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Article: Amifostine inhibits acrylamide-induced hepatotoxicity by inhibiting oxidative stress and apoptosis.

Karimi, Mostafa / Ghasemzadeh Rahbardar, Mahboobeh / Razavi, Bibi Marjan / Hosseinzadeh, Hossein

Iranian journal of basic medical sciences

2023 Volume 26, Issue 6, Page(s) 662–668

Abstract: Objectives: Acrylamide (ACR) is a toxic chemical agent that can induce hepatotoxicity through different mechanisms including oxidative stress and apoptosis. Amifostine is an important hepatoprotective and anti-oxidant compound. In this research, the ... ...

Abstract	Objectives: Acrylamide (ACR) is a toxic chemical agent that can induce hepatotoxicity through different mechanisms including oxidative stress and apoptosis. Amifostine is an important hepatoprotective and anti-oxidant compound. In this research, the hepatoprotective effect of amifostine on ACR-induced hepatotoxicity in rats has been investigated. Materials and methods: Male Wistar rats were randomly divided into 7 groups, including: 1. Control group, 2. ACR (50 mg/kg, 11 days, IP), 3-5. ACR+ amifostine (25, 50, 100 mg/kg, 11 days, IP), 6. ACR+ N-acetyl cysteine (NAC) (200 mg/kg, 11 days, IP), and 7. Amifostine (100 mg/kg, 11 days, IP). At the end of the injection period, animals' liver samples were collected to determine the content of glutathione (GSH), malondialdehyde (MDA), and apoptotic proteins (B-cell lymphoma 2 (Bcl2), Bcl-2-associated X protein (Bax), and cleaved caspase-3. Serum samples were also collected to measure alanine transaminase (ALT) and aspartate transaminase (AST) levels. Results: Administration of ACR increased MDA, Bax/Bcl2 ratio, cleaved caspase-3, ALT, and AST levels, and decreased GSH content compared with the control group. The administration of amifostine with ACR decreased MDA, Bax/Bcl2 ratio, cleaved caspase-3, ALT, and AST levels, and increased GSH content compared with the ACR group. Receiving NAC along with ACR reversed the alterations induced by ACR. Conclusion: This study shows that pretreatment with amifostine can reduce ACR-induced toxicity in the liver tissue of rats. Since oxidative stress is one of the most important mechanisms in ACR toxicity, amifostine probably reduces the toxicity of ACR by increasing the anti-oxidant and anti-apoptotic capacity of the hepatic cells.
Language	English
Publishing date	2023-05-11
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2500485-2
ISSN	2008-3874 ; 2008-3866
ISSN (online)	2008-3874
ISSN	2008-3866
DOI	10.22038/IJBMS.2023.67815.14837
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Article ; Online: Comparison of Annual Moisture Flux Variability during Dry and Wet Years over Iran

Karimi, Mostafa / Jafari, Mahnaz / Bazgeer, Saeed / Khoshakhlagh, Faramarz / Moghbel, Masoumeh

Water Resour. 2022 Dec., v. 49, no. 6 p.959-972

2022

Abstract: The main objective of this study is to investigate the relationship between the variability of the atmospheric moisture flux and precipitation during wet and dry years over Iran. For this purpose, the ERA-Interim data including precipitation, specific ... ...

Abstract	The main objective of this study is to investigate the relationship between the variability of the atmospheric moisture flux and precipitation during wet and dry years over Iran. For this purpose, the ERA-Interim data including precipitation, specific humidity and eastward-northward wind components were extracted from European Centre for Medium-Range Weather Forecasts database with a spatial resolution of 1° × 1°. Then, wet and dry years were determined by standardized z-index during the 1981–2011 period. The vertically integrated moisture flux was calculated for the troposphere layer from 1000 to 300 hPa. Results revealed that moisture flux into the troposphere in wet years has been 16.54% more than in dry years. However, moisture changes in the lower layers of the troposphere directly affect precipitation variability over the country (r = 0.6). The atmospheric circulation patterns have transported the most moisture from the south direction toward Iran. Transported moisture from Arabian Sea in a wet year (1999–2000) has been higher than a dry year (2007–2008) by 62.9%. However, this Sea is the main source of moisture transfer into the lower layers of troposphere in both selected dry and wet years. Hereupon, it can be concluded that annual changes in the amount of moisture transfer from the Arabian Sea can be considered as one of the main reasons for drought occurrence in Iran.
Keywords	atmospheric circulation ; databases ; drought ; moisture diffusivity ; specific humidity ; troposphere ; water ; wind ; Arabian Sea ; Iran
Language	English
Dates of publication	2022-12
Size	p. 959-972.
Publishing place	Pleiades Publishing
Document type	Article ; Online
ZDB-ID	2058341-2
ISSN	1608-344X ; 0097-8078
ISSN (online)	1608-344X
ISSN	0097-8078
DOI	10.1134/S0097807822060057
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Article: Land subsidence in Isfahan metropolitan and its relationship with geological and geomorphological settings revealed by Sentinel-1A InSAR observations

Goorabi, Abolghasem / Karimi, Mostafa / Yamani, Mojtaba / Perissin, Daniele

Journal of arid environments. 2020 Oct., v. 181

2020

Abstract: Recent reports from Isfahan, Iran, have proclaimed cases of land subsidence arising from persistence of drought events and groundwater overexploitation. In this light, the present study attempts to conduct an PS-InSAR analysis of subsidence events using ... ...

Abstract	Recent reports from Isfahan, Iran, have proclaimed cases of land subsidence arising from persistence of drought events and groundwater overexploitation. In this light, the present study attempts to conduct an PS-InSAR analysis of subsidence events using the Sentinel–1A Synthetic Aperture Radar imagery technique, in order to develop a subsidence map of the target study region, covering the period from October 16, 2014 to November 22, 2019 as well as to investigate its relation to geological/geomorphological factors. Toward this end, the methodology proceeds to detect land subsidence phenomenon across the Isfahan Metropolitan area, situated on the Zayande-Rud River Terrace (ZRT) and Isfahan Alluvium Plain (IAP). A total of 198 Sentinel images were used to derive deformation specifics observed throughout ZRT & IAP by dint of incorporating ascending and descending passes of the InSAR technique. As maintained by spatial and temporal variation maps of surface deformations in the urban regions of Isfahan, ZRT & IAP have undergone various orders of subsidence throughout the observation period, at an estimated rate of −5 to −100 mm/year. Overall, land subsidence appears to increase from south towards the northern, northeastern, and eastern sectors of Isfahan Metropolitan, while the southern sectors are nearly a stable area. This study indicates that the spatial subsidence pattern during the study period corresponds closely to the spatial distribution of ZRT & IAP in the Isfahan Metropolitan area.
Keywords	alluvium ; deformation ; drought ; geomorphology ; groundwater ; metropolitan areas ; rivers ; subsidence ; synthetic aperture radar ; temporal variation ; Iran
Language	English
Dates of publication	2020-10
Publishing place	Elsevier Ltd
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	428507-4
ISSN	1095-922X ; 0140-1963
ISSN (online)	1095-922X
ISSN	0140-1963
DOI	10.1016/j.jaridenv.2020.104238
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Article: iCFN: an efficient exact algorithm for multistate protein design

Karimi, Mostafa / Shen, Yang

Bioinformatics. 2018 Sept. 01, v. 34, no. 17

2018

Abstract: Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge ... ...

Abstract	Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design. We have developed an efficient exact algorithm called interconnected cost function networks (iCFN) for multistate protein design. Its generic formulation allows for a wide array of applications such as stability, affinity and specificity designs while addressing concerns such as global flexibility of protein backbones. iCFN treats each substate design as a weighted constraint satisfaction problem (WCSP) modeled through a CFN; and it solves the coupled WCSPs using novel bounds and a depth-first branch-and-bound search over a tree structure of sequences, substates, and conformations. When iCFN is applied to specificity design of a T-cell receptor, a problem of unprecedented size to exact methods, it drastically reduces search space and running time to make the problem tractable. Moreover, iCFN generates experimentally-agreeing receptor designs with improved accuracy compared with state-of-the-art methods, highlights the importance of modeling backbone flexibility in protein design, and reveals molecular mechanisms underlying binding specificity. https://shen-lab.github.io/software/iCFN Supplementary data are available at Bioinformatics online.
Keywords	T-lymphocytes ; algorithms ; bioinformatics
Language	English
Dates of publication	2018-0901
Size	p. i811-i820.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4811 ; 1367-4803
ISSN (online)	1460-2059 ; 1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty564
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Article ; Online: Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts.

Karimi, Mostafa / Wu, Di / Wang, Zhangyang / Shen, Yang

Journal of chemical information and modeling

2020 Volume 61, Issue 1, Page(s) 46–66

Abstract: Predicting compound-protein affinity is beneficial for accelerating drug discovery. Doing so without the often-unavailable structure data is gaining interest. However, recent progress in structure-free affinity prediction, made by machine learning, ... ...

Abstract	Predicting compound-protein affinity is beneficial for accelerating drug discovery. Doing so without the often-unavailable structure data is gaining interest. However, recent progress in structure-free affinity prediction, made by machine learning, focuses on accuracy but leaves much to be desired for interpretability. Defining intermolecular contacts underlying affinities as a vehicle for interpretability; our large-scale interpretability assessment finds previously used attention mechanisms inadequate. We thus formulate a hierarchical multiobjective learning problem, where predicted contacts form the basis for predicted affinities. We solve the problem by embedding protein sequences (by hierarchical recurrent neural networks) and compound graphs (by graph neural networks) with joint attentions between protein residues and compound atoms. We further introduce three methodological advances to enhance interpretability: (1) structure-aware regularization of attentions using protein sequence-predicted solvent exposure and residue-residue contact maps; (2) supervision of attentions using known intermolecular contacts in training data; and (3) an intrinsically explainable architecture where atomic-level contacts or "relations" lead to molecular-level affinity prediction. The first two and all three advances result in DeepAffinity+ and DeepRelations, respectively. Our methods show generalizability in affinity prediction for molecules that are new and dissimilar to training examples. Moreover, they show superior interpretability compared to state-of-the-art interpretable methods: with similar or better affinity prediction, they boost the AUPRC of contact prediction by around 33-, 35-, 10-, and 9-fold for the default test, new-compound, new-protein, and both-new sets, respectively. We further demonstrate their potential utilities in contact-assisted docking, structure-free binding site prediction, and structure-activity relationship studies without docking. Our study represents the first model development and systematic model assessment dedicated to interpretable machine learning for structure-free compound-protein affinity prediction.
MeSH term(s)	Amino Acid Sequence ; Deep Learning ; Machine Learning ; Neural Networks, Computer ; Proteins
Chemical Substances	Proteins
Language	English
Publishing date	2020-12-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.0c00866
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Article ; Online: Network-principled deep generative models for designing drug combinations as graph sets.

Karimi, Mostafa / Hasanzadeh, Arman / Shen, Yang

Bioinformatics (Oxford, England)

2020 Volume 36, Issue Suppl_1, Page(s) i445–i454

Abstract: Motivation: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous ... ...

Abstract	Motivation: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results: We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jointly embed gene-gene, gene-disease and disease-disease networks. Novel attentional pooling is introduced here for learning disease representations from associated genes' representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for disease-specific drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space. Availability and implementation: https://github.com/Shen-Lab/Drug-Combo-Generator. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Drug Combinations ; Drug Discovery ; Neural Networks, Computer
Chemical Substances	Drug Combinations
Language	English
Publishing date	2020-07-12
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa317
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Article ; Online: De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks.

Karimi, Mostafa / Zhu, Shaowen / Cao, Yue / Shen, Yang

Journal of chemical information and modeling

2020 Volume 60, Issue 12, Page(s) 5667–5681

Abstract: Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal ... ...

Abstract	Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence-structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the
MeSH term(s)	Proteins
Chemical Substances	Proteins
Language	English
Publishing date	2020-09-30
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.0c00593
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Article: Semiparametric Accelerated Failure Time Model as a New Approach for Health Science Studies.

Karimi, Mostafa / Shariat, Ardalan

Iranian journal of public health

2017 Volume 46, Issue 11, Page(s) 1594–1595

Language	English
Publishing date	2017-11
Publishing country	Iran
Document type	Journal Article
ISSN	2251-6085
ISSN	2251-6085
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Article ; Online: iCFN: an efficient exact algorithm for multistate protein design.

Karimi, Mostafa / Shen, Yang

Bioinformatics (Oxford, England)

2017 Volume 34, Issue 17, Page(s) i811–i820

Abstract: Motivation: Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an ... ...

Abstract	Motivation: Multistate protein design addresses real-world challenges, such as multi-specificity design and backbone flexibility, by considering both positive and negative protein states with an ensemble of substates for each. It also presents an enormous challenge to exact algorithms that guarantee the optimal solutions and enable a direct test of mechanistic hypotheses behind models. However, efficient exact algorithms are lacking for multistate protein design. Results: We have developed an efficient exact algorithm called interconnected cost function networks (iCFN) for multistate protein design. Its generic formulation allows for a wide array of applications such as stability, affinity and specificity designs while addressing concerns such as global flexibility of protein backbones. iCFN treats each substate design as a weighted constraint satisfaction problem (WCSP) modeled through a CFN; and it solves the coupled WCSPs using novel bounds and a depth-first branch-and-bound search over a tree structure of sequences, substates, and conformations. When iCFN is applied to specificity design of a T-cell receptor, a problem of unprecedented size to exact methods, it drastically reduces search space and running time to make the problem tractable. Moreover, iCFN generates experimentally-agreeing receptor designs with improved accuracy compared with state-of-the-art methods, highlights the importance of modeling backbone flexibility in protein design, and reveals molecular mechanisms underlying binding specificity. Availability and implementation: https://shen-lab.github.io/software/iCFN. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Humans ; Proteins/chemistry ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2017-06-08
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty564
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Article ; Online: DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.

Karimi, Mostafa / Wu, Di / Wang, Zhangyang / Shen, Yang

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 18, Page(s) 3329–3338

Abstract: Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability.: ...

Abstract	Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability. Results: We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. Availability and implementation: Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Amino Acid Sequence ; Deep Learning ; Neural Networks, Computer ; Proteins ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2019-02-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz111
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