pwc-archive/papers-with-abstracts huggingface dataset:
DCI: An Accurate Quality Assessment Criteria for Protein Complex Structure Models
Authors: Wenda Wang, Jiaqi Zhai, He Huang, Xinqi Gong
Date: 2024-06-30 00:00:00
Tasks: Drug Design

The structure of proteins is the basis for studying protein function and drug design. The emergence of AlphaFold 2 has greatly promoted the prediction of protein 3D structures, and it is of great significance to give an overall and accurate evaluation of the predicted models, especially the complex models. Among the existing methods for evaluating multimer structures, DockQ is the most commonly used. However, as a more suitable metric for complex docking, DockQ cannot provide a unique and accurate evaluation in the non-docking situation. Therefore, it is necessary to propose an evaluation strategy that can directly evaluate the whole complex without limitation and achieve good results. In this work, we proposed DCI score, a new evaluation strategy for protein complex structure models, which only bases on distance map and CI (contact-interface) map, DCI focuses on the prediction accuracy of the contact interface based on the overall evaluation of complex structure, is not inferior to DockQ in the evaluation accuracy according to CAPRI classification, and is able to handle the non-docking situation better than DockQ. Besides, we calculated DCI score on CASP datasets and compared it with CASP official assessment, which obtained good results. In addition, we found that DCI can better evaluate the overall structure deviation caused by interface prediction errors in the case of multi-chains. Our DCI is available at \url{https://gitee.com/WendaWang/DCI-score.git}, and the online-server is available at \url{http://mialab.ruc.edu.cn/DCIServer/}.
Weighted mesh algorithms for general Markov decision processes: Convergence and tractability
Authors: Denis Belomestny, John Schoenmakers
Date: 2024-06-29 00:00:00

We introduce a mesh-type approach for tackling discrete-time, finite-horizon Markov Decision Processes (MDPs) characterized by state and action spaces that are general, encompassing both finite and infinite (yet suitably regular) subsets of Euclidean space. In particular, for bounded state and action spaces, our algorithm achieves a computational complexity that is tractable in the sense of Novak and Wozniakowski, and is polynomial in the time horizon. For unbounded state space the algorithm is "semi-tractable" in the sense that the complexity is proportional to $\epsilon^{-c}$ with some dimension independent $c\geq2$, for achieving an accuracy $\epsilon$, and polynomial in the time horizon with degree linear in the underlying dimension. As such the proposed approach has some flavor of the randomization method by Rust which deals with infinite horizon MDPs and uniform sampling in compact state space. However, the present approach is essentially different due to the finite horizon and a simulation procedure due to general transition distributions, and more general in the sense that it encompasses unbounded state space. To demonstrate the effectiveness of our algorithm, we provide illustrations based on Linear-Quadratic Gaussian (LQG) control problems.
AI-driven Alternative Medicine: A Novel Approach to Drug Discovery and Repurposing
Authors: Oleksandr Bilokon, Nataliya Bilokon, Paul Bilokon
Date: 2024-07-02 00:00:00
Tasks: Drug Discovery

AIAltMed is a cutting-edge platform designed for drug discovery and repurposing. It utilizes Tanimoto similarity to identify structurally similar non-medicinal compounds to known medicinal ones. This preprint introduces AIAltMed, discusses the concept of `AI-driven alternative medicine,' evaluates Tanimoto similarity's advantages and limitations, and details the system's architecture. Furthermore, it explores the benefits of extending the system to include PubChem and outlines a corresponding implementation strategy.
Frontiers in integrative structural biology: modeling disordered proteins and utilizing in situ data
Authors: Kartik Majila, Shreyas Arvindekar, Muskaan Jindal, Shruthi Viswanath
Date: 2024-06-30 00:00:00
Tasks: Electron Tomography

Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple sources of experiment data with theoretical and computational predictions. Recent advancements in AI-based structure prediction and electron cryo-microscopy have sparked renewed enthusiasm for integrative modeling; structures from AI-based methods can be integrated with in situ maps to characterize large assemblies. This approach previously allowed us and others to determine the architectures of diverse macromolecular assemblies, such as nuclear pore complexes, chromatin remodelers, and cell-cell junctions. Experimental data spanning several scales was used in these studies, ranging from high-resolution data, such as X-ray crystallography and Alphafold structures, to low-resolution data, such as cryo-electron tomography maps and data from co-immunoprecipitation experiments. Two recurrent modeling challenges emerged across a range of studies. First, modeling disordered regions, which constituted a significant portion of these assemblies, necessitated the development of new methods. Second, methods needed to be developed to utilize the information from cryo-electron tomography, a timely challenge as structural biology is increasingly moving towards in situ characterization. Here, we recapitulate recent developments in the modeling of disordered proteins and the analysis of cryo-electron tomography data and highlight opportunities for method development in the context of integrative modeling.
FoldToken2: Learning compact, invariant and generative protein structure language
Authors: Zhangyang Gao, Cheng Tan, Stan Z. Li
Date: 2024-06-11 00:00:00
Tasks: Decoder, Quantization, Representation Learning

The equivalent nature of 3D coordinates has posed long term challenges in protein structure representation learning, alignment, and generation. Can we create a compact and invariant language that equivalently represents protein structures? Towards this goal, we propose FoldToken2 to transfer equivariant structures into discrete tokens, while maintaining the recoverability of the original structures. From FoldToken1 to FoldToken2, we improve three key components: (1) invariant structure encoder, (2) vector-quantized compressor, and (3) equivalent structure decoder. We evaluate FoldToken2 on the protein structure reconstruction task and show that it outperforms previous FoldToken1 by 20\% in TMScore and 81\% in RMSD. FoldToken2 probably be the first method that works well on both single-chain and multi-chain protein structures quantization. We believe that FoldToken2 will inspire further improvement in protein structure representation learning, structure alignment, and structure generation tasks.
cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM
Authors: Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, Fredrik Lindsten
Date: 2024-05-29 00:00:00
Tasks: Protein Structure Prediction

The three-dimensional structure of proteins plays a crucial role in determining their function. Protein structure prediction methods, like AlphaFold, offer rapid access to a protein structure. However, large protein complexes cannot be reliably predicted, and proteins are dynamic, making it important to resolve their full conformational distribution. Single-particle cryo-electron microscopy (cryo-EM) is a powerful tool for determining the structures of large protein complexes. Importantly, the numerous images of a given protein contain underutilized information about conformational heterogeneity. These images are very noisy projections of the protein, and traditional methods for cryo-EM reconstruction are limited to recovering only one or a few consensus conformations. In this paper, we introduce cryoSPHERE, which is a deep learning method that uses a nominal protein structure (e.g., from AlphaFold) as input, learns how to divide it into segments, and moves these segments as approximately rigid bodies to fit the different conformations present in the cryo-EM dataset. This approach provides enough constraints to enable meaningful reconstructions of single protein structural ensembles. We demonstrate this with two synthetic datasets featuring varying levels of noise, as well as two real dataset. We show that cryoSPHERE is very resilient to the high levels of noise typically encountered in experiments, where we see consistent improvements over the current state-of-the-art for heterogeneous reconstruction.
VDAC Solvation Free Energy Calculation by a Nonuniform Size Modified Poisson-Boltzmann Ion Channel Model
Authors: Liam Jemison, Matthew Stahl, Ranjan K. Dash, Dexuan Xie
Date: 2024-05-25 00:00:00

The Voltage-Dependent Anion Channel (VDAC) protein is the primary conduit for the regulated passage of ions and metabolites into and out of mitochondria. Calculating its solvation free energy is crucial for understanding its stability, function, and interactions within the cellular environment. In this paper, we introduce a total solvation free energy, $E$, which is the sum of electrostatic, ideal gas, and excess free energies, along with a non-polar energy to yield a zero of $E$ in the absence of charges. We develop numerical schemes for computing $E$ and update the current mesh generation package to accelerate the generation of tetrahedral meshes and improve the quality of meshes for computing $E$. By integrating these schemes and the updated mesh package with our non-uniform size modified Poisson-Boltzmann ion channel (nuSMPBIC), SMPBIC, and PBIC finite element packages, the PDB2PQR package, and the OPM database, we create the VDAC Solvation Free Energy Calculation (VSFEC) package. Using the VSFEC package, we perform comparison tests on the nuSMPBIC, SMPBIC, and PBIC models by using six VDAC proteins and various ionic solutions containing up to four ionic species, including ATP$^{4-}$ and Ca$^{2+}$. We also conduct tests with different neutral voltages and permittivity constants to explore the varying patterns of $E$. Our test results underscore the importance of considering non-uniform ionic size effects and demonstrate the high performance of the VSFEC package in calculating the solvation free energy of VDAC proteins.
Multi-objective generative AI for designing novel brain-targeting small molecules
Authors: Ayush Noori, Iñaki Arango, William E. Byrd, Nada Amin
Date: 2024-04-16 00:00:00
Tasks: Drug Design, Graph Neural Network, Molecular Docking

The strict selectivity of the blood-brain barrier (BBB) represents one of the most formidable challenges to successful central nervous system (CNS) drug delivery. Computational methods to generate BBB permeable drugs in silico may be valuable tools in the CNS drug design pipeline. However, in real-world applications, BBB penetration alone is insufficient; rather, after transiting the BBB, molecules must bind to a specific target or receptor in the brain and must also be safe and non-toxic. To discover small molecules that concurrently satisfy these constraints, we use multi-objective generative AI to synthesize drug-like BBB-permeable small molecules. Specifically, we computationally synthesize molecules with predicted binding affinity against dopamine receptor D2, the primary target for many clinically effective antipsychotic drugs. After training several graph neural network-based property predictors, we adapt SyntheMol (Swanson et al., 2024), a recently developed Monte Carlo Tree Search-based algorithm for antibiotic design, to perform a multi-objective guided traversal over an easily synthesizable molecular space. We design a library of 26,581 novel and diverse small molecules containing hits with high predicted BBB permeability and favorable predicted safety and toxicity profiles, and that could readily be synthesized for experimental validation in the wet lab. We also validate top scoring molecules with molecular docking simulation against the D2 receptor and demonstrate predicted binding affinity on par with risperidone, a clinically prescribed D2-targeting antipsychotic. In the future, the SyntheMol-based computational approach described here may enable the discovery of novel neurotherapeutics for currently intractable disorders of the CNS.
Kermut: Composite kernel regression for protein variant effects
Authors: Peter Mørch Groth, Mads Herbert Kerrn, Lars Olsen, Jesper Salomon, Wouter Boomsma
Date: 2024-04-09 00:00:00
Tasks: Prediction, Property Prediction, regression, Uncertainty Quantification

Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty estimates for our predictions, and while prediction accuracy has seen much progress in recent years, uncertainty metrics are rarely reported. We here provide a Gaussian process regression model, Kermut, with a novel composite kernel for modeling mutation similarity, which obtains state-of-the-art performance for supervised protein variant effect prediction while also offering estimates of uncertainty through its posterior. An analysis of the quality of the uncertainty estimates demonstrates that our model provides meaningful levels of overall calibration, but that instance-specific uncertainty calibration remains more challenging.
Understanding Multistationarity of Fully Open Reaction Networks
Authors: Shenghao Yao, AmirHosein Sadeghimanesh, Matthew England
Date: 2024-07-01 00:00:00
Tasks: Epidemiology, Graph Attention, Graph Learning

This work addresses multistationarity of fully open reaction networks equipped with mass action kinetics. We improve upon the existing results relating existence of positive feedback loops in a reaction network and multistationarity; and we provide a novel deterministic operation to generate new non-multistationary networks. This is interesting because while there were many operations to create infinitely many new multistationary networks from a multistationary example, this is the first such operation for the non-multistationary counterpart. Such tools for the generation of example networks have a use-case in the application of data science to reaction network theory. We demonstrate this by using the new data, along with a novel graph representation of reaction networks that is unique up to a permutation on the name of species of the network, to train a graph attention neural network model to predict multistationarity of reaction networks. This is the first time machine learning tools being used for studying classification problems of reaction networks.
WENDY: Covariance Dynamics Based Gene Regulatory Network Inference
Authors: Yue Wang, Peng Zheng, Yu-Chen Cheng, Zikun Wang, Aleksandr Aravkin
Date: 2024-06-17 00:00:00

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.
Comment on Deterministic Information Bottleneck
Authors: Sarah Marzen
Date: 2024-07-01 00:00:00
Tasks: Clustering

We make the case that although Deterministic Information Bottleneck may be a contribution to clustering, it should not be used to aid lossy compression without the addition of blocklength. We therefore suggest a new objective function that does so and leave its testing to future work.
ODIN: Open Data In Neurophysiology: Advancements, Solutions & Challenges
Authors: Colleen J. Gillon, Cody Baker, Ryan Ly, Edoardo Balzani, Bingni W. Brunton, Manuel Schottdorf, Satrajit Ghosh, Nima Dehghani
Date: 2024-07-01 00:00:00

Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and vastly accelerated scientific progress. For example, structural biology and genomics have undertaken systematic collection and publication of protein sequences and structures over the past half-century, and these data have led to scientific breakthroughs that were unthinkable when data collection first began. We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. To this end, new social structures along with active and open scientific communities are essential to facilitate and expand the still limited adoption of open science practices in our field. Unified by shared values of openness, we set out to organize a symposium for Open Data in Neuroscience (ODIN) to strengthen our community and facilitate transformative neuroscience research at large. In this report, we share what we learned during this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.
Synaptic effects on the intermittent synchronization of gamma rhythms
Authors: Quynh-Anh Nguyen, Leonid L Rubchinsky
Date: 2024-06-30 00:00:00

Synchronization of neural activity in the gamma frequency band is associated with various cognitive phenomena. Abnormalities of gamma synchronization may underlie symptoms of several neurological and psychiatric disorders such as schizophrenia and autism spectrum disorder. Properties of neural oscillations in the gamma band depend critically on the synaptic properties of the underlying circuits. This study explores how synaptic properties in pyramidal-interneuronal circuits affect not only the average synchronization strength but also the fine temporal patterning of neural synchrony. If two signals show only moderate synchrony strength, it may be possible to consider these dynamics as alternating between synchronized and desynchronized states. We use a model of connected circuits that produces pyramidal-interneuronal gamma (PING) oscillations to explore the temporal patterning of synchronized and desynchronized intervals. Changes in synaptic strength may alter the temporal patterning of synchronized dynamics (even if the average synchrony strength is not changed). Larger values of local synaptic connections promote longer desynchronization durations, while larger values of long-range synaptic connections promote shorter desynchronization durations. Furthermore, we show that circuits with different temporal patterning of synchronization may have different sensitivity to synaptic input. Thus, the alterations of synaptic strength may mediate physiological properties of neural circuits not only through change in the average synchrony level of gamma oscillations, but also through change in how synchrony is patterned in time over very short time scales.
Optimal Transport for Latent Integration with An Application to Heterogeneous Neuronal Activity Data
Authors: Yubai Yuan, Babak Shahbaba, Norbert Fortin, Keiland Cooper, Qing Nie, Annie Qu
Date: 2024-06-27 00:00:00
Tasks: Data Integration

Detecting dynamic patterns of task-specific responses shared across heterogeneous datasets is an essential and challenging problem in many scientific applications in medical science and neuroscience. In our motivating example of rodent electrophysiological data, identifying the dynamical patterns in neuronal activity associated with ongoing cognitive demands and behavior is key to uncovering the neural mechanisms of memory. One of the greatest challenges in investigating a cross-subject biological process is that the systematic heterogeneity across individuals could significantly undermine the power of existing machine learning methods to identify the underlying biological dynamics. In addition, many technically challenging neurobiological experiments are conducted on only a handful of subjects where rich longitudinal data are available for each subject. The low sample sizes of such experiments could further reduce the power to detect common dynamic patterns among subjects. In this paper, we propose a novel heterogeneous data integration framework based on optimal transport to extract shared patterns in complex biological processes. The key advantages of the proposed method are that it can increase discriminating power in identifying common patterns by reducing heterogeneity unrelated to the signal by aligning the extracted latent spatiotemporal information across subjects. Our approach is effective even with a small number of subjects, and does not require auxiliary matching information for the alignment. In particular, our method can align longitudinal data across heterogeneous subjects in a common latent space to capture the dynamics of shared patterns while utilizing temporal dependency within subjects.
Module control of network analysis in psychopathology
Authors: Chunyu Pan, Quan Zhang, Yue Zhu, Shengzhou Kong, Juan Liu, Changsheng Zhang, Fei Wang, Xizhe Zhang
Date: 2024-05-30 00:00:00

The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network's behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.
A Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment
Authors: Alwani Liyana Ahmad, Jose Sanchez-Bornot, Roberto C. Sotero, Damien Coyle, Zamzuri Idris, Ibrahima Faye
Date: 2024-05-29 00:00:00
Tasks: Bayesian Optimization, Hippocampus

Alzheimer Disease poses a significant challenge, necessitating early detection for effective intervention. MRI is a key neuroimaging tool due to its ease of use and cost effectiveness. This study analyzes machine learning methods for MRI based biomarker selection and classification to distinguish between healthy controls and those who develop mild cognitive impairment within five years. Using 3 Tesla MRI data from ADNI and OASIS 3, we applied various machine learning techniques, including MATLAB Classification Learner app, nested cross validation, and Bayesian optimization. Data harmonization with polynomial regression improved performance. Consistent features identified were the entorhinal, hippocampus, lateral ventricle, and lateral orbitofrontal regions. For balanced ADNI data, Naive Bayes with z score harmonization performed best. For balanced OASIS 3, SVM with z score correction excelled. In imbalanced data, RUSBoost showed strong performance on ADNI and OASIS 3. Z score harmonization highlighted the potential of a semi automatic pipeline for early AD detection using MRI.