Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Sun, Jianfeng; Kulandaisamy, A.; Liu, Jacklyn; Hu, Kai; Gromiha, M. Michael; Zhang, Yuan

doi:10.1016/j.csbj.2023.01.036

Cited by 8 publications

(5 citation statements)

References 255 publications

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“…Computational approaches, in particular molecular dynamics (MD) simulations, have been increasingly used to evaluate the interaction of drugs with the cell membrane. [77][78][79] The interaction of lead complexes 1 and 4 with a membrane model was studied using MD simulations, to evaluate the impact of specific chemical groups, introduced in the Cp ring (hydrazide or acetyl groups, respectively), of the reference complex TM34, in its mode of interaction with the cell membrane. In particular, their effects on the membrane insertion depth, preferred Paper Dalton Transactions orientation, and membrane permeation were studied, and a possible correlation with the cytotoxic activities was evaluated.…”

Section: Membrane Interactions and Permeability From MD Simulationsmentioning

confidence: 99%

Dual FGFR-targeting and pH-activatable ruthenium–peptide conjugates for targeted therapy of breast cancer

Franco Machado,

Sá,

Pires

et al. 2024

Dalton Trans.

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Section: Membrane Interactions and Permeability From MD Simulationsmentioning

confidence: 99%

Dual FGFR-targeting and pH-activatable ruthenium–peptide conjugates for targeted therapy of breast cancer

Franco Machado,

Sá,

Pires

et al. 2024

Dalton Trans.

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“…Computa�onal approaches, in par�cular molecular dynamics (MD) simula�ons, have been increasingly used to evaluate the interac�on of drugs with the cell membrane. [75][76][77] The interac�on of lead complexes 1 and 4 with a membrane model was studied using MD simula�ons, to evaluate the impact of specific chemical groups, introduced in the Cp ring (hydrazide or acetyl groups, respec�vely), of the reference complex TM34, in its mode of interac�on with the cell membrane. In par�cular, their effects on the membrane inser�on depth, preferred orienta�on, and membrane permea�on, and consequently relate them to the cytotoxic ac�vi�es.…”

Section: Membrane Interactions and Permeability From MD Simulationsmentioning

confidence: 99%

Dual FGFR-targeting and pH-activable Smart Ruthenium-Peptide Conjugates for targeted therapy of Breast Cancer

Franco Machado,

Sá,

Pires

et al. 2023

Preprint

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Dysregulation of Fibroblast Growth Factor Receptors (FGFRs) signaling has been associated with breast cancer, yet employing FGFR-targeted delivery systems to improve cytotoxic agents is still sparsely exploited. Herein, we report four new bi-functional ruthenium-peptide conjugates (RuPCs) with FGFR-targeting and pH-dependent releasing abilities, envisioning the selective delivery of cytotoxic Ru complexes to FGFR(+)-breast cancer cells, upon specific accumulation and controlled activation at the acidic tumoral microenvironment. The antiproliferative potential of the RuPCs and active Ru complexes was evaluated in four breast cancer cell lines with different FGFR expression levels (SK-BR-3, MDA-MB-134-VI, MCF-7, and MDA-MB-231) and in the normal human dermal fibroblasts (HDF) cell line, at pH 6.8 and 7.4 that mimics the tumor microenvironment and normal tissues/bloodstream, respectively. The RuPCs showed higher cytotoxicity in cells with higher level of FGFR expression at acidic pH. Additionally, RuPCs showed up to 6-fold higher activity in the FGFR(+) breast cancer lines compared to the normal line. The release profile of Ru complexes from RuPCs corroborates the antiproliferative effects observed. Remarkably, the cytotoxicity and releasing ability of RuPCs were shown to be strongly dependent on the peptide conjugation position in the Ru complex. Complementary molecular dynamic simulations and computational calculations were performed to help interpret these findings at the molecular level. In summary, we identified a lead bi-functional RuPC that holds strong potential as a FGFR-targeted chemotherapeutic agent.

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“…Membrane proteins are important biological molecules that play a crucial role in various cellular processes, such as cell signaling, ion transport, and energy production [1][2][3][4][5][6][7]. They are essential components of cell membranes and are involved in a wide range of physiological functions.…”

Section: Introductionmentioning

confidence: 99%

“…However, the study of membrane proteins is challenging due to their complex structures and properties that make them difficult to isolate and analyze experimentally. Therefore, computational methods have emerged as a powerful tool for studying membrane proteins [1,2,[9][10][11]. In recent years, there has been a growing interest in developing more accurate and efficient methods for generating features from protein sequences, which can be used to extract important sequence patterns and descriptors for downstream analysis.…”

Section: Introductionmentioning

confidence: 99%

Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to pre‐trained language models

Ou,

Ho,

Chang

2023

Proteomics

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Membrane proteins play a crucial role in various cellular processes and are essential components of cell membranes. Computational methods have emerged as a powerful tool for studying membrane proteins due to their complex structures and properties that make them difficult to analyze experimentally. Traditional features for protein sequence analysis based on amino acid types, composition, and pair composition have limitations in capturing higher‐order sequence patterns. Recently, multiple sequence alignment (MSA) and pre‐trained language models (PLMs) have been used to generate features from protein sequences. However, the significant computational resources required for MSA‐based features generation can be a major bottleneck for many applications. Several methods and tools have been developed to accelerate the generation of MSAs and reduce their computational cost, including heuristics and approximate algorithms. Additionally, the use of PLMs such as BERT has shown great potential in generating informative embeddings for protein sequence analysis. In this review, we provide an overview of traditional and more recent methods for generating features from protein sequences, with a particular focus on MSAs and PLMs. We highlight the advantages and limitations of these approaches and discuss the methods and tools developed to address the computational challenges associated with features generation. Overall, the advancements in computational methods and tools provide a promising avenue for gaining deeper insights into the function and properties of membrane proteins, which can have significant implications in drug discovery and personalized medicine.

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Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Cited by 8 publications

References 255 publications

Dual FGFR-targeting and pH-activatable ruthenium–peptide conjugates for targeted therapy of breast cancer

Dual FGFR-targeting and pH-activatable ruthenium–peptide conjugates for targeted therapy of breast cancer

Dual FGFR-targeting and pH-activable Smart Ruthenium-Peptide Conjugates for targeted therapy of Breast Cancer

Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to pre‐trained language models

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