Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer

The strand exchange domain of tumor suppressor PALB2 is intrinsically disordered and promotes oligomerization-dependent DNA compaction

The partner and localizer of BRCA2 (PALB2) is a scaffold protein linking BRCA1 with BRCA2 and RAD51 during homologous recombination (HR). PALB2 interaction with DNA strongly enhances HR in cells, while the PALB2 DNA-binding domain (PALB2-DBD) supports DNA strand exchange in vitro. We determined that PALB2-DBD is intrinsically disordered beyond a single N-terminal α-helix. Coiled-coil mediated dimerization is stabilized by interaction between intrinsically disordered regions (IDRs) leading to a 2-fold structural compaction. Single-stranded (ss)DNA binding promotes additional structural compaction and protein tetramerization. Using confocal single-molecule FRET, we observed bimodal and oligomerization-dependent compaction of ssDNA bound to PALB2-DBD, suggesting a novel strand exchange mechanism. Bioinformatics analysis and preliminary observations indicate that PALB2 forms protein-nucleic acids condensates. Intrinsically disordered DBDs are prevalent in the human proteome. PALB2-DBD and similar IDRs may use a chaperone-like mechanism to aid formation and resolution of DNA and RNA multichain intermediates during DNA replication, repair and recombination.

In Silico Modeling and Characterization of Epstein-Barr Virus Latent Membrane Protein 1 Protein

Latent membrane protein 1 (LMP1) plays a crucial role in Epstein-Barr virus (EBV)'s ability to establish latency and is involved in developing and progressing EBV-associated cancers. Additionally, EBV-infected cells affect the immune responses, making it challenging for the immune system to eliminate them. Due to the aforementioned reasons, it is crucial to understand the structural features of LMP1, which are essential for the development of novel cancer therapies that target its signaling pathways. To date, there is yet to be a complete LMP1 protein structure; therefore, in our work, we modeled the full-length LMP1 containing the short cytoplasmic N-terminus, six transmembrane domains (TMDs), and a long-simulated C-terminus. Our model showed good stability and protein compactness evaluated through accelerated-molecular dynamics, where the conformational ensemble exhibited compact folds, particularly in the TMDs. Our results suggest that specific domains or motifs, predominantly in the C-terminal domain of LMP1, show promise as potential drug targets. As a whole, our work provides insights into key structural features of LMP1 that will allow the development of novel LMP1 therapies.

Evaluation of Three Mutations in Codon 385 of Glucose-6-Phosphate Dehydrogenase via Biochemical and In Silico Analysis

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is an enzymopathy that affects approximately 500 million people worldwide. A great number of mutations in the G6PD gene have been described. However, three class A G6PD variants known as G6PD Tomah (C385R), G6PD Kangnam (C385G), and G6PD Madrid (C385W) have been reported to be clinically important due to their associations with severe clinical manifestations such as hemolytic anemia. Therefore, this work aimed to perform, for the first time, biochemical and functional characterizations of these variants. The G6PD variants were cloned and purified for this purpose, followed by analyses of their kinetic parameters and thermal stability, as well as in silico studies. The results showed that the mutations induced changes in the proteins. Regarding the kinetic parameters, it was observed that the three variants showed lower affinities for G6P and NADP+, as well as lower thermal stability compared to WT-G6PD. Molecular dynamics simulations showed that C385 mutations induced changes around neighboring amino acids. Metadynamics simulations showed that most remarkable changes account for the binding pocket volumes, particularly in the structural NADP+ binding site, with a concomitant loss of affinity for catalytic processes.

Prediction of protein structure and AI

AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-function relationships from protein 3D structure models and can serve as a template or a reference for experimental structural analysis including X-ray crystallography, NMR and cryo-EM analysis. Its use is expanding among researchers, not only in structural biology but also in other research fields. Researchers are currently exploring the full potential of AlphaFold-generated protein models. Predicting disease severity caused by missense mutations is one such application. This article provides an overview of the 3D structural modeling of AlphaFold based on deep learning techniques and highlights the challenges in predicting the pathogenicity of missense mutations.

In Silico Deciphering of the Potential Impact of Variants of Uncertain Significance in Hereditary Colorectal Cancer Syndromes

Colorectal cancer (CRC) ranks third in terms of cancer incidence worldwide and is responsible for 8% of all deaths globally. Approximately 10% of CRC cases are caused by inherited pathogenic mutations in driver genes involved in pathways that are crucial for CRC tumorigenesis and progression. These hereditary mutations significantly increase the risk of initial benign polyps or adenomas developing into cancer. In recent years, the rapid and accurate sequencing of CRC-specific multigene panels by next-generation sequencing (NGS) technologies has enabled the identification of several recurrent pathogenic variants with established functional consequences. In parallel, rare genetic variants that are not characterized and are, therefore, called variants of uncertain significance (VUSs) have also been detected. The classification of VUSs is a challenging task because each amino acid has specific biochemical properties and uniquely contributes to the structural stability and functional activity of proteins. In this scenario, the ability to computationally predict the effect of a VUS is crucial. In particular, in silico prediction methods can provide useful insights to assess the potential impact of a VUS and support additional clinical evaluation. This approach can further benefit from recent advances in artificial intelligence-based technologies. In this review, we describe the main in silico prediction tools that can be used to evaluate the structural and functional impact of VUSs and provide examples of their application in the analysis of gene variants involved in hereditary CRC syndromes.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

The strand exchange domain of tumor suppressor PALB2 is intrinsically disordered and promotes oligomerization-dependent DNA compaction

The Partner and Localizer of BRCA2 (PALB2) is a scaffold protein that links BRCA1 with BRCA2 to initiate homologous recombination (HR). PALB2 interaction with DNA strongly enhances HR efficiency in cells. The PALB2 DNA-binding domain (PALB2-DBD) supports strand exchange, a complex multistep reaction conducted by only a few proteins such as RecA-like recombinases and Rad52. Using bioinformatics analysis, small-angle X-ray scattering, circular dichroism, and electron paramagnetic spectroscopy, we determined that PALB2-DBD is an intrinsically disordered region (IDR) forming compact molten globule-like dimer. IDRs contribute to oligomerization synergistically with the coiled-coil interaction. Using confocal single-molecule FRET we demonstrated that PALB2-DBD compacts single-stranded DNA even in the absence of DNA secondary structures. The compaction is bimodal, oligomerization-dependent, and is driven by IDRs, suggesting a novel strand exchange mechanism. Intrinsically disordered proteins (IDPs) are prevalent in the human proteome. Novel DNA binding properties of PALB2-DBD and the complexity of strand exchange mechanism significantly expands the functional repertoire of IDPs. Multivalent interactions and bioinformatics analysis suggest that PALB2 function is likely to depend on formation of protein-nucleic acids condensates. Similar intrinsically disordered DBDs may use chaperone-like mechanism to aid formation and resolution of DNA and RNA multichain intermediates during DNA replication, repair and recombination.

Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Whole genome sequencing in clinical practice

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.

Rosetta Energy Analysis of AlphaFold2 models: Point Mutations and Conformational Ensembles

There has been an explosive growth in the applications of AlphaFold2, and other structure prediction platforms, to accurately predict protein structures from a multiple sequence alignment (MSA) for downstream structural analysis. However, two outstanding questions persist in the field regarding the robustness of AlphaFold2 predictions of the consequences of point mutations and the completeness of its prediction of protein conformational ensembles. We combined our previously developed method SPEACH_AF with model relaxation and energetic analysis with Rosetta to address these questions. SPEACH_AF introduces residue substitutions across the MSA and not just within the input sequence. With respect to conformational ensembles, we combined SPEACH_AF and a new MSA subsampling method, AF_cluster, and for a benchmarked set of proteins, we found that the energetics of the conformational ensembles generated by AlphaFold2 correspond to those of experimental structures and explored by standard molecular dynamic methods. With respect to point mutations, we compared the structural and energetic consequences of having the mutation(s) in the input sequence versus in the whole MSA (SPEACH_AF). Both methods yielded models different from the wild-type sequence, with more robust changes when the mutation(s) were in the whole MSA. While our findings demonstrate the robustness of AlphaFold2 in analyzing point mutations and exploring conformational ensembles, they highlight the need for multi parameter structural and energetic analyses of these models to generate experimentally testable hypotheses.

Unmasking crucial residues in adipose triglyceride lipase for coactivation with comparative gene identification-58

Lipolysis is an essential metabolic process that releases unesterified fatty acids from neutral lipid stores to maintain energy homeostasis in living organisms. Adipose triglyceride lipase (ATGL) plays a key role in intracellular lipolysis and can be coactivated upon interaction with the protein comparative gene identification-58 (CGI-58). The underlying molecular mechanism of ATGL stimulation by CGI-58 is incompletely understood. Based on analysis of evolutionary conservation, we used site directed mutagenesis to study a C-terminally truncated variant and full-length mouse ATGL providing insights in the protein coactivation on a per-residue level. We identified the region from residues N209-N215 in ATGL as essential for coactivation by CGI-58. ATGL variants with amino acids exchanges in this region were still able to hydrolyze triacylglycerol at the basal level and to interact with CGI-58, yet could not be activated by CGI-58. Our studies also demonstrate that full-length mouse ATGL showed higher tolerance to specific single amino acid exchanges in the N209-N215 region upon CGI-58 coactivation compared to C-terminally truncated ATGL variants. The region is either directly involved in protein-protein interaction or essential for conformational changes required in the coactivation process. Three-dimensional models of the ATGL/CGI-58 complex with the artificial intelligence software AlphaFold demonstrated that a large surface area is involved in the protein-protein interaction. Mapping important amino acids for coactivation of both proteins, ATGL and CGI-58, onto the 3D model of the complex locates these essential amino acids at the predicted ATGL/CGI-58 interface thus strongly corroborating the significance of these residues in CGI-58-mediated coactivation of ATGL.

The PALB2 DNA-binding domain is an intrinsically disordered recombinase

The Partner and Localizer of BRCA2 (PALB2) tumor suppressor is a scaffold protein that links BRCA1 with BRCA2 to initiate homologous recombination (HR). PALB2 interaction with DNA strongly enhances HR efficiency. The PALB2 DNA-binding domain (PALB2-DBD) supports DNA strand exchange, a complex multistep reaction supported by only a few protein families such as RecA-like recombinases or Rad52. The mechanisms of PALB2 DNA binding and strand exchange are unknown. We performed circular dichroism, electron paramagnetic spectroscopy, and small-angle X-ray scattering analyses and determined that PALB2-DBD is intrinsically disordered, even when bound to DNA. The intrinsically disordered nature of this domain was further supported by bioinformatics analysis. Intrinsically disordered proteins (IDPs) are prevalent in the human proteome and have many important biological functions. The complexity of the strand exchange reaction significantly expands the functional repertoire of IDPs. The results of confocal single-molecule FRET indicated that PALB2-DBD binding leads to oligomerization-dependent DNA compaction. We hypothesize that PALB2-DBD uses a chaperone-like mechanism to aid formation and resolution of complex DNA and RNA multichain intermediates during DNA replication and repair. Since PALB2-DBD alone or within the full-length PALB2 is predicted to have strong liquid-liquid phase separation (LLPS) potential, protein-nucleic acids condensates are likely to play a role in complex functionality of PALB2-DBD. Similar DNA-binding intrinsically disordered regions may represent a novel class of functional domains that evolved to function in eukaryotic nucleic acid metabolism complexes.

Rapid protein stability prediction using deep learning representations

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.