Using Attention-UNet Models to Predict Protein Contact Maps.

Proteins are essential to life, and understanding their intrinsic roles requires determining their structure. The field of proteomics has opened up new opportunities by applying deep learning algorithms to large databases of solved protein structures. With the availability of large data sets and advanced machine learning methods, the prediction of protein residue interactions has greatly improved. Protein contact maps provide empirical evidence of the interacting residue pairs within a protein sequence. Template-free protein structure prediction systems rely heavily on this information. This article proposes UNet-CON, an attention-integrated UNet architecture, trained to predict residue-residue contacts in protein sequences. With the predicted contacts being more accurate than state-of-the-art methods on the PDB25 test set, the model paves the way for the development of more powerful deep learning algorithms for predicting protein residue interactions.

DeepBindPPI: Protein-Protein Binding Site Prediction Using Attention Based Graph Convolutional Network.

Due to the importance of protein-protein interactions in defence mechanism of living body, attempts were made to investigate its attributes, including, but not limited to, binding affinity, and binding region. Contemporary strategies for binding site prediction largely resort to deep learning techniques but turned out to be low precision models. As laboratory experiments for drug discovery tasks utilize this information, increased false positives devalue the computational methods. This emphasize the need to develop enhanced strategies. DeepBindPPI employs deep learning technique to predict the binding regions of proteins, particularly antigen-antibody interaction sites. The results obtained are applied in a docking environment to confirm their correctness. An integration of graph convolutional network with attention mechanism predicts interacting amino acids with improved precision. The model learns the determining factors in interaction from a general pool of proteins and is then fine-tuned using antigen-antibody data. Comparison of the proposed method with existing techniques shows that the developed model has comparable performance. The use of a separate spatial network clearly improved the precision of the proposed method from 0.4 to 0.5. An attempt to utilize the interface information for docking using the HDOCK server gives promising results, with high-quality structures appearing in the top10 ranks.

Fan beam CT image synthesis from cone beam CT image using nested residual UNet based conditional generative adversarial network.

A radiotherapy technique called Image-Guided Radiation Therapy adopts frequent imaging throughout a treatment session. Fan Beam Computed Tomography (FBCT) based planning followed by Cone Beam Computed Tomography (CBCT) based radiation delivery drastically improved the treatment accuracy. Furtherance in terms of radiation exposure and cost can be achieved if FBCT could be replaced with CBCT. This paper proposes a Conditional Generative Adversarial Network (CGAN) for CBCT-to-FBCT synthesis. Specifically, a new architecture called Nested Residual UNet (NR-UNet) is introduced as the generator of the CGAN. A composite loss function, which comprises adversarial loss, Mean Squared Error (MSE), and Gradient Difference Loss (GDL), is used with the generator. The CGAN utilises the inter-slice dependency in the input by taking three consecutive CBCT slices to generate an FBCT slice. The model is trained using Head-and-Neck (H&N) FBCT-CBCT images of 53 cancer patients. The synthetic images exhibited a Peak Signal-to-Noise Ratio of 34.04±0.93 dB, Structural Similarity Index Measure of 0.9751±0.001 and a Mean Absolute Error of 14.81±4.70 HU. On average, the proposed model guarantees an improvement in Contrast-to-Noise Ratio four times better than the input CBCT images. The model also minimised the MSE and alleviated blurriness. Compared to the CBCT-based plan, the synthetic image results in a treatment plan closer to the FBCT-based plan. The three-slice to single-slice translation captures the three-dimensional contextual information in the input. Besides, it withstands the computational complexity associated with a three-dimensional image synthesis model. Furthermore, the results demonstrate that the proposed model is superior to the state-of-the-art methods.

Prescribing patterns of low vision devices in patients with cone-related dystrophies.

Purpose: The aim of this study was to elucidate the type of low vision devices (LVDs) prescribed for patients with cone dystrophy, cone-rod dystrophy, and rod-cone dystrophy and to analyze the visual improvement with the devices. Methods: A retrospective review of 300 electronic medical records of patients with cone dystrophy, cone-rod dystrophy, and rod-cone dystrophy referred to the low vision care (LVC) clinic for the first time between 2014 and 2016 at a tertiary eye care center was done. Collected data included the demographic profile of patients, details of LVDs, and best-corrected vision. Results: Out of 300 patients, 62.6% (n = 188) were male and 37.3% (n = 112) were female. Of the cases, 50% (n = 150) had cone-rod dystrophy, 45% (n = 135) had cone dystrophy, and 5% (n = 15) had rod-cone dystrophy. The most commonly prescribed LVD was SEE-TV binocular telescope (n = 6, 2.0%) for distance and dome magnifier (n = 60, 20%) for near. ET-40 dark grey tint (20.6%) was preferred for managing photophobia. There was a statistically significant difference in both distance and near visual acuities with LVDs (P < 0.05) in all categories, except rod-cone dystrophy. Conclusion: Early diagnosis with appropriate prescription of LVDs including tints helps in achieving good quality of vision in patients with cone-related dystrophies.

Ligand Based Virtual Screening Using Self-organizing Maps.

Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly time-consuming and requires high capital for facilitation. Virtual screening, a computational technique used to reduce this search space and identify lead molecules, can speed up the drug discovery process. This paper proposes a ligand-based virtual screening method using an artificial neural network called self-organizing map (SOM). The proposed work uses two SOMs to predict the active and inactive molecules separately. This SOM based technique can uniquely label a small molecule as active, inactive, and undefined as well. This can reduce the number of false positives in the screening process and improve recall; compared to support vector machine and random forest based models. Additionally, by exploiting the parallelism present in the learning and classification phases of a SOM, a graphics processing unit (GPU) based model yields much better execution time. The proposed GPU-based SOM tool can successfully evaluate a large number of molecules in training and screening phases. The source code of the implementation and related files are available at https://github.com/jayarajpbalakrishnan/2_SOM_SCREEN.

ThoraxNet: a 3D U-Net based two-stage framework for OAR segmentation on thoracic CT images.

An important phase of radiation treatment planning is the accurate contouring of the organs at risk (OAR), which is necessary for the dose distribution calculation. The manual contouring approach currently used in clinical practice is tedious, time-consuming, and prone to inter and intra-observer variation. Therefore, a deep learning-based auto contouring tool can solve these issues by accurately delineating OARs on the computed tomography (CT) images. This paper proposes a two-stage deep learning-based segmentation model with an attention mechanism that automatically delineates OARs in thoracic CT images. After preprocessing the input CT volume, a 3D U-Net architecture will locate each organ to generate cropped images for the segmentation network. Next, two differently configured U-Net-based networks will perform the segmentation of large organs-left lung, right lung, heart, and small organs-esophagus and spinal cord, respectively. A post-processing step integrates all the individually-segmented organs to generate the final result. The suggested model outperformed the state-of-the-art approaches in terms of dice similarity coefficient (DSC) values for the lungs and the heart. It is worth mentioning that the proposed model achieved a dice score of 0.941, which is 1.1% higher than the best previous dice score, in the case of the heart, an important organ in the human body. Moreover, the clinical acceptance of the results is verified using dosimetric analysis. To delineate all five organs on a CT scan of size [Formula: see text], our model takes only 8.61 s. The proposed open-source automatic contouring tool can generate accurate contours in minimal time, consequently speeding up the treatment time and reducing the treatment cost.

Protein-Protein Docking: Past, Present, and Future.

The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.

FPDock: Protein-protein docking using flower pollination algorithm.

Proteins play their vital role in biological systems through interaction and complex formation with other biological molecules. Indeed, abnormalities in the interaction patterns affect the proteins' structure and have detrimental effects on living organisms. Research in structure prediction gains its gravity as the functions of proteins depend on their structures. Protein-protein docking is one of the computational methods devised to understand the interaction between proteins. Metaheuristic algorithms are promising to use owing to the hardness of the structure prediction problem. In this paper, a variant of the Flower Pollination Algorithm (FPA) is applied to get an accurate protein-protein complex structure. The algorithm begins execution from a randomly generated initial population, which gets flourished in different isolated islands, trying to find their local optimum. The abiotic and biotic pollination applied in different generations brings diversity and intensity to the solutions. Each round of pollination applies an energy-based scoring function whose value influences the choice to accept a new solution. Analysis of final predictions based on CAPRI quality criteria shows that the proposed method has a success rate of 58% in top10 ranks, which in comparison with other methods like SwarmDock, pyDock, ZDOCK is better. Source code of the work is available at: https://github.com/Sharon1989Sunny/_FPDock_.

Protein Structure Prediction: Conventional and Deep Learning Perspectives.

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein-protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.

Ligand based virtual screening using SVM on GPU.

In silico methods play an essential role in modern drug discovery methods. Virtual screening, an in silico method, is used to filter out the chemical space on which actual wet lab experiments are need to be conducted. Ligand based virtual screening is a computational strategy using which one can build a model of the target protein based on the knowledge of the ligands that bind successfully to the target. This model is then used to predict if the new molecule is likely to bind to the target. Support vector machine, a supervised learning algorithm used for classification, can be utilized for virtual screening the ligand data. When used for virtual screening purpose, SVM could produce interesting results. But since we have a huge ligand data, the time taken for training the SVM model is quite high compared to other learning algorithms. By parallelizing these algorithms on multi-core processors, one can easily expedite these discoveries. In this paper, a GPU based ligand based virtual screening tool (GpuSVMScreen) which uses SVM have been proposed and bench-marked. This data parallel virtual screening tool provides high throughput by running in short time. The proposed GpuSVMScreen can successfully screen large number of molecules (billions) also. The source code of this tool is available at http://ccc.nitc.ac.in/project/GPUSVMSCREEN.

GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

BACKGROUND: In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). RESULTS: Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. CONCLUSION: Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.

Acute oxalate nephropathy due to 'Averrhoa bilimbi' fruit juice ingestion.

Irumban puli (Averrhoa bilimbi) is commonly used as a traditional remedy in the state of Kerala. Freshly made concentrated juice has a very high oxalic acid content and consumption carries a high risk of developing acute renal failure (ARF) by deposition of calcium oxalate crystals in renal tubules. Acute oxalate nephropathy (AON) due to secondary oxalosis after consumption of Irumban puli juice is uncommon. AON due to A. bilimbi has not been reported before. We present a series of ten patients from five hospitals in the State of Kerala who developed ARF after intake of I. puli fruit juice. Seven patients needed hemodialysis whereas the other three improved with conservative management.

Epigenetic inactivation of the E-cadherin gene in eyelid sebaceous gland carcinoma.

BACKGROUND: E-cadherin and ß-catenin are crucial components of the cell-cell adhesion complex. Their loss has often been associated with tumour metastasis and poor clinical outcome. Both loss of E-cadherin at the cell membrane and a stabilizing mutation in CTNNB1 (ß-catenin gene) have been associated with ovarian, colorectal, hepatocellular and nonmelanoma skin cancer, such as squamous and basal cell carcinomas. Absence of E-cadherin may be caused by promoter hypermethylation of the E-cadherin gene (CDH1). OBJECTIVES: To determine the role of E-cadherin promoter hypermethylation and CTNNB1 gene mutation in the aggressive behaviour of sebaceous gland carcinoma of the eyelid. METHODS: Thirty-six cases of sebaceous gland carcinoma were subjected to E-cadherin methylation-specific polymerase chain reaction and mutational analysis for the CTNNB1 gene. E-cadherin and ß-catenin staining was evaluated by immunohistochemistry. Results were correlated with the clinicopathological features of sebaceous gland carcinoma. RESULTS: nMethylation of the E-cadherin promoter region was detected in 72% of eyelid sebaceous gland carcinoma cases and loss of E-cadherin immunostaining in 83%. E-cadherin promoter hypermethylation showed a significant association with the loss of membranous E-cadherin (P = 0·038) and it was of borderline significance with reduced disease-free survival (P = 0·05). It was also found to be associated with advanced age (73%), tumour size ≥ 2 cm (77%), orbital invasion (83%), lymph node metastasis (60%), tumour recurrence (60%) and poor histological differentiation (90%). DNA sequencing revealed no stabilizing ß-catenin gene mutation in sebaceous gland carcinoma. Loss of membranous ß-catenin was observed in 61% cases, which associated significantly with both E-cadherin promoter methylation (P = 0·0262) and loss of E-cadherin membranous localization (P=0·0015). CONCLUSION: Epigenetic inactivation of the E-cadherin gene causes loss of membrane-bound E-cadherin and could contribute to the reduced disease-free survival in eyelid sebaceous gland carcinoma. Mutations in the ß-catenin gene do not seem to be involved in the pathogenesis of eyelid sebaceous gland carcinoma.

Analgesic and antipyretic effects of Sansevieria trifasciata leaves.

The ethanol and water extracts of Sansevieria trifasciata leaves showed dose-dependent and significant (P < 0.05) increase in pain threshold in tail-immersion test. Moreover, both the extracts (100 - 200 mg/kg) exhibited a dose-dependent inhibition of writhing and also showed a significant (P < 0.001) inhibition of both phases of the formalin pain test. The ethanol extract (200 mg/kg) significantly (P < 0.01) reversed yeast-induced fever. Preliminary phytochemical screening of the extracts showed the presence of alkaloids, flavonoids, saponins, glycosides, terpenoids, tannins, proteins and carbohydrates.

The JNK pathway regulates the In vivo deletion of immature CD4(+)CD8(+) thymocytes.

The extracellular signal-regulated kinase (ERK), the c-Jun NH2-terminal kinase (JNK), and p38 MAP kinase pathways are triggered upon ligation of the antigen-specific T cell receptor (TCR). During the development of T cells in the thymus, the ERK pathway is required for differentiation of CD4(-)CD8(-) into CD4(+)CD8(+) double positive (DP) thymocytes, positive selection of DP cells, and their maturation into CD4(+) cells. However, the ERK pathway is not required for negative selection. Here, we show that JNK is activated in DP thymocytes in vivo in response to signals that initiate negative selection. The activation of JNK in these cells appears to be mediated by the MAP kinase kinase MKK7 since high levels of MKK7 and low levels of Sek-1/MKK4 gene expression were detected in thymocytes. Using dominant negative JNK transgenic mice, we show that inhibition of the JNK pathway reduces the in vivo deletion of DP thymocytes. In addition, the increased resistance of DP thymocytes to cell death in these mice produces an accelerated reconstitution of normal thymic populations upon in vivo DP elimination. Together, these data indicate that the JNK pathway contributes to the deletion of DP thymocytes by apoptosis in response to TCR-derived and other thymic environment- mediated signals.