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1.
Nat Commun ; 15(1): 2875, 2024 Apr 03.
Artigo em Inglês | MEDLINE | ID: mdl-38570497

RESUMO

The characterization of protein-protein interactions (PPIs) is fundamental to the understanding of biochemical processes. Many methods have been established to identify and study direct PPIs; however, screening and investigating PPIs involving large or poorly soluble proteins remains challenging. Here, we introduce ReLo, a simple, rapid, and versatile cell culture-based method for detecting and investigating interactions in a cellular context. Our experiments demonstrate that ReLo specifically detects direct binary PPIs. Furthermore, we show that ReLo bridging experiments can also be used to determine the binding topology of subunits within multiprotein complexes. In addition, ReLo facilitates the identification of protein domains that mediate complex formation, allows screening for interfering point mutations, and it is sensitive to drugs that mediate or disrupt an interaction. In summary, ReLo is a simple and rapid alternative for the study of PPIs, especially when studying structurally complex proteins or when established methods fail.


Assuntos
Mapeamento de Interação de Proteínas , Proteínas , Mapeamento de Interação de Proteínas/métodos , Proteínas/metabolismo
2.
Science ; 383(6690): eadk8544, 2024 Mar 29.
Artigo em Inglês | MEDLINE | ID: mdl-38547289

RESUMO

Cytoplasmic dynein is a microtubule motor vital for cellular organization and division. It functions as a ~4-megadalton complex containing its cofactor dynactin and a cargo-specific coiled-coil adaptor. However, how dynein and dynactin recognize diverse adaptors, how they interact with each other during complex formation, and the role of critical regulators such as lissencephaly-1 (LIS1) protein (LIS1) remain unclear. In this study, we determined the cryo-electron microscopy structure of dynein-dynactin on microtubules with LIS1 and the lysosomal adaptor JIP3. This structure reveals the molecular basis of interactions occurring during dynein activation. We show how JIP3 activates dynein despite its atypical architecture. Unexpectedly, LIS1 binds dynactin's p150 subunit, tethering it along the length of dynein. Our data suggest that LIS1 and p150 constrain dynein-dynactin to ensure efficient complex formation.


Assuntos
1-Alquil-2-acetilglicerofosfocolina Esterase , Proteínas Adaptadoras de Transdução de Sinal , Complexo Dinactina , Dineínas , Proteínas Associadas aos Microtúbulos , Proteínas do Tecido Nervoso , Microscopia Crioeletrônica , Complexo Dinactina/química , Complexo Dinactina/genética , Complexo Dinactina/metabolismo , Dineínas/química , Dineínas/genética , Dineínas/metabolismo , Proteínas Associadas aos Microtúbulos/química , Proteínas Associadas aos Microtúbulos/metabolismo , Microtúbulos/metabolismo , Ligação Proteica , Humanos , Células HeLa , Proteínas do Tecido Nervoso/química , Proteínas do Tecido Nervoso/metabolismo , Proteínas Adaptadoras de Transdução de Sinal/química , Proteínas Adaptadoras de Transdução de Sinal/metabolismo , Repetições WD40 , Mapeamento de Interação de Proteínas
3.
FEBS Lett ; 598(7): 725-742, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38439692

RESUMO

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.


Assuntos
Mapeamento de Interação de Proteínas , Proteínas , Domínios e Motivos de Interação entre Proteínas , Proteínas/metabolismo , Bases de Dados de Proteínas
5.
Comput Biol Med ; 172: 108287, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38503089

RESUMO

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.


Assuntos
Aprendizado de Máquina , Mapeamento de Interação de Proteínas , Mapeamento de Interação de Proteínas/métodos
6.
Bioinformatics ; 40(3)2024 Mar 04.
Artigo em Inglês | MEDLINE | ID: mdl-38449296

RESUMO

MOTIVATION: The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes. RESULTS: We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments. AVAILABILITY AND IMPLEMENTATION: Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.


Assuntos
Biologia Computacional , Mapeamento de Interação de Proteínas , Humanos , Mapeamento de Interação de Proteínas/métodos , Microscopia Crioeletrônica , Biologia Computacional/métodos , Algoritmos , Proteínas/metabolismo
7.
Biochem Biophys Res Commun ; 703: 149658, 2024 Apr 09.
Artigo em Inglês | MEDLINE | ID: mdl-38387229

RESUMO

Adaptor proteins play a pivotal role in cellular signaling mediating a multitude of protein-protein interaction critical for cellular homeostasis. Dysregulation of these interactions has been linked to the onset of various cancer pathologies and exploited by viral pathogens during host cell takeover. CrkL is an adaptor protein composed of an N-terminal SH2 domain followed by two SH3 domains that mediate interactions with diverse partners through the recognition of specific binding motifs. In this study, we employed proteomic peptide-phage display (ProP-PD) to comprehensively explore the short linear motif (SLiM)-based interactions of CrkL. Furthermore, we scrutinized how the binding affinity for selected peptides was influenced in the context of the full-length CrkL versus the isolated N-SH3 domain. Importantly, our results provided insights into SLiM-binding sites within previously reported interactors, as well as revealing novel human and viral ligands, expanding our understanding of the interactions mediated by CrkL and highlighting the significance of SLiM-based interactions in mediating adaptor protein function, with implications for cancer and viral pathologies.


Assuntos
Proteínas Adaptadoras de Transdução de Sinal , Técnicas de Visualização da Superfície Celular , Mapeamento de Interação de Proteínas , Humanos , Sítios de Ligação , Neoplasias , Peptídeos , Ligação Proteica , Proteômica/métodos , Domínios de Homologia de src/fisiologia , Técnicas de Visualização da Superfície Celular/métodos , Proteínas Adaptadoras de Transdução de Sinal/metabolismo
8.
Curr Opin Struct Biol ; 85: 102775, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38330793

RESUMO

Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning methods, coupled with the vast genomic sequence data, have significantly boosted the accuracy of predicting protein structures and modeling protein complexes, approaching levels comparable to experimental techniques. Herein, we review the latest advances in the computational methods for modeling 3D protein complexes and the prediction of protein interaction partners, emphasizing the application of deep learning methods deriving from coevolution analysis. The review also highlights biomedical applications of PPI prediction and outlines challenges in the field.


Assuntos
Aprendizado Profundo , Mapeamento de Interação de Proteínas , Mapeamento de Interação de Proteínas/métodos , Algoritmos , Proteínas/química , Genômica , Biologia Computacional/métodos
9.
Bioinformatics ; 40(2)2024 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-38200587

RESUMO

MOTIVATION: Protein-protein interactions (PPIs) are essential to understanding biological pathways as well as their roles in development and disease. Computational tools, based on classic machine learning, have been successful at predicting PPIs in silico, but the lack of consistent and reliable frameworks for this task has led to network models that are difficult to compare and discrepancies between algorithms that remain unexplained. RESULTS: To better understand the underlying inference mechanisms that underpin these models, we designed an open-source framework for benchmarking that accounts for a range of biological and statistical pitfalls while facilitating reproducibility. We use it to shed light on the impact of network topology and how different algorithms deal with highly connected proteins. By studying functional genomics-based and sequence-based models on human PPIs, we show their complementarity as the former performs best on lone proteins while the latter specializes in interactions involving hubs. We also show that algorithm design has little impact on performance with functional genomic data. We replicate our results between both human and S. cerevisiae data and demonstrate that models using functional genomics are better suited to PPI prediction across species. With rapidly increasing amounts of sequence and functional genomics data, our study provides a principled foundation for future construction, comparison, and application of PPI networks. AVAILABILITY AND IMPLEMENTATION: The code and data are available on GitHub: https://github.com/Llannelongue/B4PPI.


Assuntos
Mapas de Interação de Proteínas , Saccharomyces cerevisiae , Humanos , Mapas de Interação de Proteínas/genética , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Reprodutibilidade dos Testes , Proteínas/metabolismo , Algoritmos , Aprendizado de Máquina , Mapeamento de Interação de Proteínas/métodos
10.
Rev Med Virol ; 34(1): e2517, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-38282401

RESUMO

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.


Assuntos
Viroses , Vírus , Humanos , Proteínas Virais/metabolismo , Mapeamento de Interação de Proteínas/métodos , Inteligência Artificial , Interações Hospedeiro-Patógeno
11.
ACS Chem Biol ; 19(2): 428-441, 2024 Feb 16.
Artigo em Inglês | MEDLINE | ID: mdl-38289242

RESUMO

Protein-protein interactions (PPIs) can be detected through selective complementation of split fluorescent reporters made of two complementary fragments that reassemble into a functional fluorescent reporter when in close proximity. We previously introduced splitFAST, a chemogenetic PPI reporter with rapid and reversible complementation. Here, we present the engineering of splitFAST2, an improved reporter displaying higher brightness, lower self-complementation, and higher dynamic range for optimal monitoring of PPI using an original protein engineering strategy that exploits proteins with orthology relationships. Our study allowed the identification of a system with improved properties and enabled a better understanding of the molecular features controlling the complementation properties. Because of the rapidity and reversibility of its complementation, its low self-complementation, high dynamic range, and improved brightness, splitFAST2 is well suited to study PPI with high spatial and temporal resolution, opening great prospects to decipher the role of PPI in various biological contexts.


Assuntos
Mapeamento de Interação de Proteínas , Proteínas , Proteínas/genética , Proteínas/metabolismo , Engenharia de Proteínas
12.
Elife ; 132024 Jan 16.
Artigo em Inglês | MEDLINE | ID: mdl-38226900

RESUMO

The study of protein interactions in living organisms is fundamental for understanding biological processes and central metabolic pathways. Yet, our knowledge of the bacterial interactome remains limited. Here, we combined gene deletion mutant analysis with deep-learning protein folding using AlphaFold2 to predict the core bacterial essential interactome. We predicted and modeled 1402 interactions between essential proteins in bacteria and generated 146 high-accuracy models. Our analysis reveals previously unknown details about the assembly mechanisms of these complexes, highlighting the importance of specific structural features in their stability and function. Our work provides a framework for predicting the essential interactomes of bacteria and highlight the potential of deep-learning algorithms in advancing our understanding of the complex biology of living organisms. Also, the results presented here offer a promising approach to identify novel antibiotic targets.


Assuntos
Biologia Computacional , Mapeamento de Interação de Proteínas , Mapeamento de Interação de Proteínas/métodos , Biologia Computacional/métodos , Proteínas , Bactérias/genética , Redes e Vias Metabólicas
13.
Plant J ; 117(4): 1281-1297, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-37965720

RESUMO

Phytoplasmas are pathogenic bacteria that reprogram plant host development for their own benefit. Previous studies have characterized a few different phytoplasma effector proteins that destabilize specific plant transcription factors. However, these are only a small fraction of the potential effectors used by phytoplasmas; therefore, the molecular mechanisms through which phytoplasmas modulate their hosts require further investigation. To obtain further insights into the phytoplasma infection mechanisms, we generated a protein-protein interaction network between a broad set of phytoplasma effectors and a large, unbiased collection of Arabidopsis thaliana transcription factors and transcriptional regulators. We found widespread, but specific, interactions between phytoplasma effectors and host transcription factors, especially those related to host developmental processes. In particular, many unrelated effectors target specific sets of TCP transcription factors, which regulate plant development and immunity. Comparison with other host-pathogen protein interaction networks shows that phytoplasma effectors have unusual targets, indicating that phytoplasmas have evolved a unique and unusual infection strategy. This study contributes a rich and solid data source that guides further investigations of the functions of individual effectors, as demonstrated for some herein. Moreover, the dataset provides insights into the underlying molecular mechanisms of phytoplasma infection.


Assuntos
Arabidopsis , Phytoplasma , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Plantas/metabolismo , Arabidopsis/metabolismo , Mapeamento de Interação de Proteínas , Doenças das Plantas/microbiologia
14.
Comput Biol Med ; 168: 107683, 2024 01.
Artigo em Inglês | MEDLINE | ID: mdl-37984202

RESUMO

Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.


Assuntos
Mapeamento de Interação de Proteínas , Inibidores da Bomba de Prótons , Mapeamento de Interação de Proteínas/métodos , Temperatura Alta , Algoritmos , Proteínas/química
15.
Protein Sci ; 33(2): e4853, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38078680

RESUMO

Comparing accuracies of structural protein-protein interaction (PPI) models for different complexes on an absolute scale is a challenge, requiring normalization of scores across structures of different sizes and shapes. To help address this challenge, we have developed a statistical significance metric for docking models, called random-docking (RD) p-value. This score evaluates a PPI model based on how likely a random docking process is to produce a model of better or equal accuracy. The binding partners are randomly docked against each other a large number of times, and the probability of sampling a model of equal or greater accuracy from this reference distribution is the RD p-value. Using a subset of top predicted models from CAPRI (Critical Assessment of PRediction of Interactions) rounds over 2017-2020, we find that the ease of achieving a given root mean squared deviation or DOCKQ score varies considerably by target; achieving the same relative metric can be thousands of times easier for one complex compared to another. In contrast, RD p-values inherently normalize scores for models of different complexes, making them globally comparable. Furthermore, one can calculate RD p-values after generating a reference distribution that accounts for prior information about the interface geometry, such as residues involved in binding, by giving the random-docking process access the same information. Thus, one can decouple improvements in prediction accuracy that arise solely from basic modeling constraints from those due to the rest of the method. We provide efficient code for computing RD p-values at https://github.com/Grigoryanlab/RDP.


Assuntos
Mapeamento de Interação de Proteínas , Proteínas , Proteínas/química , Mapeamento de Interação de Proteínas/métodos , Simulação de Acoplamento Molecular , Conformação Proteica , Ligação Proteica , Software , Algoritmos , Biologia Computacional/métodos , Sítios de Ligação
16.
J Proteome Res ; 23(1): 494-499, 2024 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-38069805

RESUMO

Plant-pathogen protein-protein interactions (PPIs) play crucial roles in the arm race between plants and pathogens. Therefore, the identification of these interspecies PPIs is very important for the mechanistic understanding of pathogen infection and plant immunity. Computational prediction methods can complement experimental efforts, but their predictive performance still needs to be improved. Motivated by the rapid development of natural language processing and its successful applications in the field of protein bioinformatics, here we present an improved XGBoost-based plant-pathogen PPI predictor (i.e., AraPathogen2.0), in which sequence encodings from the pretrained protein language model ESM2 and Arabidopsis PPI network-related node representations from the graph embedding technique struc2vec are used as input. Stringent benchmark experiments showed that AraPathogen2.0 could achieve a better performance than its precedent version, especially for processing the test data set with novel proteins unseen in the training data.


Assuntos
Arabidopsis , Mapeamento de Interação de Proteínas , Mapeamento de Interação de Proteínas/métodos , Processamento de Linguagem Natural , Plantas , Proteínas/metabolismo , Arabidopsis/metabolismo
17.
J Chem Inf Model ; 63(23): 7363-7372, 2023 Dec 11.
Artigo em Inglês | MEDLINE | ID: mdl-38037990

RESUMO

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.


Assuntos
Aprendizado Profundo , Mapeamento de Interação de Proteínas , Mapeamento de Interação de Proteínas/métodos , Simulação de Acoplamento Molecular , Proteína 1 Associada a ECH Semelhante a Kelch , Fator 2 Relacionado a NF-E2
18.
BMC Bioinformatics ; 24(1): 473, 2023 Dec 14.
Artigo em Inglês | MEDLINE | ID: mdl-38097937

RESUMO

PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.


Assuntos
Aprendizado Profundo , Mapeamento de Interação de Proteínas , Mapeamento de Interação de Proteínas/métodos , Redes Neurais de Computação , Sequência de Aminoácidos , Proteínas/metabolismo
19.
J Integr Bioinform ; 20(4)2023 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-38097366

RESUMO

Proteins are important parts of the biological structures and encode a lot of biological information. Protein-protein interaction network alignment is a model for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network alignment aims at finding the mapping relationship among multiple network nodes, so as to transfer the knowledge across species. However, with the increasing complexity of PPI networks, how to perform network alignment more accurately and efficiently is a new challenge. This paper proposes a new global network alignment algorithm called Simulated Annealing Multiple Network Alignment (SAMNA), using both network topology and sequence homology information. To generate the alignment, SAMNA first generates cross-network candidate clusters by a clustering algorithm on a k-partite similarity graph constructed with sequence similarity information, and then selects candidate cluster nodes as alignment results and optimizes them using an improved simulated annealing algorithm. Finally, the SAMNA algorithm was experimented on synthetic and real-world network datasets, and the results showed that SAMNA outperformed the state-of-the-art algorithm in biological performance.


Assuntos
Algoritmos , Mapas de Interação de Proteínas , Proteínas/química , Análise por Conglomerados , Mapeamento de Interação de Proteínas/métodos , Biologia Computacional/métodos
20.
Protein Eng Des Sel ; 362023 Jan 21.
Artigo em Inglês | MEDLINE | ID: mdl-38102755

RESUMO

Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.


Assuntos
Aprendizado Profundo , Mapeamento de Interação de Proteínas , Proteínas , Proteínas/química , Mapeamento de Interação de Proteínas/métodos
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