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1.
Sci Adv ; 9(25): eadg7865, 2023 06 23.
Article in English | MEDLINE | ID: mdl-37343087

ABSTRACT

Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.


Subject(s)
Antibodies, Viral , COVID-19 , Humans , Antibodies, Viral/chemistry , SARS-CoV-2/metabolism , Protein Binding , Amino Acid Sequence
3.
Nat Biomed Eng ; 5(6): 613-623, 2021 06.
Article in English | MEDLINE | ID: mdl-33707779

ABSTRACT

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.


Subject(s)
Anti-Bacterial Agents/pharmacology , Antimicrobial Cationic Peptides/pharmacology , Deep Learning , Drug Design , Drug Discovery/methods , Drug Resistance, Bacterial/drug effects , Acinetobacter baumannii/drug effects , Acinetobacter baumannii/growth & development , Acinetobacter baumannii/ultrastructure , Amino Acid Sequence , Animals , Anti-Bacterial Agents/chemical synthesis , Antimicrobial Cationic Peptides/chemical synthesis , Escherichia coli/drug effects , Escherichia coli/growth & development , Escherichia coli/ultrastructure , Female , Klebsiella Infections/drug therapy , Klebsiella pneumoniae/drug effects , Klebsiella pneumoniae/growth & development , Klebsiella pneumoniae/ultrastructure , Mice , Mice, Inbred BALB C , Microbial Sensitivity Tests , Molecular Dynamics Simulation , Pseudomonas aeruginosa/drug effects , Pseudomonas aeruginosa/growth & development , Pseudomonas aeruginosa/ultrastructure , Staphylococcus aureus/drug effects , Staphylococcus aureus/growth & development , Staphylococcus aureus/ultrastructure , Structure-Activity Relationship
4.
Epidemics ; 27: 59-65, 2019 06.
Article in English | MEDLINE | ID: mdl-30902616

ABSTRACT

The recent Zika virus (ZIKV) epidemic in the Americas ranks among the largest outbreaks in modern times. Like other mosquito-borne flaviviruses, ZIKV circulates in sylvatic cycles among primates that can serve as reservoirs of spillover infection to humans. Identifying sylvatic reservoirs is critical to mitigating spillover risk, but relevant surveillance and biological data remain limited for this and most other zoonoses. We confronted this data sparsity by combining a machine learning method, Bayesian multi-label learning, with a multiple imputation method on primate traits. The resulting models distinguished flavivirus-positive primates with 82% accuracy and suggest that species posing the greatest spillover risk are also among the best adapted to human habitations. Given pervasive data sparsity describing animal hosts, and the virtual guarantee of data sparsity in scenarios involving novel or emerging zoonoses, we show that computational methods can be useful in extracting actionable inference from available data to support improved epidemiological response and prevention.


Subject(s)
Primates/virology , Zika Virus Infection/epidemiology , Zika Virus/pathogenicity , Zoonoses/epidemiology , Zoonoses/virology , Animals , Bayes Theorem , Humans , Risk , Zika Virus Infection/pathology , Zoonoses/pathology
5.
IEEE Trans Image Process ; 14(10): 1524-36, 2005 Oct.
Article in English | MEDLINE | ID: mdl-16238058

ABSTRACT

We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low-resolution, degraded, and compressed images.


Subject(s)
Algorithms , Artificial Intelligence , Color , Colorimetry/methods , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , Pattern Recognition, Automated/methods , Feedback , Imaging, Three-Dimensional/methods , Information Storage and Retrieval/methods , Numerical Analysis, Computer-Assisted , Signal Processing, Computer-Assisted , Visual Perception
6.
IEEE Trans Image Process ; 14(5): 690-9, 2005 May.
Article in English | MEDLINE | ID: mdl-15887562

ABSTRACT

The extraction of high-level color descriptors is an increasingly important problem, as these descriptions often provide links to image content. When combined with image segmentation, color naming can be used to select objects by color, describe the appearance of the image, and generate semantic annotations. This paper presents a computational model for color categorization and naming and extraction of color composition. In this paper, we start from the National Bureau of Standards' recommendation for color names, and through subjective experiments, we develop our color vocabulary and syntax. To assign a color name from the vocabulary to an arbitrary input color, we then design a perceptually based color-naming metric. The proposed algorithm follows relevant neurophysiological findings and studies on human color categorization. Finally, we extend the algorithm and develop a scheme for extracting the color composition of a complex image. According to our results, the proposed method identifies known color regions in different color spaces accurately, the color names assigned to randomly selected colors agree with human judgments, and the description of the color composition of complex scenes is consistent with human observations.


Subject(s)
Algorithms , Artificial Intelligence , Color , Colorimetry/methods , Image Interpretation, Computer-Assisted/methods , Information Storage and Retrieval/methods , Pattern Recognition, Automated/methods , Computer Graphics , Models, Statistical , Numerical Analysis, Computer-Assisted , Reproducibility of Results , Sensitivity and Specificity , Signal Processing, Computer-Assisted
7.
Anal Quant Cytol Histol ; 24(3): 125-33, 2002 Jun.
Article in English | MEDLINE | ID: mdl-12102123

ABSTRACT

OBJECTIVE: To explore how a multidisciplinary approach, combining modern visualization and image processing techniques with innovative experimental studies, can augment the understanding of tumor development. STUDY DESIGN: We analyzed histologic sections of a microscopic brain tumor and reconstructed these slices into a 3D representation. We processed these slices to: (1) identify tumor boundaries, (2) isolate proliferating tumor cells, and (3) segment the tumor into regions based on the density of proliferating cells. We then reconstructed the 3D shape of the tumor using a constrained deformable surface approach. RESULTS: This novel method allows the analyst to (1) see specific properties of histologic slices in the 3D environment with animation, (2) switch 2D "views" dynamically, and (3) see relationships between the 3D structure and structure on a plane. CONCLUSION: Using this method to analyze a specific "case," we were also able to shed light on the limitations of a widely held assumption about the shape of expanding microscopic solid tumors as well as find more indications that such tumors behave as adaptive biosystems. Implications of these case study results, as well as future applications of the method for tumor biology research, are discussed.


Subject(s)
Brain Neoplasms/pathology , Computational Biology/methods , Imaging, Three-Dimensional/methods , Antibodies, Monoclonal , Humans , Image Processing, Computer-Assisted , Immunophenotyping , Mathematical Computing , Models, Biological , Spheroids, Cellular
8.
IEEE Trans Image Process ; 11(11): 1238-48, 2002.
Article in English | MEDLINE | ID: mdl-18249694

ABSTRACT

Color descriptors are among the most important features used in image analysis and retrieval. Due to its compact representation and low complexity, direct histogram comparison is a commonly used technique for measuring the color similarity. However, it has many serious drawbacks, including a high degree of dependency on color codebook design, sensitivity to quantization boundaries, and inefficiency in representing images with few dominant colors. In this paper, we present a new algorithm for color matching that models behavior of the human visual system in capturing color appearance of an image. We first develop a new method for color codebook design in the Lab space. The method is well suited for creating small fixed color codebooks; for image analysis, matching, and retrieval. Then we introduce a statistical technique to extract perceptually relevant colors. We also propose a new color distance measure that is based on the optimal mapping between two sets of color components representing two images. Experiments comparing the new algorithm to some existing techniques show that these novel elements lead to better match to human perception in judging image similarity in terms of color composition.

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