Parallel implementation of AutoDock

Computational docking of ligands to protein structures is a key step in structure-based drug design. Currently, the time required for each docking run is high and thus limits the use of docking in a high-throughput manner, warranting parallelization of docking algorithms. AutoDock, a widely used tool, has been chosen for parallelization. Near-linear increases in speed were observed with 96 processors, reducing the time required for docking ligands to HIV-protease from 81 min, as an example, on a single IBM Power-5 processor ( 1.65 GHz), to about 1 min on an IBM cluster, with 96 such processors. This implementation would make it feasible to perform virtual ligand screening using AutoDock.

Photoresist functionalisation method for high-density protein microarrays using photolithography

Since the last decade, there is a growing need for patterned biomolecules for various applications ranging from diagnostic devices to enabling fundamental biological studies with high throughput. Protein arrays facilitate the study of protein-protein, protein-drug or protein-DNA interactions as well as highly multiplexed immunosensors based on antibody-antigen recognition. Protein microarrays are typically fabricated using piezoelectric inkjet printing with resolution limit of similar to 70-100 mu m limiting the array density. A considerable amount of research has been done on patterning biomolecules using customised biocompatible photoresists. Here, a simple photolithographic process for fabricating protein microarrays on a commercially available diazo-naphthoquinone-novolac-positive tone photoresist functionalised with 3-aminopropyltriethoxysilane is presented. The authors demonstrate that proteins immobilised using this procedure retain their activity and therefore form functional microarrays with the array density limited only by the resolution of lithography, which is more than an order of magnitude compared with inkjet printing. The process described here may be useful in the integration of conventional semiconductor manufacturing processes with biomaterials relevant for the creation of next-generation bio-chips.

Charge Injection through Nanocomposite Electrode in Microfluidic Channel for Electrical Lysis of Biological Cells

Several concepts have been developed in the recent years for nanomaterial based integrated MEMS platform in order to accelerate the process of biological sample preparation followed by selective screening and identification of target molecules. In this context, there exist several challenges which need to be addressed in the process of electrical lysis of biological cells. These are due to (i) low resource settings while achieving maximal lysis (ii) high throughput of target molecules to be detected (iii) automated extraction and purification of relevant molecules such as DNA and protein from extremely small volume of sample (iv) requirement of fast, accurate and yet scalable methods (v) multifunctionality toward process monitoring and (vi) downward compatibility with already existing diagnostic protocols. This paper reports on the optimization of electrical lysis process based on various different nanocomposite coated electrodes placed in a microfluidic channel. The nanocomposites are synthesized using different nanomaterials like Zinc nanorod dispersion in polymer. The efficiency of electrical lysis with various different electrode coatings has been experimentally verified in terms of DNA concentration, amplification and protein yield. The influence of the coating thickness on the injection current densities has been analyzed. We further correlate experimentally the current density vs. voltage relationship with the extent of bacterial cell lysis. A coupled multiphysics based simulation model is used to predict the cell trajectories and lysis efficiencies under various electrode boundary conditions as estimated from experimental results. Detailed in-situ fluorescence imaging and spectroscopy studies are performed to validate various hypotheses.

Framework for morphometric classification of cells in imaging flow cytometry

Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis. Lay description In this article, we propose a novel framework for processing the raw data generated using microfluidics based imaging flow cytometers. Microfluidics microscopy or microfluidics based imaging flow cytometry (mIFC) is a recent microscopy paradigm, that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy, which allows us imaging cells while they are in flow. In comparison to the conventional slide-based imaging systems, mIFC is a nascent technology enabling high throughput imaging of cells and is yet to take the form of a clinical diagnostic tool. The proposed framework process the raw data generated by the mIFC systems. The framework incorporates several steps: beginning from pre-processing of the raw video frames to enhance the contents of the cell, localising the cell by a novel, fully automatic, non-iterative graph based algorithm, extraction of different quantitative morphological parameters and subsequent classification of cells. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using cost-effective microfluidics based imaging flow cytometer. The cell lines of HL60, K562 and MOLT were obtained from ATCC (American Type Culture Collection) and are separately cultured in the lab. Thus, each culture contains cells from its own category alone and thereby provides the ground truth. Each cell is localised by finding a closed cell contour by defining a directed, weighted graph from the Canny edge images of the cell such that the closed contour lies along the shortest weighted path surrounding the centroid of the cell from a starting point on a good curve segment to an immediate endpoint. Once the cell is localised, morphological features reflecting size, shape and complexity of the cells are extracted and used to develop a support vector machine based classification system. We could classify the cell-lines with good accuracy and the results were quite consistent across different cross validation experiments. We hope that imaging flow cytometers equipped with the proposed framework for image processing would enable cost-effective, automated and reliable disease screening in over-loaded facilities, which cannot afford to hire skilled personnel in large numbers. Such platforms would potentially facilitate screening camps in low income group countries; thereby transforming the current health care paradigms by enabling rapid, automated diagnosis for diseases like cancer.

PocketMatch: A new algorithm to compare binding sites in protein structures

Recognizing similarities and deriving relationships among protein molecules is a fundamental requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison. Results: Here we present a new algorithm PocketMatch for comparison of binding sites in a frame invariant manner. Each binding site is represented by 90 lists of sorted distances capturing shape and chemical nature of the site. The sorted arrays are then aligned using an incremental alignment method and scored to obtain PMScores for pairs of sites. A comprehensive sensitivity analysis and an extensive validation of the algorithm have been carried out. A comparison with other site matching algorithms is also presented. Perturbation studies where the geometry of a given site was retained but the residue types were changed randomly, indicated that chance similarities were virtually non-existent. Our analysis also demonstrates that shape information alone is insufficient to discriminate between diverse binding sites, unless combined with chemical nature of amino acids. Conclusion: A new algorithm has been developed to compare binding sites in accurate, efficient and high-throughput manner. Though the representation used is conceptually simplistic, we demonstrate that along with the new alignment strategy used, it is sufficient to enable binding comparison with high sensitivity. Novel methodology has also been presented for validating the algorithm for accuracy and sensitivity with respect to geometry and chemical nature of the site. The method is also fast and takes about 1/250(th) second for one comparison on a single processor. A parallel version on BlueGene has also been implemented.

targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

A multi-microprocessor architecture for solving partial differential equations

This paper presents the architecture of a fault-tolerant, special-purpose multi-microprocessor system for solving Partial Differential Equations (PDEs). The modular nature of the architecture allows the use of hundreds of Processing Elements (PEs) for high throughput. Its performance is evaluated by both analytical and simulation methods. The results indicate that the system can achieve high operation rates and is not sensitive to inter-processor communication delay.

Analysis of proteins with the `hot dog' fold: Prediction of function and identification of catalytic residues of hypothetical proteins

Background: The hot dog fold has been found in more than sixty proteins since the first report of its existence about a decade ago. The fold appears to have a strong association with fatty acid biosynthesis, its regulation and metabolism, as the proteins with this fold are predominantly coenzyme A-binding enzymes with a variety of substrates located at their active sites. Results: We have analyzed the structural features and sequences of proteins having the hot dog fold. This study reveals that though the basic architecture of the fold is well conserved in these proteins, significant differences exist in their sequence, nature of substrate and oligomerization. Segments with certain conserved sequence motifs seem to play crucial structural and functional roles in various classes of these proteins. Conclusion: The analysis led to predictions regarding the functional classification and identification of possible catalytic residues of a number of hot dog fold-containing hypothetical proteins whose structures were determined in high throughput structural genomics projects.

Serum proteomics of glioma: methods and applications

The prognosis of patients with glioblastoma, the most malignant adult glial brain tumor, remains poor in spite of advances in treatment procedures, including surgical resection, irradiation and chemotherapy.Genetic heterogeneity of glioblastoma warrants extensive studies in order to gain a thorough understanding of the biology of this tumor. While there have been several studies of global transcript profiling of glioma with the identification of gene signatures for diagnosis and disease management, translation into clinics is yet to happen. Serum biomarkers have the potential to revolutionize the process of cancer diagnosis, grading, prognostication and treatment response monitoring. Besides having the advantage that serum can be obtained through a less invasive procedure, it contains molecules at an extraordinary dynamic range of ten orders of magnitude in terms of their concentrations. While the conventional methods, such as 2DE, have been in use for many years, the ability to identify the proteins through mass spectrometry techniques such as MALDI-TOF led to an explosion of interest in proteomics. Relatively new high-throughput proteomics methods such as SELDI-TOF and protein microarrays are expected to hasten the process of serum biomarker discovery. This review will highlight the recent advances in the proteomics platform in discovering serum biomarkers and the current status of glioma serum markers. We aim to provide the principles and potential of the latest proteomic approaches and their applications in the biomarker discovery process. Besides providing a comprehensive list of available serum biomarkers of glioma, we will also propose how these markers will revolutionize the clinical management of glioma patients.

Computational systems approach for drug target discovery

Importance of the field: The shift in focus from ligand based design approaches to target based discovery over the last two to three decades has been a major milestone in drug discovery research. Currently, it is witnessing another major paradigm shift by leaning towards the holistic systems based approaches rather the reductionist single molecule based methods. The effect of this new trend is likely to be felt strongly in terms of new strategies for therapeutic intervention, new targets individually and in combinations, and design of specific and safer drugs. Computational modeling and simulation form important constituents of new-age biology because they are essential to comprehend the large-scale data generated by high-throughput experiments and to generate hypotheses, which are typically iterated with experimental validation. Areas covered in this review: This review focuses on the repertoire of systems-level computational approaches currently available for target identification. The review starts with a discussion on levels of abstraction of biological systems and describes different modeling methodologies that are available for this purpose. The review then focuses on how such modeling and simulations can be applied for drug target discovery. Finally, it discusses methods for studying other important issues such as understanding targetability, identifying target combinations and predicting drug resistance, and considering them during the target identification stage itself. What the reader will gain: The reader will get an account of the various approaches for target discovery and the need for systems approaches, followed by an overview of the different modeling and simulation approaches that have been developed. An idea of the promise and limitations of the various approaches and perspectives for future development will also be obtained. Take home message: Systems thinking has now come of age enabling a `bird's eye view' of the biological systems under study, at the same time allowing us to `zoom in', where necessary, for a detailed description of individual components. A number of different methods available for computational modeling and simulation of biological systems can be used effectively for drug target discovery.

An efficient algorithm for scheduling in bluetooth piconets and scatternets

Bluetooth is a short-range radio technology operating in the unlicensed industrial-scientific-medical (ISM) band at 2.45 GHz. A piconet is basically a collection of slaves controlled by a master. A scatternet, on the other hand, is established by linking several piconets together in an ad hoc fashion to yield a global wireless ad hoc network. This paper proposes a scheduling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in bluetooth piconets and scatternets. We propose a novel algorithm for scheduling slots to slaves for both piconets and scatternets using multi-layered parameterized policies. Our scheduling scheme works with real data and obtains an optimal feedback policy within prescribed parameterized classes of these by using an efficient two-timescale simultaneous perturbation stochastic approximation (SPSA) algorithm. We show the convergence of our algorithm to an optimal multi-layered policy. We also propose novel polling schemes for intra- and inter-piconet scheduling that are seen to perform well. We present an extensive set of simulation results and performance comparisons with existing scheduling algorithms. Our results indicate that our proposed scheduling algorithm performs better overall on a wide range of experiments over the existing algorithms for both piconets (Das et al. in INFOCOM, pp. 591–600, 2001; Lapeyrie and Turletti in INFOCOM conference proceedings, San Francisco, US, 2003; Shreedhar and Varghese in SIGCOMM, pp. 231–242, 1995) and scatternets (Har-Shai et al. in OPNETWORK, 2002; Saha and Matsumot in AICT/ICIW, 2006; Tan and Guttag in The 27th annual IEEE conference on local computer networks(LCN). Tampa, 2002). Our studies also confirm that our proposed scheme achieves a high throughput and low packet delays with reasonable fairness among all the connections.

Architecture of a polymorphic ASIC for interoperability across multi-mode H.264 decoders

Run-time interoperability between different applications based on H.264/AVC is an emerging need in networked infotainment, where media delivery must match the desired resolution and quality of the end terminals. In this paper, we describe the architecture and design of a polymorphic ASIC to support this. The H.264 decoding flow is partitioned into modules, such that the polymorphic ASIC meets the design goals of low-power, low-area, high flexibility, high throughput and fast interoperability between different profiles and levels of H.264. We demonstrate the idea with a multi-mode decoder that can decode baseline, main and high profile H.264 streams and can interoperate at run.time across these profiles. The decoder is capable of processing frame sizes of up to 1024 times 768 at 30 fps. The design synthesized with UMC 0.13 mum technology, occupies 250 k gates and runs at 100 MHz.

Inferring molecular function: contributions from functional linkages

In the current era of high-throughput sequencing and structure determination, functional annotation has become a bottleneck in biomedical science. Here, we show that automated inference of molecular function using functional linkages among genes increases the accuracy of functional assignments by >= 8% and enriches functional descriptions in >= 34% of top assignments. Furthermore, biochemical literature supports >80% of automated inferences for previously unannotated proteins. These results emphasize the benefit of incorporating functional linkages in protein annotation.

Mass Spectrometry-Based Systems Approach for Identification of Inhibitors of Plasmodium falciparum Fatty Acid Synthase{triangledown}

The emergence of strains of Plasmodium falciparum resistant to the commonly used antimalarials warrants the development of new antimalarial agents. The discovery of type II fatty acid synthase (FAS) in Plasmodium distinct from the FAS in its human host (type I FAS) opened up new avenues for the development of novel antimalarials. The process of fatty acid synthesis takes place by iterative elongation of butyryl-acyl carrier protein (butyryl-ACP) by two carbon units, with the successive action of four enzymes constituting the elongation module of FAS until the desired acyl length is obtained. The study of the fatty acid synthesis machinery of the parasite inside the red blood cell culture has always been a challenging task. Here, we report the in vitro reconstitution of the elongation module of the FAS of malaria parasite involving all four enzymes, FabB/F (β-ketoacyl-ACP synthase), FabG (β-ketoacyl-ACP reductase), FabZ (β-ketoacyl-ACP dehydratase), and FabI (enoyl-ACP reductase), and its analysis by matrix-assisted laser desorption-time of flight mass spectrometry (MALDI-TOF MS). That this in vitro systems approach completely mimics the in vivo machinery is confirmed by the distribution of acyl products. Using known inhibitors of the enzymes of the elongation module, cerulenin, triclosan, NAS-21/91, and (–)-catechin gallate, we demonstrate that accumulation of intermediates resulting from the inhibition of any of the enzymes can be unambiguously followed by MALDI-TOF MS. Thus, this work not only offers a powerful tool for easier and faster throughput screening of inhibitors but also allows for the study of the biochemical properties of the FAS pathway of the malaria parasite.

Mass Spectrometry-Based Systems Approach for Identification of Inhibitors of Plasmodium falciparum Fatty Acid Synthase{triangledown}

The emergence of strains of Plasmodium falciparum resistant to the commonly used antimalarials warrants the development of new antimalarial agents. The discovery of type II fatty acid synthase (FAS) in Plasmodium distinct from the FAS in its human host (type I FAS) opened up new avenues for the development of novel antimalarials. The process of fatty acid synthesis takes place by iterative elongation of butyryl-acyl carrier protein (butyryl-ACP) by two carbon units, with the successive action of four enzymes constituting the elongation module of FAS until the desired acyl length is obtained. The study of the fatty acid synthesis machinery of the parasite inside the red blood cell culture has always been a challenging task. Here, we report the in vitro reconstitution of the elongation module of the FAS of malaria parasite involving all four enzymes, FabB/F (β-ketoacyl-ACP synthase), FabG (β-ketoacyl-ACP reductase), FabZ (β-ketoacyl-ACP dehydratase), and FabI (enoyl-ACP reductase), and its analysis by matrix-assisted laser desorption-time of flight mass spectrometry (MALDI-TOF MS). That this in vitro systems approach completely mimics the in vivo machinery is confirmed by the distribution of acyl products. Using known inhibitors of the enzymes of the elongation module, cerulenin, triclosan, NAS-21/91, and (–)-catechin gallate, we demonstrate that accumulation of intermediates resulting from the inhibition of any of the enzymes can be unambiguously followed by MALDI-TOF MS. Thus, this work not only offers a powerful tool for easier and faster throughput screening of inhibitors but also allows for the study of the biochemical properties of the FAS pathway of the malaria parasite.