Protein Mutations

The following sections are included:

• Dedication
• Preface
• Acknowledgments
• Contents

A substantial portion of the cellular homeostasis network is dedicated to managing the consequences of protein aggregation, a process that cannot be avoided. In recent decades, research into human diseases, including neurodegenerative disorders, diabetes, and various forms of amyloidosis, has highlighted the profound impact of protein aggregation and its variants on human health. While numerous experimental techniques exist for studying protein aggregation, in silico methods have become invaluable due to their speed, cost-effectiveness, and adaptability. This chapter provides a concise overview of the decades-long development of computational tools aimed at identifying, quantifying, and exploring the aggregation propensity (AP) and kinetics of proteins and their variants. With a particular emphasis on the influence of protein mutations, we delve into the prediction of protein aggregation kinetics and the comprehension of the underlying mechanisms through molecular dynamic simulations.

Protein stability refers to the free energy difference between the folded and unfolded states of a protein. Amino acid mutations in a protein may alter the stability and are often associated with various diseases. Experimentally, the effect of mutations on protein stability is determined by thermal and chemical denaturation methods. These experimental data on the stability of proteins and their mutants are accumulated in several databases, and ProThermDB is the primary resource that offers more than 30,000 thermodynamic data (ΔG^H2O, ΔG, T_m , ΔΔG^H2O, ΔΔG, and ΔT_m) from 1,200 proteins. Utilizing this database, protein stability change upon mutation is related to various protein sequence and structure-based features such as physicochemical properties, inter-residue interactions, solvent accessibility, and energetic parameters. These informations are effectively used for developing computational methods for predicting protein stability upon mutations. In summary, this chapter provides comprehensive details on experimental approaches for determining protein stability and the development of databases and computational tools, as well as insights to understand the complex relationship among protein sequence, structure, function, and stability. In addition, we discussed the effect of protein stability on disease-causing mutations that help for designing the stable mutants and mutation-specific drug design strategies.

Understanding the importance of sequence and structure-based parameters of proteins for determining the folding rates of proteins and accurately predicting protein folding rates, as well as changes in folding rates upon mutations, provides deep insights into protein folding kinetics and design. In this chapter, we briefly outline the databases available for protein folding rates, followed by the development of algorithms for predicting protein folding rates using sequence and structure information and changes in folding rates upon mutation.

Intrinsically disordered regions/proteins (IDRs/IDPs) are highly flexible and lack well-defined three-dimensional structures in solution. The flexibility of IDRs is crucial for their functions, such as binding multiple partners, forming membrane-less compartments, and functional moonlighting. Disordered regions can be experimentally determined by nuclear magnetic resonance (NMR), X-ray crystallography, cryogenic electron microscopy (cryo-EM), and fluorescence resonance energy transfer (FRET) methods. Mutations in IDRs could lead to diseases including neurodegenerative disorders and cancer. Although disease-causing missense mutations more commonly occur in evolutionarily conserved structured regions, more than 20% of human disease mutations are found in IDRs. In this chapter, we will survey the list of databases available for IDRs in proteins, explore computational tools for identifying IDRs, and the influence of mutations on IDRs. Further, the relationship between IDRs and disease-causing mutations will be discussed.

Protein–protein interactions (PPIs) are central to the functioning of living organisms, orchestrating various biological processes. Intermolecular forces and binding interfaces between proteins govern the specificity and strength of these interactions. Genetic mutations may disrupt or alter the strength of binding between proteins. Understanding the effect of mutations on binding affinity in protein–protein complexes is essential for elucidating the structure–function relationship of proteins, predicting disease-associated phenotypes, and designing therapeutic interventions. This chapter provides a comprehensive overview of the impact of mutations on PPIs, encompassing experimental approaches, available databases, factors influencing binding affinity change, and specific computational tools for predicting the change in affinity upon mutation. Finally, this chapter highlights the necessity for continuous innovation in experimental and computational techniques to fully unravel the complex mechanisms underlying binding affinity changes and harness this knowledge for the development of novel therapeutic strategies.

Protein–nucleic acid interactions are inevitable in maintaining the homeostasis of cells. It is important to have a quantitative understanding of these interactions, generally described in terms of the dissociation constant or free energy change of protein–DNA and protein–RNA complexes. These interactions are impaired in the presence of mutations in nucleic acids or their interacting proteins, leading to numerous diseases. Hence, it is important to understand the binding affinity change upon mutation in protein–nucleic acid complexes. Different experimental techniques are available to study the binding affinities, although they are accurate, it is time and labor-intensive. On the other hand, computational techniques are emerging with numerous databases and computational tools to study protein–DNA complexes. In this chapter, we discuss various databases for the binding affinity of complexes and change upon mutation and the tools available to extract different structural and interaction features from the complexes. Further, we provide details on prediction methods reported for predicting the change in binding affinity upon mutation, along with hotspot residue prediction in protein–DNA complexes.

Protein–nucleic acid interactions play a crucial role in maintaining cellular homeostasis. Quantitatively, these interactions are described in terms of the dissociation constant or free energy change observed during protein–nucleic acid complex formation. These interactions are impaired in the presence of mutations affecting the binding affinity of the complexes, in turn leading to numerous diseases. Therefore, understanding how binding affinity changes due to mutations in protein-nucleic acid complexes is vital. While experimental techniques are highly accurate, they are also time-consuming and labor-intensive. On the other hand, computational techniques are emerging as valuable alternatives, with numerous databases and computational tools to study protein–RNA complexes. In this chapter, we discuss various databases that provide information on binding affinities and their changes upon mutation in protein–RNA complexes. Additionally, tools available to extract different structural and interaction features from the complexes are given in detail. Furthermore, we offer insights into prediction methods reported to predict the change in binding affinity upon mutation of protein–RNA complexes. We also cover the existing methods for hotspot residue identification in these complexes.

Carbohydrates are known as sugars or saccharides, which range from simple sugars (glucose) to complex polysaccharides (cellulose). Proteins that interact with carbohydrates are called carbohydrate-binding proteins, and the interaction between proteins and carbohydrates is important for several biological processes, such as cell–cell adhesion, immune response, pathogen recognition, and enzyme catalysis. Mutations in these proteins may impact their structure, binding affinity and function, and understanding the mutational effects on protein–carbohydrate interactions is essential for elucidating their molecular mechanisms. In this chapter, we provide a comprehensive overview of (i) databases available for binding affinity of protein–carbohydrate complexes and their mutants, (ii) analysis and prediction of mutational effects on binding affinity of protein–carbohydrate complexes, (iii) effects of mutation at the interaction sites based on diseases, and (iv) potential applications. The information provided in this chapter will provide insights to understand mutational effects on binding affinity and disease-causing mutations in protein–carbohydrate complexes and design therapeutic strategies.

Elucidating the consequences of mutations on protein function is important for identifying the functionally critical residues and designing proteins with enhanced function. Currently, several databases accumulate information on protein functions for experimentally known variants. On the other hand, prediction tools have been developed to reveal the effects of mutations on protein function. In this chapter, the advancements in experimental methods from traditional site-directed mutagenesis to recent deep mutational scanning experiments are discussed. In addition, we explain variant effect prediction methods ranging from simple amino acid substitution probabilities to state-of-the-art zero-shot language models, along with potential applications. Further, recent efforts such as the “Atlas of variant effects alliance” to accelerate research in variant effect prediction and the Critical Assessment of Genome Interpretation (CAGI) experiment for assessing the performance of the different variant effect predictors are outlined.

Amino acid substitutions in a protein alter its structure, which may affect the function and lead to diseases. Identifying disease-causing mutations is important for disease diagnosis and the development of personalized medicine. The advent of vast and diverse datasets and the availability of machine learning and deep learning algorithms has paved the way for the development of computational techniques to identify disease-causing mutations. This chapter comprehends the list of available tools for identifying disease-causing mutations, with an emphasis on the role of mutations in conferring drug resistance in human immunodeficiency virus (HIV) infection.

Advancements in the field of cancer genomics have yielded a wealth of information regarding genes and their associated mutations in the context of cancer. Researchers have conducted various experiments, including deep mutational scanning and polymerase chain reaction (PCR)-based methods, with the aim of pinpointing specific driver mutations. Some of these driver mutations tend to congregate together, forming clusters known as “hotspots” due to their distinctive nature. Nevertheless, the task of precisely identifying the quantity and specific driver mutations and hotspots essential for each cancer type remains highly challenging. Additionally, the experimental methods employed for this purpose are both costly and time-consuming. To address these challenges, numerous meticulously organized databases have been established and regularly updated to catalog mutations and hotspots. Leveraging these databases, coupled with the application of machine learning and statistical techniques, various resources have been created to classify driver mutations and hotspots effectively. These methods draw upon significant sequence- and structure-based attributes of proteins and their evolutionary history to forecast their mutational impact, facilitating the computational prioritization of driver mutations and hotspots. This chapter serves as a comprehensive summary of cancer-specific mutation databases, encompassing critical experimental and computational methodologies as well as characteristic protein and mutation properties. These collective approaches represent significant strides toward advancing existing therapeutic strategies and enhancing the realm of personalized medicine.

Transmembrane proteins (TMPs) are integral components of cellular and organelle membranes, playing pivotal roles as transporters, enzymes, receptors, and mediators in essential cellular functions. Mutations occurring in TMPs can disrupt their functions, and leading to various diseases such as cystic fibrosis, cancer, and many others. Experimental information on disease-associated and neutral mutations in TMPs has been curated and cataloged in databases such as MutHTP and TMSNP. These repositories offer comprehensive features, encompassing the sequence, structure, topology, and disease associations of TMPs. Relating these features with disease-causing mutations provides intricate relationships between sequence/structural parameters and diseases and the development of computational tools. Machine learning-based computational tools utilize diverse features, including evolutionary data, physiochemical properties, atomic contacts, contact potentials, and energetic contributions for identifying disease-causing mutations. These specialized tools serve to characterize the impact of novel variants across the entire human membrane proteome. In this chapter, we discuss about the identification of genetic variants using experimental methods, recent progress on computational databases and tools for disease-causing and neutral mutations in TMPs. These information provide additional insights for designing mutation-specific strategies for different diseases.

The COVID-19 pandemic had a major global impact and caused significant loss of lives. SARS-CoV-2, the causative virus for COVID-19, harbors spike protein that interacts with the human ACE2 receptor to infect the host cell. Mutations in the spike protein led to the emergence of several SARS-CoV-2 variants, such as Alpha, Beta, Gamma, Delta, and Omicron, which led to increased transmission rates, virulence, and evasion from the immune response. In this chapter, we reviewed the progress in different directions: (i) phylogeny of SARS-CoV-2, (ii) mutational effects at the interface of spike protein-ACE2 and spike protein-antibodies, (iii) computational tools and techniques to enhance our understanding of SARS-CoV-2 mutations, (iv) genomic studies that offer insights into the evolving patterns of mutations over time, and (iv) mutations that led to reduced neutralization efficacy of vaccines. This comprehensive review reveals the effect of mutations on virus–host interaction, which is pivotal for improved treatments and understanding the evolutionary path of the virus and immune evasion, and highlights the need to enhance our knowledge about viral mutations to be better prepared for future health crises.

Neurodegenerative diseases are the most common cause of death after cancer. They are characterized by gradual loss of neurons, resulting in a spectrum of symptoms ranging from motor impairments to memory loss, culminating in fatality. Neurodegenerative diseases are affected by a range of factors including lifestyle, cellular stress, aging, and genetic variants. These variants play a significant role in understanding disease susceptibility by modifying gene regulatory elements. Investigating the role of variants provides deep insights into the disease association. Next-generation sequencing (NGS) techniques have increased the scope of understanding diseases from a genetic perspective. Covering both protein-coding and noncoding regions for identifying various disease susceptible variants using computational tools and pipelines is crucial. In this chapter, with a brief introduction to different neurodegenerative diseases and the associated variants, we explain various sequencing techniques and pipelines used to identify plausible disease-causing variants and study their effects on different functional events that occur during post-transcription. In essence, this review helps to understand various techniques and pipelines used for variant calling and predicting their effects.

The following sections are included:

• Appendix A: List of databases for understanding the effects of mutations in proteins
• Appendix B: List of tools for understanding the effects of mutations in proteins
• Index