The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

Dissecting the Specificity of Protein-Protein Interaction in Bacterial Two-Component Signaling: Orphans and Crosstalks

Predictive understanding of the myriads of signal transduction pathways in a cell is an outstanding challenge of systems biology. Such pathways are primarily mediated by specific but transient protein-protein interactions, which are difficult to study experimentally. In this study, we dissect the specificity of protein-protein interactions governing two-component signaling (TCS) systems ubiquitously used in bacteria. Exploiting the large number of sequenced bacterial genomes and an operon structure which packages many pairs of interacting TCS proteins together, we developed a computational approach to extract a molecular interaction code capturing the preferences of a small but critical number of directly interacting residue pairs. This code is found to reflect physical interaction mechanisms, with the strongest signal coming from charged amino acids. It is used to predict the specificity of TCS interaction: Our results compare favorably to most available experimental results, including the prediction of 7 (out of 8 known) interaction partners of orphan signaling proteins in Caulobacter crescentus. Surveying among the available bacterial genomes, our results suggest 15~25% of the TCS proteins could participate in out-of-operon "crosstalks". Additionally, we predict clusters of crosstalking candidates, expanding from the anecdotally known examples in model organisms. The tools and results presented here can be used to guide experimental studies towards a system-level understanding of two-component signaling.Comment: Supplementary information available on http://www.plosone.org/article/info:doi/10.1371/journal.pone.001972

Examination of Molecular Recognition in Protein-Ligand Interactions

This dissertation is a compilation of two main projects that were investigated during my thesis research. The first project was a prospective study which identified and characterized drug-like inhibitors of a prototype of bacterial two-component signal transduction response regulator using computational and experimental methods. The second project was the development and validation of a scoring function, PHOENIX, derived using high-resolution structures and calorimetry measurements to predict binding affinities of protein-ligand interactions. Collectively, my thesis research aimed to better understand the underlying driving forces and principles which govern molecular recognition and molecular design. A prospective study coupled computational predictions with experimental validation resulted in the discovery of first-in-class inhibitors targeting a signal transduction module important for bacterial virulence. Development and validation of the PHOENIX scoring function for binding affinity prediction derived using high-resolution structures and calorimetry measurements should guide future molecular recognition studies and endeavors in computer-aided molecular design. To request for an electronic copy of this dissertation, please email the author: yattang at gmail dot com)

Protein complex compositions predicted by structural similarity

Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain—porcine α–amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE ()

Towards Structural Classification of Proteins based on Contact Map Overlap

A multitude of measures have been proposed to quantify the similarity between protein 3-D structure. Among these measures, contact map overlap (CMO) maximization deserved sustained attention during past decade because it offers a fine estimation of the natural homology relation between proteins. Despite this large involvement of the bioinformatics and computer science community, the performance of known algorithms remains modest. Due to the complexity of the problem, they got stuck on relatively small instances and are not applicable for large scale comparison. This paper offers a clear improvement over past methods in this respect. We present a new integer programming model for CMO and propose an exact B &B algorithm with bounds computed by solving Lagrangian relaxation. The efficiency of the approach is demonstrated on a popular small benchmark (Skolnick set, 40 domains). On this set our algorithm significantly outperforms the best existing exact algorithms, and yet provides lower and upper bounds of better quality. Some hard CMO instances have been solved for the first time and within reasonable time limits. From the values of the running time and the relative gap (relative difference between upper and lower bounds), we obtained the right classification for this test. These encouraging result led us to design a harder benchmark to better assess the classification capability of our approach. We constructed a large scale set of 300 protein domains (a subset of ASTRAL database) that we have called Proteus 300. Using the relative gap of any of the 44850 couples as a similarity measure, we obtained a classification in very good agreement with SCOP. Our algorithm provides thus a powerful classification tool for large structure databases

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/cod