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Computational techniques such as structure-based virtual screening require carefully prepared 3D models of potential small-molecule ligands. Though powerful, existing commercial programs for virtual-library preparation have restrictive and/or expensive licenses. Freely available alternatives, though often effective, do not fully account for all possible ionization, tautomeric, and ring-conformational variants. We here present Gypsum-DL, a free, robust open-source program that addresses these challenges. As input, Gypsum-DL accepts virtual compound libraries in SMILES or flat SDF formats. For each molecule in the virtual library, it enumerates appropriate ionization, tautomeric, chiral, cis/trans isomeric, and ring-conformational forms. As output, Gypsum-DL produces an SDF file containing each molecular form, with 3D coordinates assigned. To demonstrate its utility, we processed 1558 molecules taken from the NCI Diversity Set VI and 56,608 molecules taken from a Distributed Drug Discovery (D3) combinatorial virtual library. We also used 4463 high-quality protein-ligand complexes from the PDBBind database to show that Gypsum-DL processing can improve virtual-screening pose prediction. Gypsum-DL is available free of charge under the terms of the Apache License, Version 2.0.

Small-molecule protonation can promote or discourage protein binding by altering hydrogen-bond, electrostatic, and van-der-Waals interactions. To improve virtual-screen pose and affinity predictions, researchers must account for all major small-molecule ionization states. But existing programs for calculating these states have notable limitations such as high cost, restrictive licenses, slow execution times, and poor modularity. Here, we present dimorphite-DL 1.0, a fast, accurate, accessible, and modular open-source program for enumerating small-molecule ionization states. Dimorphite-DL uses a straightforward empirical algorithm that leverages substructure searching and draws on a database of experimentally characterized ionizable molecules. We have tested dimorphite-DL using several versions of Python and RDKit on all major operating systems. We release it under the terms of the Apache License, Version 2.0. A copy is available free of charge from http://durrantlab.com/dimorphite-dl/ .

Studies of pathogen-host specificity, virulence, and transmissibility are critical for basic research as well as for assessing the pandemic potential of emerging infectious diseases. This is especially true for viruses such as influenza, which continue to affect millions of people annually through both seasonal and occasional pandemic events. Although the influenza virus has been fairly well studied for decades, our understanding of host-cell binding and its relation to viral transmissibility and infection is still incomplete. Assessing the binding mechanisms of complex biological systems with atomic-scale detail is challenging given current experimental limitations. Much remains to be learned, for example, about how the terminal residue of influenza-binding host-cell receptors (sialic acid) interacts with the viral surface. Here, we present an integrative structural-modeling and physics-based computational assay that reveals the sialic acid association rate constants ( k on ) to three influenza sites: the hemagglutinin (HA), neuraminidase (NA) active, and NA secondary binding sites. We developed a series of highly detailed (atomic-resolution) structural models of fully intact influenza viral envelopes. Brownian dynamics simulations of these systems showed how structural properties, such as stalk height and secondary-site binding, affect sialic acid k on values. Comparing the k on values of the three sialic acid binding sites across different viral strains suggests a detailed model of encounter-complex formation and indicates that the secondary NA binding site may play a compensatory role in host-cell receptor binding. Our method elucidates the competition among these sites, all present on the same virion, and provides a new technology for directly studying the functional balance between HA and NA., Competing Interests: The authors declare the following competing financial interest(s): REA is a cofounder of, has equity interest in, and is on the scientific advisory board of Actavalon, Inc.

Purpose: The purpose of this study was to evaluate the ability of persons with aphasia, with and without hearing loss, to complete a commonly used open-set word recognition test that requires a verbal response. Furthermore, phonotactic probabilities and neighborhood densities of word recognition errors were assessed to explore potential underlying linguistic complexities that might differentially influence performance among groups., Method: Four groups of adult participants were tested: participants with no brain injury with normal hearing, participants with no brain injury with hearing loss, participants with brain injury with aphasia and normal hearing, and participants with brain injury with aphasia and hearing loss. The Northwestern University Auditory Test No. 6 (NU-6; Tillman & Carhart, 1966) was administered. Those participants who were unable to respond orally (repeating words as heard) were assessed with the Picture Identification Task (Wilson & Antablin, 1980), permitting a picture-pointing response instead. Error patterns from the NU-6 were assessed to determine whether phonotactic probability influenced performance., Results: All participants with no brain injury and 72.7% of the participants with aphasia (24 out of 33) completed the NU-6. Furthermore, all participants who were unable to complete the NU-6 were able to complete the Picture Identification Task. There were significant group differences on NU-6 performance. The 2 groups with normal hearing had significantly higher scores than the 2 groups with hearing loss, but the 2 groups with normal hearing and the 2 groups with hearing loss did not differ from one another, implying that their performance was largely determined by hearing loss rather than by brain injury or aphasia. The neighborhood density, but not phonotactic probabilities, of the participants' errors differed across groups with and without aphasia., Conclusions: Because the vast majority of the participants with aphasia examined could be tested readily using an instrument such as the NU-6, clinicians should not be reticent to use this test if patients are able to repeat single words, but routine use of alternative tests is encouraged for populations of people with brain injuries.

Given that many antifungal medications are susceptible to evolved resistance, there is a need for novel drugs with unique mechanisms of action. Inhibiting the essential proton pump Pma1p, a P-type ATPase, is a potentially effective therapeutic approach that is orthogonal to existing treatments. We identify NSC11668 and hitachimycin as structurally distinct antifungals that inhibit yeast ScPma1p. These compounds provide new opportunities for drug discovery aimed at this important target.

Virtual molecular catalogs have limited utility if member compounds are (i) difficult to synthesize or (ii) unlikely to have biological activity. The Distributed Drug Discovery (D3) program addresses the synthesis challenge by providing scientists with a free virtual D3 catalog of 73,024 easy-to-synthesize N-acyl unnatural α-amino acids, their methyl esters, and primary amides. The remaining challenge is to document and exploit the bioactivity potential of these compounds. In the current work, a search process is described that retrospectively identifies all virtual D3 compounds classified as bioactive hits in PubChem-cataloged experimental assays. The results provide insight into the broad range of drug-target classes amenable to inhibition and/or agonism by D3-accessible molecules. To encourage computer-aided drug discovery centered on these compounds, a publicly available virtual database of D3 molecules prepared for use with popular computer docking programs is also presented., (© 2017 John Wiley & Sons A/S.)

Third-party packages have transformed the Python programming language into a powerful computational-biology tool. Package installation is easy for experienced users, but novices sometimes struggle with dependencies and compilers. This presents a barrier that can hinder the otherwise broad adoption of new tools. We present Scoria, a Python package for manipulating three-dimensional molecular data. Unlike similar packages, Scoria requires no dependencies, compilation, or system-wide installation. One can incorporate the Scoria source code directly into their own programs. But Scoria is not designed to compete with other similar packages. Rather, it complements them. Our package leverages others (e.g. NumPy, SciPy), if present, to speed and extend its own functionality. To show its utility, we use Scoria to analyze a molecular dynamics trajectory. Our FootPrint script colors the atoms of one chain by the frequency of their contacts with a second chain. We are hopeful that Scoria will be a useful tool for the computational-biology community. A copy is available for download free of charge (Apache License 2.0) at http://durrantlab.com/scoria/ . Graphical abstract .

Recent advances in cell-based, high-throughput phenotypic screening have identified new chemical compounds that are active against eukaryotic pathogens. A challenge to their future development lies in identifying these compounds' molecular targets and binding modes. In particular, subsequent structure-based chemical optimization and target-based screening require a detailed understanding of the binding event. Here, we use directed evolution and whole-genome sequencing of a drug-sensitive S. cerevisiae strain to identify the yeast ortholog of TcCyp51, lanosterol-14-alpha-demethylase (TcCyp51), as the target of MMV001239, a benzamide compound with activity against Trypanosoma cruzi, the etiological agent of Chagas disease. We show that parasites treated with MMV0001239 phenocopy parasites treated with another TcCyp51 inhibitor, posaconazole, accumulating both lanosterol and eburicol. Direct drug-protein binding of MMV0001239 was confirmed through spectrophotometric binding assays and X-ray crystallography, revealing a binding site shared with other antitrypanosomal compounds that target Cyp51. These studies provide a new probe chemotype for TcCyp51 inhibition.

Deletions in the stalk of the influenza neuraminidase (NA) surface protein are associated with increased virulence, but the mechanisms responsible for this enhanced virulence are unclear. Here we use microsecond molecular dynamics simulations to explore the effect of stalk deletion on enzymatic activity, contrasting NA proteins from the A/swine/Shandong/N1/2009 strain both with and without a stalk deletion. By modeling and simulating neuraminidase apo glycoproteins embedded in complex-mixture lipid bilayers, we show that the geometry and dynamics of the neuraminidase enzymatic pocket may differ depending on stalk length, with possible repercussions on the binding of the endogenous sialylated-oligosaccharide receptors. We also use these simulations to predict previously unrecognized druggable "hotspots" on the neuraminidase surface that may prove useful for future efforts aimed at structure-based drug design.

The spiroindolones, a new class of antimalarial medicines discovered in a cellular screen, are rendered less active by mutations in a parasite P-type ATPase, PfATP4. We show here that S. cerevisiae also acquires mutations in a gene encoding a P-type ATPase (ScPMA1) after exposure to spiroindolones and that these mutations are sufficient for resistance. KAE609 resistance mutations in ScPMA1 do not confer resistance to unrelated antimicrobials, but do confer cross sensitivity to the alkyl-lysophospholipid edelfosine, which is known to displace ScPma1p from the plasma membrane. Using an in vitro cell-free assay, we demonstrate that KAE609 directly inhibits ScPma1p ATPase activity. KAE609 also increases cytoplasmic hydrogen ion concentrations in yeast cells. Computer docking into a ScPma1p homology model identifies a binding mode that supports genetic resistance determinants and in vitro experimental structure-activity relationships in both P. falciparum and S. cerevisiae. This model also suggests a shared binding site with the dihydroisoquinolones antimalarials. Our data support a model in which KAE609 exerts its antimalarial activity by directly interfering with P-type ATPase activity.

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Purpose: The purpose of this study was to measure magnitude changes of auditory steady-state responses (ASSRs) and respective transient middle- and long-latency responses as a function of stimulus intensity and carrier frequency. The literature lacks clear consensus, including relationship to loudness., Method: A cohort of 48 adults with normal hearing was examined from a companion study (Tlumak, Durrant, & Delgado, 2015) on effects of aging. ASSRs were elicited by repeated tone-burst stimuli presented at rates of 40 and 0.75 Hz at 3 frequencies and 5 levels of stimulus intensity. The design also permitted scrutiny of any gender bias to the results., Results: Similar to derived transient response findings, ASSR magnitude (harmonic sum) systematically increased with intensity. Input-output function only at 0.75 Hz approximated a log-log linear function. However, slopes fell well below that of doubling of loudness per 10 dB SPL. Results failed to demonstrate significance as a function of carrier frequency and gender for both repetition rates., Conclusion: Effects of stimulus intensity, carrier frequency, and gender on ASSRs were similar to those of their transient counterparts. Findings remain disappointing for objective loudness estimation. Results suggest only a clear linkage to the long-latency response and the 0.75-Hz magnitude but require careful consideration of limitations/underlying mechanisms when measuring loudness-related effects.

Purpose: The purpose of the study was to objectively detect age-specific changes that occur in equivalent auditory steady-state responses (ASSRs), corresponding to transient middle- and long-latency auditory evoked potentials as a function of repetition rate and advancing age., Method: The study included 48 healthy hearing adults who were equally divided into 3 groups by age: 20-39, 40-59, and 60-79 years. ASSRs were recorded at 7 repetition rates from 40 down to 0.75 Hz, elicited by trains of repeated tone burst stimuli., Results: Temporal analysis of middle- and long-latency equivalent ASSRs revealed no appreciable changes in the magnitudes of the response across the age groups. Likewise, the spectral analysis revealed that advancing age did not substantially affect the spectral content of the response at each repetition rate. Furthermore, the harmonic sum was not significantly different across the 3 age groups, between the younger adults versus the combined Older Group Sample 1 and Sample 2, and between the two extreme age groups (i.e., 20-39 vs. 60-79) for the middle- and long-latency equivalent ASSRs., Conclusion: Advancing age has no effect on the long-latency equivalent ASSRs; however, aging does affect the middle-latency equivalent ASSRs when the mean age difference is ≥ 40 years.

The magnitude of the investment required to bring a drug to the market hinders medical progress, requiring hundreds of millions of dollars and years of research and development. Any innovation that improves the efficiency of the drug-discovery process has the potential to accelerate the delivery of new treatments to countless patients in need. "Virtual screening," wherein molecules are first tested in silico in order to prioritize compounds for subsequent experimental testing, is one such innovation. Although the traditional scoring functions used in virtual screens have proven useful, improved accuracy requires novel approaches. In the current work, we use the estrogen receptor to demonstrate that neural networks are adept at identifying structurally novel small molecules that bind to a selected drug target, ultimately allowing experimentalists to test fewer compounds in the earliest stages of lead identification while obtaining higher hit rates. We describe 39 novel estrogen-receptor ligands identified in silico with experimentally determined Ki values ranging from 460 nM to 20 μM, presented here for the first time.

Objective: Normal auditory systems appear well habituated to time/phase delays inherent to sound encoding along the hearing organ, sending frequency information non-simultaneously to the central auditory system. Eliminating, or simply perturbing, the cochlear delay might be expected to decrease speech recognition ability, especially under demanding listening conditions. Resources of a larger-scale investigation permitted a preliminary examination of this issue, particularly on a relevant timescale of empirically demonstrated cochlear delays., Design: In a randomized controlled trial study, word recognition was tested for mono-syllabic tokens treated digitally to exacerbate, if not diminish/nullify, such delays. Speech-weighted noise was used to interfere with listening to time-frequency reversed (nominally no delay) versus non-reversed (natural timing) transforms under three treatments of speech tokens: (1) original-digitally recorded; digitally processed to emphasize (2) transient versus (3) quasi-steady-state components., Study Sample: Ten normal-hearing young-adult females., Results: The findings failed to demonstrate statistically significant differences between delay conditions for any of the three speech-token treatments., Conclusions: An algorithm putatively diminishing frequency-dependent cochlear delays failed to systematically deteriorate performance in all subjects for the fixed time-frequency transform, stimulus parameters, and test materials employed. Yet, trends were evident such that some effect of perturbing cochlear delays could not be ruled out completely.

Recent outbreaks of highly pathogenic and occasional drug-resistant influenza strains have highlighted the need to develop novel anti-influenza therapeutics. Here, we report computational and experimental efforts to identify influenza neuraminidase inhibitors from among the 3000 natural compounds in the Malaysian-Plants Natural-Product (NADI) database. These 3000 compounds were first docked into the neuraminidase active site. The five plants with the largest number of top predicted ligands were selected for experimental evaluation. Twelve specific compounds isolated from these five plants were shown to inhibit neuraminidase, including two compounds with IC50 values less than 92 μM. Furthermore, four of the 12 isolated compounds had also been identified in the top 100 compounds from the virtual screen. Together, these results suggest an effective new approach for identifying bioactive plant species that will further the identification of new pharmacologically active compounds from diverse natural-product resources.

The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field., (© 2015 John Wiley & Sons A/S.)

Analysis of macromolecular/small-molecule binding pockets can provide important insights into molecular recognition and receptor dynamics. Since its release in 2011, the POVME (POcket Volume MEasurer) algorithm has been widely adopted as a simple-to-use tool for measuring and characterizing pocket volumes and shapes. We here present POVME 2.0, which is an order of magnitude faster, has improved accuracy, includes a graphical user interface, and can produce volumetric density maps for improved pocket analysis. To demonstrate the utility of the algorithm, we use it to analyze the binding pocket of RNA editing ligase 1 from the unicellular parasite Trypanosoma brucei , the etiological agent of African sleeping sickness. The POVME analysis characterizes the full dynamics of a potentially druggable transient binding pocket and so may guide future antitrypanosomal drug-discovery efforts. We are hopeful that this new version will be a useful tool for the computational- and medicinal-chemist community.

Enoyl-acyl carrier protein reductase (ENR), a critical enzyme in type II fatty acid biosynthesis, is a promising target for drug discovery against hepatocyte-stage Plasmodium falciparum. In order to identify PfENR-specific inhibitors, we docked 70 FDA-approved, bioactive, and/or natural product small molecules known to inhibit the growth of whole-cell blood-stage P. falciparum into several PfENR crystallographic structures. Subsequent in vitro activity assays identified a noncompetitive low-micromolar PfENR inhibitor, celastrol, from this set of compounds., (Copyright © 2014. Published by Elsevier Ltd.)

As ever larger and more complex biological systems are modeled in silico, approximating physiological lipid bilayers with simple planar models becomes increasingly unrealistic. In order to build accurate large-scale models of subcellular environments, models of lipid membranes with carefully considered, biologically relevant curvature will be essential. In the current work, we present a multi-scale utility called LipidWrapper capable of creating curved membrane models with geometries derived from various sources, both experimental and theoretical. To demonstrate its utility, we use LipidWrapper to examine an important mechanism of influenza virulence. A copy of the program can be downloaded free of charge under the terms of the open-source FreeBSD License from http://nbcr.ucsd.edu/lipidwrapper. LipidWrapper has been tested on all major computer operating systems.

Background: Sharing sets of chemical data (e.g., chemical properties, docking scores, etc.) among collaborators with diverse skill sets is a common task in computer-aided drug design and medicinal chemistry. The ability to associate this data with images of the relevant molecular structures greatly facilitates scientific communication. There is a need for a simple, free, open-source program that can automatically export aggregated reports of entire chemical data sets to files viewable on any computer, regardless of the operating system and without requiring the installation of additional software., Results: We here present a program called WebChem Viewer that automatically generates these types of highly portable reports. Furthermore, in designing WebChem Viewer we have also created a useful online web application for remotely generating molecular structures from SMILES strings. We encourage the direct use of this online application as well as its incorporation into other software packages., Conclusions: With these features, WebChem Viewer enables interdisciplinary collaborations that require the sharing and visualization of small molecule structures and associated sets of heterogeneous chemical data. The program is released under the FreeBSD license and can be downloaded from http://nbcr.ucsd.edu/WebChemViewer. The associated web application (called "Smiley2png 1.0") can be accessed through freely available web services provided by the National Biomedical Computation Resource at http://nbcr.ucsd.edu.

Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Objective: (1) Determine whether tuning fork material (aluminum vs stainless steel) affects Rinne testing in the clinical assessment of conductive hearing loss (CHL). (2) Determine the relative acoustic and mechanical outputs of 512-Hz tuning forks made of aluminum and stainless steel., Study Design: Prospective, observational., Setting: Outpatient otology clinic., Subjects and Methods: Fifty subjects presenting May 2011 to May 2012 with negative or equivocal Rinne in at least 1 ear and same-day audiometry. Rinne test results using aluminum and steel forks were compared and correlated with the audiometric air-bone gap. Bench top measurements using sound-level meter, microphone, and artificial mastoid., Results: Patients with CHL were more likely to produce a negative Rinne test with a steel fork than with an aluminum fork. Logistic regression revealed that the probability of a negative Rinne reached 50% at a 19 dB air-bone gap for stainless steel versus 27 dB with aluminum. Bench top testing revealed that steel forks demonstrate, in effect, more comparable air and bone conduction efficiencies while aluminum forks have relatively lower bone conduction efficiency., Conclusion: We have found that steel tuning forks can detect a lesser air-bone gap compared to aluminum tuning forks. This is substantiated by observations of clear differences in the relative acoustic versus mechanical outputs of steel and aluminum forks, reflecting underlying inevitable differences in acoustic versus mechanical impedances of these devices, and thus efficiency of coupling sound/vibratory energy to the auditory system. These findings have clinical implications for using tuning forks to determine candidacy for stapes surgery.

We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.

We here present an improved version of AutoGrow (version 3.0), an evolutionary algorithm that works in conjunction with existing open-source software to automatically optimize candidate ligands for predicted binding affinity and other druglike properties. Though no substitute for the medicinal chemist, AutoGrow 3.0, unlike its predecessors, attempts to introduce some chemical intuition into the automated optimization process. AutoGrow 3.0 uses the rules of click chemistry to guide optimization, greatly enhancing synthesizability. Additionally, the program discards any growing ligand whose physical and chemical properties are not druglike. By carefully crafting chemically feasible druglike molecules, we hope that AutoGrow 3.0 will help supplement the chemist's efforts. To demonstrate the utility of the program, we use AutoGrow 3.0 to generate predicted inhibitors of three important drug targets: Trypanosoma brucei RNA editing ligase 1, peroxisome proliferator-activated receptor γ, and dihydrofolate reductase. In all cases, AutoGrow generates druglike molecules with high predicted binding affinities. AutoGrow 3.0 is available free of charge (http://autogrow.ucsd.edu) under the terms of the GNU General Public License and has been tested on Linux and Mac OS X., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: Individuals with phenylketonuria (PKU) can exhibit deficits in executive functions, intelligence, attention, visual-spatial processing abilities, and efficiency stemming presumably from deficient neurotransmitter synthesis, even when the disorder is diagnosed and treated early. Basic audiological and electrophysiological evaluations were used to examine the peripheral and central auditory pathways of children with early-treated PKU, who followed adequate versus inadequate diets. Results were compared with those of age-matched children without PKU., Design: The control group included 35 children aged 5 to 16 years, and the experimental group included 25 children with classic PKU, all of whom were diagnosed and treated early. The experimental group comprised children of two subgroups, divided according to their dietary control, as follows: 8 children aged 6 to 13 years with adequate diets (group A); 17 children ages 6 to 15 years demonstrating inadequate diets (group B). All participants underwent auditory evaluations, including otoscopy, pure-tone and speech audiometry, immittance testing (tympanometry and assessment of contralateral stapedial reflex thresholds), and evaluations of auditory brainstem and middle latency responses. The demographic variables and audiological examination results were analyzed for all groups. Results among groups and subgroups were compared using analysis of variance with repeated measures to test statistical significance across factors and measures at the p = 0.05 level., Results: Audiometric evaluation revealed results within normal limits for all groups, except for one child from the inadequate diet group, who showed a mild bilateral conductive hearing loss. Results of speech and immittance audiometry suggested the children in group B as having poorer average speech-discrimination scores and higher stapedial reflex thresholds at 4000 Hz. Auditory brainstem response results revealed longer average latencies for waves III and V and greater interaural differences for wave V in group B, although both groups A and B showed longer average latencies for the interpeak interval I-V, compared with the control group. Middle latency responses showed no significant differences in the latencies of the Na and Pa waves or the Na-Pa amplitude for the experimental group, but electrode or ear effects were present in 87.5% of group A and 58.8% of group B., Conclusions: Overall, differences observed tended to be small (by clinical diagnostic standards), yet suggest that children with PKU exhibit some effect of this disorder on the pontine auditory pathway, even when diagnosed/treated early and independent of the appropriateness of diet. It thus seems prudent to follow PKU children with auditory processing assessments to evaluate functional implications of these findings.

The protozoan parasite Trypanosoma cruzi, the etiological agent of Chagas' disease, affects millions of individuals and continues to be an important global health concern. The poor efficacy and unfavorable side effects of current treatments necessitate novel therapeutics. Cruzain, the major cysteine protease of T. cruzi, is one potential novel target. Recent advances in a class of vinyl sulfone inhibitors are encouraging; however, as most potential therapeutics fail in clinical trials and both disease progression and resistance call for combination therapy with several drugs, the identification of additional classes of inhibitory molecules is essential. Using an exhaustive virtual-screening and experimental validation approach, we identify several additional small-molecule cruzain inhibitors. Further optimization of these chemical scaffolds could lead to the development of novel drugs useful in the treatment of Chagas' disease., (© 2012 John Wiley & Sons A/S.)

One common practice in drug discovery is to optimize known or suspected ligands in order to improve binding affinity. In performing these optimizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by altering certain chemical moieties of known/endogenous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically 'swapping' the chemical moieties of known ligands to generate novel ligands with potentially improved potency. To address this need, we have created a novel algorithm called 'LigMerge'. LigMerge identifies the maximum (largest) common substructure of two three-dimensional ligand models, superimposes these two substructures, and then systematically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demonstrate the utility of LigMerge, we identify compounds predicted to inhibit peroxisome proliferator-activated receptor gamma, HIV reverse transcriptase, and dihydrofolate reductase with affinities higher than those of known ligands. We hope that LigMerge will be a helpful tool for the drug design community., (© 2012 John Wiley & Sons A/S.)

During the past century, several epidemics of human African trypanosomiasis, a deadly disease caused by the protist Trypanosoma brucei, have afflicted sub-Saharan Africa. Over 10 000 new victims are reported each year, with hundreds of thousands more at risk. As current drug treatments are either highly toxic or ineffective, novel trypanocides are urgently needed. The T. brucei galactose synthesis pathway is one potential therapeutic target. Although galactose is essential for T. brucei survival, the parasite lacks the transporters required to intake galactose from the environment. UDP-galactose 4'-epimerase (TbGalE) is responsible for the epimerization of UDP-glucose to UDP-galactose and is therefore of great interest to medicinal chemists. Using molecular dynamics simulations, we investigate the atomistic motions of TbGalE in both the apo and holo states. The sampled conformations and protein dynamics depend not only on the presence of a UDP-sugar ligand, but also on the chirality of the UDP-sugar C4 atom. This dependence provides important insights into TbGalE function and may help guide future computer-aided drug discovery efforts targeting this protein., (© 2012 John Wiley & Sons A/S.)

Objective: Auditory steady-state responses (ASSR) evoked by recurrent brief tones were assessed over a wide range of stimulus repetition rates apropos the traditionally measured obligatory, transient, auditory evoked potentials. Repetition rates of ≤ 10 Hz have received little attention in the context of the ASSR stimulus-response analysis approach, speculated to provide technical advantages/additional information over more traditional transient stimulus-response paradigms., Design: Magnitudes were measured at repetition rates from 0.75 to 80 Hz, using trains of repeated tone-burst stimuli., Study Sample: Twelve normal-hearing children and a reference sample of 25 young adults., Results: Results show that response magnitudes were significantly larger in children than adults at repetition rates of ≤ 5 Hz. Magnitudes were largest at the two lowest repetition rates, following the trends expected from the transient auditory evoked potential (AEP) literature. The harmonic sum is proposed as a more appropriate measure of response magnitude than amplitude of the fundamental alone., Conclusions: The analysis methods used in this paper may give information that will have applications for clinical testing. Of pragmatic importance is that the stimulus rate profile could be determined without subjective wave identification and/or interpretation, and thus by a method that is inherently more objective than conventional AEP analysis.

Objective: Auditory steady-state responses (ASSR) evoked by recurrent tones were assessed over a wide range of stimulus repetition rates embracing well the traditionally measured transient AEPs. Repetition rates of ≤ 10 Hz have received little attention in the context of the ASSR stimulus-response analysis approach which is speculated to provide technical advantages, if not additional/supplemental information, over more traditional transient stimulus-response paradigms., Design: Magnitudes were measured at repetition rates from 0.75 to 80 Hz, using trains of recurrent tone-burst stimuli., Study Sample: Twenty-five normal-hearing adults during sleep and awake., Results: Results show that response magnitudes for adults tested during sleep were significantly larger than those for adults while awake at repetition rates <5 Hz. Magnitudes were largest at the two lowest repetition rates, as expected from corresponding results obtained using conventional methods., Conclusions: The analysis methods used in this paper may give information that will have applications for clinical testing. Results confirm and extend knowledge of the effects of repetition rate on AEPs over a range embracing the gamut of responses as traditionally classified, specifically at the beginning stages of natural sleep.

Academic researchers and many in industry often lack the financial resources available to scientists working in "big pharma." High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu.

NNScore is a neural-network-based scoring function designed to aid the computational identification of small-molecule ligands. While the test cases included in the original NNScore article demonstrated the utility of the program, the application examples were limited. The purpose of the current work is to further confirm that neural-network scoring functions are effective, even when compared to the scoring functions of state-of-the-art docking programs, such as AutoDock, the most commonly cited program, and AutoDock Vina, thought to be two orders of magnitude faster. Aside from providing additional validation of the original NNScore function, we here present a second neural-network scoring function, NNScore 2.0. NNScore 2.0 considers many more binding characteristics when predicting affinity than does the original NNScore. The network output of NNScore 2.0 also differs from that of NNScore 1.0; rather than a binary classification of ligand potency, NNScore 2.0 provides a single estimate of the pK(d). To facilitate use, NNScore 2.0 has been implemented as an open-source python script. A copy can be obtained from http://www.nbcr.net/software/nnscore/ .

In the current work, we present a hydrogen-bond analysis of 2673 ligand-receptor complexes that suggests the total number of hydrogen bonds formed between a ligand and its receptor is a poor predictor of ligand potency; furthermore, even that poor prediction does not suggest a statistically significant correlation between hydrogen-bond formation and potency. While we are not the first to suggest that hydrogen bonds on average do not generally contribute to ligand binding affinities, this additional evidence is nevertheless interesting. The primary role of hydrogen bonds may instead be to ensure specificity, to correctly position the ligand within the active site, and to hold the protein active site in a ligand-friendly conformation. We also present a new computer program called HBonanza (hydrogen-bond analyzer) that aids the analysis and visualization of hydrogen-bond networks. HBonanza, which can be used to analyze single structures or the many structures of a molecular dynamics trajectory, is open source and python implemented, making it easily editable, customizable, and platform independent. Unlike many other freely available hydrogen-bond analysis tools, HBonanza provides not only a text-based table describing the hydrogen-bond network, but also a Tcl script to facilitate visualization in VMD, a popular molecular visualization program. Visualization in other programs is also possible. A copy of HBonanza can be obtained free of charge from http://www.nbcr.net/hbonanza., (Copyright © 2011 Elsevier Inc. All rights reserved.)

This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.

We present a novel algorithm called CrystalDock that analyzes a molecular pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound experimental structures. Germane fragments from the crystallographic or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, respectively. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.

The relaxed complex scheme, a virtual-screening methodology that accounts for protein receptor flexibility, was used to identify a low-micromolar, non-bisphosphonate inhibitor of farnesyl diphosphate synthase. Serendipitously, we also found that several predicted farnesyl diphosphate synthase inhibitors were low-micromolar inhibitors of undecaprenyl diphosphate synthase. These results are of interest because farnesyl diphosphate synthase inhibitors are being pursued as both anti-infective and anticancer agents, and undecaprenyl diphosphate synthase inhibitors are antibacterial drug leads., (© 2011 John Wiley & Sons A/S.)

Background: Trypanosoma brucei (T. brucei) is an infectious agent for which drug development has been largely neglected. We here use a recently developed computer program called AutoGrow to add interacting molecular fragments to S5, a known inhibitor of the validated T. brucei drug target RNA editing ligase 1, in order to improve its predicted binding affinity., Results: The proposed binding modes of the resulting compounds mimic that of ATP, the native substrate, and provide insights into novel protein-ligand interactions that may be exploited in future drug-discovery projects., Conclusions: We are hopeful that these new predicted inhibitors will aid medicinal chemists in developing novel therapeutics to fight human African trypanosomiasis.

Matrix metalloproteinases are zinc-containing enzymes capable of degrading all components of the extracellular matrix. Owing to their role in human disease, matrix metalloproteinase have been the subject of extensive study. A bioinorganic approach was recently used to identify novel inhibitors based on a maltol zinc-binding group, but accompanying molecular-docking studies failed to explain why one of these inhibitors, AM-6, had approximately 2500-fold selectivity for MMP-3 over MMP-2. A number of studies have suggested that the matrix-metalloproteinase active site is highly flexible, leading some to speculate that differences in active-site flexibility may explain inhibitor selectivity. To extend the bioinorganic approach in a way that accounts for MMP-2 and MMP-3 dynamics, we here investigate the predicted binding modes and energies of AM-6 docked into multiple structures extracted from matrix-metalloproteinase molecular dynamics simulations. Our findings suggest that accounting for protein dynamics is essential for the accurate prediction of binding affinity and selectivity. Additionally, AM-6 and other similar inhibitors likely select for and stabilize only a subpopulation of all matrix-metalloproteinase conformations sampled by the apo protein. Consequently, when attempting to predict ligand affinity and selectivity using an ensemble of protein structures, it may be wise to disregard protein conformations that cannot accommodate the ligand., (© 2011 John Wiley & Sons A/S.)

Objective: Quasi-steady-state responses were assessed over a wide range of stimulus repetition rates embracing well the traditionally measured transient AEPs (obligatory auditory evoked potentials of all latencies). Repetition rates of ≤10 Hz have received little attention in the context of the ASSR stimulus-response analysis approach which is speculated to provide technical advantages, if not additional information, over more traditional transient stimulus-response paradigms., Design: A measure introduced and defined as the sum of the response at the stimulus frequency and its harmonics. The magnitude of steady-state responses were measured at repetition rates from 0.75 to 80 Hz, using trains of repeated tone-burst stimuli., Study Sample: Twenty-five normal-hearing adults., Results: Results show that the magnitudes of the response across repetition rates are largest at the two lowest rates, following trends expected from the transient AEP literature. Good reliability overall was observed for the harmonic sum., Conclusions: The analysis methods used in this paper may give information that will have application for clinical testing. Of pragmatic importance is that the rate profile could be determined without subjective wave identification and/or interpretation, and thus by a method that is inherently more objective than conventional AEP tests.

Undecaprenyl pyrophosphate synthase is a cis-prenyltransferase enzyme, which is required for cell wall biosynthesis in bacteria. Undecaprenyl pyrophosphate synthase is an attractive target for antimicrobial therapy. We performed long molecular dynamics simulations and docking studies on undecaprenyl pyrophosphate synthase to investigate its dynamic behavior and the influence of protein flexibility on the design of undecaprenyl pyrophosphate synthase inhibitors. We also describe the first X-ray crystallographic structure of Escherichia coli apo-undecaprenyl pyrophosphate synthase. The molecular dynamics simulations indicate that undecaprenyl pyrophosphate synthase is a highly flexible protein, with mobile binding pockets in the active site. By carrying out docking studies with experimentally validated undecaprenyl pyrophosphate synthase inhibitors using high- and low-populated conformational states extracted from the molecular dynamics simulations, we show that structurally dissimilar compounds can bind preferentially to different and rarely sampled conformational states. By performing structural analyses on the newly obtained apo-undecaprenyl pyrophosphate synthase and other crystal structures previously published, we show that the changes observed during the molecular dynamics simulation are very similar to those seen in the crystal structures obtained in the presence or absence of ligands. We believe that this is the first time that a rare 'expanded pocket' state, key to drug design and verified by crystallography, has been extracted from a molecular dynamics simulation., (© 2011 John Wiley & Sons A/S.)

Computational chemists and structural biologists are often interested in characterizing ligand-receptor complexes for hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions in order to assess ligand binding. When done by hand, this characterization can become tedious, especially when many complexes need be analyzed. In order to facilitate the characterization of ligand binding, we here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer), which is freely available for download at http://www.nbcr.net/binana/. To demonstrate the utility of the new algorithm, we use BINANA to confirm that the number of hydrophobic contacts between a ligand and its protein receptor is positively correlated with ligand potency. Additionally, we show how BINANA can be used to search through a large ligand-receptor database to identify those complexes that are remarkable for selected binding features, and to identify lead candidates from a virtual screen with specific, desirable binding characteristics. We are hopeful that BINANA will be useful to computational chemists and structural biologists who wish to automatically characterize many ligand-receptor complexes for key binding characteristics., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Researchers engaged in computer-aided drug design often wish to measure the volume of a ligand-binding pocket in order to predict pharmacology. We have recently developed a simple algorithm, called POVME (POcket Volume MEasurer), for this purpose. POVME is Python implemented, fast, and freely available. To demonstrate its utility, we use the new algorithm to study three members of the matrix-metalloproteinase family of proteins. Despite the structural similarity of these proteins, differences in binding-pocket dynamics are easily identified., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Protein flexibility plays a critical role in ligand binding to both orthosteric and allosteric sites. We here review some of the computer-aided drug-design techniques currently used to account for protein flexibility, ranging from methods that probe local receptor flexibility in the region of the protein immediately adjacent to the binding site, to those that account for general flexibility in all protein regions., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein-ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The scoring function presented here, used either on its own or in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.

Background: Neglected tropical diseases, including diseases caused by trypanosomatid parasites such as Trypanosoma brucei, cost tens of millions of disability-adjusted life-years annually. As the current treatments for African trypanosomiasis and other similar infections are limited, new therapeutics are urgently needed. RNA Editing Ligase 1 (REL1), a protein unique to trypanosomes and other kinetoplastids, was identified recently as a potential drug target., Methodology/principal Findings: Motivated by the urgent need for novel trypanocidal therapeutics, we use an ensemble-based virtual-screening approach to discover new naphthalene-based TbREL1 inhibitors. The predicted binding modes of the active compounds are evaluated within the context of the flexible receptor model and combined with computational fragment mapping to determine the most likely binding mechanisms. Ultimately, four new low-micromolar inhibitors are presented. Three of the four compounds may bind to a newly revealed cleft that represents a putative druggable site not evident in any crystal structure., Conclusions/significance: Pending additional optimization, the compounds presented here may serve as precursors for future novel therapies useful in the fight against several trypanosomatid pathogens, including human African trypanosomiasis, a devastating disease that afflicts the vulnerable patient populations of sub-Saharan Africa.

Trypanosoma brucei, the causative agent of human African trypanosomiasis, affects tens of thousands of sub-Saharan Africans. As current therapeutics are inadequate due to toxic side effects, drug resistance, and limited effectiveness, novel therapies are urgently needed. UDP-galactose 4'-epimerase (TbGalE), an enzyme of the Leloir pathway of galactose metabolism, is one promising T. brucei drug target. We here use the relaxed complex scheme, an advanced computer-docking methodology that accounts for full protein flexibility, to identify inhibitors of TbGalE. An initial hit rate of 62% was obtained at 100 microM, ultimately leading to the identification of 14 low-micromolar inhibitors. Thirteen of these inhibitors belong to a distinct series with a conserved binding motif that may prove useful in future drug design and optimization.

Chagas disease, caused by the unicellular parasite Trypanosoma cruzi, claims 50,000 lives annually and is the leading cause of infectious myocarditis in the world. As current antichagastic therapies like nifurtimox and benznidazole are highly toxic, ineffective at parasite eradication, and subject to increasing resistance, novel therapeutics are urgently needed. Cruzain, the major cysteine protease of Trypanosoma cruzi, is one attractive drug target. In the current work, molecular dynamics simulations and a sequence alignment of a non-redundant, unbiased set of peptidase C1 family members are used to identify uncharacterized cruzain binding sites. The two sites identified may serve as targets for future pharmacological intervention.

Pandemic (H1N1) influenza poses an imminent threat. Nations have stockpiled inhibitors of the influenza protein neuraminidase in hopes of protecting their citizens, but drug-resistant strains have already emerged, and novel therapeutics are urgently needed. In the current work, the computer program AutoGrow is used to generate novel predicted neuraminidase inhibitors. Given the great flexibility of the neuraminidase active site, protein dynamics are also incorporated into the computer-aided drug-design process. Several potential inhibitors are identified that are predicted to bind to neuraminidase better than currently approved drugs., (Copyright 2010 Elsevier Ltd. All rights reserved.)