Current Drug Targets - Volume 17, Issue 14, 2016

For many years the assumption that “Chemical compounds with similar structures may have similar activities” has been a foundation for lead identification. The similarity can be computed based upon topological, steric, electronic, and/or physical properties. The chemical structure similarity search differs from the chemical substructure search in that the former requires assessment of the properties of each compound and thus no filter can be applied for skipping structures before they are assessed to accelerate the computation. The latter can be accelerated by pre-screening compounds and omitting those that miss one (or more) specified fragments from the query. Moreover, three-dimensional similarity search requires superimposing many conformation pairs for each compound in the library. This makes 3-D similarity search algorithms time-consuming, and in general requires high performance computing (HPC) resources. This review will summarize recent progress in the techniques for HPC-supported two and three-dimensional chemical structure similarity search algorithms, and their applications in ligand-based virtual screening.

Protein-protein interactions (PPIs) play important roles in a variety of biological processes, and many PPIs have been regarded as biologically compelling targets for drug discovery. Extensive efforts have been made to develop feasible proteinprotein docking approaches to study PPIs in silico. Most of these approaches are composed of two stages: sampling and scoring. Sampling is used to generate a number of plausible protein-protein binding conformations and scoring can rank all those conformations. Due to large and flexible binding interface of PPI, determination of the near native structures is computationally expensive, and therefore computational efficiency is the most challenging issue in protein-protein docking. Here, we have reviewed the basic concepts and implementations of the sampling, scoring and acceleration algorithms in some established docking programs, and the limitations of these algorithms have been discussed. Then, some suggestions to the future directions for sampling, scoring and acceleration algorithms have been proposed. This review is expected to provide a better understanding of protein-protein docking and give some clues for the optimization and improvement of available approaches.

Computer-aided design is one of the critical components of modern drug discovery. Drug development is routinely streamlined using computational approaches to improve hit identification and lead selection, enhance bioavailability, and reduce toxicity. A mounting body of genomic knowledge accumulated during the last decade or so presents great opportunities for pharmaceutical research. However, new challenges also arose because processing this large volume of data demands unprecedented computing resources. On the other hand, the state-of-the-art heterogeneous systems deliver petaflops of peak performance to accelerate scientific discovery. In this communication, we review modern parallel accelerator architectures, mainly focusing on Intel Xeon Phi many-core devices. Xeon Phi is a relatively new platform that features tens of computing cores with hundreds of threads offering massively parallel capabilities for a broad range of application. We also discuss common parallel programming frameworks targeted to this accelerator, including OpenMP, OpenCL, MPI and HPX. Recent advances in code development for many-core devices are described to demonstrate the advantages of heterogeneous implementations over the traditional, serial computing. Finally, we highlight selected algorithms, eFindSite, a ligand binding site predictor, a force field for bio-molecular simulations, and BUDE, a structure-based virtual screening engine, to demonstrate how modern drug discovery is accelerated by heterogeneous systems equipped with parallel computing devices.

Allostery is a long-range macromolecular mechanism of internal regulation, in which the binding of a ligand in an allosteric site induces distant conformational changes in a distant portion of the protein, modifying its activity. From the drug design point of view, this mechanism can be exploited to achieve important therapeutic effects, since ligands able to bind allosteric sites may be designed to regulate target proteins. Computational tools are a valid support in this sense, since they allow the characterization of allosteric communications within proteins, which are essential to design modulator ligands. While considering long-range interactions in macromolecules, the principal drug design tool available to researcher is molecular dynamics, and related applications, since it allows the evaluation of conformational changes of a protein bound to a ligand. In particular, all-atoms molecular dynamics is suitable to verify the internal mechanisms that orchestrate allosteric communications, in order to identify key residues and internal pathways that modify the protein behaviour. The problem is that these techniques are heavily time-consuming and computationally intensive, thus high performance computing systems, including parallel computing and GPU-accelerated computations, are necessary to achieve results in a reasonable time. In this review, we will discuss how it is possible to exploit in silico approaches to characterize allosteric modulations and long-range interactions within proteins, describing the case study of the Heat Shock Proteins, a class of chaperons regulated by stress conditions, which is particularly important since it is involved in many cancers and neurodegenerative diseases.

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.

Virtual screening for active compounds has become an essential step within the drug development pipeline. The computer based prediction of compound binding modes is one of the most time and cost efficient methods for screening ligand libraries and enrich results of potential drugs. Here we present an overview about currently available online resources regarding compound databases, docking applications, and science gateways for drug discovery and virtual screening, in order to help structural biologists in choosing the best tools for their analysis. The appearance of the user interface, authentication and security aspects, data management, and computational performance will be discussed. We anticipate a broad overview about currently available solutions, guiding computational chemists and users from related fields towards scientifically reliable results.

Over the years, polo-like kinase 1 (PLK1) has garnered great interest as a therapeutic target. The PLK1 is a member of a highly conserved serine/threonine kinase family that plays pivotal roles in mitosis, cytokinesis and DNA damage response in eukaryotic cells. In this review, we summarize the functions of PLK1 during cell cycle progression, its roles in human pediatric cancer and its value as a prognostic factor. Furthermore, we introduce the advances in pharmacological inhibition and the newly chemotherapeutic development of small-molecules to target PLK1 in cancer treatment. Finally, clinical trials with PLK1 inhibitors are briefly reviewed.

Rheumatoid arthritis (RA) is a chronic systemic disorder characterized by persistent synovitis and systemic inflammation. Currently, the widely used drugs for the treatment of RA are disease-modifying antirheumatic drugs, biological agents and glucocorticoids. But their clinical use has been limited because of their adverse effects with a high frequency and high cost of treatment. It is essential to find novel candidate agents. Traditional Chinese medicine (TCM) has been used for RA treatment for a long period of time. In recent years, significant amounts of studies have shown that some TCMs and their active ingredients have obvious therapeutic effects on RA. In this review, the compounds in TCMs that have an effect in clinic or animal experiments of RA are critically reviewed and summarized. Moreover, the relationship between chemical structures of the compound and their activities is analyzed. The relevant researches are described from the aspects of source, methods, result, and related mechanism analysis. The existing studies show that most effective compounds in TCM for RA treatment belong to alkaloids, flavonoids, terpenoids, phenols and quinines. It is hoped that the data summarized in this review will be beneficial to the screening of new nature-derived antirheumatic drugs.

Receptor tyrosine kinases (RTKs) family is comprised of different cell surface glycoproteins. These enzymes participate in and regulate vital processes such as cell proliferation, polarity, differentiation, cell to cell interactions, signaling, and cell survival. Dysregulation of RTKs contributes to the development of different types of tumors. RTKs deregulation in different types of cancer has been reported for more than 30 RTKs. Due to their critical roles, the specific targeting of RTKs in malignancies is a promising approach. Targeted cellular and molecular therapies (personalized medicine) have been known as new types of therapeutics, which prevent tumor cell proliferation and invasion by interfering with molecules essential for tumor growth and survival. Specific targeting of RTKs using monoclonal antibodies (mAbs) in malignancies as well as in autoimmune disorders is of great interest. The growing number of mAbs approved by the authorities implies on the increasing attentions and applications of these therapeutic tools. Due to the high specificity, mAbs are the most promising substances that target RTKs expressed on the tumor cell surface. In this communication, we review the recent progresses in the development of mAbs targeting oncogenic RTKs for cancer treatment.