Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets

Protein phosphorylation prediction: limitations, merits and pitfalls

Protein phosphorylation is a major protein post-translational modification process that plays a pivotal role in numerous cellular processes, such as recognition, signaling or degradation. It can be studied experimentally by various methodologies, including western blot analysis, site-directed mutagenesis, 2D gel electrophoresis, mass spectrometry etc. A number of in silico tools have also been developed in order to predict plausible phosphorylation sites in a given protein. In this review, we conducted a benchmark study including the leading protein phosphorylation prediction software, in an effort to determine which performs best. The first place was taken by GPS 2.2, having predicted all phosphorylation sites with a 83% fidelity while in second place came NetPhos 2.0 with 69%.  

Analyzing Effects of Naturally Occurring Missense Mutations

Single-point mutation in genome, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding protein and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well

G-protein coupled receptors activation mechanism: from ligand binding to the transmission of the signal inside the cell

G-protein coupled receptors (GPCRs) are the largest family of pharmaceutical drug targets in the human genome and are modulated by a large variety of en- dogenous and synthetic ligands. GPCRs activation usually depends on agonist binding (except for receptors with basal activity), which stabilizes receptor con- formations and allow the requirement and activation of intracellular transducers. GPCRs are unique receptors and very well studied, since they play an important role in a great number of diseases. They interact with different type of ligands (such as light, peptides, proteins) and different partners in the intracellular part (such as G-proteins or β-arrestins). Based on homology and function GPCRs are divided in five classes: Class A or Rhodopsin, Class B1 or Secretin, Class B2 or Adhesion, Class C or Glutamate, Class F or Frizzled. What is still missing in the state of the art of these receptor, and in particular in Class A, is a global study on different binding cavities with divergent properties, with the aim to discover common binding characteristics, preserved during years of evolution. Gaining more knowledge on common features for ligand recognition shared among all the recep- tors may become crucial to deeply understand the mechanism used to transmit the signal into the cell. In the first step of this thesis we have used all the solved Class A receptors structures to analyze and find, if exist, a common way to transmit the signal inside the cell. We identified and validated ten positions shared between all the binding cavities and always involved in the interaction with ligands. We demonstrated that residues in these positions are conserved and have co-evolved together. In a second step, we used these positions to understand how ligands could be positioned in the binding cavities of three study cases: Muscarinic receptors, Kisspeptin receptors and the GPR3 receptor. We did not have any experimental information a priori. We used homology modeling and docking techniques for the first two cases, adding molecular dynamics simulations in the third case. All the predictions and suggestions from the computational point of view, turned out to be very successful. In particular for the GPR3 receptor we were able to identify and validate by alanine-scanning mutagenesis the role of three functionally relevant residues. The latter were correlated with the constitutive and agonist-stimulated adenylate cyclase activity of GPR3 receptor. Taken together, these results suggest an important role of computational structural biology and pave the way of strong collaborations between computational and experimental researches

A Brief View of Molecular Modeling Approaches to P2 Receptors

Purinergic receptors are a class of receptors distributed into two groups, P1 and P2. P1 receptors are activated by nucleosides, like adenosine, while nucleotides active P2 receptors. In turn, P2 receptors comprise two families, metabotropic P2Y and ionotropic P2X. P2Y receptors consist in eight members, namely, P2Y1, P2Y2, P2Y4, P2Y6, P2Y11, P2Y12, P2Y13, and P2Y14, described in mammals, while P2X includes seven members, numbered P2X1 to P2X7. These receptors have been described as expressed in practically all cells studied to date. In this context, P2 receptors are suggested as participating in certain diseases. The general approach applied in the discovery of new drugs is expensive and lengthy. Alternatively, in the last 20 years, molecular modeling has emerged as an exciting tool for the design of new drugs, in less time and at low costs. These tools allow for in silico testing of thousands of molecules against a target protein, as well as toxicity, absorption, distribution, metabolism, and constant affinity predictions of a given interaction. Thus, molecular modeling algorithms emerge as an increasingly important tool for the design of drugs targeting purinergic receptors as therapeutic targets of many diseases, including cancer, pain, inflammation, cardiovascular, and endocrine conditions

On the human taste perception: Molecular-level understanding empowered by computational methods

Background: The perception of taste is a prime example of complex signal transduction at the subcellular level, involving an intricate network of molecular machinery, which can be investigated to great extent by the tools provided by Computational Molecular Modelling. The present review summarises the current knowledge on the molecular mechanisms at the root of taste transduction, in particular involving taste receptors, highly specialised proteins driving the activation/deactivation of specific cell signalling pathways and ultimately leading to the perception of the five principal tastes: sweet, umami, bitter, salty and sour. The former three are detected by similar G protein-coupled receptors, while the latter two are transduced by ion channels. Scope and approach: The main objective of the present review is to provide a general overview of the molecular structures investigated to date of all taste receptors and the techniques employed for their molecular modelling. In addition, we provide an analysis of the various ligands known to date for the above-listed receptors, including how they are activated in the presence of their target molecule. Key findings and conclusions: In the last years, numerous advances have been made in molecular research and computational investigation of ligand-receptor interaction related to taste receptors. This work aims to outline the progress in scientific knowledge about taste perception and understand the molecular mechanisms involved in the transfer of taste information

Evolutionary action and structural basis of the allosteric switch controlling β(2)AR functional selectivity

Functional selectivity of G-protein-coupled receptors is believed to originate from ligand-specific conformations that activate only subsets of signaling effectors. In this study, to identify molecular motifs playing important roles in transducing ligand binding into distinct signaling responses, we combined in silico evolutionary lineage analysis and structure-guided site-directed mutagenesis with large-scale functional signaling characterization and non-negative matrix factorization clustering of signaling profiles. Clustering based on the signaling profiles of 28 variants of the β(2)-adrenergic receptor reveals three clearly distinct phenotypical clusters, showing selective impairments of either the Gi or βarrestin/endocytosis pathways with no effect on Gs activation. Robustness of the results is confirmed using simulation-based error propagation. The structural changes resulting from functionally biasing mutations centered around the DRY, NPxxY, and PIF motifs, selectively linking these micro-switches to unique signaling profiles. Our data identify different receptor regions that are important for the stabilization of distinct conformations underlying functional selectivity

Developing a powerful In Silico tool for the discovery of novel caspase-3 substrates: a preliminary screening of the human proteome

<p>Abstract</p> <p>Background</p> <p>Caspases are a family of cysteinyl proteases that regulate apoptosis and other biological processes. Caspase-3 is considered the central executioner member of this family with a wide range of substrates. Identification of caspase-3 cellular targets is crucial to gain further insights into the cellular mechanisms that have been implicated in various diseases including: cancer, neurodegenerative, and immunodeficiency diseases. To date, over 200 caspase-3 substrates have been identified experimentally. However, many are still awaiting discovery.</p> <p>Results</p> <p>Here, we describe a powerful bioinformatics tool that can predict the presence of caspase-3 cleavage sites in a given protein sequence using a Position-Specific Scoring Matrix (PSSM) approach. The present tool, which we call CAT3, was built using 227 confirmed caspase-3 substrates that were carefully extracted from the literature. Assessing prediction accuracy using 10 fold cross validation, our method shows AUC (area under the ROC curve) of 0.94, sensitivity of 88.83%, and specificity of 89.50%. The ability of CAT3 in predicting the precise cleavage site was demonstrated in comparison to existing state-of-the-art tools. In contrast to other tools which were trained on cleavage sites of various caspases as well as other similar proteases, CAT3 showed a significant decrease in the false positive rate. This cost effective and powerful feature makes CAT3 an ideal tool for high-throughput screening to identify novel caspase-3 substrates.</p> <p>The developed tool, CAT3, was used to screen 13,066 human proteins with assigned gene ontology terms. The analyses revealed the presence of many potential caspase-3 substrates that are not yet described. The majority of these proteins are involved in signal transduction, regulation of cell adhesion, cytoskeleton organization, integrity of the nucleus, and development of nerve cells.</p> <p>Conclusions</p> <p>CAT3 is a powerful tool that is a clear improvement over existing similar tools, especially in reducing the false positive rate. Human proteome screening, using CAT3, indicate the presence of a large number of possible caspase-3 substrates that exceed the anticipated figure. In addition to their involvement in various expected functions such as cytoskeleton organization, nuclear integrity and adhesion, a large number of the predicted substrates are remarkably associated with the development of nerve tissues.</p