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In multiple sclerosis (MS) the circulating metabolome is dysregulated, with indole lactate (ILA) being one of the most significantly reduced metabolites. We demonstrate that oral supplementation of ILA impacts key MS disease processes in two preclinical models. ILA reduces neuroinflammation by dampening immune cell activation as well as infiltration; and promotes remyelination and in vitro oligodendrocyte differentiation through the aryl hydrocarbon receptor (AhR). Supplementation of ILA, a reductive indole metabolite, restores the gut microbiome's oxidative/reductive metabolic balance by lowering circulating indole acetate (IAA), an oxidative indole metabolite, that blocks remyelination and oligodendrocyte maturation. The ILA-induced reduction in circulating IAA is linked to changes in IAA-producing gut microbiota taxa and pathways that are also dysregulated in MS. Notably, a lower ILA:IAA ratio correlates with worse MS outcomes. Overall, these findings identify ILA as a potential anti-inflammatory remyelinating agent and provide insights into the role of gut dysbiosis-related metabolic alterations in MS progression., Competing Interests: Competing interests: The authors declare that they have no competing interests in relation to this work, except for a pending patent (PCT Application No. PCT/US24/27239) concerning the application of ILA as a therapeutic agent for multiple sclerosis. The patent applicant is Johns Hopkins University, and the authors of the patent are Pavan Bhargava, Kathryn Fitzgerlad, Peter Calabresi and Michael Kornberg. This patent is currently under review.

Motivation: Large language models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains such as biomedicine. Solutions such as pretraining and domain-specific fine-tuning add substantial computational overhead, requiring further domain-expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.5-Turbo, and GPT-4, to generate meaningful biomedical text rooted in established knowledge., Results: Compared to the existing RAG technique for Knowledge Graphs, the proposed method utilizes minimal graph schema for context extraction and uses embedding methods for context pruning. This optimization in context extraction results in more than 50% reduction in token consumption without compromising the accuracy, making a cost-effective and robust RAG implementation on proprietary LLMs. KG-RAG consistently enhanced the performance of LLMs across diverse biomedical prompts by generating responses rooted in established knowledge, accompanied by accurate provenance and statistical evidence (if available) to substantiate the claims. Further benchmarking on human curated datasets, such as biomedical true/false and multiple-choice questions (MCQ), showed a remarkable 71% boost in the performance of the Llama-2 model on the challenging MCQ dataset, demonstrating the framework's capacity to empower open-source models with fewer parameters for domain-specific questions. Furthermore, KG-RAG enhanced the performance of proprietary GPT models, such as GPT-3.5 and GPT-4. In summary, the proposed framework combines explicit and implicit knowledge of KG and LLM in a token optimized fashion, thus enhancing the adaptability of general-purpose LLMs to tackle domain-specific questions in a cost-effective fashion., Availability and Implementation: SPOKE KG can be accessed at https://spoke.rbvi.ucsf.edu/neighborhood.html. It can also be accessed using REST-API (https://spoke.rbvi.ucsf.edu/swagger/). KG-RAG code is made available at https://github.com/BaranziniLab/KG_RAG. Biomedical benchmark datasets used in this study are made available to the research community in the same GitHub repository., (© The Author(s) 2024. Published by Oxford University Press.)

Neuron-specific enolase (NSE) is one of the biomarkers used as an indicator of brain disorder, but since it is also found in blood cell components, there is a concern that a spurious increase in NSE may occur after cardiovascular surgery, where cardiopulmonary bypass (CPB) causes hemolysis. In the present study, we investigated the relationship between the degree of hemolysis and NSE after cardiovascular surgery and the usefulness of immediate postoperative NSE values in the diagnosis of brain disorder. A retrospective study of 198 patients who underwent surgery with CPB in the period from May 2019 to May 2021 was conducted. Postoperative NSE levels and Free hemoglobin (F-Hb) levels were compared in both groups. In addition, to verify the relationship between hemolysis and NSE, we examined the correlation between F-Hb levels and NSE levels. We also examined whether different surgical procedures could produce an association between hemolysis and NSE. Among 198 patients, 20 had postoperative stroke (Group S) and 178 had no postoperative stroke (Group U). There was no significant difference in postoperative NSE levels and F-Hb levels between Group S and Group U (p = 0.264, p = 0.064 respectively). F-Hb and NSE were weakly correlated (r = 0.29. p < 0.01). In conclusion, NSE level immediately after cardiac surgery with CPB is modified by hemolysis rather than brain injury, therefore it would be unreliable as a biomarker of brain disorder., (© 2023. The Author(s).)

Purpose: Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence., Methods: In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes., Results: Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization., Conclusions: The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE., (© 2024 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses., (© 2024. The Author(s).)

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3 -related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system., Competing Interests: JF receives additional funding from the Rady Children’s Institute for Genomic Medicine, and her spouse is Founder and Principal of Friedman Bioventure. JH receives grant/contract support (paid to institution) from: Pfizer; Novartis; Janssen; BMS; and Gilead. PJM receives grant/research support from: AbbVie; Amgen; Bristol Myers Squibb; Eli Lilly; Galapagos; Gilead; Janssen; Novartis; Pfizer; Sun Pharma; and UCB. PJM also serves as a consultant at: AbbVie; Acelyrin; Aclaris; Amgen; Boehringer Ingelheim; Bristol Myers Squibb; Eli Lilly; Galapagos; Gilead; GlaxoSmithKline; Inmagene; Janssen; Pfizer; Moonlake Pharma; Novartis; Sun Pharma; and UCB. In addition, PJM receives speakers’ bureau fees from: AbbVie; Amgen; Eli Lilly; Janssen; Novartis; Pfizer; and Union Chimique Belge. SHS is supported by the National Institute on Aging, Intramural Research Program. All other primary authors have no conflicts of interest to declare., (© The Author(s) 2023.)

Introduction: Early diagnosis of Parkinson's disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR)., Methods: To predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group., Results: The classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification., Discussion: The proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence., Competing Interests: SEB is cofounder and holds shares in MATE Bioservices, a company that commercializes uses of SPOKE knowledge graph. CAN holds shares of MATE Bioservices. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Soman, Nelson, Cerono, Goldman, Baranzini and Brown.)

Motivation: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information., Results: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts., Availability and Implementation: The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2023. Published by Oxford University Press.)

Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia. Delayed diagnosis and overpriced tests are the prime reason for the high mortality rate, especially in underdeveloped countries. A time and cost-efficient diagnosis tool: Chest X-rays, was thus accepted as the standard diagnostic test for pediatric pneumonia. However, the lower radiation levels for diagnosis in children make the task much more onerous and time-consuming. The mentioned challenges initiate the need for a computer-aided detection model that is instantaneous and accurate. Our work proposes a stacked ensemble learning of deep learning-based features for pediatric pneumonia classification. The extracted features from the global average pooling layer of the fine-tuned Xception model pretrained on ImageNet weights are sent to the Kernel Principal Component Analysis for dimensionality reduction. The dimensionally reduced features are further trained and validated on the stacking classifier. The stacking classifier consists of two stages; the first stage uses the Random-Forest classifier, K-Nearest Neighbors, Logistic Regression, XGB classifier, Support Vector Classifier (SVC), Nu-SVC, and MLP classifier. The second stage operates on Logistic Regression using the first stage predictions for the final classification with Stratified K-fold cross-validation to prevent overfitting. The model was tested on the publicly available pediatric pneumonia dataset, achieving an accuracy of 98.3%, precision of 99.29%, recall of 98.36%, F1-score of 98.83%, and an AUC score of 98.24%. The performance shows its reliability for real-time deployment in assisting radiologists and physicians., Competing Interests: Conflicts of interestThe authors declare no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Meaningful representations of clinical data using embedding vectors is a pivotal step to invoke any machine learning (ML) algorithm for data inference. In this article, we propose a time-aware embedding approach of electronic health records onto a biomedical knowledge graph for creating machine readable patient representations. This approach not only captures the temporal dynamics of patient clinical trajectories, but also enriches it with additional biological information from the knowledge graph. To gauge the predictivity of this approach, we propose an ML pipeline called TANDEM (Temporal and Non-temporal Dynamics Embedded Model) and apply it on the early detection of Parkinson's disease. TANDEM results in a classification AUC score of 0.85 on unseen test dataset. These predictions are further explained by providing a biological insight using the knowledge graph. Taken together, we show that temporal embeddings of clinical data could be a meaningful predictive representation for downstream ML pipelines in clinical decision-making.

Pediatric pneumonia has drawn immense awareness due to the high mortality rates over recent years. The acute respiratory infection caused by bacteria, viruses, or fungi infects the lung region and hinders oxygen transport, making breathing difficult due to inflamed or pus and fluid-filled alveoli. Being non-invasive and painless, chest X-rays are the most common modality for pediatric pneumonia diagnosis. However, the low radiation levels for diagnosis in children make accurate detection challenging. This challenge initiates the need for an unerring computer-aided diagnosis model. Our work proposes Contrast Limited Adaptive Histogram Equalization for image enhancement and a stacking classifier based on the fusion of deep learning-based features for pediatric pneumonia diagnosis. The extracted features from the global average pooling layers of the fine-tuned MobileNet, DenseNet121, DenseNet169, and DenseNet201 are concatenated for the final classification using a stacked ensemble classifier. The stacking classifier uses Support Vector Classifier, Nu-SVC, Logistic Regression, K-Nearest Neighbor, Random Forest Classifier, Gaussian Naïve Bayes, AdaBoost classifier, Bagging Classifier, and Extra-trees Classifier for the first stage, and Nu-SVC as the meta-classifier. The stacking classifier validated using Stratified K-Fold cross-validation achieves an accuracy of 98.62%, precision of 98.99%, recall of 99.53%, F1 score of 99.26%, and an AUC score of 93.17% on the publicly available pediatric pneumonia dataset. We expect this model to greatly help the real-time diagnosis of pediatric pneumonia., Competing Interests: Conflicts of interest/competing interestsThe authors declare no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science., (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article we describe concrete uses of SPOKE, an open knowledge network that connects curated information from 37 specialized and human-curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID-19 research and chronic disease diagnosis and management.

A novel approach of preprocessing EEG signals by generating spectrum image for effective Convolutional Neural Network (CNN) based classification for Motor Imaginary (MI) recognition is proposed. The approach involves extracting the Variational Mode Decomposition (VMD) modes of EEG signals, from which the Short Time Fourier Transform (STFT) of all the modes are arranged to form EEG spectrum images. The EEG spectrum images generated are provided as input image to CNN. The two generic CNN architectures for MI classification (EEGNet and DeepConvNet) and the architectures for pattern recognition (AlexNet and LeNet) are used in this study. Among the four architectures, EEGNet provides average accuracies of 91.37%, 94.41%, 85.67% and 90.21% for the four datasets used to validate the proposed approach. Consistently better results in comparison with results in recent literature demonstrate that the EEG spectrum image generation using VMD-STFT is a promising method for the time frequency analysis of EEG signals., Competing Interests: Conflict of interestAll authors declare that they have no conflict of interest., (© Korean Society of Medical and Biological Engineering 2021.)

Segmentation of fundamental heart sounds-S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This pa-per proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© Korean Society of Medical and Biological Engineering 2019.)

Neurons in the dorsal pathway of the visual cortex are thought to be involved in motion processing. The first site of motion processing is the primary visual cortex (V1), encoding the direction of motion in local receptive fields, with higher order motion processing happening in the middle temporal area (MT). Complex motion properties like optic flow are processed in higher cortical areas of the Medial Superior Temporal area (MST). In this study, a hierarchical neural field network model of motion processing is presented. The model architecture has an input layer followed by either one or cascade of two neural fields (NF): the first of these, NF1, represents V1, while the second, NF2, represents MT. A special feature of the model is that lateral connections used in the neural fields are trained by asymmetric Hebbian learning, imparting to the neural field the ability to process sequential information in motion stimuli. The model was trained using various traditional moving patterns such as bars, squares, gratings, plaids, and random dot stimulus. In the case of bar stimuli, the model had only a single NF, the neurons of which developed a direction map of the moving bar stimuli. Training a network with two NFs on moving square and moving plaids stimuli, we show that, while the neurons in NF1 respond to the direction of the component (such as gratings and edges) motion, the neurons in NF2 (analogous to MT) responding to the direction of the pattern (plaids, square object) motion. In the third study, a network with 2 NFs was simulated using random dot stimuli (RDS) with translational motion, and show that the NF2 neurons can encode the direction of the concurrent dot motion (also called translational flow motion), independent of the dot configuration. This translational RDS flow motion is decoded by a simple perceptron network (a layer above NF2) with an accuracy of 100% on train set and 90% on the test set, thereby demonstrating that the proposed network can generalize to new dot configurations. Also, the response properties of the model on different input stimuli closely resembled many of the known features of the neurons found in electrophysiological studies.

Grid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern is originally considered to be independent of the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al., 2015) and in connected environments (Carpenter et al., 2015). Aforementioned experimental results demonstrated the dependence of grid cell activity on environmental geometry. Analysis of grid cell periodicity on practically infinite variations of environmental geometry imposes a limitation on the experimental study. Hence we analyze the dependence of grid cell periodicity on the environmental geometry purely from a computational point of view. We use a hierarchical oscillatory network model where velocity inputs are presented to a layer of Head Direction cells, outputs of which are projected to a Path Integration layer. The Lateral Anti-Hebbian Network (LAHN) is used to perform feature extraction from the Path Integration neurons thereby producing a spectrum of spatial cell responses. We simulated the model in five types of environmental geometries such as: (1) connected environments, (2) convex shapes, (3) concave shapes, (4) regular polygons with varying number of sides, and (5) transforming environment. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model encode not just the local position but also more global information like the shape of the environment. Furthermore, the model is able to capture the invariant attributes of the physical space ingrained in its LAHN layer, thereby revealing its ability to classify an environment using this information. The proposed model is interesting not only because it is able to capture the experimental results but, more importantly, it is able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity and suggesting the possibility of grid cells encoding the invariant properties of an environment.

Grid cells and place cells are believed to be cellular substrates for the spatial navigation functions of hippocampus as experimental animals physically navigated in 2D and 3D spaces. However, a recent saccade study on head fixated monkey has also reported grid-like representations on saccadic trajectory while the animal scanned the images on a computer screen. We present two computational models that explain the formation of grid patterns on saccadic trajectory formed on the novel Images. The first model named Saccade Velocity Driven Oscillatory Network -Direct PCA (SVDON-DPCA) explains how grid patterns can be generated on saccadic space using Principal Component Analysis (PCA) like learning rule. The model adopts a hierarchical architecture. We extend this to a network model viz. Saccade Velocity Driven Oscillatory Network-Network PCA (SVDON-NPCA) where the direct PCA stage is replaced by a neural network that can implement PCA using a neurally plausible algorithm. This gives the leverage to study the formation of grid cells at a network level. Saccade trajectory for both models is generated based on an attention model which attends to the salient location by computing the saliency maps of the images. Both models capture the spatial characteristics of grid cells such as grid scale variation on the dorso-ventral axis of Medial Entorhinal cortex. Adding one more layer of LAHN over the SVDON-NPCA model predicts the Place cells in saccadic space, which are yet to be discovered experimentally. To the best of our knowledge, this is the first attempt to model grid cells and place cells from saccade trajectory.

Three-dimensional (3D) spatial cells in the mammalian hippocampal formation are believed to support the existence of 3D cognitive maps. Modeling studies are crucial to comprehend the neural principles governing the formation of these maps, yet to date very few have addressed this topic in 3D space. Here we present a hierarchical network model for the formation of 3D spatial cells using anti-Hebbian network. Built on empirical data, the model accounts for the natural emergence of 3D place, border, and grid cells, as well as a new type of previously undescribed spatial cell type which we call plane cells. It further explains the plausible reason behind the place and grid-cell anisotropic coding that has been observed in rodents and the potential discrepancy with the predicted periodic coding during 3D volumetric navigation. Lastly, it provides evidence for the importance of unsupervised learning rules in guiding the formation of higher-dimensional cognitive maps.

Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, their universal computational properties have not been explored much unlike in the case of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. If neuronal ensembles are taken to be the basic functional units of brain dynamics, it is desirable to develop oscillator-based models that can explain a wide variety of neural phenomena. Autoencoders are a special type of feed forward networks that have been used for construction of large-scale deep networks. Although autoencoders based on rate-coded and spiking neuron networks have been proposed, there are no autoencoders based on oscillators. We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals.

Spatial cells in the hippocampal complex play a pivotal role in the navigation of an animal. Exact neural principles behind these spatial cell responses have not been completely unraveled yet. Here we present two models for spatial cells, namely the Velocity Driven Oscillatory Network (VDON) and Locomotor Driven Oscillatory Network. Both models have basically three stages in common such as direction encoding stage, path integration (PI) stage, and a stage of unsupervised learning of PI values. In the first model, the following three stages are implemented: head direction layer, frequency modulation by a layer of oscillatory neurons, and an unsupervised stage that extracts the principal components from the oscillator outputs. In the second model, a refined version of the first model, the stages are extraction of velocity representation from the locomotor input, frequency modulation by a layer of oscillators, and two cascaded unsupervised stages consisting of the lateral anti-hebbian network. The principal component stage of VDON exhibits grid cell-like spatially periodic responses including hexagonal firing fields. Locomotor Driven Oscillatory Network shows the emergence of spatially periodic grid cells and periodically active border-like cells in its lower layer; place cell responses are found in its higher layer. This model shows the inheritance of phase precession from grid cell to place cell in both one- and two-dimensional spaces. It also shows a novel result on the influence of locomotion rhythms on the grid cell activity. The study thus presents a comprehensive, unifying hierarchical model for hippocampal spatial cells., (© 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Invasive pulmonary aspergillosis (IPA) is an opportunistic fungal infection in patients undergoing chemotherapy for hematological malignancy, hematopoietic stem cell transplant, or other forms of immunosuppression. In this group, Aspergillus infections account for the majority of deaths due to mold pathogens. Although early detection is associated with improved outcomes, current diagnostic regimens lack sensitivity and specificity. Patients undergoing chemotherapy, stem cell transplantation and lung transplantation were enrolled in a multi-site prospective observational trial. Proven and probable IPA cases and matched controls were subjected to discovery proteomics analyses using a biofluid analysis platform, fractionating plasma into reproducible protein and peptide pools. From 556 spots identified by 2D gel electrophoresis, 66 differentially expressed post-translationally modified plasma proteins were identified in the leukemic subgroup only. This protein group was rich in complement components, acute-phase reactants and coagulation factors. Low molecular weight peptides corresponding to abundant plasma proteins were identified. A candidate marker panel of host response (9 plasma proteins, 4 peptides), fungal polysaccharides (galactomannan), and cell wall components (β-D glucan) were selected by statistical filtering for patients with leukemia as a primary underlying diagnosis. Quantitative measurements were developed to qualify the differential expression of the candidate host response proteins using selective reaction monitoring mass spectrometry assays, and then applied to a separate cohort of 57 patients with leukemia. In this verification cohort, a machine learning ensemble-based algorithm, generalized pathseeker (GPS) produced a greater case classification accuracy than galactomannan (GM) or host proteins alone. In conclusion, Integration of host response proteins with GM improves the diagnostic detection of probable IPA in patients undergoing treatment for hematologic malignancy. Upon further validation, early detection of probable IPA in leukemia treatment will provide opportunities for earlier interventions and interventional clinical trials.

The Cochin Backwaters in India are part of the Vembanad-Kol system, which is a protected wetland and one of the largest estuarine ecosystems in South Asia. The backwaters are a major supplier of fisheries resources and are developed as tourist destination. Periyar River discharges into the northern arm of the system and receives effluents from chemical, petrochemical and metal processing industries which release huge amounts of wastewaters after little treatment. We investigated water and sediment contamination in the industrial vicinity and at one station further away including organic and inorganic contaminants. In total 83 organic contaminants were found, e.g. well known priority pollutants such as endosulfan, hexachlorobenzene, DDT, hexachlorocyclohexane and their metabolites, which likely stem from the industrial manufacturing of organochlorine pesticides. Furthermore, several benzothiazole, dibenzylamine and dicyclohexylamine derivatives were detected, which indicated inputs from rubber producing facilities. Several of these compounds have not been reported as environmental contaminants so far. A comparison of organic contaminant and trace hazardous element concentrations in sediments with reported sediment quality guidelines revealed that adverse effects on benthic species are likely at all stations. The chemical assessment was combined with an investigation of macrobenthic diversity and community composition. Benthic organisms were completely lacking at the site with the highest trace hazardous element concentrations. Highest species numbers, diversity indices and abundances were recorded at the station with the greatest distance to the industrial area. Filter feeders were nearly completely lacking, probably leading to an impairment of the filter function in this area. This study shows that a combination of chemical and biological methods is an innovative approach to achieve a comprehensive characterization of industrial contamination, to evaluate associated risks for bottom dwelling consumers regarding sediment quality guidelines, and to observe related adverse effects on the benthic community directly in the field., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Secondary dengue viral infection can produce capillary leakage associated with increased mortality known as dengue hemorrhagic fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, nine cytokines, and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject's gender, clinical parameters, two cytokines, and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on eight discriminant features with an area under the receiver operator curve (AUC) of 0.999. Model analysis indicated that the feature-outcome relationship were nonlinear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches., (© 2012 Wiley Periodicals, Inc.)

Cysteinyl S-nitrosylation has emerged as an important post-translational modification affecting protein function in health and disease. Great emphasis has been placed on global, unbiased quantification of S-nitrosylated proteins because of physiologic and oxidative stimuli. However, current strategies have been hampered by sample loss and altered protein electrophoretic mobility. Here, we describe a novel quantitative approach that uses accurate, sensitive fluorescence modification of cysteine S-nitrosylation that leaves electrophoretic mobility unaffected (SNOFlo) and introduce unique concepts for measuring changes in S-nitrosylation status relative to protein abundance. Its efficacy in defining the functional S-nitrosoproteome is demonstrated in two diverse biological applications: an in vivo rat hypoxia-ischemia/reperfusion model and antimicrobial S-nitrosoglutathione-driven transnitrosylation of an enteric microbial pathogen. The suitability of this approach for investigating endogenous S-nitrosylation is further demonstrated using Ingenuity Pathways analysis that identified nervous system and cellular development networks as the top two networks. Functional analysis of differentially S-nitrosylated proteins indicated their involvement in apoptosis, branching morphogenesis of axons, cortical neurons, and sympathetic neurites, neurogenesis, and calcium signaling. Major abundance changes were also observed for fibrillar proteins known to be stress-responsive in neurons and glia. Thus, both examples demonstrate the technique's power in confirming the widespread involvement of S-nitrosylation in hypoxia-ischemia/reperfusion injury and in antimicrobial host responses.

We propose a new approach for calculating the three-dimensional (3D) structure of a protein from distance and dihedral angle constraints derived from experimental data. We suggest that such constraints can be obtained from experiments such as tritium planigraphy, chemical or enzymatic cleavage of the polypeptide chain, paramagnetic perturbation of nuclear magnetic resonance (NMR) spectra, measurement of hydrogen-exchange rates, mutational studies, mass spectrometry, and electron paramagnetic resonance. These can be supplemented with constraints from theoretical prediction of secondary structures and of buried/exposed residues. We report here distance geometry calculations to generate the structures of a test protein Staphylococcal nuclease (STN), and the HIV-1 rev protein (REV) of unknown structure. From the available 3D atomic coordinates of STN, we set up simulated data sets consisting of varying number and quality of constraints, and used our group's Self Correcting Distance Geometry (SECODG) program DIAMOD to generate structures. We could generate the correct tertiary fold from qualitative (approximate) as well as precise distance constraints. The root mean square deviations of backbone atoms from the native structure were in the range of 2.0 A to 8.3 A, depending on the number of constraints used. We could also generate the correct fold starting from a subset of atoms that are on the surface and those that are buried. When we used data sets containing a small fraction of incorrect distance constraints, the SECODG technique was able to detect and correct them. In the case of REV, we used a combination of constraints obtained from mutagenic data and structure predictions. DIAMOD generated helix-loop-helix models, which, after four self-correcting cycles, populated one family exclusively. The features of the energy-minimized model are consistent with the available data on REV-RNA interaction. Our method could thus be an attractive alternative for calculating protein 3D structures, especially in cases where the traditional methods of X-ray crystallography and multidimensional NMR spectroscopy have been unsuccessful.

The Jun a 3 protein from mountain cedar (Juniperus ashei) pollen, a member of group 5 of the family of plant pathogenesis-related proteins (PR-proteins), reacts with serum IgE from patients with cedar hypersensitivity. We used the crystal structures of two other proteins of this group, thaumatin and an antifungal protein from tobacco, both approximately 50% identical in sequence to Jun a 3, as templates to build homology models for the allergen. The in-house programs EXDIS and FANTOM were used to extract distance and dihedral angle constraints from the Protein Data Bank files and determine energy-minimized structures. The mean backbone deviations for the energy-refined model structures from either of the templates is <1 A, their conformational energies are low, and their stereochemical properties (determined with PROCHECK) are acceptable. The circular dichroism spectrum of Jun a 3 is consistent with the postulated beta-sheet core. Tryptic fragments of Jun a 3 that reacted with IgE from allergic patients all mapped to one helical/loop surface of the models. The Jun a 3 models have features common to aerosol allergens from completely different protein families, suggesting that tertiary structural elements may mediate the triggering of an allergic response.

APOBEC1 is the catalytic subunit of an enzyme complex that mediates apolipoprotein (apo) B mRNA editing. It dimerizes in vitro and requires complementation factor(s) for its editing activity. We have performed a systematic analysis of the structure-functional relationship of APOBEC1 by targeted mutagenesis of various sequence motifs within the protein. Using in vitro RNA editing assay, we found that basic amino acid clusters at the amino-terminal region R15R16R17 and R33K34, are essential for apoB mRNA editing. Mutation of R15R16R17 to K15K16K17 and mutation of R33K34 simultaneously to A33A34 almost completely abolished in vitro editing activity. The carboxy-terminal region of APOBEC1 contains a leucine-rich motif. Deletion analysis of this region indicates that residues 181 to 210 are important for in vitro apoB mRNA editing. Single amino acid substitutions demonstrate that L182, I185, and L189 are important residues required for normal editing function. Furthermore, the double mutant P190A/P191A also lost >90% of editing activity which suggests that a beta turn in this region of the molecule may be essential for proper functioning of APOBEC1. It was suggested that dimerization of APOBEC1 creates an active structure for deamination of apoB mRNA. When we examined the dimerization potential of truncated APOBEC1s using both amino and carboxy termini deletion mutants, we found that amino-terminal deletions up to residue A117 did not impair dimerization activity whereas carboxy-terminal deletions showed diminished dimerization. The systematic and extensive mutagenesis experiments in this study provide information on the role of various sequence motifs identified in APOBEC1 in enzyme catalysis and dimerization.

Tryptic proteolysis of the small GTPases Rab4 and Rab5 is a multi-step, nucleotide-dependent process. Using N-terminal peptide sequencing, matrix-assisted laser desorption ionization-time-of-flight MS and molecular modelling, we identified the three initial sites of proteolysis in Rab5 as Arg-4, Arg-81 and Arg-197. Arg-4 and Arg-81 lie within regions previously implicated in Rab5 endocytic function, and Arg-197 lies in a region involved in membrane targeting. Topologically, Arg-81 lies within the conformationally variable Switch II region shown to be important for protein-protein interactions of other GTPases. Homology modelling studies on Rab5 indicate that the Arg-81 side chain is buried in the Rab5 GTP conformation, but is solvent-accessible in the GDP conformation, explaining the dependence of proteolysis on nucleotides. Peptide mapping of Rab4 was performed to take advantage of additional scissile bonds within Switch II to determine more precisely the limits of the nucleotide-dependent protease-accessible region. The Rab4 cleavage sites corresponded to Arg-81 and Pro-87 of Rab5, and taken together with the finding that Rab5 was not cleaved at Arg-91 this analysis defines an eight-residue surface-exposed conformationally variable region lying in the centre of Switch II. A sequence comparison of Rab proteins shows these eight residues to have a loosely conserved motif that we term Switch II(v) for its relative variability. C-terminal to Switch II(v) is a highly conserved Rab-specific YYRGA motif that we term Switch II(c) for its constant sequence. N-terminal to Switch II(v) is a sequence-invariant G-domain involved in nucleotide binding and hydrolysis. We propose that the Rab Switch II(v) region imparts specificity to nucleotide-dependent protein-protein interactions.

A conserved proline-rich motif (PRM) in the cytoplasmic domain of cytokine receptors has been suggested to be a signaling switch regulated by the action of the FK506 binding protein (FKBP) family of peptidylprolyl isomerases (O'Neal KD, Yu-Lee LY, Shearer WT, 1995, Ann NY Acad Sci 766:282-284). We have docked the prolactin receptor PRM (Ile1-Phe2-Pro3-Pro4-Val5-Pro6-Gly7-Pro8) to the ligand binding site of FKBP12. The procedure involved conformational search restricted by NMR restraints (O'Neal KD et al., 1996, Biochem J 315:833-844), energy minimization of the octapeptide conformers so obtained, template-based docking of a selected conformer to FKBP12, and energy refinement of the resulting complex. The template used was the crystal structure of a cyclic FK506-peptide hybrid bound to FKBP12. Val5-Pro6 of the PRM was taken to be the biologically relevant Xaa-Pro bond. The docked conformer is stabilized by two intramolecular hydrogen bonds, N7H7-->O4 and N2H2-->O8, and two intermolecular ones, Ile56; N-H-->O = C:Pro6 and Tyr82:O-H-->O = C:Gly7. This conformer features a Type I beta-turn and has extensive hydrophobic contacts with the FKBP12 binding surface. The observed interactions support the hypothesis that FKBP12 catalyzes cis-trans isomerization in the PRM when it is part of the longer cytoplasmic domain of a cytokine receptor, and suggest a significant role for the PRM in signal transduction.

Changes in the chemical structure of the tetraethylammonium (TEA) ion reduce binding affinity at the external TEA receptor of outwardly rectifying potassium channels. To study the mechanism of selective binding, we applied a variety of hydrophilic quaternary ammonium (QA) ions to the noninactivating mutant of Shaker B T449Y, to Kv3.1, and to Kv3.1 mutants, expressed in Xenopus oocytes. In outside-out patches, QA ions in which ethyl groups of TEA were replaced by methyl groups had a lower affinity than TEA, whereas changes in binding affinity were minor when propyl groups were substituted for ethyl groups. All channels tested showed this pattern. Changes in free energy of binding correlated well with changes in the computed free energy of hydration of the TEA derivatives that we used. The affinity for TEA derivatives was reduced in Kv3.1Y407T, which is in support of the hypothesis that cation pi-electron interaction is involved. Binding affinities of QA ions were higher in Kv3.1 Y407F than in the wild-type, suggesting that the hydroxyl groups of the tyrosines reduce QA binding. The rank order of potency of the QA ions toward the different channels studied was the same. These results indicate that external QA ions bind strongly to hydrophobic pi-electron-rich functions. The selectivity, however, is determined by the physical properties of the QA ion.

Tetraethylammonium (TEA) is thought to be the most effective quaternary ammonium (QA) ion blocker at the external site of K+ channels, and small changes to the TEA ion reduce its potency. To examine the properties of the external QA receptor, we applied a variety of QA ions to excised patches from human embryonic kidney cells or Xenopus oocytes transfected with the delayed rectifying K+ channels Kv 2.1 and Kv 3.1. In outside-out patches of Kv 3.1, the relative potencies were TEA > tetrapropylammonium (TPA) > tetrabutylammonium (TBA). In contrast to Kv 3.1, the relative potencies in Kv 2.1 were TBA > TEA > TPA. In Kv 3.1 and Kv 2.1, external tetrapentylammonium (TPeA) blocked K+ currents in a fast, reversible and, in contrast to TEA, time-dependent manner. The external binding of TPeA appeared to be voltage independent, unlike the effects of TPeA applied to inside-out patches. External n-alkyl-triethylammonium compounds (C8, C10 chain length) had a lower affinity than TEA in Kv 3.1, but a higher affinity than TEA in Kv 2.1. In Kv 3.1, the decrease in QA affinity was large when one or two methyl groups were substituted for ethyl groups in TEA, but minor when propyl groups replaced ethyl groups. Changes in the free energy of binding could be correlated to changes in the free energy of hydration of TEA derivatives calculated by continuum methodology. These results reveal a substantial hydrophobic component of external QA ion binding to Kv 2.1, and to a lesser degree to Kv 3.1, in addition to the generally accepted electrostatic interactions. The chain length of hydrophobic TEA derivatives affects the affinity for the hydrophobic binding site, whereas the hydropathy of QA ions determines the electrostatic interaction energy.

The 'H5' segment located between the putative fifth and sixth transmembrane helices is the most highly conserved region in voltage-gated potassium channels and it is believed to constitute a major part of the ion conduction path (pore). Here we present a two-step procedure, comprising secondary structure prediction and hydrophobic moment profiling, to predict the structure of this important region. Combined results from the application of the procedure to the H5 region of four voltage-gated and five other K+ channel sequences lead to the prediction of a beta-strand-turn-beta-strand structure for H5. The reasons for the application of these soluble protein methods to parts of membrane proteins are: (i) that pore-lining residues are accessible to water and (ii) that a large enough database of high-resolution membrane protein structures does not yet exist. The results are compared with experimental results, in particular spectroscopic studies of two peptides based on the H5 sequence of SHAKER potassium channel. The procedure developed here may be applicable to water-accessible regions of other membrane proteins.

We have carried out a nanosecond molecular dynamics simulation of an analogue of the ribonuclease C-peptide in water. The overall conformation has an extended region for the first three amino acids connected to an alpha-helix for residues 4-13, and this basic structure is preserved throughout the simulation, with helical hydrogen bonds present 87% of the time, on average. The final helical hydrogen bond is spontaneously broken and re-formed several times, providing a detailed picture of such winding/unwinding events. The simulation was used to estimate the effects of internal motion on proton nuclear Overhauser effect spectroscopy (NOESY) intensities for several classes of important cross peaks. Within the helical regions, the effects of internal motion vary only a little from one residue to another for backbone-backbone cross peaks, and the relevant correlation functions reach plateau values within about 50 ps. The spectral simulations show, however, that it may be difficult to establish a close quantitative connection between NOESY cross-peak volumes and measures of helical content.

Conformational studies have been carried out on the X-cis-Pro tripeptide system (a system of three linked peptide units, in the trans-cis-trans configuration) using energy minimization techniques. For X, residues Gly, L-Ala, D-Ala and L-Pro have been used. The energy minima have been classified into different groups based upon the conformational similarity. There are 15, 20, 18 and 6 minima that are possible for the four cases respectively and these fall into 11 different groups. A study of these minima shows that, (i) some minima contain hydrogen bonds--either 4-->1 or 1-->2 type, (ii) the low energy minima qualify themselves as bend conformations, (iii) cis' and trans' conformations are possible for the prolyl residue as also the C gamma-endo and C gamma-exo puckerings, and (iv) for Pro-cis-Pro, cis' at the first prolyl residue is ruled out, due to the high energy. The available crystal structure data on proteins and peptides, containing cis-Pro segment have been examined with a view to find the minima that occur in solid state. The data from protein show that they fall under two groups. The conformation at X in X-cis-Pro is near extended when it is a non-glycyl residue. In both peptides and proteins there exists a preference for trans' conformation at prolyl residue over cis' when X is a non-glycyl residue. The minima obtained can be useful in modelling studies.

We describe a 1 ns molecular dynamics simulation of an 18-residue peptide (corresponding to a portion of the H helix of myoglobin) in water. The initial helical conformation progressively frays to a more disordered structure, with the loss of internal secondary structure generally proceeding from the C-terminus toward the N-terminus. Although a variety of mechanisms are involved in the breaking of helical hydrogen bonds, the formation of transient turn structures, with i----i + 3 hydrogen bonds, and bifurcated hydrogen-bond structures intermediate between alpha and turn or 3(10) structures is a common motif. In some cases a single water molecule is inserted into an internal hydrogen bond, but it is also common to have several water molecules involved in transient intermediates. Overall, the results provide new information about the detailed mechanisms by which helices are made and broken in aqueous solution.

A simple algorithm has been developed to detect beta-bends and 'loops'-chain reversals containing five amino acid residues, using only coordinates of C alpha-atoms from crystal structure data of globular proteins using the above algorithm. Analysis of bends have showed that the total number of bends in each protein (TB) is linearly related to total number of non-hydrophobic residues in that protein which in turn is related linearly to total number of amino acid residues. Secondly, we found that a large number of consecutive bends occur in each protein which give rise to on an average only three independent residues per turn. Positional preference of amino acid residues in chain reversals is stressed. Consideration of pairs of amino acid residues in positions (i + 1) and (i + 2) of bends seems to provide a more reliable basis for predicting chain reversals in proteins.

Cow and rat trypsin differ in net charge by 12.5 units yet have the same enzymatic mechanism. The role of electrical potentials in the catalytic mechanism of these trypsin isozymes is investigated by using the finite difference Poisson-Boltzmann method. The calculations reveal that the active sites are effectively shielded from surface charge, thus making it possible for the two enzymes to have essentially identical potentials in their catalytically important regions. The potentials in both active sites are dominated by local interactions arising both from partial charges and from the negative charge on Asp-102. The latter is found to stabilize the transition state by about 4 kcal/mol, a value that is consistent with the extent of reduced catalytic activity in the variant Asn-102 trypsin, in which the negative charge is absent. The calculations predict that Asp-102 is ionized and that His-57 is neutral in the resting state of the enzyme. In contrast to their negligible effect on catalytic activity, the cumulative effect of surface charges is found to raise the pK of the N-terminal alpha-amino group of Ile-16 in the rat enzyme by about 1.5 units relative to that of cow trypsin. This charged amino acid forms an ionic bond with Asp-194, which stabilizes the active conformation of the enzyme. An increase in pK of Ile-16 thus provides a possible explanation for the retention of activity of rat trypsin at high pH. The results of this study could not have been obtained from an electrostatic model based on Coulombic potentials.

A structure consisting of the polyproline-II or collagen-like helix immediately succeeded by a beta-turn is seen in several synthetic peptides and has been suggested to be the conformational requirement for proline hydroxylation in nascent procollagen. Using a simple algorithm for detecting secondary structures, we have analysed crystal structure data on 40 globular proteins and have found eight examples of the collagen-helix + beta-turn supersecondary structure in 15 proteins that contain the collagen-like helical segments.

The three-dimensional solution structure of a zinc finger nucleic acid binding motif has been determined by nuclear magnetic resonance (NMR) spectroscopy. Spectra of a synthetic peptide corresponding to a single zinc finger from the Xenopus protein Xfin yielded distance and dihedral angle constraints that were used to generate structures from distance geometry and restrained molecular dynamics calculations. The zinc finger is an independently folded domain with a compact globular structure in which the zinc atom is bound by two cysteine and two histidine ligands. The polypeptide backbone fold consists of a well-defined helix, starting as alpha and ending as 3(10) helix, packed against two beta strands that are arranged in a hairpin structure. A high density of basic and polar amino acid side chains on the exposed face of the helix are probably involved in DNA binding.

The occurrence of an eight-residue long segment of polypeptide chain in collagen helical conformation has been detected in bacteriochlorophyll a-protein by the application of an algorithm for identifying secondary structures in globular proteins from their alpha-carbon positions. This segment spans residues 277 to 284 of the protein and is the longest known stretch of collagen helix to be observed in globular proteins.

A new algorithm has been developed for identifying helices, extended structures, and bends from the positions of the alpha-carbon atoms using the virtual bond approach. The parameters used are two virtual bond angles (delta 1 and delta 2), the virtual dihedral angle (theta), and the distance (D) between the terminal alpha-carbon atoms of the tripeptide. The criteria for classification have been worked out by model building as well as from proteins whose complete secondary structures are known. These criteria are as follows: (i) magnitude of theta less than or equal to 60 degrees and (delta 1 + delta 2) less than or equal to 230 degrees for a bend, (ii) for a helix, successive thetas should not differ by more than 30 degrees, and (iii) for an extended structure, the cumulative deviation of the above parameters should not vary by more than 20% from the ideal extended chain. The method developed has been applied successfully to three proteins wherein the coordinates of alpha-carbon atoms alone are known and a complete mapping of the secondary structures has now been obtained. One interesting observation is that the percentage of residues not taking part in helices, extended structures, and bends is very small--of the order of 4%.