Pedro A. Fernandes

Protein–ligand docking: Current status and future challenges

Understanding the ruling principles whereby protein receptors recognize, interact, and associate with molecular substrates and inhibitors is of paramount importance in drug discovery efforts. Protein–ligand docking aims to predict and rank the structure(s) arising from the association between a given ligand and a target protein of known 3D structure. Despite the breathtaking advances in the field over the last decades and the widespread application of docking methods, several downsides still exist. In particular, protein flexibility—a critical aspect for a thorough understanding of the principles that guide ligand binding in proteins—is a major hurdle in current protein–ligand docking efforts that needs to be more efficiently accounted for. In this review the key concepts of protein–ligand docking methods are outlined, with major emphasis being given to the general strengths and weaknesses that presently characterize this methodology. Despite the size of the field, the principal types of search algorithms and scoring functions are reviewed and the most popular docking tools are briefly depicted. Recent advances that aim to address some of the traditional limitations associated with molecular docking are also described. A selection of hand‐picked examples is used to illustrate these features. Proteins 2006.

Hot spots--a review of the protein-protein interface determinant amino-acid residues.

Proteins tendency to bind to one another in a highly specific manner forming stable complexes is fundamental to all biological processes. A better understanding of complex formation has many practical applications, which include the rational design of new therapeutic agents, and the analysis of metabolic and signal transduction networks. Alanine‐scanning mutagenesis made possible the detection of the functional epitopes, and demonstrated that most of the protein–protein binding energy is related only to a group of few amino acids at intermolecular protein interfaces: the hot spots. The scope of this review is to summarize all the available information regarding hot spots for a better atomic understanding of their structure and function. The ultimate objective is to improve the rational design of complexes of high affinity and specificity as well as that of small molecules, which can mimic the functional epitopes of the proteic complexes. Proteins 2007.

Computational alanine scanning mutagenesis--an improved methodological approach.

Alanine scanning mutagenesis of protein–protein interfacial residues can be applied to a wide variety of protein complexes to understand the structural and energetic characteristics of the hot‐spots. Binding free energies have been estimated with reasonable accuracy with empirical methods, such as Molecular Mechanics/Poisson‐Boltzmann surface area (MM‐PBSA), and with more rigorous computational approaches like Free Energy Perturbation (FEP) and Thermodynamic Integration (TI). The main objective of this work is the development of an improved methodological approach, with less computational cost, that predicts accurately differences in binding free energies between the wild‐type and alanine mutated complexes (ΔΔGbinding). The method was applied to three complexes, and a mean unsigned error of 0.80 kcal/mol was obtained in a set of 46 mutations. The computational method presented here achieved an overall success rate of 80% and an 82% success rate in residues for which alanine mutation causes an increase in the binding free energy > 2.0 kcal/mol (warm‐ and hot‐spots). This fully atomistic computational methodological approach consists in a computational Molecular Dynamics simulation protocol performed in a continuum medium using the Generalized Born model. A set of three different internal dielectric constants, to mimic the different degree of relaxation of the interface when different types of amino acids are mutated for alanine, have to be used for the proteins, depending on the type of amino acid that is mutated. This method permits a systematic scanning mutagenesis of protein–protein interfaces and it is capable of anticipating the experimental results of mutagenesis, thus guiding new experimental investigations.

Computational enzymatic catalysis.

Computational methodologies are playing increasingly important roles in elucidating and presenting the complete and detailed mechanisms of enzymatic reactions because of their capacity to determine and characterize intermediates and transition states from both structural and energetics points of view, independent of their reduced lifetimes and without interfering with the natural reactional flux. These features are turning the field into an active and interesting area of research, involving a diverse range of studies, mostly directed at understanding the ways in which enzymes function under certain circumstances and predicting how they will behave under others. The accuracy of the computational data obtained for a given mechanistic hypothesis depends essentially on three mutually exclusive factors: the accuracy of the Hamiltonian of the reaction mechanism, consideration of the modulating aspect of the enzymes structure in the energetics of the active center, and consideration of the enzymes conformational fluctuations and dynamics. Although, unfortunately, it is impossible at present to optimize these crucial factors simultaneously, the success of any enzymatic mechanistic study depends on the level of equilibrium achieved among them. Different authors adopt different solutions, and this Account summarizes the most favored, with emphasis placed on our own preferences. Another crucial aspect in computational enzymatic catalysis is the model used in the calculations. Our aim is to build the simplest model that captures the essence of the catalytic power of an enzyme, allowing us to apply the highest possible theoretical level and minimize accidental errors. The choice is, however, far from obvious, ranging from simple models containing tens of atoms up to models of full enzymes plus solvent. Many factors underlie the choice of an appropriate model; here, examples are presented of very different modeling strategies that have been employed to obtain meaningful results. One particular case study, that of enzyme ribonucleotide reductase (RNR), a radical enzyme that catalyzes the reduction of ribonucleotides into deoxyribonucleotides, is one of the examples illustrating how the successive increase of the systems size does not dramatically change the thermodynamics and kinetics of the reaction. The values obtained and presented speak for themselves in that the only ones that are distinctly different are those calculated using an exceedingly small model, which omitted the amino acids that establish hydrogen bonds with the reactive unit of the substrate. This Account also describes our computational analysis of the mechanism of farnesyltransferase, a heterodimeric zinc metalloenzyme that is currently one of the most fascinating targets in cancer research. We focus on the present methodologies that we have been using, our models and understanding of the problem, and the accuracy of results and associated problems within this area of research.

Farnesyltransferase Inhibitors: A Detailed Chemical View on an Elusive Biological Problem

Farnesyltransferase (FTase) is a zinc enzyme that has been the subject of particular attention in anti-cancer research. This enzyme promotes the addition of a farnesyl group from farnesyl diphosphate (FPP) to a cysteine residue of a protein substrate containing a typical -CAAX motif at the carboxyl terminus. Initial interest in FTase inhibition was prompted by the finding that farnesylation was absolutely required for the oncogenic forms of ras proteins to transform cells, as ras proteins have been implicated in around 30% of all human cancers. This discovery led to frenetic search for FTase inhibitors (FTIs), with more than 400 patents registered in less than a decade. However, despite the very promising initial results, the outcome of Phase II and Phase III clinical trials was, is general, rather disappointing, with the most advanced FTIs failing to demonstrate anti-tumor activity in ras dependent cancers, presumably because K-ras, the most frequently mutated form of ras in human cancers, is able to bypass FTI blockade through cross-prenylation by the related enzyme geranylgeranyltransferase I (GGTase I). Surprisingly, several of these compounds were later shown to have anti-tumor activity against non-ras dependent cancers, launching the grounds for a new and exciting era in FTIs research and development, although the precise target for the FTIs activity of these compounds still remains unknown. This review reports the recent progress in the field, presenting a comprehensive summary of the most promising FTIs, in terms of their chemical structure and properties, taking into account the topology of the enzymes active-site, and the most recent mechanistic results on the catalytic activity of FTase, both at the theoretical and mechanistic level. These features are presented in close linking with the available results on the biological activity of these inhibitors, and with the outcome of the most recent clinical trials.

Protein-Ligand Docking in the New Millennium – A Retrospective of 10 Years in the Field

Protein-ligand docking is currently an important tool in drug discovery efforts and an active area of research that has been the subject of important developments over the last decade. These are well portrayed in the rising number of available protein-ligand docking software programs, increasing level of sophistication of its most recent applications, and growing number of users. While starting by summarizing the key concepts in protein-ligand docking, this article presents an analysis of the evolution of this important field of research over the past decade. Particular attention is given to the massive range of alternatives, in terms of protein-ligand docking software programs currently available. The emerging trends in this field are the subject of special attention, while old established docking alternatives are critically revisited. Current challenges in the field of protein-ligand docking such as the treatment of protein flexibility, the presence of structural water molecules and its effect in docking, and the entropy of binding are dissected and discussed, trying to anticipate the next years in the field.

Protein–protein docking dealing with the unknown

Protein–protein binding is one of the critical events in biology, and knowledge of proteic complexes three‐dimensional structures is of fundamental importance for the biochemical study of pharmacologic compounds. In the past two decades there was an emergence of a large variety of algorithms designed to predict the structures of protein–protein complexes—a procedure named docking. Computational methods, if accurate and reliable, could play an important role, both to infer functional properties and to guide new experiments. Despite the outstanding progress of the methodologies developed in this area, a few problems still prevent protein–protein docking to be a widespread practice in the structural study of proteins. In this review we focus our attention on the principles that govern docking, namely the algorithms used for searching and scoring, which are usually referred as the docking problem. We also focus our attention on the use of a flexible description of the proteins under study and the use of biological information as the localization of the hot spots, the important residues for protein–protein binding. The most common docking softwares are described too.

Vascular Endothelial Growth Factor (VEGF) Inhibition - A Critical Review

Angiogenesis, or formation of new blood capillaries from preexisting vessels, plays both beneficial and damaging roles in the organism. It is a result of a complex balance of positive and negative regulators, and vascular endothelial growth factor (VEGF) is one of the most important pro-angiogenic factors involved in tumor angiogenesis. VEGF increases vascular permeability, which might facilitate tumor dissemination via the circulation causing a greater delivery of oxygen and nutrients; it recruits circulating endothelial precursor cells, and acts as a survival factor for immature tumor blood vessels. The endotheliotropic activities of VEGF are mediated through the VEGF-specific tyrosine-kinase receptors: VEGFR-1, VEGFR-2 and VEGFR-3. VEGF and its receptors play a central role in tumor angiogenesis, and therefore the blockade of this pathway is a promising therapeutic strategy for inhibiting angiogenesis and tumor growth. A number of different strategies to inhibit VEGF signal transduction are in development and they include the development of humanized neutralizing anti-VEGF monoclonal antibodies, receptor antagonists, soluble receptors, antagonistic VEGF mutants, and inhibitors of VEGF receptor function. These agents can be divided in two broad classes, namely agents designed to target the VEGF activity and agents designed to target the surface receptor function. The main purpose of this review is to summarize all the available information regarding the importance of the pro-angiogenic factor VEGF in cancer therapy. After an overview of the VEGF family and their respective receptors, we shall focus our attention on the different VEGF-inhibitors existent nowadays. Agents based upon anti-VEGF therapy have provided solid proofs about their success, and therefore we believe that a critical review is of the utmost importance to help researchers in their future work.

Overview of Ribonucleotide Reductase Inhibitors: An Appealing Target in Anti-Tumour Therapy

This review provides up-to-date information on the inhibition of ribonucleotide reductase (RNR), the enzyme that catalyses the reduction of ribonucleotides into deoxyribonucleotides. Taking in account that DNA replication and repair are essential mechanisms for cell integrity and are dependent on the availability of deoxyribonucleotides, many researchers are giving special attention to this enzyme, since it is an attractive target to treat several diseases of our time specially cancer. This investment has already given some benefits since some of these inhibitors show potent chemotherapeutic efficacy against a wide range of tumours such as non-small cell lung cancer, adenocarcinoma of pancreas, bladder cancer, leukaemia and some solid tumours. In fact a few of them have already been approved for the clinical treatment of some kinds of cancer. All aspects of RNR inhibition and corresponding inhibitors are the subjects of this review. The inhibitors are divided in three main groups: translation inhibitors, which unable the formation of the enzyme; dimerization inhibitors that prevent the complexation of the two RNR subunits (R1 and R2); and catalytic inhibitors that inactivate subunit R1 and/or subunit R2, leading to RNR inactivity. In this last group special focus will be addressed to substrate analogues.

Microfluidic devices: useful tools for bioprocess intensification.

The dawn of the new millennium saw a trend towards the dedicated use of microfluidic devices for process intensification in biotechnology. As the last decade went by, it became evident that this pattern was not a short-lived fad, since the deliverables related to this field of research have been consistently piling-up. The application of process intensification in biotechnology is therefore seemingly catching up with the trend already observed in the chemical engineering area, where the use of microfluidic devices has already been upgraded to production scale. The goal of the present work is therefore to provide an updated overview of the developments centered on the use of microfluidic devices for process intensification in biotechnology. Within such scope, particular focus will be given to different designs, configurations and modes of operation of microreactors, but reference to similar features regarding microfluidic devices in downstream processing will not be overlooked. Engineering considerations and fluid dynamics issues, namely related to the characterization of flow in microchannels, promotion of micromixing and predictive tools, will also be addressed, as well as reflection on the analytics required to take full advantage of the possibilities provided by microfluidic devices in process intensification. Strategies developed to ease the implementation of experimental set-ups anchored in the use of microfluidic devices will be briefly tackled. Finally, realistic considerations on the current advantages and limitation on the use of microfluidic devices for process intensification, as well as prospective near future developments in the field, will be presented.