Protein informatics combined with multiple data sources enriches the clinical characterization of novel TRPV4 variant causing an intermediate skeletal dysplasia

Statistical Mechanics Metrics in Pairing and Parsing In Silico and Phenotypic Data of a Novel Genetic NFκB1 (c.T638A) Variant

(1) Background: Mutations in NFκB1, a transcriptional regulator of immunomodulating proteins, are a known cause of inborn errors of immunity. Our proband is a 22-year-old male with a diagnosis of common variable immunodeficiency (CVID), cytopenias with massive splenomegaly, and nodular regenerative hyperplasia of the liver. Genetic studies identified a novel, single-point mutation variant in NFκB1, c. T638A p. V213E. (2) Methods: Next-generation panel sequencing of the patient uncovered a novel single-point mutation in the NFκB1 gene that was modeled using the I-TASSER homology-modeling software, and molecular dynamics were assessed using the YASARA2 software (version 20.14.24). (3) Results: This variant replaces valine with glutamic acid at position 213 in the NFκB1 sequence. Molecular modeling and molecular dynamic studies showed altered dynamics in and around the rel homology domain, ankyrin regions, and death domain of the protein. We postulate that these changes alter overall protein function. (4) Conclusions: This case suggests the pathogenicity of a novel variant using protein-modeling techniques and molecular dynamic simulations.

Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics

With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.

Molecular Modeling and Phenotypic Description of a Patient with a Novel Exonic Deletion of GALNS with Resultant Morquio Syndrome with Two Successful Pregnancies

In this report, we describe phenotypic features of a patient with mucopolysaccharidosis type IVA (Morquio syndrome) harboring a novel exon 1 deletion in GALNS with enzymatic confirmation consistent with Morquio syndrome. To our knowledge, this is the first reported case of this variant. Additionally, we protein modelled wild-type GALNS and the pathogenic variant with an exon 1 deletion for comparative analysis using statistical mechanics methods described herein. We demonstrate that, even when the protein is translated, the mutation would affect protein stability and function via homodimer interaction modifications. Lastly, given the patient's 2 successful pregnancies, data about the management of pregnancies in mucopolysaccharidoses are reviewed, and we discuss the management of pregnancy in patients with Morquio syndrome.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins

Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as "undruggable", is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.

Genomic Observations of a Rare/Pathogenic SMAD3 Variant in Loeys⁻Dietz Syndrome 3 Confirmed by Protein Informatics and Structural Investigations

Background and objectives: Loeys–Dietz syndrome 3, also known as aneurysms-–osteoarthritis syndrome, is an autosomal dominant genetic connective tissue disease caused by pathogenic variants in SMAD3, a transcription factor involved in TGF-β signaling. This disorder is characterized by early-onset osteoarthritis and arterial aneurysms. Common features include scoliosis, uvula abnormalities, striae, and velvety skin. Materials and Methods: The pathogenicity of a variant of uncertain significance in the SMAD3 gene was evaluated (variant c.220C > T) through personalized protein informatics and molecular studies. Results: The case of a 44-year-old male, who was originally presumed to have Marfan syndrome, is presented. An expanded gene panel determined the probable cause to be a variant in SMAD3, c.220C > T (p.R74W). His case was complicated by a history of stroke, but his phenotype was otherwise characteristic for Loeys–Dietz syndrome 3. Conclusion: This case emphasizes the importance of comprehensive genetic testing to evaluate patients for connective tissue disorders, as well as the potential benefit of utilizing a protein informatics platform for the assessment of variant pathogenicity.

Protein informatics combined with multiple data sources enriches the clinical characterization of novel TRPV4 variant causing an intermediate skeletal dysplasia

Background

Transient receptor potential cation channel subfamily V member 4 (TRPV4) is an ion channel permeable to Ca²⁺ that is sensitive to physical, hormonal, and chemical stimuli. This protein is expressed in many cell types, including osteoclasts, chondrocytes, and sensory neurons. As such, pathogenic variants of this gene are associated with skeletal dysplasias and neuromuscular disorders. Pathogenesis of these phenotypes is not yet completely understood, but it is known that genotype–phenotype correlations for TRPV4 pathogenic variants often are not present.

Methods

Newly characterized, suspected pathogenic variant in TRPV4 was analyzed using protein informatics and personalized protein‐level molecular studies, genomic exome analysis, and clinical study.

Results

This statement is demonstrated in the family of our proband, a 47‐year‐old female having the novel c.2401A>G (p.K801E) variant of TRPV4. We discuss the common symptoms between the proband, her father, and her daughter, and compare her phenotype to known TRPV4‐associated skeletal dysplasias.

Conclusions

Protein informatics and molecular modeling are used to confirm the pathogenicity of the unique TRPV4 variant found in this family. Multiple data were combined in a comprehensive manner to give complete overall perspective on the patient disease and prognosis.