Browsing by Author "Varshney, Amitabh"

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    Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
    (Wiley, 2022-07-13) Yin, Rui; Yeng, Brandon F.; Varshney, Amitabh; Pierce, Brian G.
    High-resolution experimental structural determination of protein–protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases (43%) had near-native models (medium or high critical assessment of predicted interactions accuracy) generated as top-ranked predictions by AlphaFold, greatly surpassing the performance of unbound protein–protein docking (9% success rate for near-native top-ranked models), however AlphaFold modeling of antibody–antigen complexes within our set was unsuccessful. We identified sequence and structural features associated with lack of AlphaFold success, and we also investigated the impact of multiple sequence alignment input. Benchmarking of a multimer-optimized version of AlphaFold (AlphaFold-Multimer) with a set of recently released antibody–antigen structures confirmed a low rate of success for antibody–antigen complexes (11% success), and we found that T cell receptor–antigen complexes are likewise not accurately modeled by that algorithm, showing that adaptive immune recognition poses a challenge for the current AlphaFold algorithm and model. Overall, our study demonstrates that end-to-end deep learning can accurately model many transient protein complexes, and highlights areas of improvement for future developments to reliably model any protein–protein interaction of interest.
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    Global Contours
    (2010-05-05) Bista, Sujal; Varshney, Amitabh
    We present a multi-scale approach that uses Laplacian eigenvectors to extract globally significant contours from an image. The input images are mapped into the Laplacian space by using Laplacian eigenvectors. This mapping causes globally significant pixels along the contours to expand in the Laplacian space. The measure of the expansion is used to compute the Global Contours. We apply our scheme to real color images and compare it with several other methods that compute image and color saliency. The contours calculated by our method reflect global properties of the image and are complementary to classic center-surround image saliency methods. We believe that hybrid image saliency algorithms that combine our method of Global Contours with center-surround image saliency algorithms will be able to better characterize the most important regions of images than those from just using contours calculated using bottom-up approaches.
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    High-throughput sequence alignment using Graphics Processing Units
    (Springer Nature, 2007-12-10) Schatz, Michael C; Trapnell, Cole; Delcher, Arthur L; Varshney, Amitabh
    The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies. This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU.
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    Visualization of WiFi Signals Using Programmable Transfer Functions
    (MDPI, 2022-04-26) Rowden, Alexander; Krokos, Eric; Whitley, Kirsten; Varshney, Amitabh
    In this paper, we show how volume rendering with a Programmable Transfer Function can be used for the effective and comprehensible visualization of WiFi signals. A traditional transfer function uses a low-dimensional lookup table to map the volumetric scalar field to color and opacity. In this paper, we present the concept of a Programmable Transfer Function. We then show how generalizing traditional lookup-based transfer functions to Programmable Transfer Functions enables us to leverage view-dependent and real-time attributes of a volumetric field to depict the data variations of WiFi surfaces with low and high-frequency components. Our Programmable Transfer Functions facilitate interactive knowledge discovery and produce meaningful visualizations.