In addition, we leverage DeepCoVDR to predict COVID-19 drug candidates from existing FDA-approved drugs, showcasing the effectiveness of DeepCoVDR in identifying promising new COVID-19 medications.
DeepCoVDR, a repository on GitHub at https://github.com/Hhhzj-7/DeepCoVDR, presents its contents for review.
The project, https://github.com/Hhhzj-7/DeepCoVDR, presents a novel approach to tackling complex challenges.
An enhanced comprehension of tissue organization has been achieved by using spatial proteomics data to map cell states. These methods were subsequently adapted to investigate the repercussions of these structures on the course of disease and the survival rates of patients. Yet, until this point, the preponderance of supervised learning methods employing these data types have failed to fully exploit the spatial aspects, thereby hindering their performance and applicability.
Motivated by ecological and epidemiological frameworks, we designed innovative spatial feature extraction procedures for processing spatial proteomics data. Employing these attributes, we developed predictive models for the survival of cancer patients. The utilization of spatial features, as we demonstrate, led to a consistent upgrade in performance compared to previous methods relying on spatial proteomics data for this same objective. Analysis of feature importance uncovered new insights into the complex interactions between cells, providing crucial information on patient survival.
The computational underpinnings of this project, are available at the gitlab.com repository enable-medicine-public/spatsurv.
Access the codebase for this undertaking at gitlab.com/enable-medicine-public/spatsurv.
The selective elimination of cancer cells, a key aim in anticancer therapy, is potentially achievable through synthetic lethality. This strategy targets cancer-specific genetic mutations by inhibiting the partner genes, thereby avoiding harm to normal cells. Wet-lab SL screening methods are hampered by problems including substantial costs and unintended side effects. Addressing these concerns is facilitated by computational techniques. Prior machine learning techniques capitalize on available supervised learning pairs, and knowledge graphs (KGs) can substantially boost predictive accuracy. Nevertheless, the intricate subgraph configurations within the knowledge graph remain largely unexamined. Additionally, the inability to interpret most machine learning methods is a crucial challenge to their widespread use in the process of identifying systems for SL.
A model called KR4SL is presented to forecast SL partners for a given primary gene. By effectively constructing and learning from relational digraphs within a knowledge graph (KG), it accurately reflects the structural semantics of the KG. bio-mediated synthesis Relational digraph semantic information is encoded by merging entity textual semantics into propagated messages and improving the sequential semantics of paths using a recurrent neural network. Additionally, we develop an attentive aggregator for identifying the most impactful subgraph structures, which are key contributors to SL predictions, providing insightful explanations. Rigorous testing under different operational environments demonstrates that KR4SL performs far better than all baseline methods. Predicted gene pairs' explanatory subgraphs provide a window into the prediction process and mechanisms behind synthetic lethality. Interpretability and improved predictive power of deep learning highlight its practical value for SL-based cancer drug target discovery.
The KR4SL source code, freely usable, is found at the following GitHub link: https://github.com/JieZheng-ShanghaiTech/KR4SL.
Within the GitHub repository, https://github.com/JieZheng-ShanghaiTech/KR4SL, the KR4SL source code is freely distributed.
Though simple in their structure, Boolean networks demonstrate an impressive efficiency in modeling complicated biological systems. In spite of using only two activation levels, this framework may fail to fully capture the intricacies of the dynamics within real-world biological systems. As a result, the utilization of multi-valued networks (MVNs), an extension of Boolean networks, is indispensable. Modeling biological systems using MVNs, though important, has lagged behind in the development of corresponding theories, analysis methods, and essential supporting tools. Specifically, the contemporary implementation of trap spaces in Boolean networks has yielded substantial impacts on systems biology, however, a comparable concept for MVNs remains undefined and unexplored currently.
In this study, we extend the notion of trap spaces within Boolean networks to encompass MVNs. We subsequently elaborate on the theory and the methods of analysis related to trap spaces in MVNs. Within the Python package trapmvn, we have implemented each of the proposed methods. Our approach's practical implementation is validated by a realistic case study, and its speed is further analyzed using a sizable dataset of real-world models. The experimental results confirm the time efficiency, a factor we believe essential for more precise analysis on larger and more complex multi-valued models.
Source code and data are freely available from the GitHub repository at https://github.com/giang-trinh/trap-mvn.
One can find the open-source source code and the accompanying data files at the link https://github.com/giang-trinh/trap-mvn.
A key aspect of drug design and development is the accurate prediction of the binding affinity between proteins and ligands. Due to its promise of bolstering model interpretability, the cross-modal attention mechanism has become a fundamental aspect of various deep learning models recently. Deep drug-target interaction models, seeking to enhance their explainability, must consider non-covalent interactions (NCIs), a cornerstone of binding affinity prediction, when designing protein-ligand attention mechanisms. ArkDTA, a novel architecture for predicting binding affinities with interpretability, is suggested, drawing inspiration from NCIs.
ArkDTA's experimental results highlight comparable predictive accuracy to the most current state-of-the-art models, and demonstrate a substantial improvement in the model's explainability. Through qualitative analysis of our novel attention mechanism, ArkDTA demonstrates its capacity to locate possible non-covalent interaction (NCI) areas between candidate drug compounds and target proteins, thereby improving the interpretability and domain awareness of the model's internal functions.
ArkDTA can be accessed at the following GitHub repository: https://github.com/dmis-lab/ArkDTA.
The provided email address is [email protected], affiliated with korea.ac.kr.
The email address [email protected] is provided.
The function of proteins is fundamentally shaped by the crucial process of alternative RNA splicing. In spite of its undeniable relevance, the absence of tools for elucidating the mechanistic effects of splicing on protein interaction networks (i.e.,) is problematic. RNA splicing determines whether protein-protein interactions occur or are avoided. To fill this void, we present LINDA, a method based on Linear Integer Programming for Network reconstruction, integrating protein-protein and domain-domain interaction information, transcription factor targets, and differential splicing/transcript analysis to infer the impact of splicing-dependent effects on cellular pathways and regulatory networks.
In HepG2 and K562 cells, a panel of 54 shRNA depletion experiments from the ENCORE initiative were subjected to LINDA analysis. Through computational analysis of benchmarking data, we ascertained that incorporating splicing effects into LINDA yielded more accurate identification of pathway mechanisms implicated in known biological processes than current state-of-the-art methods, which do not account for splicing. Furthermore, we have empirically confirmed certain anticipated splicing consequences arising from HNRNPK depletion in K562 cells, impacting signaling pathways.
The ENCORE initiative provided 54 shRNA depletion experiments on HepG2 and K562 cells, which were then processed using LINDA. By computationally comparing performance, we found that the integration of splicing effects into LINDA provides superior identification of pathway mechanisms driving known biological processes, outperforming other cutting-edge methods that neglect splicing. Cardiovascular biology We have experimentally corroborated some of the projected effects of reduced HNRNPK expression on splicing events related to signaling, specifically in K562 cells.
The impressive, recent strides in protein and protein complex structural prediction hold great promise for reconstructing interactomes at a large scale with single-residue precision. Not only should modeling methods reveal the three-dimensional arrangement of interacting molecules, but they also should delineate the effect of sequence variations on the binding affinity.
We report on Deep Local Analysis, a novel and efficient deep learning framework in this work. This framework is structured on a remarkably straightforward subdivision of protein interfaces into small, locally oriented residue-centered cubes and 3D convolutions that identify patterns within those cubes. The binding affinity shift in associated complexes, meticulously calculated by DLA, is grounded in the cubes of wild-type and mutant residues. A Pearson correlation coefficient of 0.735 was achieved on approximately 400 mutations in unseen protein complexes. On blind datasets containing complex structures, this model exhibits a greater capability for generalization compared to the current state-of-the-art methods. check details Predictions are improved by taking into account the evolutionary constraints that residues impose. We likewise examine the impact of conformational diversity on effectiveness. DLA's utility extends beyond predicting the impact of mutations, functioning as a general framework for transferring insights gleaned from the comprehensive, non-redundant database of complex protein structures to various tasks. From a partially masked cube, the central residue's identification and its physicochemical classification are recoverable.