in one and the same favored phi-psi region. For residues which are either spread over different favored regions or concentrated in disallowed regions, the constraints are not enforced. The final family is then selected, after the second run, again only based on the agreement with the experimental constraints. This automated approach was implemented in DYANA and was tested on as many as 12 proteins, including some containing paramagnetic metals, whose structures had been previously solved in our laboratory. The quality of the structures, and of Ramachandran plot statistics in particular, was notably improved while preserving the agreement with the experimental constraints.