ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre- processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein–protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus

Fast automated placement of polar hydrogen atoms in protein-ligand complexes

<p>Abstract</p> <p>Background</p> <p>Hydrogen bonds play a major role in the stabilization of protein-ligand complexes. The ability of a functional group to form them depends on the position of its hydrogen atoms. An accurate knowledge of the positions of hydrogen atoms in proteins is therefore important to correctly identify hydrogen bonds and their properties. The high mobility of hydrogen atoms introduces several degrees of freedom: Tautomeric states, where a hydrogen atom alters its binding partner, torsional changes where the position of the hydrogen atom is rotated around the last heavy-atom bond in a residue, and protonation states, where the number of hydrogen atoms at a functional group may change. Also, side-chain flips in glutamine and asparagine and histidine residues, which are common crystallographic ambiguities must be identified before structure-based calculations can be conducted.</p> <p>Results</p> <p>We have implemented a method to determine the most probable hydrogen atom positions in a given protein-ligand complex. Optimality of hydrogen bond geometries is determined by an empirical scoring function which is used in molecular docking. This allows to evaluate protein-ligand interactions with an established model. Also, our method allows to resolve common crystallographic ambiguities such as as flipped amide groups and histidine residues. To ensure high speed, we make use of a dynamic programming approach.</p> <p>Conclusion</p> <p>Our results were checked against selected high-resolution structures from an external dataset, for which the positions of the hydrogen atoms have been validated manually. The quality of our results is comparable to that of other programs, with the advantage of being fast enough to be applied on-the-fly for interactive usage or during score evaluation.</p

Intuitive, But Not Simple: Including Explicit Water Molecules in Protein-Protein Docking Simulations Improves Model Quality

Characterizing the nature of interaction between proteins that have not been experimentally co-crystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high-resolution crystallographically characterized “dry” protein-protein complexes, and was shown to reliably identify native-like models. However, most current protein-protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the ‘truly’ bridging waters at the 30 protein-protein interfaces and we utilized them in “solvated” docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ~24% in the average hit-count within the top-10 predictions the protein-protein dataset was seen, compared to standard “dry” docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native-like structure predictions

Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest

DEVELOPMENT OF HINT BASED COMPUTATIONAL TOOLS FOR DRUG DESIGN: APPLICATIONS IN THE DESIGN AND DEVELOPMENT OF NOVEL ANTI-CANCER AGENTS

The overall aim of the research is to develop a computational platform based on HINT paradigm for manipulating, predicting and analyzing biomacromolecular-ligand structure. A second synergistic goal is to apply the above methodology to design novel and potent anti-cancer agents. The crucial role of the microtubule in cell division has identified tubulin as an interesting target for the development of therapeutics for cancer. Pyrrole-containing molecules derived from nature have proven to be particularly useful as lead compounds for drug development. We have designed and developed a series of substituted pyrroles that inhibit growth and promote death of breast tumor cells at nM and μM concentrations in human breast tumor cell lines. In another project, stilbene analogs were designed and developed as microtubule depolymerizing agents that showed anti-leukemic activity. A molecular modeling study was carried out to accurately represent the complex structure and the binding mode of a new class of tubulin inhibitors that bind at the αβ-tubulin colchicine site. These studies coupled with HINT interaction analyses were able to describe the complex structure and the binding modes of inhibitors. Qualitative analyses of the results showed general agreement with the experimental in vitro biological activity for these derivatives. Consequently, we have been designing new analogs that can be synthesized and tested; we believe that these molecules will be highly selective against cancer cells with minimal toxicity to the host tissue. Another goal of our research is to develop computational tools for drug design. The development and implementation of a novel cavity detection algorithm is also reported and discussed. The algorithm named VICE (Vectorial Identification of Cavity Extents) utilizes HINT toolkit functions to identify and delineate a binding pocket in a protein. The program is based on geometric criteria and applies simple integer grid maps to delineate binding sites. The algorithm was extensively tested on a diverse set of proteins and detects binding pockets of different shapes and sizes. The study also implemented the computational titration algorithm to understand the complexity of ligand binding and protonation state in the active site of HIV-1 protease. The Computational titration algorithm is a powerful tool for understanding ligand binding in a complex biochemical environment and allows generating hypothesis on the best model for binding

The Development and Applications of the HINT Scoring Function: Exploring Colchicine-Site Anticancer Agents and Tautomerism

The overall aim of this work was to apply HINT, an empirical scoring function based on the understanding of hydrophobicity, to analyze and predict the binding affinities and biological activities of colchicine-site anticancer agents. The second, concurrent aim was to improve the scoring function by incorporating tautomerism within the modeling process. Our belief is that proper evaluation of tautomeric forms for small molecules will improve performance of virtual screening. The novel pyrrole-based compounds targeting the colchicine site were docked into the receptor using HINT as a rescoring function. Two distinct binding modes dictated by the size and shape of a subpocket were predicted to differentiate the highly active compounds from the weak ones. Of the residues predicted to participate in binding for the active binding mode, Cys241β was revealed to form a weak but critical hydrogen bond with the ligand. A larger collection of colchicine-site agents, biologically tested in the same laboratory including our pyrrole-based compounds were subject to 3D quantitative structure-activity relationship (QSAR) study. Using results on docking the pyrrole compounds as a guide, relative binding poses and QSAR models were built to facilitate ligand design and optimization. A new 3D modeling approach was introduced to visually highlight the unique features of highly active compounds and the commonality of all compounds in the dataset using HINT maps and successfully tested on the colchicine-site agents. These results will provide valuable guidance in the future design and development of new colchicine-site agents. To incorporate tautomerism within HINT, we proposed and developed two workflow approaches: a general search tool using a simple and intuitive algorithm analyzing hydrogen shift patterns to identify and enumerate tautomeric structures, and a database that contains commonly observed tautomeric structures. The first approach was designed for small-scale docking studies and the second approach was designed for large-scale virtual screening. The tautomer module in HINT will give more accurate modeling results when the compound encountered is able to tautomerize

Bound Water at Protein-Protein Interfaces: Partners, Roles and Hydrophobic Bubbles as a Conserved Motif

Background There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region. Methodology A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules. Results The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be independent of the crystallographic resolution. Interactions with residue backbones are consistent for all classes and account for 21.5% of all interactions. Interactions with polar residues are significantly more common for the first group and interactions with non-polar residues dominate the last group. Waters interacting with both proteins stabilize on average the proteins\u27 interaction (−0.46 kcal mol−1), but the overall average contribution of a single water to the protein-protein interaction energy is unfavorable (+0.03 kcal mol−1). Analysis of the waters without favorable interactions with either protein suggests that this is a conserved phenomenon: 42% of these waters have SASA ≤ 10 Å2 and are thus largely buried, and 69% of these are within predominantly hydrophobic environments or “hydrophobic bubbles”. Such water molecules may have an important biological purpose in mediating protein-protein interactions

Development and Improvement of Tools and Algorithms for the Problem of Atom Type Perception and for the Assessment of Protein-Ligand-Complex Geometries

In context of the present work, a scoring function for protein-ligand complexes has been developed, not aimed at affinity prediction, but rather a good recognition rate of near native geometries. The developed program DSX makes use of the same formalism as the knowledge-based scoring function DrugScore, hence using the knowledge from crystallographic databases and atom-type specific distance-dependent distribution functions. It is based on newly defined atom-types. Additionally, the program is augmented by two novel potentials which evaluate the torsion angles and (de-)solvation effects. Validation of DSX is based on a literature-known, comprehensive data-set that allows for comparison with other popular scoring functions. DSX is intended for the recognition of near-native binding modes. In this important task, DSX outperforms the competitors, but is also among the best scoring functions regarding the ranking of different compounds. Another essential step in the development of DSX was the automatical assignment of the new atom types. A powerful programming framework was implemented to fulfill this task. Validation was done on a literature-known data-set and showed superior efficiency and quality compared to similar programs where this data was available. The front-end fconv was developed to share this functionality with the scientific community. Multiple features useful in computational drug-design workflows are also included and fconv was made freely available as Open Source Project. Based on the developed potentials for DSX, a number of further applications was created and impemented: The program HotspotsX calculates favorable interaction fields in protein binding pockets that can be used as a starting point for pharmacophoric models and that indicate possible directions for the optimization of lead structures. The program DSFP calculates scores based on fingerprints for given binding geometries. These fingerprints are compared with reference fingerprints that are derived from DSX interactions in known crystal structures of the particular target. Finally, the program DSX_wat was developed to predict stable water networks within a binding pocket. DSX interaction fields are used to calculate the putative water positions

DynaDom: structure-based prediction of T cell receptor inter-domain and T cell receptor-peptide-MHC (class I) association angles

Table S3. Per residue flip states using Reduce, Protoss and DynaDom comparing single domains and TCR complexes. (PDF 145 kb

DEVELOPMENT AND APPLICATIONS OF THE HINT FORCEFIELD IN PREDICTION OF ANTIBIOTIC EFFLUX AND VIRTUAL SCREENING FOR ANTIVIRALS

This work was aimed at developing novel tools that utilize HINT, an empirical forcefield capable of quantitating both hydrophobic and hydrophilic (hydropathic) interactions, for implementation in theoretical biology and drug discovery/design. The role of hydrophobicity in determination of macromolecular structure and formation of complexes in biological molecules is undeniable and has been the subject of research across several decades. Hydrophobicity is introduced, with a review of its history and contemporary theories. This is followed by a description of various methods that quantify this all-pervading phenomenon and their use in protein folding and contemporary drug design projects – including a detailed overview of the HINT forcefield. The specific aim of this dissertation is to introduce our attempts at developing new methods for use in the study of antibacterial drug resistance and antiviral drug discovery. Multidrug efflux is commonly regarded as a fast growing problem in the field of medicine. Several species of microbes are known to have developed resistance against almost all classes of antibiotics by various modes-of-action, which include multidrug transporters (a.k.a. efflux pumps). These proteins are present in both gram-positive and gram-negative bacteria and extrude molecules of various classes. They protect the efflux pump-expressing bacterium from harmful effects of exogenous agents by simply evacuating the latter. Perhaps the best characterized mechanism amongst these is that of the AcrA-AcrB-TolC efflux pump. Data is available in literature and perhaps also in proprietary databases available with pharmaceutical companies, characterizing this pump in terms of the minimum inhibitory concentration ratios (MIC ratios) for various antibiotics. We procured a curated dataset of 32 β-lactam and 12 antibiotics of other classes from this literature. Initial attempts at studying the MIC ratios of β-lactam antibiotics as a function of their three dimensional topology via 3D-quantitative structure activity relationship (3D-QSAR) technology yielded seemingly good models. However, this methodology is essentially designed to address single receptor-ligand interactions. Molecules being transported by the efflux pump must undoubtedly be involved in multiple interactions with the same. Notably, such methods require a pharmacophoric overlap of ligands prior to the generation of models, thereby limiting their applicability to a set of structurally-related compounds. Thus, we designed a novel method that takes various interactions between antibiotic agents and the AcrA-AcrB-TolC pump into account in conjunction with certain properties of the drugs. This method yielded mathematical models that are capable of predicting high/low efflux with significant efficiency (\u3e93% correct). The development of this method, along with the results from its validation, is presented herein. A parallel aim being pursued by us is to discover inhibitors for hemagglutinin-neuraminidase (HN) of human parainfluenza virus type 3 (HPIV3) by in silico screening. The basis for targeting HN is explored, along with commentary on the methodology adopted during this effort. This project yielded a moderate success rate of 34%, perhaps due to problems in the computational methodology utilized. We highlight one particular problem – that of emulating target flexibility – and explore new avenues for overcoming this obstacle in the long run. As a starting point towards enhancing the tools available to us for virtual screening in general (and for discovering antiviral compounds in specific), we explored the compatibility between sidechain rotamer libraries and the HINT scoring function. A new algorithm was designed to optimize amino acid residue sidechains, if provided with the backbone coordinates, by generating sidechain positions using the Dunbrack and Cohen backbone-dependent rotamer library and scoring them with the HINT scoring function. This rotamer library was previously used by its developers previously to design a very successful sidechain optimization algorithm called SCWRL. Output structures from our algorithm were compared with those from SCWRL and showed extraordinary similarities as well as significant differences, which are discussed herein. This successful implementation of HINT in our sidechain optimization algorithm establishes the compatibility between this forcefield and sidechain rotamer libraries. Future aims in this project include enhancement of our current algorithm and the design of a new algorithm to explore partial induced-fit in targets aimed at improving current docking methodology. This work shows significant progress towards the implementation of our hydropathic force field in theoretical modeling of biological systems in order to enhance our ability to understand atomistic details of inter- and intramolecular interactions which must form the basis for a wide variety of biological phenomena. Such efforts are key to not only to understanding the said phenomena, but also towards a solid basis for efficient drug design in the future