Computational Approaches and Resources in Single Amino Acid Substitutions Analysis Toward Clinical Research

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Nuclear receptor superfamily structural diversity in pacific oyster: In silico identification of estradiol binding candidates

The increasing presence of anthropogenic contaminants in aquatic environments poses challenges for species inhabiting contaminated sites. Due to their structural binding characteristics to ligands that inhibit or activate gene transcription, these xenobiotic compounds frequently target the nuclear receptor superfamily. The present work aims to understand the potential interaction between the hormone 17-β-estradiol, an environmental contaminant, and the nuclear receptors of Crassostrea gigas, the Pacific oyster. This filter-feeding, sessile oyster species is subject to environmental changes and exposure to contaminants. In the Pacific oyster, the estrogen-binding nuclear receptor is not able to bind this hormone as it does in vertebrates. However, another receptor may exhibit responsiveness to estrogen-like molecules and derivatives. We employed high-performance in silico methodologies, including three-dimensional modeling, molecular docking and atomistic molecular dynamics to identify likely binding candidates with the target moecule. Our approach revealed that among the C. gigas nuclear receptor superfamily, candidates with the most favorable interaction with the molecule of interest belonged to the NR1D, NR1H, NR1P, NR2E, NHR42, and NR0B groups. Interestingly, NR1H and NR0B were associated with planktonic/larval life cycle stages, while NR1P, NR2E, and NR0B were associated with sessile/adult life stages. The application of this computational methodological strategy demonstrated high performance in the virtual screening of candidates for binding with the target xenobiotic molecule and can be employed in other studies in the field of ecotoxicology in non-model organisms.

Computational Analysis of Gly482Ser Single-Nucleotide Polymorphism in PPARGC1A Gene Associated with CAD, NAFLD, T2DM, Obesity, Hypertension, and Metabolic Diseases

Peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PPARGC1A) regulates the expression of energy metabolism's genes and mitochondrial biogenesis. The essential roles of PPARGC1A encouraged the researchers to assess the relation between metabolism-related diseases and its variants. To study Gly482Ser (+1564G/A) single-nucleotide polymorphism (SNP) after PPARGC1A modeling, we substitute Gly482 for Ser482. Stability prediction tools showed that this substitution decreases the stability of PPARGC1A or has a destabilizing effect on this protein. We then utilized molecular dynamics simulation of both the Gly482Ser variant and wild type of the PPARGC1A protein to analyze the structural changes and to reveal the conformational flexibility of the PPARGC1A protein. We observed loss flexibility in the RMSD plot of the Gly482Ser variant, which was further supported by a decrease in the SASA value in the Gly482Ser variant structure of PPARGC1A and an increase of H-bond with the increase of β-sheet and coil and decrease of turn in the DSSP plot of the Gly482Ser variant. Such alterations may significantly impact the structural conformation of the PPARGC1A protein, and it might also affect its function. It showed that the Gly482Ser variant affects the PPARGC1A structure and makes the backbone less flexible to move. In general, molecular dynamics simulation (MDS) showed more flexibility in the native PPARGC1A structure. Essential dynamics (ED) also revealed that the range of eigenvectors in the conformational space has lower extension of motion in the Gly482Ser variant compared with WT. The Gly482Ser variant also disrupts PPARGC1A interaction. Due to this single-nucleotide polymorphism in PPARGC1A, it became more rigid and might disarray the structural conformation and catalytic function of the protein and might also induce type 2 diabetes mellitus (T2DM), coronary artery disease (CAD), and nonalcoholic fatty liver disease (NAFLD). The results obtained from this study will assist wet lab research in expanding potent treatment on T2DM.

Investigating mutations at the hotspot position of the ERBB2 and screening for the novel lead compound to treat breast cancer - a computational approach

Membrane proteins are the most common types of cancer that are active in the prognosis. Membrane proteins are a distinguishing characteristic of a cancer cell. In tumor cell therapy, the overexpressed membrane proteins are becoming ever more relevant. The 3-kinase (PI3K)/AKT phosphatidylinositol pathway is downstream triggered by different extracellular signals, and this signaling pathway activation impacts a variety of proliferation of the cellular processes like cell growth and surviving. Frequent PI3K/AKT dysregulation in human cancer has rendered proteins of this pathway desirable for diagnostic markers. Members of the ERBB family-like ERBB2 and ERBB3 activate intracellular signaling pathways such as PI3K/AKT. The mutations in these proteins dysfunctions the proteins in the downstream. Considering this importance, we have developed a computational pipeline to identify the mutation position with a highest number of mutations and to screen them for pathogenicity, stability, conservation, and structural changes using PredictSNP, iStable, ConSurf, and GROMACS simulation software respectively. Further, a virtual screening approach was initiated to find the most similar non-toxic lead compound, which could be an alternative to the currently used lapatinib. To conclude, protein-ligand dynamics were undertaken to study the actions of native and mutants with the lapatinib and the lead compound. From the overall analysis, we identified position 755 with leucine in the native condition is prone to frequent mutations. The leucine at 755th position is more prone to mutate as serine and tryptophan. Further from the computational analysis, we identified that the mutation L755S is more significant than the L755W mutation. We have witnessed CID140590176 be a potential lead compound with no toxicity. The behavior of the lead compound has shown more compactness with an increased number of intermolecular hydrogen bonds in the ERBB2 with L755S. This lead compound can be further taken for experimental validations, and we believe that this lead compound could be a potent ERBB2 inhibitor.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Proposing the Promiscuous Protein Structures in JNK1 and JNK3 for Virtual Screening in Pursuit of Potential Leads

Over the past decade, the available crystal structures have almost doubled in Protein Data Bank (PDB) providing the research community with a series of similar crystal structures to choose from for future docking studies. With the steady growth in the number of high-resolution three-dimensional protein structures, ligand docking-based virtual screening of chemical libraries to a receptor plays a critical role in the drug discovery process by identifying new drug candidates. Thus, identifying potential candidates among all the available structures in a database for docking studies is of utmost importance. Our work examined whether one could use the resolution of a number of known structures, without considering other parameters, to choose a good experimental structure for various docking studies to find more useful drug leads. We expected that a good experimental structure for docking studies to be the one that gave favorable docking with the largest number of ligands among the experimental structures to be selected. We chose three protein test systems for our study, all belonging to the family of MAPK: (1) JNK1, (2) JNK2, and (3) JNK3. On analysis of the results, the best resolution structures showed significant variations from the expected values in their result, whereas the poor resolution structures proved to be better candidates for docking studies.

In silico approaches and tools for the prediction of drug metabolism and fate: A review

The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.

Substitution impact of highly conserved arginine residue at position 75 in GJB1 gene in association with X-linked Charcot-Marie-tooth disease: A computational study

X-linked Charcot-Marie-Tooth type 1 X (CMTX1) disease is a subtype of Charcot-Marie-Tooth (CMT), which is mainly caused by mutations in the GJB1 gene. It is also known as connexin 32 (Cx32) that leads to Schwann cell abnormalities and peripheral neuropathy. CMTX1 is considered as the second most common form of CMT disease. The aim of this study is to computationally predict the potential impact of different single amino acid substitutions at position 75 of Cx32, from arginine (R) to proline (P), glutamine (Q) and tryptophan (W). This position is known to be highly conserved among the family of connexin. To understand the structural and functional changes due to these single amino acid substitutions, we employed a homology-modeling technique to build the three-dimensional structure models for the native and mutant proteins. The protein structures were further embedded into a POPC lipid bilayer, inserted into a water box, and subjected to molecular dynamics simulation for 50 ns. Our results show that the mutants R75P, R75Q and R75W display variable structural conformation and dynamic behavior compared to the native protein. Our data proves useful in predicting the potential pathogenicity of the mutant proteins and is expected to serve as a platform for drug discovery for patients with CMT.

Genotype-phenotype correlation in patients with isovaleric acidaemia: comparative structural modelling and computational analysis of novel variants

Isovaleric acidaemia (IVA) is an autosomal recessive inborn error of leucine metabolism. It is caused by a deficiency in the mitochondrial isovaleryl-CoA dehydrogenase (IVD) enzyme. In this study, we investigated eight patients with IVA. The patients' diagnoses were confirmed by urinary organic acid analysis and the blood C5-Carnitine value. A molecular genetic analysis of the IVD gene revealed nine different variants: five were missense variants (c.1193G > A; p. R398Q, c.1207T > A; p. Y403N, c.872C > T; p. A291V, c.749G > C; p. G250A, c.1136T > C; p.I379T), one was a frameshift variant (c.ins386 T; p. Y129fs), one was a splicing variant (c.465 + 2T > C), one was a polymorphism (c.732C > T; p. D244D), and one was an intronic benign variant (c.287 + 14T > C). Interestingly, all variants were in homozygous form, and four variants were novel (p. Y403N, p. Y129fs, p. A291V, p. G250A) and absent from 200 normal chromosomes. We performed protein modelling and dynamics analyses, pathogenicity and stability analyses, and a physiochemical properties analysis of the five missense variants (p.Y403N, R398Q, p.A291V, p.G250A, and p.I379T). Variants p.I379T and p.R398Q were found to be the most deleterious and destabilizing compared to variants p.A291V and p.Y403N. However, the four variants were predicted to be severe by the protein dynamic and in silico analysis, which was consistent with the patients' clinical phenotypes. The p.G250A variant was computationally predicted as mild, which was consistent with the severity of the clinical phenotype. This study reveals a potentially meaningful genotype-phenotype correlation for our patient cohort and highlights the development and use of this computational analysis for future assessments of genetic variants in the clinic.

Comparative computational assessment of the pathogenicity of mutations in the Aspartoacylase enzyme

Aspartoacylase (ASPA) is a zinc-dependent abundant enzyme in the brain, which catalyzes the conversion of N-acetyl aspartate (NAA) into acetate and aspartate. Mutations in the ASPA gene are associated with the development of Canavan disease (CD), leading to the deficiency of ASPA activity. Patients with CD were characterized by degeneration of the white matter of the brain. We reported earlier on two patients with severe form of CD that both had two novel missense mutations in the ASPA: c.427 A > G; p. I143V and c.557 T > A; p. V186D (Zaki et al. 2017a), patient 1 harbored both mutations (p.I143V and p.V186D) in a heterozygous form together with four other mutations, and patient 2 had both mutations in homozygous form. Wijayasinghe et al. (2014) crystallized the 3D structures of four different ASPA mutants (p.K213E, p.Y231C, p.E285A, and p.F295S). In this study, we used in silico prediction methods and molecular dynamics simulation (MDS) to understand the structural impact of all these mutations. Moreover, we used molecular docking (MD) to investigate the binding patterns of the NAA substrate to the native and mutant proteins. Among the mutations, p.E285A (crystallized mutant) was predicted to be the most deleterious for the protein function and the least deleteriousness mutant was the p.I143V (novel mutant). Among the novel mutations, p.V186D was observed to be disruptive for both the zinc binding and NAA binding than the p.I143V. This study provides practical insights on the effect of these mutations on the ASPA function and might serve as a platform for drug design for CD treatment.

Molecular Dynamics: New Frontier in Personalized Medicine

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.