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2. RNA-Puzzles Round V: blind predictions of 23 RNA structures

Author

Bu, Fan, Adam, Yagoub, Adamiak, Ryszard W, Antczak, Maciej, de Aquino, Belisa Rebeca H, Badepally, Nagendar Goud, Batey, Robert T, Baulin, Eugene F, Boinski, Pawel, Boniecki, Michal J, Bujnicki, Janusz M, Carpenter, Kristy A, Chacon, Jose, Chen, Shi-Jie, Chiu, Wah, Cordero, Pablo, Das, Naba Krishna, Das, Rhiju, Dawson, Wayne K, DiMaio, Frank, Ding, Feng, Dock-Bregeon, Anne-Catherine, Dokholyan, Nikolay V, Dror, Ron O, Dunin-Horkawicz, Stanisław, Eismann, Stephan, Ennifar, Eric, Esmaeeli, Reza, Farsani, Masoud Amiri, Ferré-D’Amaré, Adrian R, Geniesse, Caleb, Ghanim, George E, Guzman, Horacio V, Hood, Iris V, Huang, Lin, Jain, Dharm Skandh, Jaryani, Farhang, Jin, Lei, Joshi, Astha, Karelina, Masha, Kieft, Jeffrey S, Kladwang, Wipapat, Kmiecik, Sebastian, Koirala, Deepak, Kollmann, Markus, Kretsch, Rachael C, Kurciński, Mateusz, Li, Jun, Li, Shuang, Magnus, Marcin, Masquida, BenoÎt, Moafinejad, S Naeim, Mondal, Arup, Mukherjee, Sunandan, Nguyen, Thi Hoang Duong, Nikolaev, Grigory, Nithin, Chandran, Nye, Grace, Pandaranadar Jeyeram, Iswarya PN, Perez, Alberto, Pham, Phillip, Piccirilli, Joseph A, Pilla, Smita Priyadarshini, Pluta, Radosław, Poblete, Simón, Ponce-Salvatierra, Almudena, Popenda, Mariusz, Popenda, Lukasz, Pucci, Fabrizio, Rangan, Ramya, Ray, Angana, Ren, Aiming, Sarzynska, Joanna, Sha, Congzhou Mike, Stefaniak, Filip, Su, Zhaoming, Suddala, Krishna C, Szachniuk, Marta, Townshend, Raphael, Trachman, Robert J, Wang, Jian, Wang, Wenkai, Watkins, Andrew, Wirecki, Tomasz K, Xiao, Yi, Xiong, Peng, Xiong, Yiduo, Yang, Jianyi, Yesselman, Joseph David, Zhang, Jinwei, Zhang, Yi, Zhang, Zhenzhen, Zhou, Yuanzhe, Zok, Tomasz, Zhang, Dong, Zhang, Sicheng, Żyła, Adriana, Westhof, Eric, and Miao, Zhichao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson-Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.

Published
2024

3. RNA-Puzzles Round V: blind predictions of 23 RNA structures

Author

Bu, Fan, Adam, Yagoub, Adamiak, Ryszard W., Antczak, Maciej, de Aquino, Belisa Rebeca H., Badepally, Nagendar Goud, Batey, Robert T., Baulin, Eugene F., Boinski, Pawel, Boniecki, Michal J., Bujnicki, Janusz M., Carpenter, Kristy A., Chacon, Jose, Chen, Shi-Jie, Chiu, Wah, Cordero, Pablo, Das, Naba Krishna, Das, Rhiju, Dawson, Wayne K., DiMaio, Frank, Ding, Feng, Dock-Bregeon, Anne-Catherine, Dokholyan, Nikolay V., Dror, Ron O., Dunin-Horkawicz, Stanisław, Eismann, Stephan, Ennifar, Eric, Esmaeeli, Reza, Farsani, Masoud Amiri, Ferré-D’Amaré, Adrian R., Geniesse, Caleb, Ghanim, George E., Guzman, Horacio V., Hood, Iris V., Huang, Lin, Jain, Dharm Skandh, Jaryani, Farhang, Jin, Lei, Joshi, Astha, Karelina, Masha, Kieft, Jeffrey S., Kladwang, Wipapat, Kmiecik, Sebastian, Koirala, Deepak, Kollmann, Markus, Kretsch, Rachael C., Kurciński, Mateusz, Li, Jun, Li, Shuang, Magnus, Marcin, Masquida, BenoÎt, Moafinejad, S. Naeim, Mondal, Arup, Mukherjee, Sunandan, Nguyen, Thi Hoang Duong, Nikolaev, Grigory, Nithin, Chandran, Nye, Grace, Pandaranadar Jeyeram, Iswarya P. N., Perez, Alberto, Pham, Phillip, Piccirilli, Joseph A., Pilla, Smita Priyadarshini, Pluta, Radosław, Poblete, Simón, Ponce-Salvatierra, Almudena, Popenda, Mariusz, Popenda, Lukasz, Pucci, Fabrizio, Rangan, Ramya, Ray, Angana, Ren, Aiming, Sarzynska, Joanna, Sha, Congzhou Mike, Stefaniak, Filip, Su, Zhaoming, Suddala, Krishna C., Szachniuk, Marta, Townshend, Raphael, Trachman, III, Robert J., Wang, Jian, Wang, Wenkai, Watkins, Andrew, Wirecki, Tomasz K., Xiao, Yi, Xiong, Peng, Xiong, Yiduo, Yang, Jianyi, Yesselman, Joseph David, Zhang, Jinwei, Zhang, Yi, Zhang, Zhenzhen, Zhou, Yuanzhe, Zok, Tomasz, Zhang, Dong, Zhang, Sicheng, Żyła, Adriana, Westhof, Eric, and Miao, Zhichao

Published
2025
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4. Tight Short-Lived Signatures

Author

Mondal, Arup, Rooparaghunath, Ruthu Hulikal, and Gupta, Debayan

Subjects
Computer Science - Cryptography and Security
Abstract

A Time-lock puzzle (TLP) sends information into the future: a predetermined number of sequential computations must occur (i.e., a predetermined amount of time must pass) to retrieve the information, regardless of parallelization. Buoyed by the excitement around secure decentralized applications and cryptocurrencies, the last decade has witnessed numerous constructions of TLP variants and related applications (e.g., cost-efficient blockchain designs, randomness beacons, e-voting, etc.). In this poster, we first extend the notion of TLP by formally defining the "time-lock public key encryption" (TLPKE) scheme. Next, we introduce and construct a "tight short-lived signatures" scheme using our TLPKE. Furthermore, to test the validity of our proposed schemes, we do a proof-of-concept implementation and run detailed simulations.

Published
2023

5. RANDGENER: Distributed Randomness Beacon from Verifiable Delay Function

Author

Mondal, Arup, Rooparaghunath, Ruthu Hulikal, and Gupta, Debayan

Subjects
Computer Science - Cryptography and Security
Abstract

Buoyed by the excitement around secure decentralized applications, the last few decades have seen numerous constructions of distributed randomness beacons (DRB) along with use cases; however, a secure DRB (in many variations) remains an open problem. We further note that it is natural to want some kind of reward for participants who spend time and energy evaluating the randomness beacon value -- this is already common in distributed protocols. In this work, we present RandGener, a novel $n$-party commit-reveal-recover (or collaborative) DRB protocol with a novel reward and penalty mechanism along with a set of realistic guarantees. We design our protocol using trapdoor watermarkable verifiable delay functions in the RSA group setting (without requiring a trusted dealer or distributed key generation).

Published
2023

6. Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge

Author

Lawson, Catherine L., Kryshtafovych, Andriy, Pintilie, Grigore D., Burley, Stephen K., Černý, Jiří, Chen, Vincent B., Emsley, Paul, Gobbi, Alberto, Joachimiak, Andrzej, Noreng, Sigrid, Prisant, Michael G., Read, Randy J., Richardson, Jane S., Rohou, Alexis L., Schneider, Bohdan, Sellers, Benjamin D., Shao, Chenghua, Sourial, Elizabeth, Williams, Chris I., Williams, Christopher J., Yang, Ying, Abbaraju, Venkat, Afonine, Pavel V., Baker, Matthew L., Bond, Paul S., Blundell, Tom L., Burnley, Tom, Campbell, Arthur, Cao, Renzhi, Cheng, Jianlin, Chojnowski, Grzegorz, Cowtan, K. D., DiMaio, Frank, Esmaeeli, Reza, Giri, Nabin, Grubmüller, Helmut, Hoh, Soon Wen, Hou, Jie, Hryc, Corey F., Hunte, Carola, Igaev, Maxim, Joseph, Agnel P., Kao, Wei-Chun, Kihara, Daisuke, Kumar, Dilip, Lang, Lijun, Lin, Sean, Maddhuri Venkata Subramaniya, Sai R., Mittal, Sumit, Mondal, Arup, Moriarty, Nigel W., Muenks, Andrew, Murshudov, Garib N., Nicholls, Robert A., Olek, Mateusz, Palmer, Colin M., Perez, Alberto, Pohjolainen, Emmi, Pothula, Karunakar R., Rowley, Christopher N., Sarkar, Daipayan, Schäfer, Luisa U., Schlicksup, Christopher J., Schröder, Gunnar F., Shekhar, Mrinal, Si, Dong, Singharoy, Abhishek, Sobolev, Oleg V., Terashi, Genki, Vaiana, Andrea C., Vedithi, Sundeep C., Verburgt, Jacob, Wang, Xiao, Warshamanage, Rangana, Winn, Martyn D., Weyand, Simone, Yamashita, Keitaro, Zhao, Minglei, Schmid, Michael F., Berman, Helen M., and Chiu, Wah

Published
2024
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8. BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning

Author

Mondal, Arup, Virk, Harpreet, and Gupta, Debayan

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Federated Learning (FL) enables multiple parties to distributively train a ML model without revealing their private datasets. However, it assumes trust in the centralized aggregator which stores and aggregates model updates. This makes it prone to gradient tampering and privacy leakage by a malicious aggregator. Malicious parties can also introduce backdoors into the joint model by poisoning the training data or model gradients. To address these issues, we present BEAS, the first blockchain-based framework for N-party FL that provides strict privacy guarantees of training data using gradient pruning (showing improved differential privacy compared to existing noise and clipping based techniques). Anomaly detection protocols are used to minimize the risk of data-poisoning attacks, along with gradient pruning that is further used to limit the efficacy of model-poisoning attacks. We also define a novel protocol to prevent premature convergence in heterogeneous learning environments. We perform extensive experiments on multiple datasets with promising results: BEAS successfully prevents privacy leakage from dataset reconstruction attacks, and minimizes the efficacy of poisoning attacks. Moreover, it achieves an accuracy similar to centralized frameworks, and its communication and computation overheads scale linearly with the number of participants., Comment: The Third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)

Published
2022

9. SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation

Author

More, Yash, Ramachandran, Prashanthi, Panda, Priyam, Mondal, Arup, Virk, Harpreet, and Gupta, Debayan

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might use the model parameters to derive sensitive information about the training dataset used. To address such leakage, differential privacy and cryptographic techniques have been investigated in prior work, but these often result in large communication overheads or impact model performance. To mitigate this centralization of power, we propose SCOTCH, a decentralized m-party secure-computation framework for federated aggregation that deploys MPC primitives, such as secret sharing. Our protocol is simple, efficient, and provides strict privacy guarantees against curious aggregators or colluding data-owners with minimal communication overheads compared to other existing state-of-the-art privacy-preserving federated learning frameworks. We evaluate our framework by performing extensive experiments on multiple datasets with promising results. SCOTCH can train the standard MLP NN with the training dataset split amongst 3 participating users and 3 aggregating servers with 96.57% accuracy on MNIST, and 98.40% accuracy on the Extended MNIST (digits) dataset, while providing various optimizations., Comment: Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), Third AAAI Privacy-Preserving Artificial Intelligence (PPAI-22) Workshop

Published
2022

10. Flatee: Federated Learning Across Trusted Execution Environments

Author

Mondal, Arup, More, Yash, Rooparaghunath, Ruthu Hulikal, and Gupta, Debayan

Subjects
Computer Science - Cryptography and Security
Abstract

Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this)., Comment: IEEE Euro S&P 2021 Poster; see https://www.ieee-security.org/TC/EuroSP2021/posters.html

Published
2021

11. S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

Author

Ramachandran, Prashanthi, Agarwal, Shivam, Mondal, Arup, Shah, Aastha, and Gupta, Debayan

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural network: in our case, this virtual third party is actually a set of servers which individually learn nothing, even with a malicious (but non-colluding) adversary. Previous work in this area has been limited to just one specific activation function: ReLU, rendering the approach impractical for many use-cases. For the first time, we provide fast and verifiable protocols for all common activation functions and optimize them for running in a secret-shared manner. The ability to quickly, verifiably, and robustly compute exponentiation, softmax, sigmoid, etc., allows us to use previously written NNs without modification, vastly reducing developer effort and complexity of code. In recent times, ReLU has been found to converge much faster and be more computationally efficient as compared to non-linear functions like sigmoid or tanh. However, we argue that it would be remiss not to extend the mechanism to non-linear functions such as the logistic sigmoid, tanh, and softmax that are fundamental due to their ability to express outputs as probabilities and their universal approximation property. Their contribution in RNNs and a few recent advancements also makes them more relevant., Comment: Appeared at the Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21). (7 pages, technical paper.)

Published
2021

12. CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Author

Shekhar, Mrinal, Terashi, Genki, Gupta, Chitrak, Sarkar, Daipayan, Debussche, Gaspard, Sisco, Nicholas J, Nguyen, Jonathan, Mondal, Arup, Vant, John, Fromme, Petra, Van Horn, Wade D, Tajkhorshid, Emad, Kihara, Daisuke, Dill, Ken, Perez, Alberto, and Singharoy, Abhishek

Subjects
Macromolecular and Materials Chemistry, Chemical Sciences, Engineering, Materials Engineering, Nanotechnology, Bioengineering, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Aetiology, 1.4 Methodologies and measurements, Underpinning research, Generic health relevance, Macromolecular and materials chemistry, Materials engineering
Abstract

Cryo-electron microscopy (EM) requires molecular modeling to refine structural details from data. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins that cannot be resolved by cryo-EM. Here, we introduce CryoFold - a pipeline of molecular dynamics simulations that determines ensembles of protein structures directly from sequence by integrating density data of varying sparsity at 3-5 Å resolution with coarse-grained topological knowledge of the protein folds. We present six examples showing its broad applicability for folding proteins between 72 to 2000 residues, including large membrane and multi-domain systems, and results from two EMDB competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations, unseen by traditional methods, are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python GUI.

Published
2021

18. MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics

Author

Singh, Bhumika, Mondal, Arup, Gaalswyk, Kari, MacCallum, Justin L., and Perez, Alberto

Abstract

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A challenging step for integrating the data is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal to or slightly lower than the real value. However, the initial belief might be unknown to the user, as it depends on both the technique and the system of study. Here we introduce MELD-Adapt, designed to dynamically evaluate and infer the reliability of input data while at the same time finding the best interpretation of the data and the structures compatible with it. We demonstrate the utility of this method across different systems, particularly emphasizing its capability to correct initial assumptions and identify the correct fraction of data to produce reliable structural models. The approach is tested with two benchmark sets: the folding of 12 proteins with coarse physical insights and the binding of peptides with varying affinities to the extraterminal domain using chemical shift perturbation data. We find that subtle differences in data structure (e.g., locally clustered or globally distributed), starting belief, and force field preferences can have an impact on the predictions, limiting the possibility of a transferable protocol across all systems and data types. Nonetheless, we find a wide range of initial setup conditions that will lead to successful sampling and identification of native states, leading to a robust pipeline. Furthermore, disagreements about how much data is enforced and satisfied rapidly serve to identify incorrect setup conditions.

Published
2024
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19. Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge

Author

Lawson, Catherine, primary, Kryshtafovych, Andriy, additional, Pintilie, Grigore, additional, Burley, Stephen, additional, Cerny, Jiri, additional, Chen, Vincent, additional, Emsley, Paul, additional, Gobbi, Alberto, additional, Joachimiak, Andrzej, additional, Noreng, Sigrid, additional, Prisant, Michael, additional, Read, Randy, additional, Richardson, Jane, additional, Rohou, Alexis, additional, Schneider, Bohdan, additional, Sellers, Benjamin, additional, Chao, Chenghua, additional, Sourial, Elizabeth, additional, Williams, Chris, additional, Williams, Christopher, additional, Yang, Ying, additional, Abbaraju, Venkat, additional, Afonine, Pavel V., additional, Baker, Matthew, additional, Bond, Paul, additional, Blundell, Tom, additional, Burnley, Tom, additional, Campbell, Arthur, additional, Cao, Renzhi, additional, Cheng, Jianlin, additional, Chojnowski, Grzegorz, additional, Cowtan, Kevin, additional, Dimaio, Frank, additional, Esmaeeli, Reza, additional, Giri, Nabin, additional, Grubmüller, Helmut, additional, Hoh, Soon Wen, additional, Hou, Jie, additional, Hryc, Corey, additional, Hunte, Carola, additional, Igaev, Maxim, additional, Joseph, Agnel, additional, Kao, Wei-Chun, additional, Kihara, Daisuke, additional, Kumar, Dilip, additional, Lang, Lijun, additional, Lin, Sean, additional, Subramaniya, Sai Raghavendra Maddhuri Venkata, additional, Mittal, Sumit, additional, Mondal, Arup, additional, Moriarty, Nigel, additional, Muenks, Andrew, additional, Murshudov, Garib, additional, Nicholls, Robert, additional, Olek, Mateusz, additional, Palmer, Colin, additional, Perez, Alberto, additional, Pohjolainen, Emmi, additional, Pothula, Karunakar, additional, Rowley, Christopher, additional, Sarkar, Daipayan, additional, Schäfer, Luisa, additional, Schlicksup, Christopher, additional, Schroeder, Gunnar, additional, Shekhar, Mrinal, additional, Si, Dong, additional, Singharoy, Abhishek, additional, Sobolev, Oleg, additional, Terashi, Genki, additional, Vaiana, Andrea, additional, Vedithi, Sundeep, additional, Verburgt, Jacob, additional, Wang, Xiao, additional, Warshamanage, Rangana, additional, Winn, Martyn, additional, Weyand, Simone, additional, Yamashita, Keitaro, additional, Zhao, Minglei, additional, Schmid, Michael, additional, Berman, Helen, additional, and Chiu, Wah, additional

Published
2024
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24. A Computational Pipeline for Accurate Prioritization of Protein‐Protein Binding Candidates in High‐Throughput Protein Libraries.

Author

Mondal, Arup, Singh, Bhumika, Felkner, Roland H., De Falco, Anna, Swapna, GVT, Montelione, Gaetano T., Roth, Monica J., and Perez, Alberto

Subjects
*BINDING site assay, *PROTEINS, *PROTEIN engineering, *PROTEIN-protein interactions
Abstract

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High‐throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF‐CBA) to identify proteins that bind a target of interest from a pull‐down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide‐protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF‐CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era.

Author

Chang, Liwei, Mondal, Arup, Singh, Bhumika, Martínez‐Noa, Yisel, and Perez, Alberto

Subjects
DRUG discovery, THERMODYNAMICS, MOLECULAR dynamics, AMINO acid sequence, PEPTIDE drugs
Abstract

Peptide‐based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide‐based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide‐protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post‐AF era, we are witnessing rapid progress with the potential to revolutionize peptide‐based drug discovery such as the ability to rank peptide binders or classify them as binders/non‐binders and the ability to design novel peptide sequences. However, AI‐based methods are struggling due to the lack of well‐curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics‐based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD‐based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics‐based methods holds the promise of reshaping the landscape of peptide‐based drug discovery. This article is categorized under:Structure and Mechanism > Computational Biochemistry and BiophysicsMolecular and Statistical Mechanics > Molecular InteractionsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

Published
2024
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29. GENERALIST: A latent space based generative model for protein sequence families.

Author

Akl, Hoda, Emison, Brooke, Zhao, Xiaochuan, Mondal, Arup, Perez, Alberto, and Dixit, Purushottam D.

Subjects
AMINO acid sequence, PROTEIN models, MACHINE learning, PROBABILISTIC generative models, ORDER statistics, SEQUENCE spaces
Abstract

Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability. Author summary: Protein sequence families show tremendous sequence variation. Yet, it is thought that a large portion of the functional sequence space remains unexplored. Generative models are machine learning methods that allow us to learn what makes proteins functional using sequences of naturally occurring proteins. Here, we present a new type of generative model GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences that is accurate, easy to implement, and works with very small datasets. We believe that GENERALIST will be an important tool in the repertoire of protein scientists and engineers alike. [ABSTRACT FROM AUTHOR]

Published
2023
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36. 'Sexual Objectification' of Dalit women in Kishore Kale's Against all Odds: A Study in Critical Perspectives

Author

Mondal, Arup Kumar

Subjects
Dalit, feminism, patriarchy, objectification, commoditization
Abstract

As portrayed in Kishore Kale’s Against all Odds, the Tamasha folk dancers under the veil of artistic performance are sexually exploited. They are treated as commodities exploited by upper caste rich people. Their own community people support this practice for their monetary gain. The women thus are crushed by both internal and external patriarchy. They are forced to remain silent. Commoditization theory refers to the transformation of a human being into a commodity. When it happens, the commoditized person comes under the full control of the commoditizer. Naturally the liberty, individuality as well as humanity of the commoditized is lost. The commoditized is used to satisfy the commoditizer. The depersonalisation of women by depriving them of their human characteristics and individuality conceptualizes the forced profession of Kolhati Tamasha dancers as sexual object and not human beings. After being sexually exploited, these dancers are regarded as used and discarded objects. The stigmatized profession of these Dalit women labels them as ‘impure’ and renders them as unacceptable in society. The body of these dancers is for consumption by men. The abhorrent patriarchal society strips off their humanity and their identity becomes dependent on their sexual orientation. The paper will critically focus on the issues of sexual objectification of the Kolhati women as portrayed in Kishore Kale’s Against all Odds. Keywords: Dalit, feminism, patriarchy, objectification, commoditization

Published
2022
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40. Modeling SARS‐CoV‐2 proteins in the CASP‐commons experiment

Author

Kryshtafovych, Andriy, Moult, John, Billings, Wendy, Della Corte, Dennis, Fidelis, Krzysztof, Kwon, Sohee, Olechnovič, Kliment, Seok, Chaok, Venclovas, Česlovas, Won, Jonghun, Adhikari, Badri, Adiyaman, Recep, Aguirre-Plans, Joaquim, Anishchenko, Ivan, Baek, Minkyung, Baker, David, Baldassarre, Frederico, Barger, Jacob, Bhattacharya, Sutanu, Bhattacharya, Debswapna, Bitton, Mor, Cao, Renzhi, Cheng, Jianlin, Christoffer, Charles, Czaplewski, Cezary, Elofsson, Arne, Faraggi, Eshel, Feig, Michael, Fernandez-Fuentes, Narcis, Grishin, Nick, Grudinin, Sergei, Guo, Zhiye, Hanazono, Yuya, Hassabis, Demis, Hedelius, Bryce, Heo, Lim, Hiranuma, Naozumi, Hunt, Cassandra, Igashov, Ilia, Ishida, Takashi, Jernigan, Robert, Jones, David, Jumper, John, Kadukova, Maria, Kandathil, Shaun, Keasar, Chen, Kihara, Daisuke, Kinch, Lisa, Kiyota, Yasuomi, Kloczkowski, Andrzje, Kohli, Pushmeet, Kogut, Mateusz, Laine, Elodie, Lilley, Cade, Liu, Jian, Liwo, Adam, Lubecka, Emilia, Mondal, Arup, Morris, Connor, Mcguffin, Liam, Molina, Alexis, Nakamura, Tsukasa, Oliva, Baldo, Perez, Alberto, Pozzati, Gabriele, Sarkar, Daipayan, Sato, Rin, Schwede, Torsten, Shrestha, Bikash, Sidi, Tomer, Studer, Gabriel, Shuvo, Md Hossain, Takeda-Shitaka, Mayuko, Takei, Yuma, Terashi, Genki, Tomii, Kentaro, Tsuchiya, Yuko, Tunyasuvunakool, Kathryn, Waliner, Björn, Wu, Tianqi, Xu, Jinbo, Yamamori, Yu, Zhang, Chengxin, Zhang, Yang, Zheng, Wei, University of California [Davis] (UC Davis), University of California (UC), Institute for Bioscience and Biotechnology Research [Rockville, MD, États-Unis] (IBBR), University of Maryland [College Park], University of Maryland System-University of Maryland System, Brigham Young University (BYU), Seoul National University [Seoul] (SNU), Vilnius University [Vilnius], University of Missouri [St. Louis], University of Missouri System, University of Reading (UOR), Universitat Pompeu Fabra [Barcelona] (UPF), Biorobotics Lab (University of Washington), University of Washington [Seattle], Royal Institute of Technology [Stockholm] (KTH ), Auburn University (AU), Ben-Gurion University of the Negev (BGU), Pacific Lutheran University [Tacoma] (PLU), University of Missouri [Columbia] (Mizzou), Purdue University [West Lafayette], University of Gdańsk (UG), Stockholm University, Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Michigan State University [East Lansing], Michigan State University System, Aberystwyth University, University of Texas Southwestern Medical Center [Dallas], Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), National Institutes for Quantum and Radiological Science and Technology (QST), DeepMind [London], DeepMind Technologies, Moscow Institute of Physics and Technology [Moscow] (MIPT), Tokyo Institute of Technology [Tokyo] (TITECH), Iowa State University (ISU), University College of London [London] (UCL), Kitasato University, Ohio State University [Columbus] (OSU), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Florida [Gainesville] (UF), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Tohoku University [Sendai], University of Basel (Unibas), National Institute of Advanced Industrial Science and Technology (AIST), Linköping University (LIU), Toyota Technological Institute at Chicago [Chicago] (TTIC), University of Michigan [Ann Arbor], University of Michigan System, University of California, University of Maryland [Baltimore], Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
Models, Molecular, 2019-20 coronavirus outbreak, Research groups, Protein Conformation, Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Model accuracy, Genome, Viral, computer.software_genre, Biochemistry, SARS‐CoV‐2, Viroporin Proteins, Domain (software engineering), Viral Proteins, 03 medical and health sciences, Protein Domains, Structural Biology, EMA, Humans, CASP, Molecular Biology, Research Articles, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, COVID, 0303 health sciences, SARS-CoV-2, 030302 biochemistry & molecular biology, COVID-19, Protein structure prediction, Model quality, Critical assessment, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Research Article
Abstract

International audience; Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).

Published
2021
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47. Cryofold: Determining Protein Structures and Data- Guided Ensembles from Cryo-Em Density Maps

Author

Shekhar, Mrinal, primary, Terashi, Genki, additional, Gupta, Chitrak, additional, Sarkar, Daipayan, additional, Debussche, Gaspard, additional, Sisco, Nick, additional, Nguyen, Jonathan, additional, Mondal, Arup, additional, Zook, James, additional, Vant, John, additional, Fromme, Petra, additional, Van Horn, Wade, additional, Tajkhorshid, Emad, additional, Kihara, Diasuke, additional, Dill, Ken, additional, Perez, Alberto, additional, and Singharoy, A., additional

Published
2021
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