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

Engineering affinity of humanized ScFv targeting CD147 antibody: A combined approach of mCSM-AB2 and molecular dynamics simulations

This study aims to assess the effectiveness of mCSM-AB2, a graph-based signature machine learning method, for affinity engineering of the humanized single-chain Fv anti-CD147 (HuScFvM6-1B9). In parallel, molecular dynamics (MD) simulations were used to gain valuable insights into the dynamics and affinity of the HuScFvM6-1B9-CD147 complex. The result analysis involved integrating free energy changes calculated from the mCSM-AB2 with binding free energy predictions from MD simulations. The simulated structures of the modified HuScFvM6-1B9-CD147 domain 1 complex from MD simulations were used to highlight critical residues participating in the binding surface. Interestingly, alterations in the pattern of amino acids of HuScFvM6-1B9 at the complementarity determining regions interacting with the 31EDLGS35 epitope were observed, particularly in mutants that lost binding activity. The predicted mutants of HuScFvM6-1B9 were subsequently engineered and expressed in E. coli for subsequent binding property validation. Compared to WT HuScFvM6-1B9, the mutant HuScFvM6-1B9L1:N32Y exhibited a 1.66-fold increase in binding affinity, with a KD of 1.75 × 10-8 M. While mCSM-AB2 demonstrates insignificant improvement in predicting binding affinity enhancements, it excels at predicting negative effects, aligning well with experimental validation. In addition to binding free energies, total entropy was considered to explain the discrepancy between mCSM-AB2 predictions and experimental results. This study provides guidelines and identifies the limitations of mCSM-AB2 and MD simulations in antibody engineering.

Experimental and computational approaches for membrane protein insertion and topology determination

Membrane proteins play pivotal roles in a wide array of cellular processes and constitute approximately a quarter of the protein-coding genes across all organisms. Despite their ubiquity and biological significance, our understanding of these proteins remains notably less comprehensive compared to their soluble counterparts. This disparity in knowledge can be attributed, in part, to the inherent challenges associated with employing specialized techniques for the investigation of membrane protein insertion and topology. This review will center on a discussion of molecular biology methodologies and computational prediction tools designed to elucidate the insertion and topology of helical membrane proteins.

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

Dysregulation of Fibroblast Growth Factor Receptors (FGFRs) signaling has been associated with breast cancer, yet employing FGFR-targeted delivery systems to improve the efficacy of 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, and controlled activation at the acidic tumoral microenvironment. The antiproliferative potential of the RuPCs and free Ru complexes was evaluated in four breast cancer cell lines with different FGFR expression levels (SKBR-3, MDA-MB-134-VI, MCF-7, and MDA-MB-231) and in human dermal fibroblasts (HDF), at pH 6.8 and pH 7.4 aimed at mimicking the tumor microenvironment and normal tissues/bloodstream pHs, 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 cell 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 conjugation of the peptide 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.

Recent research progress in glycosylphosphatidylinositol-anchored protein biosynthesis, chemical/chemoenzymatic synthesis, and interaction with the cell membrane

Glycosylphosphatidylinositol (GPI) attachment to the C-terminus of proteins is a prevalent posttranslational modification in eukaryotic species, and GPIs help anchor proteins to the cell surface. GPI-anchored proteins (GPI-APs) play a key role in various biological events. However, GPI-APs are difficult to access and investigate. To tackle the problem, chemical and chemoenzymatic methods have been explored for the preparation of GPI-APs, as well as GPI probes that facilitate the study of GPIs on live cells. Substantial progress has also been made regarding GPI-AP biosynthesis, which is helpful for developing new synthetic methods for GPI-APs. This article reviews the recent advancements in the study of GPI-AP biosynthesis, GPI-AP synthesis, and GPI interaction with the cell membrane utilizing synthetic probes.

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

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.