Miniprotein engineering for inhibition of PD-1/PD-L1 interaction.

Miniproteins constitute an excellent basis for the development of structurally demanding functional molecules. The engrailed homeodomain, a three-helix-containing miniprotein, was applied as a scaffold for constructing programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) interaction inhibitors. PD-L1 binders were initially designed using the computer-aided approach and subsequently optimized iteratively. The conformational stability was assessed for each obtained miniprotein using circular dichroism spectroscopy, indicating that numerous mutations could be introduced. The formation of a sizable hydrophobic surface at the inhibitor that fits the molecular target imposed the necessity for the incorporation of additional charged amino acid residues to retain its appropriate solubility. Finally, the miniprotein effectively binding to PD-L1 (KD = 51.4 nM) that inhibits PD-1/PD-L1 interaction in cell-based studies with EC50 = 3.9 µM, was discovered.

The power of computational proteomics platforms to decipher protein-protein interactions.

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity.

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.

Mitochondria in COVID-19: from cellular and molecular perspective.

The rapid development of the COVID-19 pandemic resulted in a closer analysis of cell functioning during ß-coronavirus infection. This review will describe evidence for COVID-19 as a syndrome with a strong, albeit still underestimated, mitochondrial component. Due to the sensitivity of host mitochondria to coronavirus infection, SARS-CoV-2 affects mitochondrial signaling, modulates the immune response, modifies cellular energy metabolism, induces apoptosis and ageing, worsening COVID-19 symptoms which can sometimes be fatal. Various aberrations across human systems and tissues and their relationships with mitochondria were reported. In this review, particular attention is given to characterization of multiple alterations in gene expression pattern and mitochondrial metabolism in COVID-19; the complexity of interactions between SARS-CoV-2 and mitochondrial proteins is presented. The participation of mitogenome fragments in cell signaling and the occurrence of SARS-CoV-2 subgenomic RNA within membranous compartments, including mitochondria is widely discussed. As SARS-CoV-2 severely affects the quality system of mitochondria, the cellular background for aberrations in mitochondrial dynamics in COVID-19 is additionally characterized. Finally, perspectives on the mitigation of COVID-19 symptoms by affecting mitochondrial biogenesis by numerous compounds and therapeutic treatments are briefly outlined.

Software and Databases for Protein-Protein Docking.

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

Molecular Dynamics Simulations in Protein-Protein Docking.

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions.

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

The C-terminal sequences of Bcl-2 family proteins mediate interactions that regulate cell death.

Programmed cell death via the both intrinsic and extrinsic pathways is regulated by interactions of the Bcl-2 family protein members that determine whether the cell commits to apoptosis via mitochondrial outer membrane permeabilization (MOMP). Recently the conserved C-terminal sequences (CTSs) that mediate localization of Bcl-2 family proteins to intracellular membranes, have been shown to have additional protein-protein binding functions that contribute to the functions of these proteins in regulating MOMP. Here we review the pivotal role of CTSs in Bcl-2 family interactions including: (1) homotypic interactions between the pro-apoptotic executioner proteins that cause MOMP, (2) heterotypic interactions between pro-apoptotic and anti-apoptotic proteins that prevent MOMP, and (3) heterotypic interactions between the pro-apoptotic executioner proteins and the pro-apoptotic direct activator proteins that promote MOMP.

Multiple Functional Protein-Protein Interaction Interfaces Allosterically Regulate ATP-Binding in Cyclin-Dependent Kinase-1.

The ATP-dependent phosphorylation activity of cyclin-dependent kinase 1 (CDK1), an essential enzyme for cell cycle progression, is regulated by interactions with Cyclin-B, substrate, and Cks proteins. We have recently shown that active site acetylation in CDK1 abrogated binding to Cyclin-B which posits an intriguing long-range communication between the catalytic site and the protein-protein interaction (PPI) interface. Now, we demonstrate a general allosteric link between the CDK1 active site and all three of its PPI interfaces through atomistic molecular dynamics (MD) simulations. Specifically, we examined ATP binding free energies to CDK1 in native nonacetylated (K33wt) and acetylated (K33Ac) forms as well as the acetyl-mimic K33Q and the acetyl-null K33R mutant forms, which are accessible in vitro. In agreement with experiments, ATP binding is stronger in K33wt relative to the other three perturbed states. Free energy decomposition reveals, in addition to expected local changes, significant and selective nonlocal entropic responses to ATP binding/perturbation of K33 from the αC $$ \alpha C $$ -helix, activation loop (A-loop), and αG $$ \alpha G $$ - α $$ \alpha $$ H segments in CDK1 which interface with Cyclin-B, substrate, and Cks proteins, respectively. Statistical analysis reveals that while entropic responses of protein segments to active site perturbations are on average correlated with their dynamical changes, such correlations are lost in about 9%-48% of the dataset depending on the segment. Besides proving the bi-directional communication between the active site and the CDK1:Cyclin-B interface, our study uncovers a hitherto unknown mode of ATP binding regulation by multiple PPI interfaces in CDK1.

Proximity Ligation Assay for the Analysis of Iron-Mediated Protein-Protein Interactions in the Nucleus.

Iron forms essential cofactors used by many nuclear enzymes involved in genome maintenance. However, unchaperoned nuclear iron may represent a threat to the surrounding genetic material as it promotes redox toxicity that may affect DNA integrity. Safely handling intracellular iron implies metal transfer and cofactor assembly processes based on protein-protein interactions. Identifying those interactions commonly occurs via high-throughput approaches using affinity purification or proximity labeling coupled with mass spectrometry analysis. However, these methods do not identify the subcellular location of the interactions. The one-on-one confirmation of proposed nuclear interactions is also challenging. Many approaches used to look at protein interactions are not tailored for looking at the nucleus because the methods used to solubilize nuclear content are harsh enough to disrupt those transient interactions. Here, we describe step-by-step the use of Proximity Ligation Assay (PLA) to analyze iron-mediated protein-protein interactions in the nucleus of cultured human cells. PLA allows the subcellular visualization of the interactions via the in situ detection of the two interacting proteins using fluorescence confocal microscopy. Briefly, cells are fixed, blocked, permeabilized, and incubated with primary antibodies directed to target proteins. Primary antibodies are recognized using PLA probes consisting of one PLUS and one MINUS oligonucleotide-labeled secondary antibody. If the two proteins are close enough (<40 nm), the PLA probes are ligated and used as the template for rolling circle amplification (RCA) with fluorescently labeled oligonucleotides that yield a signal detectable using fluorescence confocal microscopy. A fluorescently labeled membrane-specific stain (WGA) and the DNA-specific probe DAPI are used to identify cellular and nuclear boundaries, respectively. Confocal images are then analyzed using the CellProfiler software to confirm the abundance and localization of the studied protein-protein interactions.

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

Point mutations at specific sites of the nsp12-nsp8 interface dramatically affect the RNA polymerization activity of SARS-CoV-2.

In a recent characterization of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variability present in 30 diagnostic samples from patients of the first COVID-19 pandemic wave, 41 amino acid substitutions were documented in the RNA-dependent RNA polymerase (RdRp) nsp12. Eight substitutions were selected in this work to determine whether they had an impact on the RdRp activity of the SARS-CoV-2 nsp12-nsp8-nsp7 replication complex. Three of these substitutions were found around the polymerase central cavity, in the template entry channel (D499G and M668V), and within the motif B (V560A), and they showed polymerization rates similar to the wild type RdRp. The remaining five mutations (P323L, L372F, L372P, V373A, and L527H) were placed near the nsp12-nsp8F contact surface; residues L372, V373, and L527 participated in a large hydrophobic cluster involving contacts between two helices in the nsp12 fingers and the long α-helix of nsp8F. The presence of any of these five amino acid substitutions resulted in important alterations in the RNA polymerization activity. Comparative primer elongation assays showed different behavior depending on the hydrophobicity of their side chains. The substitution of L by the bulkier F side chain at position 372 slightly promoted RdRp activity. However, this activity was dramatically reduced with the L372P, and L527H mutations, and to a lesser extent with V373A, all of which weaken the hydrophobic interactions within the cluster. Additional mutations, specifically designed to disrupt the nsp12-nsp8F interactions (nsp12-V330S, nsp12-V341S, and nsp8-R111A/D112A), also resulted in an impaired RdRp activity, further illustrating the importance of this contact interface in the regulation of RNA synthesis.

Structural and molecular dynamics simulation studies of CBL-interacting protein kinase CIPK and its complexes related to plant salinity stress.

CONTEXT: Calcium-dependent signaling in plants is responsible for several major cellular events, including the activation of the salinity-responsive pathways. Calcium binds to calcineurin B-like protein (CBL), and the resulting CBL-Ca2+ complex binds to CBL-interacting protein kinase (CIPK). The CBL-CIPK complex enhances the CIPK interaction with an upstream kinase. The upstream kinase phosphorylates CIPK that, in turn, phosphorylates membrane transporters. Phosphorylation influences transporter activity to kick-start many downstream functions, such as balancing the cytosolic Na+-to-K+ ratio. The CBL-CIPK interaction is pivotal for Ca2+-dependent salinity stress signaling. METHODS: Computational methods are used to model the entire Arabidopsis thaliana CIPK24 protein structure in its autoinhibited and open-activated states. Arabidopsis thaliana CIPK24-CBL4 complex is predicted based on the protein-protein docking methods. The available structural and functional data support the CIPK24 and the CIPK24-CBL4 complex models. Models are energy-minimized and subjected to molecular dynamics (MD) simulations. MD simulations for 500 ns and 300 ns enabled us to predict the importance of conserved residues of the proteins. Finally, the work is extended to predict the CIPK24-CBL4 complex with the upstream kinase GRIK2. MD simulation for 300 ns on the ternary complex structure enabled us to identify the critical CIPK24-GRIK2 interactions. Together, these data could be used to engineer the CBL-CIPK interaction network for developing salt tolerance in crops.

LazyAF, a pipeline for accessible medium-scale in silico prediction of protein-protein interactions.

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

Dissecting the structural and functional consequences of the evolutionary proline-glycine deletion in the wing 1 region of the forkhead domain of human FoxP1.

The human FoxP transcription factors dimerize via three-dimensional domain swapping, a unique feature among the human Fox family, as result of evolutionary sequence adaptations in the forkhead domain. This is the case for the conserved glycine and proline residues in the wing 1 region, which are absent in FoxP proteins but present in most of the Fox family. In this work, we engineered both glycine (G) and proline-glycine (PG) insertion mutants to evaluate the deletion events in FoxP proteins in their dimerization, stability, flexibility, and DNA-binding ability. We show that the PG insertion only increases protein stability, whereas the single glycine insertion decreases the association rate and protein stability and promotes affinity to the DNA ligand.

Plastid HSP90C C-terminal extension region plays a regulatory role in chaperone activity and client binding.

HSP90Cs are essential molecular chaperones localized in the plastid stroma that maintain protein homeostasis and assist the import and thylakoid transport of chloroplast proteins. While HSP90C contains all conserved domains as an HSP90 family protein, it also possesses a unique feature in its variable C-terminal extension (CTE) region. This study elucidated the specific function of this HSP90C CTE region. Our phylogenetic analyses revealed that this intrinsically disordered region contains a highly conserved DPW motif in the green lineages. With biochemical assays, we showed that the CTE is required for the chaperone to effectively interact with client proteins PsbO1 and LHCB2 to regulate ATP-independent chaperone activity and to effectuate its ATP hydrolysis. The CTE truncation mutants could support plant growth and development reminiscing the wild type under normal conditions except for a minor phenotype in cotyledon when expressed at a level comparable to wild type. However, higher HSP90C expression was observed to correlate with a stronger response to specific photosystem II inhibitor DCMU, and CTE truncations dampened the response. Additionally, when treated with lincomycin to inhibit chloroplast protein translation, CTE truncation mutants showed a delayed response to PsbO1 expression repression, suggesting its role in chloroplast retrograde signaling. Our study therefore provides insights into the mechanism of HSP90C in client protein binding and the regulation of green chloroplast maturation and function, especially under stress conditions.

Robust Prediction of Relative Binding Energies for Protein-Protein Complex Mutations Using Free Energy Perturbation Calculations.

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how Protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis.

Introduction: The most common primary brain tumor in adults is glioblastoma multiforme (GBM), accounting for 45.2% of all cases. The characteristics of GBM, a highly aggressive brain tumor, include rapid cell division and a propensity for necrosis. Regretfully, the prognosis is extremely poor, with only 5.5% of patients surviving after diagnosis. Methodology: To eradicate these kinds of complicated diseases, significant focus is placed on developing more effective drugs and pinpointing precise pharmacological targets. Finding appropriate biomarkers for drug discovery entails considering a variety of factors, including illness states, gene expression levels, and interactions between proteins. Using statistical techniques like p-values and false discovery rates, we identified differentially expressed genes (DEGs) as the first step in our research for identifying promising biomarkers in GBM. Of the 132 genes, 13 showed upregulation, and only 29 showed unique downregulation. No statistically significant changes in the expression of the remaining genes were observed. Results: Matrix metallopeptidase 9 (MMP9) had the greatest degree in the hub biomarker gene identification, followed by (periostin (POSTN) at 11 and Hes family BHLH transcription factor 5 (HES5) at 9. The significance of the identification of each hub biomarker gene in the initiation and advancement of glioblastoma multiforme was brought to light by the survival analysis. Many of these genes participate in signaling networks and function in extracellular areas, as demonstrated by the enrichment analysis.We also identified the transcription factors and kinases that control proteins in the proteinprotein interactions (PPIs) of the DEGs. Discussion: We discovered drugs connected to every hub biomarker. It is an appealing therapeutic target for inhibiting MMP9 involved in GBM. Molecular docking investigations indicated that the chosen complexes (carmustine, lomustine, marimastat, and temozolomide) had high binding affinities of -6.3, -7.4, -7.7, and -8.7 kcal/mol, respectively, the mean root-mean-square deviation (RMSD) value for the carmustine complex and marimastat complex was 4.2 Å and 4.9 Å, respectively, and the lomustine and temozolomide complex system showed an average RMSD of 1.2 Å and 1.6 Å, respectively. Additionally, high stability in root-mean-square fluctuation (RMSF) analysis was observed with no structural conformational changes among the atomic molecules. Thus, these in silico investigations develop a new way for experimentalists to target lethal diseases in future.

SARS-CoV-2: Prediction of critical ionic amino acid mutations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that caused coronavirus disease 2019 (COVID-19), has been studied thoroughly, and several variants are revealed across the world with their corresponding mutations. Studies and vaccines development focus on the genetic mutations of the S protein due to its vital role in allowing the virus attach and fuse with the membrane of a host cell. In this perspective, we study the effects of all ionic amino acid mutations of the SARS-CoV-2 viral spike protein S1 when bound to Antibody CC12.1 within the SARS-CoV-2:CC12.1 complex model. Binding free energy calculations between SARS-CoV-2 and antibody CC12.1 are based on the Analysis of Electrostatic Similarities of Proteins (AESOP) framework, where the electrostatic potentials are calculated using Adaptive Poisson-Boltzmann Solver (APBS). The atomic radii and charges that feed into the APBS calculations are calculated using the PDB2PQR software. Our results are the first to propose in silico potential life-threatening mutations of SARS-CoV-2 beyond the present mutations found in the five common variants worldwide. We find each of the following mutations: K378A, R408A, K424A, R454A, R457A, K458A, and K462A, to play significant roles in the binding to Antibody CC12.1, since they are turned into strong inhibitors on both chains of the S1 protein, whereas the mutations D405A, D420A, and D427A, show to play important roles in this binding, as they are turned into mild inhibitors on both chains of the S1 protein.

Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation.

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step towards comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. Evaluated using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations, AF3 complex structures give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.