Jonathan C. Fuller

Protein Binding Pocket Dynamics

The dynamics of protein binding pockets are crucial for their interaction specificity. Structural flexibility allows proteins to adapt to their individual molecular binding partners and facilitates the binding process. This implies the necessity to consider protein internal motion in determining and predicting binding properties and in designing new binders. Although accounting for protein dynamics presents a challenge for computational approaches, it expands the structural and physicochemical space for compound design and thus offers the prospect of improved binding specificity and selectivity. A cavity on the surface or in the interior of a protein that possesses suitable properties for binding a ligand is usually referred to as a binding pocket. The set of amino acid residues around a binding pocket determines its physicochemical characteristics and, together with its shape and location in a protein, defines its functionality. Residues outside the binding site can also have a long-range effect on the properties of the binding pocket. Cavities with similar functionalities are often conserved across protein families. For example, enzyme active sites are usually concave surfaces that present amino acid residues in a suitable configuration for binding low molecular weight compounds. Macromolecular binding pockets, on the other hand, are located on the protein surface and are often shallower. The mobility of proteins allows the opening, closing, and adaptation of binding pockets to regulate binding processes and specific protein functionalities. For example, channels and tunnels can exist permanently or transiently to transport compounds to and from a binding site. The influence of protein flexibility on binding pockets can vary from small changes to an already existent pocket to the formation of a completely new pocket. Here, we review recent developments in computational methods to detect and define binding pockets and to study pocket dynamics. We introduce five different classes of protein pocket dynamics: (1) appearance/disappearance of a subpocket in an existing pocket; (2) appearance/disappearance of an adjacent pocket on the protein surface in the direct vicinity of an already existing pocket; (3) pocket breathing, which may be caused by side-chain fluctuations or backbone or interdomain vibrational motion; (4) opening/closing of a channel or tunnel, connecting a pocket inside the protein with solvent, including lid motion; and (5) the appearance/disappearance of an allosteric pocket at a site on a protein distinct from an already existing pocket with binding of a ligand to the allosteric binding site affecting the original pocket. We suggest that the class of pocket dynamics, as well as the type and extent of protein motion affecting the binding pocket, should be factors considered in choosing the most appropriate computational approach to study a given binding pocket. Furthermore, we examine the relationship between pocket dynamics classes and induced fit, conformational selection, and gating models of ligand binding on binding kinetics and thermodynamics. We discuss the implications of protein binding pocket dynamics for drug design and conclude with potential future directions for computational analysis of protein binding pocket dynamics.

The HMGB1 protein induces a metabolic type of tumour cell death by blocking aerobic respiration

The high-mobility group box 1 (HMGB1) protein has a central role in immunological antitumour defense. Here we show that natural killer cell-derived HMGB1 directly eliminates cancer cells by triggering metabolic cell death. HMGB1 allosterically inhibits the tetrameric pyruvate kinase isoform M2, thus blocking glucose-driven aerobic respiration. This results in a rapid metabolic shift forcing cells to rely solely on glycolysis for the maintenance of energy production. Cancer cells can acquire resistance to HMGB1 by increasing glycolysis using the dimeric form of PKM2, and employing glutaminolysis. Consistently, we observe an increase in the expression of a key enzyme of glutaminolysis, malic enzyme 1, in advanced colon cancer. Moreover, pharmaceutical inhibition of glutaminolysis sensitizes tumour cells to HMGB1 providing a basis for a therapeutic strategy for treating cancer.

A Quick Guide for Building a Successful Bioinformatics Community

“Scientific community” refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop “The ‘How To Guide’ for Establishing a Successful Bioinformatics Network” at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB).

Ten simple rules for organizing an unconference.

An academic conference is a traditional platform for researchers and professionals to network and learn about recent developments and trends in a particular academic field [1–4]. Typically, the organizing committees and sponsors decide the main theme and sub-topics of the conference and select the presenters based on peer-reviewed papers [5]. The selected speakers usually share their research with a large audience by means of presentations and posters. However, the most stimulating discussions generally take place over coffee breaks when attendees can interact with each other and discuss various topics, including their own research interests, in a more informal manner [1, 6, 7], while expanding their own professional networks. An emphasis on facilitating such informal/networking interactions is a central focus of “unconventional conferences”—or “unconferences.” While many people may not yet have taken part in an unconference, the concept has been around for more than two decades. Events with unconference formats, beginning as early as 1985, include Open Space Technology, Foo Camp, BarCamp, Birds of a Feather, EdCamp, ScienceOnline, and many others. The success of these events has made the unconference format increasingly popular and widely known [8–11]. Unlike traditional conferences, an unconference is a participant-oriented meeting where the attendees decide on the agenda, discussion topics, workshops, and, often, even the time and venues. The informal and flexible program allows participants to suggest topics of their own interest and choose sessions accordingly. The format provides an excellent opportunity for researchers from diverse disciplines to work collaboratively on topics of common interest. The overarching goal for most unconferences is to prioritize conversation over presentation. In other words, the content for a session does not come from a select number of individuals at the front of the room, but is generated by all the attendees within the room, and, as such, every participant has an important role. Advantages of the unconference format include: a focus on topics that are relevant to the attendees (because they suggested them), an opportunity for teamwork development, flexibility of schedule, and an emphasis on contributions from every participant. The relationships built during an unconference often continue well past the event. The interactions can lead to productive collaborations, professional development opportunities, and a network of resources and are very effective at building a community amongst participants. The unconference format, therefore, gives participants experience in working together, and this can change how they think about their day-to-day work. A range of articles offer tips and advice for organizing and delivering aspects of scientific conferences and meetings or observations on features of successful meetings [5, 12, 13], including several from the PLOS Computational Biology “Ten Simple Rules” collection [14–16]. While the rules presented in this article are of particular relevance to the organization of unconferences, several of these points are also useful and complementary guidelines for organizing other kinds of events.

Biggest challenges in bioinformatics

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to discuss the ‘Biggest Challenges in Bioinformatics’ in a ‘World Café’ style event.

LigDig: a web server for querying ligand-protein interactions

Summary: LigDig is a web server designed to answer questions that previously required several independent queries to diverse data sources. It also performs basic manipulations and analyses of the structures of protein–ligand complexes. The LigDig webserver is modular in design and consists of seven tools, which can be used separately, or via linking the output from one tool to the next, in order to answer more complex questions. Currently, the tools allow a user to: (i) perform a free-text compound search, (ii) search for suitable ligands, particularly inhibitors, of a protein and query their interaction network, (iii) search for the likely function of a ligand, (iv) perform a batch search for compound identifiers, (v) find structures of protein–ligand complexes, (vi) compare three-dimensional structures of ligand binding sites and (vii) prepare coordinate files of protein–ligand complexes for further calculations. Availability and implementation: LigDig makes use of freely available databases, including ChEMBL, PubChem and SABIO-RK, and software programs, including cytoscape.js, PDB2PQR, ProBiS and Fconv. LigDig can be used by non-experts in bio- and chemoinformatics. LigDig is available at: http://mcm.h-its.org/ligdig. Contact: jonathan.fuller@h-its.org, rebecca.wade@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.

webSDA: a web server to simulate macromolecular diffusional association

Macromolecular interactions play a crucial role in biological systems. Simulation of diffusional association (SDA) is a software for carrying out Brownian dynamics simulations that can be used to study the interactions between two or more biological macromolecules. webSDA allows users to run Brownian dynamics simulations with SDA to study bimolecular association and encounter complex formation, to compute association rate constants, and to investigate macromolecular crowding using atomically detailed macromolecular structures. webSDA facilitates and automates the use of the SDA software, and offers user-friendly visualization of results. webSDA currently has three modules: ‘SDA docking’ to generate structures of the diffusional encounter complexes of two macromolecules, ‘SDA association’ to calculate bimolecular diffusional association rate constants, and ‘SDA multiple molecules’ to simulate the diffusive motion of hundreds of macromolecules. webSDA is freely available to all users and there is no login requirement. webSDA is available at http://mcm.h-its.org/webSDA/.

On calculation of the electrostatic potential of a phosphatidylinositol phosphate-containing phosphatidylcholine lipid membrane accounting for membrane dynamics.

Many signaling events require the binding of cytoplasmic proteins to cell membranes by recognition of specific charged lipids, such as phosphoinositol-phosphates. As a model for a protein-membrane binding site, we consider one charged phosphoinositol phosphate (PtdIns(3)P) embedded in a phosphatidylcholine bilayer. As the protein-membrane binding is driven by electrostatic interactions, continuum solvent models require an accurate representation of the electrostatic potential of the phosphoinositol phosphate-containing membrane. We computed and analyzed the electrostatic potentials of snapshots taken at regular intervals from molecular dynamics simulations of the bilayer. We observe considerable variation in the electrostatic potential of the bilayer both along a single simulation and between simulations performed with the GAFF or CHARMM c36 force fields. However, we find that the choice of GAFF or CHARMM c36 parameters has little effect on the electrostatic potential of a given configuration of the bilayer with a PtdIns(3)P embedded in it. From our results, we propose a remedian averaging method for calculating the electrostatic potential of a membrane system that is suitable for simulations of protein-membrane binding with a continuum solvent model.