Biomolecules

New submissions

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New submissions for Fri, 17 May 24

[1]  arXiv:2405.09647 [pdf, ps, other]

Title: Dynamics of antibody binding and neutralization during viral infection

Authors: Zhenying Chen, Hasan Ahmed, Cora Hirst, Rustom Antia

Subjects: Populations and Evolution (q-bio.PE); Biomolecules (q-bio.BM)

In vivo in infection, virions are constantly produced and die rapidly. In contrast, most antibody binding assays do not include such features. Motivated by this, we considered virions with n=100 binding sites in simple mathematical models with and without the production of virions. In the absence of viral production, at steady state, the distribution of virions by the number of sites bound is given by a binomial distribution, with the proportion being a simple function of antibody affinity (Kon/Koff) and concentration; this generalizes to a multinomial distribution in the case of two or more kinds of antibodies. In the presence of viral production, the role of affinity is replaced by an infection analog of affinity (IAA), with IAA=Kon/(Koff+dv+r), where dv is the virus decaying rate and r is the infection growth rate. Because in vivo dv can be large, the amount of binding as well as the effect of Koff on binding are substantially reduced. When neutralization is added, the effect of Koff is similarly small which may help explain the relatively high Koff reported for many antibodies. We next show that the n+2-dimensional model used for neutralization can be simplified to a 2-dimensional model. This provides some justification for the simple models that have been used in practice. A corollary of our results is that an unexpectedly large effect of Koff in vivo may point to mechanisms of neutralization beyond stoichiometry. Our results suggest reporting Kon and Koff separately, rather than focusing on affinity, until the situation is better resolved both experimentally and theoretically.

[2]  arXiv:2405.09886 [pdf, ps, other]

Title: MTLComb: multi-task learning combining regression and classification tasks for joint feature selection

Authors: Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak, Daniel Durstewitz, Emanuel Schwarz, Verena Schneider-Lindner

Comments: 33 pages, 3 figures, 5 tables

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)

Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms. Although MTL has been successfully applied to ether regression or classification tasks alone, incorporating mixed types of tasks into a unified MTL framework remains challenging, primarily due to variations in the magnitudes of losses associated with different tasks. This challenge, particularly evident in MTL applications with joint feature selection, often results in biased selections. To overcome this obstacle, we propose a provable loss weighting scheme that analytically determines the optimal weights for balancing regression and classification tasks. This scheme significantly mitigates the otherwise biased feature selection. Building upon this scheme, we introduce MTLComb, an MTL algorithm and software package encompassing optimization procedures, training protocols, and hyperparameter estimation procedures. MTLComb is designed for learning shared predictors among tasks of mixed types. To showcase the efficacy of MTLComb, we conduct tests on both simulated data and biomedical studies pertaining to sepsis and schizophrenia.

Replacements for Fri, 17 May 24

[3]  arXiv:2404.12141 (replaced) [pdf, other]

Title: MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

Authors: Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, Wei-Ying Ma

Comments: 20 pages, 11 figures

Subjects: Biomolecules (q-bio.BM); Machine Learning (cs.LG)

[4]  arXiv:2403.12995 (replaced) [pdf, other]

Title: Multi-Scale Protein Language Model for Unified Molecular Modeling

Authors: Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao Zhou

Comments: ICML2024 camera-ready

Subjects: Biomolecules (q-bio.BM); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

[5]  arXiv:2405.06642 (replaced) [pdf, other]

Title: PPFlow: Target-aware Peptide Design with Torsional Flow Matching

Authors: Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li

Comments: 18 pages

Subjects: Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

[6]  arXiv:2308.04098 (replaced) [pdf, other]

Title: Molecular docking via quantum approximate optimization algorithm

Authors: Qi-Ming Ding, Yi-Ming Huang, Xiao Yuan

Comments: 23 pages, 17 figures, All comments are welcome

Journal-ref: Phys. Rev. Applied 21, 034036 (2024)

Subjects: Quantum Physics (quant-ph); Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)

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