Prediction of Function Determining and Buried Residues Through Analysis of Saturation Mutagenesis Datasets

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

Rare deleterious mutations in Bruton's tyrosine kinase as biomarkers for ibrutinib-based therapy: an in silico insight

CONTEXT

Squamous cell carcinoma (SCC) is the second most common type of skin cancer caused by malignant keratinocytes. Multiple studies have shown that protein mutations have a significant impact on the development and progression of cancer, including SCC. We attempted to decode the effect of single amino acid mutations in the Bruton's tyrosine kinase (BTK) protein in this study. Molecular dynamic (MD) simulations were performed on selected deleterious mutations of the BTK protein, revealing that the variants adversely affect the protein, indicating that they may contribute to the prognosis of SCC by making the protein unstable. Then, we investigated the interaction between the protein and its mutants with ibrutinib, a drug designed to treat SCC. Even though the mutations have deleterious effects on protein structure, they bind to ibrutinib similarly to their wild type counterpart. This study demonstrates that the effect of detected missense mutations is unfavorable and can result in function loss, which is severe for SCC, but that ibrutinib-based therapy can still be effective on them, and the mutations can be used as biomarkers for Ibrutinib-based treatment.

METHODS

Seven different computational techniques were used to compute the effect of SAVs in accordance with the experimental requirements of this study. To understand the differences in protein and mutant dynamics, MD simulation and trajectory analysis, including RMSD, RMSF, PCA, and contact analysis, were performed. The free binding energy and its decomposition for each protein-drug complex were determined using docking, MM-GBSA, MM-PBSA, and interaction analysis (wild and mutants).

Bioinformatics Analysis of Mutations Sheds Light on the Evolution of Dengue NS1 Protein With Implications in the Identification of Potential Functional and Druggable Sites

Non-structural protein (NS1) is a 350 amino acid long conserved protein in the dengue virus. Conservation of NS1 is expected due to its importance in dengue pathogenesis. The protein is known to exist in dimeric and hexameric states. The dimeric state is involved in its interaction with host proteins and viral replication, and the hexameric state is involved in viral invasion. In this work, we performed extensive structure and sequence analysis of NS1 protein, and uncovered the role of NS1 quaternary states in its evolution. A three-dimensional modeling of unresolved loop regions in NS1 structure is performed. "Conserved" and "Variable" regions within NS1 protein were identified from sequences obtained from patient samples and the role of compensatory mutations in selecting destabilizing mutations were identified. Molecular dynamics (MD) simulations were performed to extensively study the effect of a few mutations on NS1 structure stability and compensatory mutations. Virtual saturation mutagenesis, predicting the effect of every individual amino acid substitution on NS1 stability sequentially, revealed virtual-conserved and variable sites. The increase in number of observed and virtual-conserved regions across NS1 quaternary states suggest the role of higher order structure formation in its evolutionary conservation. Our sequence and structure analysis could enable in identifying possible protein-protein interfaces and druggable sites. Virtual screening of nearly 10,000 small molecules, including FDA-approved drugs, permitted us to recognize six drug-like molecules targeting the dimeric sites. These molecules could be promising due to their stable interactions with NS1 throughout the simulation.

Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA

Unlike globular proteins, mutational effects on the function of Intrinsically Disordered Proteins (IDPs) are not well-studied. Deep Mutational Scanning of a yeast surface displayed mutant library yields insights into sequence-function relationships in the CcdA IDP. The approach enables facile prediction of interface residues and local structural signatures of the bound conformation. In contrast to previous titration-based approaches which use a number of ligand concentrations, we show that use of a single rationally chosen ligand concentration can provide quantitative estimates of relative binding constants for large numbers of protein variants. This is because the extended interface of IDP ensures that energetic effects of point mutations are spread over a much smaller range than for globular proteins. Our data also provides insights into the much-debated role of helicity and disorder in partner binding of IDPs. Based on this exhaustive mutational sensitivity dataset, a rudimentary model was developed in an attempt to predict mutational effects on binding affinity of IDPs that form alpha-helical structures upon binding.

Deep mutational scanning of essential bacterial proteins can guide antibiotic development

Deep mutational scanning is a powerful approach to investigate a wide variety of research questions including protein function and stability. Here, we perform deep mutational scanning on three essential E. coli proteins (FabZ, LpxC and MurA) involved in cell envelope synthesis using high-throughput CRISPR genome editing, and study the effect of the mutations in their original genomic context. We use more than 17,000 variants of the proteins to interrogate protein function and the importance of individual amino acids in supporting viability. Additionally, we exploit these libraries to study resistance development against antimicrobial compounds that target the selected proteins. Among the three proteins studied, MurA seems to be the superior antimicrobial target due to its low mutational flexibility, which decreases the chance of acquiring resistance-conferring mutations that simultaneously preserve MurA function. Additionally, we rank anti-LpxC lead compounds for further development, guided by the number of resistance-conferring mutations against each compound. Our results show that deep mutational scanning studies can be used to guide drug development, which we hope will contribute towards the development of novel antimicrobial therapies.

Prediction of Residue-specific Contributions to Binding and Thermal Stability Using Yeast Surface Display

Accurate prediction of residue burial as well as quantitative prediction of residue-specific contributions to protein stability and activity is challenging, especially in the absence of experimental structural information. This is important for prediction and understanding of disease causing mutations, and for protein stabilization and design. Using yeast surface display of a saturation mutagenesis library of the bacterial toxin CcdB, we probe the relationship between ligand binding and expression level of displayed protein, with in vivo solubility in E. coli and in vitro thermal stability. We find that both the stability and solubility correlate well with the total amount of active protein on the yeast cell surface but not with total amount of expressed protein. We coupled FACS and deep sequencing to reconstruct the binding and expression mean fluorescent intensity of each mutant. The reconstructed mean fluorescence intensity (MFIseq) was used to differentiate between buried site, exposed non active-site and exposed active-site positions with high accuracy. The MFIseq was also used as a criterion to identify destabilized as well as stabilized mutants in the library, and to predict the melting temperatures of destabilized mutants. These predictions were experimentally validated and were more accurate than those of various computational predictors. The approach was extended to successfully identify buried and active-site residues in the receptor binding domain of the spike protein of SARS-CoV-2, suggesting it has general applicability.

Artificial intelligence based methods for hot spot prediction

Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high therapeutic potential. However, discovering such molecules is challenging. Most protein-protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design.

Rapid Identification of Secondary Structure and Binding Site Residues in an Intrinsically Disordered Protein Segment

Mycobacterium tuberculosis harbours nine toxin-antitoxin (TA) systems of the MazEF family. MazEF TA modules are of immense importance due to the perceived role of the MazF toxin in M. tuberculosis persistence and disease. The MazE antitoxin has a disordered C-terminal domain that binds the toxin, MazF and neutralizes its endoribonuclease activity. However, the structure of most MazEF TA complexes remains unsolved till date, obscuring structural and functional information about the antitoxins. We present a facile method to identify toxin binding residues on the disordered antitoxin. Charged residue scanning mutagenesis was used to screen a yeast surface displayed MazE6 antitoxin library against its purified cognate partner, the MazF6 toxin. Binding residues were deciphered by probing the relative reduction in binding to the ligand by flow cytometry. We have used this to identify putative antitoxin interface residues and local structure attained by the antitoxin upon interaction in the MazEF6 TA system and the same methodology is readily applicable to other intrinsically disordered protein regions.